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Zhou S, Huang X, Liu N, Zhang W, Zhang YT, Chung FL. Open-world electrocardiogram classification via domain knowledge-driven contrastive learning. Neural Netw 2024; 179:106551. [PMID: 39068675 DOI: 10.1016/j.neunet.2024.106551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 05/16/2024] [Accepted: 07/16/2024] [Indexed: 07/30/2024]
Abstract
Automatic electrocardiogram (ECG) classification provides valuable auxiliary information for assisting disease diagnosis and has received much attention in research. The success of existing classification models relies on fitting the labeled samples for every ECG type. However, in practice, well-annotated ECG datasets usually cover only limited ECG types. It thus raises an issue: conventional classification models trained with limited ECG types can only identify those ECG types that have already been observed in the training set, but fail to recognize unseen (or unknown) ECG types that exist in the wild and are not included in training data. In this work, we investigate an important problem called open-world ECG classification that can predict fine-grained observed ECG classes and identify unseen classes. Accordingly, we propose a customized method that first incorporates clinical knowledge into contrastive learning by generating "hard negative" samples to guide learning diagnostic ECG features (i.e., distinguishable representations), and then performs multi-hypersphere learning to learn compact ECG representations for classification. The experiment results on 12-lead ECG datasets (CPSC2018, PTB-XL, and Georgia) demonstrate that the proposed method outperforms the state-of-the-art methods. Specifically, our method achieves superior accuracy than the comparative methods on the unseen ECG class and certain seen classes. Overall, the investigated problem (i.e., open-world ECG classification) helps to draw attention to the reliability of automatic ECG diagnosis, and the proposed method is proven effective in tackling the challenges. The code and datasets are released at https://github.com/betterzhou/Open_World_ECG_Classification.
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Affiliation(s)
- Shuang Zhou
- Department of Computing, Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China.
| | - Xiao Huang
- Department of Computing, Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China
| | - Ninghao Liu
- Department of Computer Science, University of Georgia, Athens, USA
| | - Wen Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Yuan-Ting Zhang
- Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Fu-Lai Chung
- Department of Computing, Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China.
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2
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Lee S, Ho DH, Jekal J, Cho SY, Choi YJ, Oh S, Choi YY, Lee T, Jang KI, Cho JH. Fabric-based lamina emergent MXene-based electrode for electrophysiological monitoring. Nat Commun 2024; 15:5974. [PMID: 39358330 PMCID: PMC11446925 DOI: 10.1038/s41467-024-49939-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 06/20/2024] [Indexed: 10/04/2024] Open
Abstract
Commercial wearable biosignal sensing technologies encounter challenges associated with irritation or discomfort caused by unwanted objects in direct contact with the skin, which can discourage the widespread adoption of wearable devices. To address this issue, we propose a fabric-based lamina emergent MXene-based electrode, a lightweight and flexible shape-morphing wearable bioelectrode. This work offers an innovative approach to biosignal sensing by harnessing the high electrical conductivity and low skin-to-electrode contact impedance of MXene-based dry electrodes. Its design, inspired by Nesler's pneumatic interference actuator, ensures stable skin-to-electrode contact, enabling robust biosignal detection in diverse situations. Extensive research is conducted on key design parameters, such as the width and number of multiple semicircular legs, the radius of the anchoring frame, and pneumatic pressure, to accommodate a wide range of applications. Furthermore, a real-time wireless electrophysiological monitoring system has been developed, with a signal-to-noise ratio and accuracy comparable to those of commercial bioelectrodes. This work excels in recognizing various hand gestures through a convolutional neural network, ultimately introducing a shape-morphing electrode that provides reliable, high-performance biosignal sensing for dynamic users.
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Affiliation(s)
- Sanghyun Lee
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, Republic of Korea
| | - Dong Hae Ho
- Department of Energy Science and Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of Korea
| | - Janghwan Jekal
- Department of Robotics and Mechatronics Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of Korea
| | - Soo Young Cho
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, Republic of Korea
| | - Young Jin Choi
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, Republic of Korea
| | - Saehyuck Oh
- Department of Robotics and Mechatronics Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of Korea
| | - Yoon Young Choi
- Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Taeyoon Lee
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
- Department of Bio and Brain Engineering, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea
| | - Kyung-In Jang
- Department of Robotics and Mechatronics Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of Korea.
| | - Jeong Ho Cho
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, Republic of Korea.
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Kwak J, Jung J. Classification of imbalanced ECGs through segmentation models and augmented by conditional diffusion model. PeerJ Comput Sci 2024; 10:e2299. [PMID: 39314720 PMCID: PMC11419651 DOI: 10.7717/peerj-cs.2299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 08/10/2024] [Indexed: 09/25/2024]
Abstract
Electrocardiograms (ECGs) provide essential data for diagnosing arrhythmias, which can potentially cause serious health complications. Early detection through continuous monitoring is crucial for timely intervention. The Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia dataset employed for arrhythmia analysis research comprises imbalanced data. It is necessary to create a robust model independent of data imbalances to classify arrhythmias accurately. To mitigate the pronounced class imbalance in the MIT-BIH arrhythmia dataset, this study employs advanced augmentation techniques, specifically variational autoencoder (VAE) and conditional diffusion, to augment the dataset. Furthermore, accurately segmenting the continuous heartbeat dataset into individual heartbeats is crucial for confidently detecting arrhythmias. This research compared a model that employed annotation-based segmentation, utilizing R-peak labels, and a model that utilized an automated segmentation method based on a deep learning model to segment heartbeats. In our experiments, the proposed model, utilizing MobileNetV2 along with annotation-based segmentation and conditional diffusion augmentation to address minority class, demonstrated a notable 1.23% improvement in the F1 score and 1.73% in the precision, compared to the model classifying arrhythmia classes with the original imbalanced dataset. This research presents a model that accurately classifies a wide range of arrhythmias, including minority classes, moving beyond the previously limited arrhythmia classification models. It can serve as a basis for better data utilization and model performance improvement in arrhythmia diagnosis and medical service research. These achievements enhance the applicability in the medical field and contribute to improving the quality of healthcare services by providing more sophisticated and reliable diagnostic tools.
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Affiliation(s)
- Jinhee Kwak
- Department of Information and Communication Engineering, Myongji University, Yongin, Gyeonggi-do, Republic of South Korea
| | - Jaehee Jung
- Department of Information and Communication Engineering, Myongji University, Yongin, Gyeonggi-do, Republic of South Korea
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Shi J, Liu W, Zhang H, Chang S, Wang H, He J, Huang Q. CPSS: Fusing consistency regularization and pseudo-labeling techniques for semi-supervised deep cardiovascular disease detection using all unlabeled electrocardiograms. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108315. [PMID: 38991373 DOI: 10.1016/j.cmpb.2024.108315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 06/12/2024] [Accepted: 06/29/2024] [Indexed: 07/13/2024]
Abstract
BACKGROUND AND OBJECTIVE Deep learning usually achieves good performance in the supervised way, which requires a large amount of labeled data. However, manual labeling of electrocardiograms (ECGs) is laborious that requires much medical knowledge. Semi-supervised learning (SSL) provides an effective way of leveraging unlabeled data to improve model performance, providing insight for solving this problem. The objective of this study is to improve the performance of cardiovascular disease (CVD) detection by fully utilizing unlabeled ECG. METHODS A novel SSL algorithm fusing consistency regularization and pseudo-labeling techniques (CPSS) is proposed. CPSS consists of supervised learning and unsupervised learning. For supervised learning, the labeled ECGs are mapped into prediction vectors by the classifier. The cross-entropy loss function is used to optimize the classifier. For unsupervised learning, the unlabeled ECGs are weakly and strongly augmented, and a consistency loss is used to minimize the difference between the classifier's predictions for the two augmentations. Pseudo-labeling techniques include positive pseudo-labeling (PL) and ranking-based negative pseudo-labeling (RNL). PL introduces pseudo-labels for data with high prediction confidence. RNL assigns negative pseudo-labels to the lower-ranked categories in the prediction vectors to leverage data with low prediction confidence. In this study, VGGNet and ResNet are used as classifiers, which are jointly optimized by labeled and unlabeled ECGs. RESULTS CPSS has been validated on several databases. With the same number of labeled ECGs (10%), it improves the accuracies over pure supervised learning by 13.59%, 4.60%, and 5.38% in the CPSC2018, PTB-XL, and Chapman databases, respectively. CPSS achieves comparable results to the fully supervised method with only 10% of labeled ECGs, which reduces the labeling workload by 90%. In addition, to verify the practicality of CPSS, a cardiovascular disease monitoring system is designed by heterogeneously deploying the trained classifiers on an SoC (system-on-a-chip), which can detect CVD in real time. CONCLUSION The results of this study indicate that the proposed CPSS can significantly improve the performance of CVD detection using unlabeled ECG, which reduces the burden of ECG labeling in deep learning. In addition, the designed monitoring system makes the proposed CPSS promising for real-world applications.
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Affiliation(s)
- Jiguang Shi
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Wenhan Liu
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Huaicheng Zhang
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Sheng Chang
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Hao Wang
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Jin He
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Qijun Huang
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China.
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Wang Q, Pan M, Kreiss L, Samaei S, Carp SA, Johansson JD, Zhang Y, Wu M, Horstmeyer R, Diop M, Li DDU. A comprehensive overview of diffuse correlation spectroscopy: Theoretical framework, recent advances in hardware, analysis, and applications. Neuroimage 2024; 298:120793. [PMID: 39153520 DOI: 10.1016/j.neuroimage.2024.120793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2024] [Revised: 07/23/2024] [Accepted: 08/14/2024] [Indexed: 08/19/2024] Open
Abstract
Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However, newcomers might feel overwhelmed, not only by the already-complex DCS theoretical framework but also by the broad range of component options and system architectures. To facilitate new entry to this exciting field, we present a comprehensive review of DCS hardware architectures (continuous-wave, frequency-domain, and time-domain) and summarize corresponding theoretical models. Further, we discuss new applications of highly integrated silicon single-photon avalanche diode (SPAD) sensors in DCS, compare SPADs with existing sensors, and review other components (lasers, sensors, and correlators), as well as data analysis tools, including deep learning. Potential applications in medical diagnosis are discussed and an outlook for the future directions is provided, to offer effective guidance to embark on DCS research.
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Affiliation(s)
- Quan Wang
- Department of Biomedical Engineering, Faculty of Engineering, University of Strathclyde, Glasgow, United Kingdom
| | - Mingliang Pan
- Department of Biomedical Engineering, Faculty of Engineering, University of Strathclyde, Glasgow, United Kingdom
| | - Lucas Kreiss
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Saeed Samaei
- Department of Medical and Biophysics, Schulich School of Medical & Dentistry, Western University, London, Ontario, Canada; Lawson Health Research Institute, Imaging Program, London, Ontario, Canada
| | - Stefan A Carp
- Massachusetts General Hospital, Optics at Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Charlestown, MA, United States
| | | | - Yuanzhe Zhang
- Department of Biomedical Engineering, Faculty of Engineering, University of Strathclyde, Glasgow, United Kingdom
| | - Melissa Wu
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Roarke Horstmeyer
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Mamadou Diop
- Department of Medical and Biophysics, Schulich School of Medical & Dentistry, Western University, London, Ontario, Canada; Lawson Health Research Institute, Imaging Program, London, Ontario, Canada
| | - David Day-Uei Li
- Department of Biomedical Engineering, Faculty of Engineering, University of Strathclyde, Glasgow, United Kingdom.
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Bayani A, Kargar M. LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network. Physiol Rep 2024; 12:e16182. [PMID: 39218586 PMCID: PMC11366442 DOI: 10.14814/phy2.16182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 07/30/2024] [Accepted: 07/30/2024] [Indexed: 09/04/2024] Open
Abstract
The electrocardiogram (ECG) is a fundamental and widely used tool for diagnosing cardiovascular diseases. It involves recording cardiac electrical signals using electrodes, which illustrate the functioning of cardiac muscles during contraction and relaxation phases. ECG is instrumental in identifying abnormal cardiac activity, heart attacks, and various cardiac conditions. Arrhythmia detection, a critical aspect of ECG analysis, entails accurately classifying heartbeats. However, ECG signal analysis demands a high level of expertise, introducing the possibility of human errors in interpretation. Hence, there is a clear need for robust automated detection techniques. Recently, numerous methods have emerged for arrhythmia detection from ECG signals. In our research, we developed a novel one-dimensional deep neural network technique called linear deep convolutional neural network (LDCNN) to identify arrhythmias from ECG signals. We compare our suggested method with several state-of-the-art algorithms for arrhythmia detection. We evaluate our methodology using benchmark datasets, including the PTB Diagnostic ECG and MIT-BIH Arrhythmia databases. Our proposed method achieves high accuracy rates of 99.24% on the PTB Diagnostic ECG dataset and 99.38% on the MIT-BIH Arrhythmia dataset.
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Affiliation(s)
- Ali Bayani
- Department of Computer Engineering, Tabriz BranchIslamic Azad UniversityTabrizIran
| | - Masoud Kargar
- Department of Computer Engineering, Tabriz BranchIslamic Azad UniversityTabrizIran
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Wang W, Han D, Duan X, Yong Y, Wu Z, Ma X, Zhang H, Dai K. Fast-Activated Minimal Gated Unit: Lightweight Processing and Feature Recognition for Multiple Mechanical Impact Signals. SENSORS (BASEL, SWITZERLAND) 2024; 24:5245. [PMID: 39204941 PMCID: PMC11359475 DOI: 10.3390/s24165245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Revised: 08/10/2024] [Accepted: 08/11/2024] [Indexed: 09/04/2024]
Abstract
Multiple dynamic impact signals are widely used in a variety of engineering scenarios and are difficult to identify accurately and quickly due to the signal adhesion phenomenon caused by nonlinear interference. To address this problem, an intelligent algorithm combining wavelet transforms with lightweight neural networks is proposed. First, the features of multiple impact signals are analyzed by establishing a transfer model for multiple impacts in multibody dynamical systems, and interference is suppressed using wavelet transformation. Second, a lightweight neural network, i.e., fast-activated minimal gated unit (FMGU), is elaborated for multiple impact signals, which can reduce computational complexity and improve real-time performance. Third, the experimental results show that the proposed method maintains excellent feature recognition results compared to gate recurrent unit (GRU) and long short-term memory (LSTM) networks under all test datasets with varying impact speeds, while its metrics for computational complexity are 50% lower than those of the GRU and LSTM. Therefore, the proposed method is of great practical value for weak hardware application platforms that require the accurate identification of multiple dynamic impact signals in real time.
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Affiliation(s)
- Wenrui Wang
- School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; (W.W.); (X.D.); (Y.Y.); (Z.W.); (H.Z.)
| | - Dong Han
- The Third Military Representative Office of the Army Armament Department in Nanjing, Nanjing 210000, China;
| | - Xinyi Duan
- School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; (W.W.); (X.D.); (Y.Y.); (Z.W.); (H.Z.)
| | - Yaxin Yong
- School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; (W.W.); (X.D.); (Y.Y.); (Z.W.); (H.Z.)
| | - Zhengqing Wu
- School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; (W.W.); (X.D.); (Y.Y.); (Z.W.); (H.Z.)
| | - Xiang Ma
- School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; (W.W.); (X.D.); (Y.Y.); (Z.W.); (H.Z.)
| | - He Zhang
- School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; (W.W.); (X.D.); (Y.Y.); (Z.W.); (H.Z.)
| | - Keren Dai
- School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; (W.W.); (X.D.); (Y.Y.); (Z.W.); (H.Z.)
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Ma L, Zhang F. A Novel Real-Time Detection and Classification Method for ECG Signal Images Based on Deep Learning. SENSORS (BASEL, SWITZERLAND) 2024; 24:5087. [PMID: 39204785 PMCID: PMC11360666 DOI: 10.3390/s24165087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2024] [Revised: 07/28/2024] [Accepted: 08/02/2024] [Indexed: 09/04/2024]
Abstract
In this paper, a novel deep learning method Mamba-RAYOLO is presented, which can improve detection and classification in the processing and analysis of ECG images in real time by integrating three advanced modules. The feature extraction module in our work with a multi-branch structure during training can capture a wide range of features to ensure efficient inference and rich feature extraction. The attention mechanism module utilized in our proposed network can dynamically focus on the most relevant spatial and channel-wise features to improve detection accuracy and computational efficiency. Then, the extracted features can be refined for efficient spatial feature processing and robust feature fusion. Several sets of experiments have been carried out to test the validity of the proposed Mamba-RAYOLO and these indicate that our method has made significant improvements in the detection and classification of ECG images. The research offers a promising framework for more accurate and efficient medical ECG diagnostics.
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Affiliation(s)
- Linjuan Ma
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China;
| | - Fuquan Zhang
- College of Computer and Control Engineering, Minjiang University, Fuzhou 350108, China
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Irlik K, Aldosari H, Hendel M, Kwiendacz H, Piaśnik J, Kulpa J, Ignacy P, Boczek S, Herba M, Kegler K, Coenen F, Gumprecht J, Zheng Y, Lip GYH, Alam U, Nabrdalik K. Artificial intelligence-enhanced electrocardiogram analysis for identifying cardiac autonomic neuropathy in patients with diabetes. Diabetes Obes Metab 2024; 26:2624-2633. [PMID: 38603589 DOI: 10.1111/dom.15578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/12/2024] [Accepted: 03/15/2024] [Indexed: 04/13/2024]
Abstract
AIM To develop and employ machine learning (ML) algorithms to analyse electrocardiograms (ECGs) for the diagnosis of cardiac autonomic neuropathy (CAN). MATERIALS AND METHODS We used motif and discord extraction techniques, alongside long short-term memory networks, to analyse 12-lead, 10-s ECG tracings to detect CAN in patients with diabetes. The performance of these methods with the support vector machine classification model was evaluated using 10-fold cross validation with the following metrics: accuracy, precision, recall, F1 score, and area under the receiver-operating characteristic curve (AUC). RESULTS Among 205 patients (mean age 54 ± 17 years, 54% female), 100 were diagnosed with CAN, including 38 with definite or severe CAN (dsCAN) and 62 with early CAN (eCAN). The best model performance for dsCAN classification was achieved using both motifs and discords, with an accuracy of 0.92, an F1 score of 0.92, a recall at 0.94, a precision of 0.91, and an excellent AUC of 0.93 (95% confidence interval [CI] 0.91-0.94). For the detection of any stage of CAN, the approach combining motifs and discords yielded the best results, with an accuracy of 0.65, F1 score of 0.68, a recall of 0.75, a precision of 0.68, and an AUC of 0.68 (95% CI 0.54-0.81). CONCLUSION Our study highlights the potential of using ML techniques, particularly motifs and discords, to effectively detect dsCAN in patients with diabetes. This approach could be applied in large-scale screening of CAN, particularly to identify definite/severe CAN where cardiovascular risk factor modification may be initiated.
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Affiliation(s)
- Krzysztof Irlik
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool, UK
- Student's Scientific Association at the Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
- Doctoral School, Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
| | - Hanadi Aldosari
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool, UK
- Department of Computer Science, School of Electrical Engineering, Electronics and Computer Science, University of Liverpool, Liverpool, UK
| | - Mirela Hendel
- Student's Scientific Association at the Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
| | - Hanna Kwiendacz
- Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
| | - Julia Piaśnik
- Student's Scientific Association at the Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
| | - Justyna Kulpa
- Student's Scientific Association at the Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
| | - Paweł Ignacy
- Doctoral School, Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
| | - Sylwia Boczek
- Student's Scientific Association at the Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
| | - Mikołaj Herba
- Student's Scientific Association at the Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
| | - Kamil Kegler
- Student's Scientific Association at the Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
| | - Frans Coenen
- Department of Computer Science, School of Electrical Engineering, Electronics and Computer Science, University of Liverpool, Liverpool, UK
| | - Janusz Gumprecht
- Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
| | - Yalin Zheng
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool, UK
- Department of Eye and Vision Science, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
- St Paul's Eye Unit, Royal Liverpool University Hospital, Liverpool, UK
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool, UK
- Danish Center for Health Services Research, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Uazman Alam
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool, UK
- Diabetes & Endocrinology Research and Pain Research Institute, Institute of Life Course and Medical Sciences, University of Liverpool and Liverpool University Hospital NHS Foundation Trust, Liverpool, UK
- Centre for Biomechanics and Rehabilitation Technologies, Staffordshire University, Stoke-on-Trent, UK
| | - Katarzyna Nabrdalik
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool, UK
- Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
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10
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Zhang S, Fang Y, Ren Y. ECG autoencoder based on low-rank attention. Sci Rep 2024; 14:12823. [PMID: 38834839 DOI: 10.1038/s41598-024-63378-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 05/28/2024] [Indexed: 06/06/2024] Open
Abstract
The prevalence of cardiovascular disease (CVD) has surged in recent years, making it the foremost cause of mortality among humans. The Electrocardiogram (ECG), being one of the pivotal diagnostic tools for cardiovascular diseases, is increasingly gaining prominence in the field of machine learning. However, prevailing neural network models frequently disregard the spatial dimension features inherent in ECG signals. In this paper, we propose an ECG autoencoder network architecture incorporating low-rank attention (LRA-autoencoder). It is designed to capture potential spatial features of ECG signals by interpreting the signals from a spatial perspective and extracting correlations between different signal points. Additionally, the low-rank attention block (LRA-block) obtains spatial features of electrocardiogram signals through singular value decomposition, and then assigns these spatial features as weights to the electrocardiogram signals, thereby enhancing the differentiation of features among different categories. Finally, we utilize the ResNet-18 network classifier to assess the performance of the LRA-autoencoder on both the MIT-BIH Arrhythmia and PhysioNet Challenge 2017 datasets. The experimental results reveal that the proposed method demonstrates superior classification performance. The mean accuracy on the MIT-BIH Arrhythmia dataset is as high as 0.997, and the mean accuracy and F 1 -score on the PhysioNet Challenge 2017 dataset are 0.850 and 0.843.
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Affiliation(s)
- Shilin Zhang
- School of Information Science and Engineering (Institute of Data Science and Technology), Shandong Normal University, Jinan, 250014, China
| | - Yixian Fang
- School of Information Engineering, Shandong Management University, Jinan, 250357, China.
| | - Yuwei Ren
- School of Information Science and Engineering (Institute of Data Science and Technology), Shandong Normal University, Jinan, 250014, China
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Rodríguez-Abreo O, Cruz-Fernandez M, Fuentes-Silva C, Quiroz-Juárez MA, Aragón JL. Modeling the Electrical Activity of the Heart via Transfer Functions and Genetic Algorithms. Biomimetics (Basel) 2024; 9:300. [PMID: 38786509 PMCID: PMC11118079 DOI: 10.3390/biomimetics9050300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/13/2024] [Accepted: 05/14/2024] [Indexed: 05/25/2024] Open
Abstract
Although healthcare and medical technology have advanced significantly over the past few decades, heart disease continues to be a major cause of mortality globally. Electrocardiography (ECG) is one of the most widely used tools for the detection of heart diseases. This study presents a mathematical model based on transfer functions that allows for the exploration and optimization of heart dynamics in Laplace space using a genetic algorithm (GA). The transfer function parameters were fine-tuned using the GA, with clinical ECG records serving as reference signals. The proposed model, which is based on polynomials and delays, approximates a real ECG with a root-mean-square error of 4.7% and an R2 value of 0.72. The model achieves the periodic nature of an ECG signal by using a single periodic impulse input. Its simplicity makes it possible to adjust waveform parameters with a predetermined understanding of their effects, which can be used to generate both arrhythmic patterns and healthy signals. This is a notable advantage over other models that are burdened by a large number of differential equations and many parameters.
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Affiliation(s)
- Omar Rodríguez-Abreo
- Centro de Física Aplicada y Tecnología Avanzada, Universidad Nacional Autónoma de México, Santiago de Querétaro 76230, Mexico;
| | - Mayra Cruz-Fernandez
- Division de Tecnologías Industriales, Universidad Politécnica de Querétaro, Santiago de Querétaro 76240, Mexico (C.F.-S.)
| | - Carlos Fuentes-Silva
- Division de Tecnologías Industriales, Universidad Politécnica de Querétaro, Santiago de Querétaro 76240, Mexico (C.F.-S.)
| | - Mario A. Quiroz-Juárez
- Centro de Física Aplicada y Tecnología Avanzada, Universidad Nacional Autónoma de México, Santiago de Querétaro 76230, Mexico;
| | - José L. Aragón
- Centro de Física Aplicada y Tecnología Avanzada, Universidad Nacional Autónoma de México, Santiago de Querétaro 76230, Mexico;
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12
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Kalmady SV, Salimi A, Sun W, Sepehrvand N, Nademi Y, Bainey K, Ezekowitz J, Hindle A, McAlister F, Greiner R, Sandhu R, Kaul P. Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level. NPJ Digit Med 2024; 7:133. [PMID: 38762623 PMCID: PMC11102430 DOI: 10.1038/s41746-024-01130-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 04/26/2024] [Indexed: 05/20/2024] Open
Abstract
Artificial intelligence-enabled electrocardiogram (ECG) algorithms are gaining prominence for the early detection of cardiovascular (CV) conditions, including those not traditionally associated with conventional ECG measures or expert interpretation. This study develops and validates such models for simultaneous prediction of 15 different common CV diagnoses at the population level. We conducted a retrospective study that included 1,605,268 ECGs of 244,077 adult patients presenting to 84 emergency departments or hospitals, who underwent at least one 12-lead ECG from February 2007 to April 2020 in Alberta, Canada, and considered 15 CV diagnoses, as identified by International Classification of Diseases, 10th revision (ICD-10) codes: atrial fibrillation (AF), supraventricular tachycardia (SVT), ventricular tachycardia (VT), cardiac arrest (CA), atrioventricular block (AVB), unstable angina (UA), ST-elevation myocardial infarction (STEMI), non-STEMI (NSTEMI), pulmonary embolism (PE), hypertrophic cardiomyopathy (HCM), aortic stenosis (AS), mitral valve prolapse (MVP), mitral valve stenosis (MS), pulmonary hypertension (PHTN), and heart failure (HF). We employed ResNet-based deep learning (DL) using ECG tracings and extreme gradient boosting (XGB) using ECG measurements. When evaluated on the first ECGs per episode of 97,631 holdout patients, the DL models had an area under the receiver operating characteristic curve (AUROC) of <80% for 3 CV conditions (PTE, SVT, UA), 80-90% for 8 CV conditions (CA, NSTEMI, VT, MVP, PHTN, AS, AF, HF) and an AUROC > 90% for 4 diagnoses (AVB, HCM, MS, STEMI). DL models outperformed XGB models with about 5% higher AUROC on average. Overall, ECG-based prediction models demonstrated good-to-excellent prediction performance in diagnosing common CV conditions.
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Affiliation(s)
- Sunil Vasu Kalmady
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada
- Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Amir Salimi
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Weijie Sun
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Nariman Sepehrvand
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada
- Department of Medicine, University of Calgary, Calgary, AB, Canada
| | - Yousef Nademi
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Kevin Bainey
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada
- Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Justin Ezekowitz
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada
- Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Abram Hindle
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Finlay McAlister
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada
- Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Russel Greiner
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
| | - Roopinder Sandhu
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada
- Smidt Heart Institute, Cedars-Sinai Medical Center Hospital System, Los Angeles, CA, USA
| | - Padma Kaul
- Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada.
- Department of Medicine, University of Alberta, Edmonton, AB, Canada.
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13
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Lin CH, Liu ZY, Chen JS, Fann YC, Wen MS, Kuo CF. ECG-surv: A deep learning-based model to predict time to 1-year mortality from 12-lead electrocardiogram. Biomed J 2024:100732. [PMID: 38697480 DOI: 10.1016/j.bj.2024.100732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 03/12/2024] [Accepted: 04/18/2024] [Indexed: 05/05/2024] Open
Abstract
BACKGROUND Electrocardiogram (ECG) abnormalities have demonstrated potential as prognostic indicators of patient survival. However, the traditional statistical approach is constrained by structured data input, limiting its ability to fully leverage the predictive value of ECG data in prognostic modeling. METHODS This study aims to introduce and evaluate a deep-learning model to simultaneously handle censored data and unstructured ECG data for survival analysis. We herein introduce a novel deep neural network called ECG-surv, which includes a feature extraction neural network and a time-to-event analysis neural network. The proposed model is specifically designed to predict the time to 1-year mortality by extracting and analyzing unique features from 12-lead ECG data. ECG-surv was evaluated using both an independent test set and an external set, which were collected using different ECG devices. RESULTS The performance of ECG-surv surpassed that of the Cox proportional model, which included demographics and ECG waveform parameters, in predicting 1-year all-cause mortality, with a significantly higher concordance index (C-index) in ECG-surv than in the Cox model using both the independent test set (0.860 [95% CI: 0.859- 0.861] vs. 0.796 [95% CI: 0.791- 0.800]) and the external test set (0.813 [95% CI: 0.807- 0.814] vs. 0.764 [95% CI: 0.755- 0.770]). ECG-surv also demonstrated exceptional predictive ability for cardiovascular death (C-index of 0.891 [95% CI: 0.890- 0.893]), outperforming the Framingham risk Cox model (C-index of 0.734 [95% CI: 0.715-0.752]). CONCLUSION ECG-surv effectively utilized unstructured ECG data in a survival analysis. It outperformed traditional statistical approaches in predicting 1-year all-cause mortality and cardiovascular death, which makes it a valuable tool for predicting patient survival.
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Affiliation(s)
- Ching-Heng Lin
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan
| | - Zhi-Yong Liu
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Jung-Sheng Chen
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Yang C Fann
- Division of Intramural Research, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, United States
| | - Ming-Shien Wen
- Division of Cardiology, Chang Gung Memorial Hospital, Taoyuan, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chang-Fu Kuo
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan; Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital, Taoyuan, Taiwan.
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14
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Moreno-Sánchez PA, García-Isla G, Corino VDA, Vehkaoja A, Brukamp K, van Gils M, Mainardi L. ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review. Comput Biol Med 2024; 172:108235. [PMID: 38460311 DOI: 10.1016/j.compbiomed.2024.108235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 02/07/2024] [Accepted: 02/25/2024] [Indexed: 03/11/2024]
Abstract
Cardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different challenges still remain, such as an increasing unmet demand for skilled cardiologists capable of accurately interpreting ECG. This leads to higher workload and potential diagnostic inaccuracies. Data-driven approaches, such as machine learning (ML) and deep learning (DL) have emerged to improve existing computer-assisted solutions and enhance physicians' ECG interpretation of the complex mechanisms underlying CVD. However, many ML and DL models used to detect ECG-based CVD suffer from a lack of explainability, bias, as well as ethical, legal, and societal implications (ELSI). Despite the critical importance of these Trustworthy Artificial Intelligence (AI) aspects, there is a lack of comprehensive literature reviews that examine the current trends in ECG-based solutions for CVD diagnosis or prognosis that use ML and DL models and address the Trustworthy AI requirements. This review aims to bridge this knowledge gap by providing a systematic review to undertake a holistic analysis across multiple dimensions of these data-driven models such as type of CVD addressed, dataset characteristics, data input modalities, ML and DL algorithms (with a focus on DL), and aspects of Trustworthy AI like explainability, bias and ethical considerations. Additionally, within the analyzed dimensions, various challenges are identified. To these, we provide concrete recommendations, equipping other researchers with valuable insights to understand the current state of the field comprehensively.
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Affiliation(s)
| | - Guadalupe García-Isla
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Italy
| | - Valentina D A Corino
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Italy
| | - Antti Vehkaoja
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | - Mark van Gils
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Luca Mainardi
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Italy
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15
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Liu LR, Huang MY, Huang ST, Kung LC, Lee CH, Yao WT, Tsai MF, Hsu CH, Chu YC, Hung FH, Chiu HW. An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection. Heliyon 2024; 10:e27200. [PMID: 38486759 PMCID: PMC10937691 DOI: 10.1016/j.heliyon.2024.e27200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 02/18/2024] [Accepted: 02/26/2024] [Indexed: 03/17/2024] Open
Abstract
Arrhythmia, a frequently encountered and life-threatening cardiac disorder, can manifest as a transient or isolated event. Traditional automatic arrhythmia detection methods have predominantly relied on QRS-wave signal detection. Contemporary research has focused on the utilization of wearable devices for continuous monitoring of heart rates and rhythms through single-lead electrocardiogram (ECG), which holds the potential to promptly detect arrhythmias. However, in this study, we employed a convolutional neural network (CNN) to classify distinct arrhythmias without QRS wave detection step. The ECG data utilized in this study were sourced from the publicly accessible PhysioNet databases. Taking into account the impact of the duration of ECG signal on accuracy, this study trained one-dimensional CNN models with 5-s and 10-s segments, respectively, and compared their results. In the results, the CNN model exhibited the capability to differentiate between Normal Sinus Rhythm (NSR) and various arrhythmias, including Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Wolff-Parkinson-White syndrome (WPW), Ventricular Fibrillation (VF), Ventricular Tachycardia (VT), Ventricular Flutter (VFL), Mobitz II AV Block (MII), and Sinus Bradycardia (SB). Both 10-s and 5-s ECG segments exhibited comparable results, with an average classification accuracy of 97.31%. It reveals the feasibility of utilizing even shorter 5-s recordings for detecting arrhythmias in everyday scenarios. Detecting arrhythmias with a single lead aligns well with the practicality of wearable devices for daily use, and shorter detection times also align with their clinical utility in emergency situations.
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Affiliation(s)
- Liong-Rung Liu
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
| | - Ming-Yuan Huang
- Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
| | - Shu-Tien Huang
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
| | - Lu-Chih Kung
- Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
| | - Chao-hsiung Lee
- Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
| | - Wen-Teng Yao
- Division of Plastic Surgery, Department of Surgery, Mackay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
| | - Ming-Feng Tsai
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Division of Plastic Surgery, Department of Surgery, Mackay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
| | - Cheng-Hung Hsu
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Yu-Chang Chu
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Fei-Hung Hung
- Health Data Analytics and Statistics Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan
| | - Hung-Wen Chiu
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan
- Bioinformatics Data Science Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
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16
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V JP, S AAV, P GK, N K K. A novel attention-based cross-modal transfer learning framework for predicting cardiovascular disease. Comput Biol Med 2024; 170:107977. [PMID: 38217974 DOI: 10.1016/j.compbiomed.2024.107977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/19/2023] [Accepted: 01/08/2024] [Indexed: 01/15/2024]
Abstract
Cardiovascular disease (CVD) remains a leading cause of death globally, presenting significant challenges in early detection and treatment. The complexity of CVD arises from its multifaceted nature, influenced by a combination of genetic, environmental, and lifestyle factors. Traditional diagnostic approaches often struggle to effectively integrate and interpret the heterogeneous data associated with CVD. Addressing this challenge, we introduce a novel Attention-Based Cross-Modal (ABCM) transfer learning framework. This framework innovatively merges diverse data types, including clinical records, medical imagery, and genetic information, through an attention-driven mechanism. This mechanism adeptly identifies and focuses on the most pertinent attributes from each data source, thereby enhancing the model's ability to discern intricate interrelationships among various data types. Our extensive testing and validation demonstrate that the ABCM framework significantly surpasses traditional single-source models and other advanced multi-source methods in predicting CVD. Specifically, our approach achieves an accuracy of 93.5%, precision of 92.0%, recall of 94.5%, and an impressive area under the curve (AUC) of 97.2%. These results not only underscore the superior predictive capability of our model but also highlight its potential in offering more accurate and early detection of CVD. The integration of cross-modal data through attention-based mechanisms provides a deeper understanding of the disease, paving the way for more informed clinical decision-making and personalized patient care.
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Affiliation(s)
- Jothi Prakash V
- Karpagam College of Engineering, Myleripalayam Village, Coimbatore, 641032, Tamil Nadu, India.
| | - Arul Antran Vijay S
- Karpagam College of Engineering, Myleripalayam Village, Coimbatore, 641032, Tamil Nadu, India.
| | - Ganesh Kumar P
- College of Engineering, Guindy, Anna University, Chennai, 600025, Tamil Nadu, India.
| | - Karthikeyan N K
- Coimbatore Institute of Technology, Peelamedu, Coimbatore, 641014, Tamil Nadu, India.
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17
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Safdar MF, Nowak RM, Pałka P. Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review. Comput Biol Med 2024; 170:107908. [PMID: 38217973 DOI: 10.1016/j.compbiomed.2023.107908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/19/2023] [Accepted: 12/24/2023] [Indexed: 01/15/2024]
Abstract
Electrocardiogram (ECG) are the physiological signals and a standard test to measure the heart's electrical activity that depicts the movement of cardiac muscles. A review study has been conducted on ECG signals analysis with the help of artificial intelligence (AI) methods over the last ten years i.e., 2012-22. Primarily, the method of ECG analysis by software systems was divided into classical signal processing (e.g. spectrograms or filters), machine learning (ML) and deep learning (DL), including recursive models, transformers and hybrid. Secondly, the data sources and benchmark datasets were depicted. Authors grouped resources by ECG acquisition methods into hospital-based portable machines and wearable devices. Authors also included new trends like advanced pre-processing, data augmentation, simulations and agent-based modeling. The study found improvement in ECG examination perfection made each year through ML, DL, hybrid models, and transformers. Convolutional neural networks and hybrid models were more targeted and proved efficient. The transformer model extended the accuracy from 90% to 98%. The Physio-Net library helps acquire ECG signals, including the popular benchmark databases such as MIT-BIH, PTB, and challenging datasets. Similarly, wearable devices have been established as a appropriate option for monitoring patient health without the time and place limitations and are also helpful for AI model calibration with so far accuracy of 82%-83% on Samsung smartwatch. In the pre-processing signals, spectrogram generation through Fourier and wavelet transformations are erected leading approaches promoting on average accuracy of 90%-95%. Likewise, data enhancement using geometrical techniques is well-considered; however, extraction and concatenation-based methods need attention. As the what-if analysis in healthcare or cardiac issues can be performed using a complex simulation, the study reviews agent-based modeling and simulation approaches for cardiovascular risk event assessment.
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Affiliation(s)
- Muhammad Farhan Safdar
- Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland.
| | - Robert Marek Nowak
- Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland
| | - Piotr Pałka
- Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland
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18
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Rahmani R, Gholami Z, Ghanavati K, Ayati A, Shafiee A. Diagnostic value of electrocardiographic indices in discriminating the culprit vessel based on the coronary dominancy in inferior acute myocardial infarction. J Electrocardiol 2024; 83:111-116. [PMID: 38422574 DOI: 10.1016/j.jelectrocard.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 02/13/2024] [Accepted: 02/19/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND Identifying the culprit during inferior myocardial infarction (MI) is still challenging. We determined the diagnostic effect of electrocardiographic (ECG) indices in identifying the culprit vessel of acute MI and the impact of coronary artery dominance on it. METHODS This cross-sectional study included patients with acute inferior MI who presented to Imam Khomeini Hospital and Tehran Heart Center and underwent primary PCI within 12 h of the onset of symptoms. A standard 12‑lead ECG was recorded and interpreted by two cardiologists. Based on the coronary angiography, the patients were divided into two groups of LCX or RCA involvement and were compared for general variables and ECG indices. The diagnostic values of the ECG indices for predicting the culprit vessel were then calculated. RESULTS We evaluated 411 patients with inferior STEMI (321 [77.5%] male, age 58.1 ± 11.1 years). RCA was the culprit vessel in 286 patients (69.1%) and LCX in 128 patients (30.9%). 321 patients (77.5%) were right dominant, 40 (9.7%) patients were left dominant, and 53 patients (12.8%), were codominant. Coronary dominance had minimal impact on the ECG indices regarding culprit identification even after adjustment for confounders. STE in lead III > lead II had the highest sensitivity for detecting RCA as the culprit (sensitivity: 89.2% and specificity: 57.8%). STE ≥0.1 mV in V5 or V6 leads had the highest sensitivity for detecting LCX as the culprit (sensitivity: 51.6, specificity: 93.7%). CONCLUSION In inferior STEMI, ECG indices can predict the culprit vessel with acceptable sensitivity and specificity independent of coronary artery dominance.
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Affiliation(s)
- Reza Rahmani
- Department of Cardiology, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran.
| | - Zahra Gholami
- Department of Cardiology, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Kimia Ghanavati
- School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Aryan Ayati
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Akbar Shafiee
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.
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19
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Wang J, Pei S, Yang Y, Wang H. Convolutional transformer-driven robust electrocardiogram signal denoising framework with adaptive parametric ReLU. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:4286-4308. [PMID: 38549328 DOI: 10.3934/mbe.2024189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
The electrocardiogram (ECG) is a widely used diagnostic tool for cardiovascular diseases. However, ECG recording is often subject to various noises, which can limit its clinical evaluation. To address this issue, we propose a novel Transformer-based convolutional neural network framework with adaptively parametric ReLU (APtrans-CNN) for ECG signal denoising. The proposed APtrans-CNN architecture combines the strengths of transformers in global feature learning and CNNs in local feature learning to address the inadequacy of learning with long sequence time-series features. By fully exploiting the global features of ECG signals, our framework can effectively extract critical information that is necessary for signal denoising. We also introduce an adaptively parametric ReLU that can assign a value to the negative information contained in the ECG signal, thereby overcoming the limitation of ReLU to retain negative information. Additionally, we introduce a dynamic feature aggregation module that enables automatic learning and retention of valuable features while discarding useless noise information. Results obtained from two datasets demonstrate that our proposed APtrans-CNN can accurately extract pure ECG signals from noisy datasets and is adaptable to various applications. Specifically, when the input consists of ECG signals with a signal-to-noise ratio (SNR) of -4 dB, APtrans-CNN successfully increases the SNR to more than 6 dB, resulting in the diagnostic model's accuracy exceeding 96%.
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Affiliation(s)
- Jing Wang
- School of Computer Science, Xi'an Polytechnic University, Xi'an 710021, China
| | - Shicheng Pei
- Glasgow College, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yihang Yang
- School of Computer Science, Xi'an Polytechnic University, Xi'an 710021, China
| | - Huan Wang
- Glasgow College, University of Electronic Science and Technology of China, Chengdu 611731, China
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20
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Nishikimi R, Nakano M, Kashino K, Tsukada S. Variational autoencoder-based neural electrocardiogram synthesis trained by FEM-based heart simulator. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2024; 5:19-28. [PMID: 38390581 PMCID: PMC10879006 DOI: 10.1016/j.cvdhj.2023.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024] Open
Abstract
Background For comprehensive electrocardiogram (ECG) synthesis, a recent promising approach has been based on a heart model with physical and chemical cardiac parameters. However, the problem is that such approach requires a high-cost and limited environment using supercomputers owing to the massive computation. Objective The purpose of this study is to develop an efficient method for synthesizing 12-lead ECG signals from cardiac parameters. Methods The proposed method is based on a variational autoencoder (VAE). The encoder and decoder of the VAE are conditioned by the cardiac parameters so that it can model the relationship between the ECG signals and the cardiac parameters. The training data are produced by a comprehensive, finite element method (FEM)-based heart simulator. New ECG signals can then be synthesized by inputting the cardiac parameters into the trained VAE decoder without relying on enormous computational resources. We used 2 metrics to evaluate the quality of ECG signals synthesized by the proposed model. Results Experimental results showed that the proposed model synthesized adequate ECG signals while preserving empirically important feature points and the overall signal shapes. We also explored the optimal model by varying the number of layers and the size of latent variables in the proposed model that balances the model complexity and the simulation accuracy. Conclusion The proposed method has the potential to become an alternative to computationally expensive FEM-based heart simulators. It is able to synthesize ECGs from various cardiac parameters within seconds on a personal laptop computer.
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Affiliation(s)
- Ryo Nishikimi
- NTT Communication Science Laboratories, Atsugi, Japan
| | - Masahiro Nakano
- NTT Communication Science Laboratories, Atsugi, Japan
- NTT Basic Research Laboratories, Atsugi, Japan
| | - Kunio Kashino
- NTT Communication Science Laboratories, Atsugi, Japan
- NTT Basic Research Laboratories, Atsugi, Japan
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21
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Chen J, Huang S, Zhang Y, Chang Q, Zhang Y, Li D, Qiu J, Hu L, Peng X, Du Y, Gao Y, Chen DZ, Bellou A, Wu J, Liang H. Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts. Nat Commun 2024; 15:976. [PMID: 38302502 PMCID: PMC10834950 DOI: 10.1038/s41467-024-44930-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 01/10/2024] [Indexed: 02/03/2024] Open
Abstract
Early detection is critical to achieving improved treatment outcomes for child patients with congenital heart diseases (CHDs). Therefore, developing effective CHD detection techniques using low-cost and non-invasive pediatric electrocardiogram are highly desirable. We propose a deep learning approach for CHD detection, CHDdECG, which automatically extracts features from pediatric electrocardiogram and wavelet transformation characteristics, and integrates them with key human-concept features. Developed on 65,869 cases, CHDdECG achieved ROC-AUC of 0.915 and specificity of 0.881 on a real-world test set covering 12,000 cases. Additionally, on two external test sets with 7137 and 8121 cases, the overall ROC-AUC were 0.917 and 0.907 while specificities were 0.937 and 0.907. Notably, CHDdECG surpassed cardiologists in CHD detection performance comparison, and feature importance scores suggested greater influence of automatically extracted electrocardiogram features on CHD detection compared with human-concept features, implying that CHDdECG may grasp some knowledge beyond human cognition. Our study directly impacts CHD detection with pediatric electrocardiogram and demonstrates the potential of pediatric electrocardiogram for broader benefits.
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Affiliation(s)
- Jintai Chen
- State Key Laboratory of Transvascular Implantation Devices of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, 310009, Hangzhou, China
| | - Shuai Huang
- Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080, Guangzhou, Guangdong Province, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, Guangdong Province, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), 510080, Guangzhou, Guangdong Province, China
| | - Ying Zhang
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, Guangdong Province, China
- Department of Cardiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080, Guangzhou, Guangdong Province, China
| | - Qing Chang
- Liaoning Engineering Research Center of Intelligent Diagnosis and Treatment Ecosystem, 110004, Shenyang, Liaoning Province, China
- Clinical Research Center of Shengjing Hospital of China Medical University, 110004, Shenyang, Liaoning Province, China
| | - Yixiao Zhang
- Liaoning Engineering Research Center of Intelligent Diagnosis and Treatment Ecosystem, 110004, Shenyang, Liaoning Province, China
- Department of Urology Surgery, Shengjing Hospital of China Medical University, 110004, Shenyang, Liaoning Province, China
| | - Dantong Li
- Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080, Guangzhou, Guangdong Province, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, Guangdong Province, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), 510080, Guangzhou, Guangdong Province, China
| | - Jia Qiu
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, Guangdong Province, China
- Department of Cardiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080, Guangzhou, Guangdong Province, China
| | - Lianting Hu
- Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080, Guangzhou, Guangdong Province, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, Guangdong Province, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), 510080, Guangzhou, Guangdong Province, China
| | - Xiaoting Peng
- Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080, Guangzhou, Guangdong Province, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, Guangdong Province, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), 510080, Guangzhou, Guangdong Province, China
| | - Yunmei Du
- College of Information Technology and Engineering, Guangzhou College of Commerce, 510363, Guangzhou, Guangdong Province, China
- Guangzhou Women and Children's Medical Center, Guangzhou Medical University, 510623, Guangzhou, Guangdong Province, China
| | - Yunfei Gao
- Zhuhai Precision Medical Center, Zhuhai People's Hospital/ Zhuhai Hospital Affiliated with Jinan University, Jinan University, 519000, Zhuhai, Guangdong Province, China
- The Biomedical Translational Research Institute, Jinan University Faculty of Medical Science, Jinan University, 510632, Guangzhou, Guangdong Province, China
| | - Danny Z Chen
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Abdelouahab Bellou
- Institute of Sciences in Emergency Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, China.
- Department of Emergency Medicine, Wayne State University School of Medicine, Detroit, MI, 48201, USA.
| | - Jian Wu
- State Key Laboratory of Transvascular Implantation Devices of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, 310009, Hangzhou, China.
- School of Public Health, Zhejiang University, 310058, Hangzhou, China.
| | - Huiying Liang
- Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080, Guangzhou, Guangdong Province, China.
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, Guangdong Province, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), 510080, Guangzhou, Guangdong Province, China.
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22
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Huang Y, Yen GG, Tseng VS. Snippet Policy Network V2: Knee-Guided Neuroevolution for Multi-Lead ECG Early Classification. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:2167-2181. [PMID: 35816519 DOI: 10.1109/tnnls.2022.3187741] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Early time series classification predicts the class label of a given time series before it is completely observed. In time-critical applications, such as arrhythmia monitoring in ICU, early treatment contributes to the patient's fast recovery, and early warning could even save lives. Hence, in these cases, it is worthy of trading, to some extent, classification accuracy in favor of earlier decisions when the time series data are collected over time. In this article, we propose a novel deep reinforcement learning-based framework, snippet policy network V2 (SPN-V2), for long and varied-length multi-lead electrocardiogram (ECG) early classification. The proposed SNP-V2 contains two main components: snippet representation learning (SRL) and early classification timing learning (ECTL). The SRL is proposed to encode inner-snippet spatial correlations and inter-snippet temporal correlations into the hidden representations of the subsegment (snippet) of the input ECG. ECTL aims to learn a decision agent to classify the time series early and accurately. To optimize the proposed framework, we design a novel knee-guided neuroevolution algorithm (KGNA) to solve cardiovascular diseases' early classification problem, automatically optimizing the proposed SPN-V2 regarding the tradeoff between accuracy and earliness. In addition, we conduct a series of experiments on two real-world ECG datasets. The experimental results show the superiority of the proposed algorithm over the state-of-the-art competing methods.
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23
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Gong S, Lu Y, Yin J, Levin A, Cheng W. Materials-Driven Soft Wearable Bioelectronics for Connected Healthcare. Chem Rev 2024; 124:455-553. [PMID: 38174868 DOI: 10.1021/acs.chemrev.3c00502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
In the era of Internet-of-things, many things can stay connected; however, biological systems, including those necessary for human health, remain unable to stay connected to the global Internet due to the lack of soft conformal biosensors. The fundamental challenge lies in the fact that electronics and biology are distinct and incompatible, as they are based on different materials via different functioning principles. In particular, the human body is soft and curvilinear, yet electronics are typically rigid and planar. Recent advances in materials and materials design have generated tremendous opportunities to design soft wearable bioelectronics, which may bridge the gap, enabling the ultimate dream of connected healthcare for anyone, anytime, and anywhere. We begin with a review of the historical development of healthcare, indicating the significant trend of connected healthcare. This is followed by the focal point of discussion about new materials and materials design, particularly low-dimensional nanomaterials. We summarize material types and their attributes for designing soft bioelectronic sensors; we also cover their synthesis and fabrication methods, including top-down, bottom-up, and their combined approaches. Next, we discuss the wearable energy challenges and progress made to date. In addition to front-end wearable devices, we also describe back-end machine learning algorithms, artificial intelligence, telecommunication, and software. Afterward, we describe the integration of soft wearable bioelectronic systems which have been applied in various testbeds in real-world settings, including laboratories that are preclinical and clinical environments. Finally, we narrate the remaining challenges and opportunities in conjunction with our perspectives.
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Affiliation(s)
- Shu Gong
- Department of Chemical & Biological Engineering, Monash University, Clayton, Victoria 3800, Australia
| | - Yan Lu
- Department of Chemical & Biological Engineering, Monash University, Clayton, Victoria 3800, Australia
| | - Jialiang Yin
- Department of Chemical & Biological Engineering, Monash University, Clayton, Victoria 3800, Australia
| | - Arie Levin
- Department of Chemical & Biological Engineering, Monash University, Clayton, Victoria 3800, Australia
| | - Wenlong Cheng
- Department of Chemical & Biological Engineering, Monash University, Clayton, Victoria 3800, Australia
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24
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Nyström A, Olsson de Capretz P, Björkelund A, Lundager Forberg J, Ohlsson M, Björk J, Ekelund U. Prior electrocardiograms not useful for machine learning predictions of major adverse cardiac events in emergency department chest pain patients. J Electrocardiol 2024; 82:42-51. [PMID: 38006763 DOI: 10.1016/j.jelectrocard.2023.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 09/07/2023] [Accepted: 11/02/2023] [Indexed: 11/27/2023]
Abstract
At the emergency department (ED), it is important to quickly and accurately determine which patients are likely to have a major adverse cardiac event (MACE). Machine learning (ML) models can be used to aid physicians in detecting MACE, and improving the performance of such models is an active area of research. In this study, we sought to determine if ML models can be improved by including a prior electrocardiogram (ECG) from each patient. To that end, we trained several models to predict MACE within 30 days, both with and without prior ECGs, using data collected from 19,499 consecutive patients with chest pain, from five EDs in southern Sweden, between the years 2017 and 2018. Our results indicate no improvement in AUC from prior ECGs. This was consistent across models, both with and without additional clinical input variables, for different patient subgroups, and for different subsets of the outcome. While contradicting current best practices for manual ECG analysis, the results are positive in the sense that ML models with fewer inputs are more easily and widely applicable in practice.
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Affiliation(s)
- Axel Nyström
- Lund University, Department of Laboratory Medicine, Lund, Sweden.
| | - Pontus Olsson de Capretz
- Skåne University Hospital, Department of Internal and Emergency Medicine, Lund, Sweden; Lund University, Department of Clinical Sciences, Lund, Sweden
| | - Anders Björkelund
- Lund University, Center for Environmental and Climate Science, Lund, Sweden
| | - Jakob Lundager Forberg
- Lund University, Department of Clinical Sciences, Lund, Sweden; Helsingborg Hospital, Department of Emergency Medicine, Helsingborg, Sweden
| | - Mattias Ohlsson
- Lund University, Center for Environmental and Climate Science, Lund, Sweden; Halmstad University, Center for Applied Intelligent Systems Research (CAISR), Halmstad, Sweden
| | - Jonas Björk
- Lund University, Department of Laboratory Medicine, Lund, Sweden; Clinical Studies Sweden, Forum South, Skåne University Hospital, Lund, Sweden
| | - Ulf Ekelund
- Skåne University Hospital, Department of Internal and Emergency Medicine, Lund, Sweden; Lund University, Department of Clinical Sciences, Lund, Sweden
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25
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Chopannejad S, Roshanpoor A, Sadoughi F. Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE-Tomek method to detect cardiac arrhythmia based on 12 -lead electrocardiogram signals. Digit Health 2024; 10:20552076241234624. [PMID: 38449680 PMCID: PMC10916475 DOI: 10.1177/20552076241234624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 01/26/2024] [Indexed: 03/08/2024] Open
Abstract
Objectives Cardiac arrhythmia is one of the most severe cardiovascular diseases that can be fatal. Therefore, its early detection is critical. However, detecting types of arrhythmia by physicians based on visual identification is time-consuming and subjective. Deep learning can develop effective approaches to classify arrhythmias accurately and quickly. This study proposed a deep learning approach developed based on a Chapman-Shaoxing electrocardiogram (ECG) dataset signal to detect seven types of arrhythmias. Method Our DNN model is a hybrid CNN-BILSTM-BiGRU algorithm assisted by a multi-head self-attention mechanism regarding the challenging problem of classifying various arrhythmias of ECG signals. Additionally, the synthetic minority oversampling technique (SMOTE)-Tomek technique was utilized to address the data imbalance problem to detect and classify cardiac arrhythmias. Result The proposed model, trained with a single lead, was tested using a dataset containing 10,466 participants. The performance of the algorithm was evaluated using a random split validation approach. The proposed algorithm achieved an accuracy of 98.57% by lead II and 98.34% by lead aVF for the classification of arrhythmias. Conclusion We conducted an analysis of single-lead ECG signals to evaluate the effectiveness of our proposed hybrid model in diagnosing and classifying different types of arrhythmias. We trained separate classification models using each individual signal lead. Additionally, we implemented the SMOTE-Tomek technique along with cross-entropy loss as a cost function to address the class imbalance problem. Furthermore, we utilized a multi-headed self-attention mechanism to adjust the network structure and classify the seven arrhythmia classes. Our model achieved high accuracy and demonstrated good generalization ability in detecting ECG arrhythmias. However, further testing of the model with diverse datasets is crucial to validate its performance.
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Affiliation(s)
- Sara Chopannejad
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Arash Roshanpoor
- Department of Computer, Yadegar-e-Imam Khomeini (RAH), Janat-abad Branch, Islamic Azad University, Tehran, Iran
| | - Farahnaz Sadoughi
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
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26
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Ansari MY, Qaraqe M, Charafeddine F, Serpedin E, Righetti R, Qaraqe K. Estimating age and gender from electrocardiogram signals: A comprehensive review of the past decade. Artif Intell Med 2023; 146:102690. [PMID: 38042607 DOI: 10.1016/j.artmed.2023.102690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 10/13/2023] [Accepted: 10/18/2023] [Indexed: 12/04/2023]
Abstract
Twelve lead electrocardiogram signals capture unique fingerprints about the body's biological processes and electrical activity of heart muscles. Machine learning and deep learning-based models can learn the embedded patterns in the electrocardiogram to estimate complex metrics such as age and gender that depend on multiple aspects of human physiology. ECG estimated age with respect to the chronological age reflects the overall well-being of the cardiovascular system, with significant positive deviations indicating an aged cardiovascular system and a higher likelihood of cardiovascular mortality. Several conventional, machine learning, and deep learning-based methods have been proposed to estimate age from electronic health records, health surveys, and ECG data. This manuscript comprehensively reviews the methodologies proposed for ECG-based age and gender estimation over the last decade. Specifically, the review highlights that elevated ECG age is associated with atherosclerotic cardiovascular disease, abnormal peripheral endothelial dysfunction, and high mortality, among many other cardiovascular disorders. Furthermore, the survey presents overarching observations and insights across methods for age and gender estimation. This paper also presents several essential methodological improvements and clinical applications of ECG-estimated age and gender to encourage further improvements of the state-of-the-art methodologies.
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Affiliation(s)
- Mohammed Yusuf Ansari
- Texas A&M University, College Station, TX, USA; Texas A&M University at Qatar, Doha, Qatar.
| | - Marwa Qaraqe
- Division of Information and Computing Technology, Hamad Bin Khalifa University, Doha, Qatar; Texas A&M University at Qatar, Doha, Qatar
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27
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Wang J, Ji B, Lei Y, Liu T, Mao H, Yang X. Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal-to-noise ratio and speed of MRS. Med Phys 2023; 50:7955-7966. [PMID: 37947479 PMCID: PMC10872746 DOI: 10.1002/mp.16831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 10/05/2023] [Accepted: 10/25/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND While magnetic resonance imaging (MRI) provides high resolution anatomical images with sharp soft tissue contrast, magnetic resonance spectroscopy (MRS) enables non-invasive detection and measurement of biochemicals and metabolites. However, MRS has low signal-to-noise ratio (SNR) when concentrations of metabolites are in the range of millimolar. Standard approach of using a high number of signal averaging (NSA) to achieve sufficient SNR comes at the cost of a long acquisition time. PURPOSE We propose to use deep-learning approaches to denoise MRS data without increasing NSA. This method has potential to reduce the acquisition time as well as improve SNR and quality of spectra, which could enhance the diagnostic value and broaden the clinical applications of MRS. METHODS The study was conducted using data collected from the brain spectroscopy phantom and human subjects. We utilized a stack auto-encoder (SAE) network to train deep learning models for denoising low NSA data (NSA = 1, 2, 4, 8, and 16) randomly truncated from high SNR data collected with high NSA (NSA = 192), which were also used to obtain the ground truth. We applied both self-supervised and fully-supervised training approaches and compared their performance of denoising low NSA data based on improvement in SNR. To prevent overfitting, the SAE network was trained in a patch-based manner. We then tested the denoising methods on noise-containing data collected from the phantom and human subjects, including data from brain tumor patients. We evaluated their performance by comparing the SNR levels and mean squared errors (MSEs) calculated for the whole spectra against high SNR "ground truth", as well as the value of chemical shift of N-acetyl-aspartate (NAA) before and after denoising. RESULTS With the SAE model, the SNR of low NSA data (NSA = 1) obtained from the phantom increased by 28.5% and the MSE decreased by 42.9%. For low NSA data of the human parietal and temporal lobes, the SNR increased by 32.9% and the MSE decreased by 63.1%. In all cases, the chemical shift of NAA in the denoised spectra closely matched with the high SNR spectra without significant distortion to the spectra after denoising. Furthermore, the denoising performance of the SAE model was more effective in denoising spectra with higher noise levels. CONCLUSIONS The reported SAE denoising method is a model-free approach to enhance the SNR of MRS data collected with low NSA. With the denoising capability, it is possible to acquire MRS data with a few NSA, shortening the scan time while maintaining adequate spectroscopic information for detecting and quantifying the metabolites of interest. This approach has the potential to improve the efficiency and effectiveness of clinical MRS data acquisition by reducing the scan time and increasing the quality of spectroscopic data.
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Affiliation(s)
- Jing Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Bing Ji
- Department of Radiology and Imaging Science and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Tian Liu
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Hui Mao
- Department of Radiology and Imaging Science and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
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28
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Gao H, Wang X, Chen Z, Wu M, Li J, Liu C. ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning. IEEE J Biomed Health Inform 2023; 27:5225-5236. [PMID: 37713232 DOI: 10.1109/jbhi.2023.3315715] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/16/2023]
Abstract
The value of Electrocardiogram (ECG) monitoring in early cardiovascular disease (CVD) detection is undeniable, especially with the aid of intelligent wearable devices. Despite this, the requirement for expert interpretation significantly limits public accessibility, underscoring the need for advanced diagnosis algorithms. Deep learning-based methods represent a leap beyond traditional rule-based algorithms, but they are not without challenges such as small databases, inefficient use of local and global ECG information, high memory requirements for deploying multiple models, and the absence of task-to-task knowledge transfer. In response to these challenges, we propose a multi-resolution model adept at integrating local morphological characteristics and global rhythm patterns seamlessly. We also introduce an innovative ECG continual learning (ECG-CL) approach based on parameter isolation, designed to enhance data usage effectiveness and facilitate inter-task knowledge transfer. Our experiments, conducted on four publicly available databases, provide evidence of our proposed continual learning method's ability to perform incremental learning across domains, classes, and tasks. The outcome showcases our method's capability in extracting pertinent morphological and rhythmic features from ECG segmentation, resulting in a substantial enhancement of classification accuracy. This research not only confirms the potential for developing comprehensive ECG interpretation algorithms based on single-lead ECGs but also fosters progress in intelligent wearable applications. By leveraging advanced diagnosis algorithms, we aspire to increase the accessibility of ECG monitoring, thereby contributing to early CVD detection and ultimately improving healthcare outcomes.
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29
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Liang L, Liu M, Elefteriades J, Sun W. Synergistic Integration of Deep Neural Networks and Finite Element Method with Applications of Nonlinear Large Deformation Biomechanics. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2023; 416:116347. [PMID: 38370344 PMCID: PMC10871671 DOI: 10.1016/j.cma.2023.116347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Patient-specific finite element analysis (FEA) holds great promise in advancing the prognosis of cardiovascular diseases by providing detailed biomechanical insights such as high-fidelity stress and deformation on a patient-specific basis. Albeit feasible, FEA that incorporates three-dimensional, complex patient-specific geometry can be time-consuming and unsuitable for time-sensitive clinical applications. To mitigate this challenge, machine learning (ML) models, e.g., deep neural networks (DNNs), have been increasingly utilized as potential alternatives to finite element method (FEM) for biomechanical analysis. So far, efforts have been made in two main directions: (1) learning the input-to-output mapping of traditional FEM solvers and replacing FEM with data-driven ML surrogate models; (2) solving equilibrium equations using physics-informed loss functions of neural networks. While these two existing strategies have shown improved performance in terms of speed or scalability, ML models have not yet provided practical advantages over traditional FEM due to generalization issues. This has led us to the question: instead of abandoning or replacing the traditional FEM framework that can reliably solve biomechanical problems, can we integrate FEM and DNNs to enhance performance? In this study, we propose a synergistic integration of DNNs and FEM to overcome their individual limitations. Using biomechanical analysis of the human aorta as the test bed, we demonstrated two novel integrative strategies in forward and inverse problems. For the forward problem, we developed DNNs with state-of-the-art architectures to predict a nodal displacement field, and this initial DNN solution was then updated by a FEM-based refinement process, yielding a fast and accurate computing framework. For the inverse problem of heterogeneous material parameter identification, our method employs DNN as a regularizer of the spatial distribution of material parameters, aiding the optimizer in locating the optimal solution. In our demonstrative examples, despite that the DNN-only forward models yielded small displacement errors in most test cases; stress errors were considerably large, and for some test cases, the peak stress errors were greater than 50%. Our DNN-FEM integration eliminated these non-negligible errors in DNN-only models and was magnitudes faster than the FEM-only approach. Additionally, compared to FEM-only inverse method with errors greater than 50%, our DNN-FEM inverse approach significantly improved the parameter identification accuracy and reduced the errors to less than 1%.
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Affiliation(s)
- Liang Liang
- Department of Computer Science, University of Miami, Coral Gables, FL
| | - Minliang Liu
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA
| | - John Elefteriades
- Aortic Institute, School of Medicine, Yale University, New Haven, CT
| | - Wei Sun
- Sutra Medical Inc, Lake Forest, CA
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30
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Zhang F, Li M, Song L, Wu L, Wang B. Multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion. Front Physiol 2023; 14:1253907. [PMID: 37841309 PMCID: PMC10569425 DOI: 10.3389/fphys.2023.1253907] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 09/12/2023] [Indexed: 10/17/2023] Open
Abstract
Since ECG contains key characteristic information of arrhythmias, extracting this information is crucial for identifying arrhythmias. Based on this, in order to effectively extract ECG data features and realize automatic detection of arrhythmia, a multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion is proposed. First, the features of single-lead ECG signals are extracted and converted into two-dimensional images, and the feature data sets are labeled and divided according to different types of arrhythmias. The improved residual neural network is trained on the training set to obtain the classification model of the neural network. Finally, the classification model is applied to the automatic detection of arrhythmias during exercise. The accuracy of the classification model of this method is as high as 99.60%, and it has high accuracy and generalization ability. The automatic identification of arrhythmia also contributes to the research and development of future wearable devices.
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Affiliation(s)
- Fuchun Zhang
- School of Information Science and Engineering, Linyi University, Linyi, China
| | - Meng Li
- School of Information Science and Engineering, Linyi University, Linyi, China
| | - Li Song
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
| | - Liang Wu
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
| | - Baiyang Wang
- School of Information Science and Engineering, Shandong University, Qingdao, China
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31
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Hu L, Huang S, Liu H, Du Y, Zhao J, Peng X, Li D, Chen X, Yang H, Kong L, Tang J, Li X, Liang H, Liang H. A cardiologist-like computer-aided interpretation framework to improve arrhythmia diagnosis from imbalanced training datasets. PATTERNS (NEW YORK, N.Y.) 2023; 4:100795. [PMID: 37720326 PMCID: PMC10499877 DOI: 10.1016/j.patter.2023.100795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 03/06/2023] [Accepted: 06/16/2023] [Indexed: 09/19/2023]
Abstract
Arrhythmias can pose a significant threat to cardiac health, potentially leading to serious consequences such as stroke, heart failure, cardiac arrest, shock, and sudden death. In computer-aided electrocardiogram interpretation systems, the inclusion of certain classes of arrhythmias, which we term "aggressive" or "bullying," can lead to the underdiagnosis of other "vulnerable" classes. To address this issue, a method for arrhythmia diagnosis is proposed in this study. This method combines morphological-characteristic-based waveform clustering with Bayesian theory, drawing inspiration from the diagnostic reasoning of experienced cardiologists. The proposed method achieved optimal performance in macro-recall and macro-precision through hyperparameter optimization, including spliced heartbeats and clusters. In addition, with increasing bullying by aggressive arrhythmias, our model obtained the highest average recall and the lowest average drop in recall on the nine vulnerable arrhythmias. Furthermore, the maximum cluster characteristics were found to be consistent with established arrhythmia diagnostic criteria, lending interpretability to the proposed method.
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Affiliation(s)
- Lianting Hu
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Guangzhou, Guangdong 510080, China
- Medical Big Data Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Guangzhou, Guangdong 510080, China
| | - Shuai Huang
- Medical Big Data Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Guangzhou, Guangdong 510080, China
| | - Huazhang Liu
- Medical Big Data Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Guangzhou, Guangdong 510080, China
| | - Yunmei Du
- Medical Big Data Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
| | - Junfei Zhao
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Guangzhou, Guangdong 510080, China
| | - Xiaoting Peng
- Medical Big Data Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Guangzhou, Guangdong 510080, China
| | - Dantong Li
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Guangzhou, Guangdong 510080, China
- Medical Big Data Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Guangzhou, Guangdong 510080, China
| | - Xuanhui Chen
- Medical Big Data Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Guangzhou, Guangdong 510080, China
| | - Huan Yang
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Guangzhou, Guangdong 510080, China
- Medical Big Data Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Guangzhou, Guangdong 510080, China
| | - Lingcong Kong
- Medical Big Data Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Guangzhou, Guangdong 510080, China
| | - Jiajie Tang
- School of Information Management, Wuhan University, Wuhan, Hubei 430072, China
| | - Xin Li
- School of Information Management, Wuhan University, Wuhan, Hubei 430072, China
| | - Heng Liang
- School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
| | - Huiying Liang
- Medical Big Data Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Guangzhou, Guangdong 510080, China
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Ding C, Wang S, Jin X, Wang Z, Wang J. A novel transformer-based ECG dimensionality reduction stacked auto-encoders for arrhythmia beat detection. Med Phys 2023; 50:5897-5912. [PMID: 37470489 DOI: 10.1002/mp.16534] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 05/12/2023] [Accepted: 05/12/2023] [Indexed: 07/21/2023] Open
Abstract
BACKGROUND Electrocardiogram (ECG) is a powerful tool for studying cardiac activity and diagnosing various cardiovascular diseases, including arrhythmia. While machine learning and deep learning algorithms have been applied to ECG interpretation, there is still room for improvement. For instance, the commonly used Recurrent Neural Networks (RNNs), reply on its previous state to update and is therefore ineffective for parallel computing. RNN also struggles to efficiently address the issue of long-distance reliance. PURPOSE To reduce computational complexity by dimensionality reduction of ECG signals we constructed a Stacked Auto-encoders model using Transformer for ECG-based arrhythmia detection. And overcome the challenges of long-term dependencies and limited parallelizability in traditional RNNs when applied to ECG signal processing. METHODS In this paper, a Transformer-Based ECG Dimensionality Reduction Stacked Auto-encoders model is proposed for ECG-based arrhythmia detection. The transformer is used to encode ECG signals into a feature matrix, which is then dimensionally reduced using unsupervised greedy training through the four linear layers. This resulted in a low-dimensional representation of ECG features, which are subsequently classified using support vector machines (SVM) to minimize overfitting. RESULTS The proposed method is benchmarked on the MIT-BIH Arrhythmia database. In the 10-fold cross validation of beat-based arrhythmia detection, the average accuracy, sensitivity, specificity and F1 score of the proposed method are 99.83%, 98.84%, 99.84% and 99.13%, respectively, for the record-based arrhythmia detection which refers to the approach where the training and testing sets use ECG data from independent recorded patients are 88.10%, 49.79%, 91.56% and 39.95%, respectively. CONCLUSIONS Compared to other existing ECG-based arrhythmia detection methods, our proposed approach exhibits improved detection accuracy and stronger generalization for arrhythmia beats. Additionally, the use of the record-based data division method makes our approach more suitable for clinical practice.
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Affiliation(s)
- Chun Ding
- School of Software, Yunnan University, Kunming, Yunnan, China
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, Guangdong, China
| | - Shenglun Wang
- School of Software, Yunnan University, Kunming, Yunnan, China
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, Guangdong, China
| | - Xiaopeng Jin
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, Guangdong, China
| | - Zhaoze Wang
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Junsong Wang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, Guangdong, China
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Kent M, Vasconcelos L, Ansari S, Ghanbari H, Nenadic I. Fourier space approach for convolutional neural network (CNN) electrocardiogram (ECG) classification: A proof-of-concept study. J Electrocardiol 2023; 80:24-33. [PMID: 37141727 DOI: 10.1016/j.jelectrocard.2023.04.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 02/15/2023] [Accepted: 04/04/2023] [Indexed: 05/06/2023]
Abstract
There has been a proliferation of machine learning (ML) electrocardiogram (ECG) classification algorithms reaching > 85% accuracy for various cardiac pathologies. Although the accuracy within institutions might be high, models trained at one institution might not be generalizable enough for accurate detection when deployed in other institutions due to differences in type of signal acquisition, sampling frequency, time of acquisition, device noise characteristics and number of leads. In this proof-of-concept study, we leverage the publicly available PTB-XL dataset to investigate the use of time-domain (TD) and frequency-domain (FD) convolutional neural networks (CNN) to detect myocardial infarction (MI), ST/T-wave changes (STTC), atrial fibrillation (AFIB) and sinus arrhythmia (SARRH). To simulate interinstitutional deployment, the TD and FD implementations were also compared on adapted test sets using different sampling frequencies 50 Hz, 100 Hz and 250 Hz, and acquisition times of 5 s and 10s at 100 Hz sampling frequency from the training dataset. When tested on the original sampling frequency and duration, the FD approach showed comparable results to TD for MI (0.92 FD - 0.93 TD AUROC) and STTC (0.94 FD - 0.95 TD AUROC), and better performance for AFIB (0.99 FD - 0.86 TD AUROC) and SARRH (0.91 FD - 0.65 TD AUROC). Although both methods were robust to changes in sampling frequency, changes in acquisition time were detrimental to the TD MI and STTC AUROCs, at 0.72 and 0.58 respectively. Alternatively, the FD approach was able to maintain the same level of performance, and, therefore, showed better potential for interinstitutional deployment.
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Affiliation(s)
- Madeline Kent
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | | | - Sardar Ansari
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Hamid Ghanbari
- Cardiovascular Disease, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Ivan Nenadic
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA; Department of Radiology, Mayo Clinic, Rochester, MN, USA
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Liu W, Li Z, Zhang H, Chang S, Wang H, He J, Huang Q. Dense lead contrast for self-supervised representation learning of multilead electrocardiograms. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.03.099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
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Lai J, Tan H, Wang J, Ji L, Guo J, Han B, Shi Y, Feng Q, Yang W. Practical intelligent diagnostic algorithm for wearable 12-lead ECG via self-supervised learning on large-scale dataset. Nat Commun 2023; 14:3741. [PMID: 37353501 PMCID: PMC10290151 DOI: 10.1038/s41467-023-39472-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 06/15/2023] [Indexed: 06/25/2023] Open
Abstract
Cardiovascular disease is a major global public health problem, and intelligent diagnostic approaches play an increasingly important role in the analysis of electrocardiograms (ECGs). Convenient wearable ECG devices enable the detection of transient arrhythmias and improve patient health by making it possible to seek intervention during continuous monitoring. We collected 658,486 wearable 12-lead ECGs, among which 164,538 were annotated, and the remaining 493,948 were without diagnostic. We present four data augmentation operations and a self-supervised learning classification framework that can recognize 60 ECG diagnostic terms. Our model achieves an average area under the receiver-operating characteristic curve (AUROC) and average F1 score on the offline test of 0.975 and 0.575. The average sensitivity, specificity and F1-score during the 2-month online test are 0.736, 0.954 and 0.468, respectively. This approach offers real-time intelligent diagnosis, and detects abnormal segments in long-term ECG monitoring in the clinical setting for further diagnosis by cardiologists.
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Affiliation(s)
- Jiewei Lai
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Guangzhou, China
| | - Huixin Tan
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Guangzhou, China
| | - Jinliang Wang
- CardioCloud Medical Technology (Beijing) Co., Ltd., Beijing, China
| | - Lei Ji
- IT Department, Chinese PLA General Hospital, Beijing, China
| | - Jun Guo
- Department of Cardiology, Chinese PLA General Hospital, Beijing, China
| | - Baoshi Han
- Department of Cardiology, Chinese PLA General Hospital, Beijing, China
| | - Yajun Shi
- Department of Cardiology, Chinese PLA General Hospital, Beijing, China
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China.
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Guangzhou, China.
| | - Wei Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China.
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Guangzhou, China.
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Lyu H, Li X, Zhang J, Zhou C, Tang X, Xu F, Yang Y, Huang Q, Xiang W, Li D. Automated inter-patient arrhythmia classification with dual attention neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 236:107560. [PMID: 37116424 DOI: 10.1016/j.cmpb.2023.107560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 04/13/2023] [Accepted: 04/18/2023] [Indexed: 05/21/2023]
Abstract
BACKGROUND AND OBJECTIVES Arrhythmia classification based on electrocardiograms (ECG) can enhance clinical diagnostic efficiency. However, due to the significant differences in the number of different categories of heartbeats, the performance of classes with fewer samples in arrhythmia classification have not met expectations under the inter-patient paradigm. This paper aims to mitigate the adverse effects of category imbalance and improve arrhythmia classification performance. METHODS We constructed a novel dual attention hybrid network (DA-Net) for arrhythmia classification under sample imbalance, based on modified convolutional networks with channel attention (MCC-Net) and sequence-to-sequence network with global attention (Seq2Seq). The refined local features of the input heartbeat are first extracted by MCC-Net and then sent to Seq2Seq for further feature fusion. By applying local and global attention in the feature extraction and fusion parts, respectively, the method fully fuses low-level feature details and high-level context information and enhances the ability to extract discriminative features. RESULTS Based on the MIT-BIH arrhythmia database, under the inter-patient paradigm without any data augmentation methods, the proposed method achieved 99.98% accuracy (ACC) for five categories. The various performance indicators are as follows: Class N: sensitivity (SEN) = 99.96%, specificity (SPEC) = 99.93%, positive predictive value (PPV) = 99.99%; Class S: SEN = 99.67%, SPEC = 99.98%, PPV = 99.56%; Class V: SEN = 100%, SPEC = 99.99%, PPV = 99.91%; Class F: SEN = 100%, PPV = 99.98%, SPEC = 97.17%. In further experiments simulating extreme cases, the model still achieved ACC of 99.54% and 98.91% in the three-category and five-category categories when the training sample size was much smaller than the test sample. CONCLUSIONS Without any data augmentation methods, the proposed model not only alleviates the negative impact of class imbalance and achieves excellent performance in all categories but also provides a new approach for dealing with class imbalance in arrhythmia classification. Additionally, our method demonstrates potential in conditions with fewer samples.
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Affiliation(s)
- He Lyu
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China
| | - Xiangkui Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University. 37 Guoxue Alley, Chengdu 610041. China
| | - Jian Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University. 37 Guoxue Alley, Chengdu 610041. China
| | - Chenchen Zhou
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China
| | - Xuezhi Tang
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China
| | - Fanxin Xu
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China
| | - Ye Yang
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China
| | - Qinzhen Huang
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China.
| | - Wei Xiang
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission (Southwest Minzu University). Chengdu, China.
| | - Dong Li
- Division of Hospital Medicine, Emory School of Medicine, 201 Dowman Dr, Atlanta, GA 30322, USA
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Wang J, Sohn JJ, Lei Y, Nie W, Zhou J, Avery S, Liu T, Yang X. Deep learning-based protoacoustic signal denoising for proton range verification. Biomed Phys Eng Express 2023; 9:045006. [PMID: 37141867 DOI: 10.1088/2057-1976/acd257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 05/04/2023] [Indexed: 05/06/2023]
Abstract
Proton therapy is a type of radiation therapy that can provide better dose distribution compared to photon therapy by delivering most of the energy at the end of range, which is called the Bragg peak (BP). The protoacoustic technique was developed to determine the BP locationsin vivo, but it requires a large dose delivery to the tissue to obtain a high number of signal averaging (NSA) to achieve a sufficient signal-to-noise ratio (SNR), which is not suitable for clinical use. A novel deep learning-based technique has been proposed to denoise acoustic signals and reduce BP range uncertainty with much lower doses. Three accelerometers were placed on the distal surface of a cylindrical polyethylene (PE) phantom to collect protoacoustic signals. In total, 512 raw signals were collected at each device. Device-specific stack autoencoder (SAE) denoising models were trained to denoise the noise-containing input signals, which were generated by averaging only 1, 2, 4, 8, 16, or 24 raw signals (low NSA signals), while the clean signals were obtained by averaging 192 raw signals (high NSA). Both supervised and unsupervised training strategies were employed, and the evaluation of the models was based on mean squared error (MSE), SNR, and BP range uncertainty. Overall, the supervised SAEs outperformed the unsupervised SAEs in BP range verification. For the high accuracy detector, it achieved a BP range uncertainty of 0.20 ± 3.44 mm by averaging over 8 raw signals, while for the other two low accuracy detectors, they achieved the BP uncertainty of 1.44 ± 6.45 mm and -0.23 ± 4.88 mm by averaging 16 raw signals, respectively. This deep learning-based denoising method has shown promising results in enhancing the SNR of protoacoustic measurements and improving the accuracy in BP range verification. It greatly reduces the dose and time for potential clinical applications.
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Affiliation(s)
- Jing Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - James J Sohn
- Department of Radiation Oncology, Northwestern University, Chicago IL, United States of America
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Wei Nie
- Radiation Oncology Division, Inova Schar Cancer Institute, Fairfax, VA, United States of America
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Stephen Avery
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Tian Liu
- Department of Radiation Oncology, Mount Sinai Medical Center, New York, NY 10029, United States of America
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
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Liang L, Liu M, Elefteriades J, Sun W. Synergistic Integration of Deep Neural Networks and Finite Element Method with Applications for Biomechanical Analysis of Human Aorta. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.03.535423. [PMID: 37066215 PMCID: PMC10104001 DOI: 10.1101/2023.04.03.535423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Motivation: Patient-specific finite element analysis (FEA) has the potential to aid in the prognosis of cardiovascular diseases by providing accurate stress and deformation analysis in various scenarios. It is known that patient-specific FEA is time-consuming and unsuitable for time-sensitive clinical applications. To mitigate this challenge, machine learning (ML) techniques, including deep neural networks (DNNs), have been developed to construct fast FEA surrogates. However, due to the data-driven nature of these ML models, they may not generalize well on new data, leading to unacceptable errors. Methods We propose a synergistic integration of DNNs and finite element method (FEM) to overcome each other’s limitations. We demonstrated this novel integrative strategy in forward and inverse problems. For the forward problem, we developed DNNs using state-of-the-art architectures, and DNN outputs were then refined by FEM to ensure accuracy. For the inverse problem of heterogeneous material parameter identification, our method employs a DNN as regularization for the inverse analysis process to avoid erroneous material parameter distribution. Results We tested our methods on biomechanical analysis of the human aorta. For the forward problem, the DNN-only models yielded acceptable stress errors in majority of test cases; yet, for some test cases that could be out of the training distribution (OOD), the peak stress errors were larger than 50%. The DNN-FEM integration eliminated the large errors for these OOD cases. Moreover, the DNN-FEM integration was magnitudes faster than the FEM-only approach. For the inverse problem, the FEM-only inverse method led to errors larger than 50%, and our DNN-FEM integration significantly improved performance on the inverse problem with errors less than 1%.
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Ran S, Li X, Zhao B, Jiang Y, Yang X, Cheng C. Label correlation embedding guided network for multi-label ECG arrhythmia diagnosis. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
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40
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Yoo H, Yum Y, Park SW, Lee JM, Jang M, Kim Y, Kim JH, Park HJ, Han KS, Park JH, Joo HJ. Standardized Database of 12-Lead Electrocardiograms with a Common Standard for the Promotion of Cardiovascular Research: KURIAS-ECG. Healthc Inform Res 2023; 29:132-144. [PMID: 37190737 PMCID: PMC10209728 DOI: 10.4258/hir.2023.29.2.132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 02/22/2023] [Accepted: 03/10/2023] [Indexed: 05/17/2023] Open
Abstract
OBJECTIVES Electrocardiography (ECG)-based diagnosis by experts cannot maintain uniform quality because individual differences may occur. Previous public databases can be used for clinical studies, but there is no common standard that would allow databases to be combined. For this reason, it is difficult to conduct research that derives results by combining databases. Recent commercial ECG machines offer diagnoses similar to those of a physician. Therefore, the purpose of this study was to construct a standardized ECG database using computerized diagnoses. METHODS The constructed database was standardized using Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Observational Medical Outcomes Partnership-common data model (OMOP-CDM), and data were then categorized into 10 groups based on the Minnesota classification. In addition, to extract high-quality waveforms, poor-quality ECGs were removed, and database bias was minimized by extracting at least 2,000 cases for each group. To check database quality, the difference in baseline displacement according to whether poor ECGs were removed was analyzed, and the usefulness of the database was verified with seven classification models using waveforms. RESULTS The standardized KURIAS-ECG database consists of high-quality ECGs from 13,862 patients, with about 20,000 data points, making it possible to obtain more than 2,000 for each Minnesota classification. An artificial intelligence classification model using the data extracted through SNOMED-CT showed an average accuracy of 88.03%. CONCLUSIONS The KURIAS-ECG database contains standardized ECG data extracted from various machines. The proposed protocol should promote cardiovascular disease research using big data and artificial intelligence.
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Affiliation(s)
- Hakje Yoo
- Korea University Research Institute for Medical Bigdata Science, Korea University College of Medicine, Seoul,
Korea
| | - Yunjin Yum
- Department of Biostatistics, Korea University College of Medicine, Seoul,
Korea
| | - Soo Wan Park
- Korea University Research Institute for Medical Bigdata Science, Korea University College of Medicine, Seoul,
Korea
| | - Jeong Moon Lee
- Korea University Research Institute for Medical Bigdata Science, Korea University College of Medicine, Seoul,
Korea
| | - Moonjoung Jang
- Korea University Research Institute for Medical Bigdata Science, Korea University College of Medicine, Seoul,
Korea
| | - Yoojoong Kim
- School of Computer Science and Information Engineering, The Catholic University of Korea, Bucheon,
Korea
| | - Jong-Ho Kim
- Department of Cardiology, Cardiovascular Center, Korea University College of Medicine, Seoul,
Korea
| | - Hyun-Joon Park
- Korea University Research Institute for Healthcare Service Innovation, Korea University College of Medicine, Seoul,
Korea
| | - Kap Su Han
- Department of Emergency Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul,
Korea
| | - Jae Hyoung Park
- Department of Cardiology, Cardiovascular Center, Korea University College of Medicine, Seoul,
Korea
| | - Hyung Joon Joo
- Korea University Research Institute for Medical Bigdata Science, Korea University College of Medicine, Seoul,
Korea
- Department of Cardiology, Cardiovascular Center, Korea University College of Medicine, Seoul,
Korea
- Department of Medical Informatics, Korea University College of Medicine, Seoul,
Korea
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41
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Park HB, Lee J, Hong Y, Byungchang S, Kim W, Lee BK, Lin FY, Hadamitzky M, Kim YJ, Conte E, Andreini D, Pontone G, Budoff MJ, Gottlieb I, Chun EJ, Cademartiri F, Maffei E, Marques H, Gonçalves PDA, Leipsic JA, Shin S, Choi JH, Virmani R, Samady H, Chinnaiyan K, Stone PH, Berman DS, Narula J, Shaw LJ, Bax JJ, Min JK, Kook W, Chang HJ. Risk factors based vessel-specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method: Results from the PARADIGM registry. Clin Cardiol 2023; 46:320-327. [PMID: 36691990 PMCID: PMC10018106 DOI: 10.1002/clc.23964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/12/2022] [Accepted: 12/19/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND AND HYPOTHESIS The recently introduced Bayesian quantile regression (BQR) machine-learning method enables comprehensive analyzing the relationship among complex clinical variables. We analyzed the relationship between multiple cardiovascular (CV) risk factors and different stages of coronary artery disease (CAD) using the BQR model in a vessel-specific manner. METHODS From the data of 1,463 patients obtained from the PARADIGM (NCT02803411) registry, we analyzed the lumen diameter stenosis (DS) of the three vessels: left anterior descending (LAD), left circumflex (LCx), and right coronary artery (RCA). Two models for predicting DS and DS changes were developed. Baseline CV risk factors, symptoms, and laboratory test results were used as the inputs. The conditional 10%, 25%, 50%, 75%, and 90% quantile functions of the maximum DS and DS change of the three vessels were estimated using the BQR model. RESULTS The 90th percentiles of the DS of the three vessels and their maximum DS change were 41%-50% and 5.6%-7.3%, respectively. Typical anginal symptoms were associated with the highest quantile (90%) of DS in the LAD; diabetes with higher quantiles (75% and 90%) of DS in the LCx; dyslipidemia with the highest quantile (90%) of DS in the RCA; and shortness of breath showed some association with the LCx and RCA. Interestingly, High-density lipoprotein cholesterol showed a dynamic association along DS change in the per-patient analysis. CONCLUSIONS This study demonstrates the clinical utility of the BQR model for evaluating the comprehensive relationship between risk factors and baseline-grade CAD and its progression.
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Affiliation(s)
- Hyung-Bok Park
- CONNECT-AI Research Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea
- Department of Cardiology, Catholic Kwandong University International St. Mary's Hospital, Incheon, South Korea
| | - Jina Lee
- CONNECT-AI Research Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea
- Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul, South Korea
| | - Yongtaek Hong
- CONNECT-AI Research Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea
| | - So Byungchang
- Department of Mathematical Sciences, Seoul National University, Seoul, South Korea
| | - Wonse Kim
- Department of Mathematical Sciences, Seoul National University, Seoul, South Korea
- MetaEyes, Seoul, South Korea
| | - Byoung K Lee
- Department of Cardiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Fay Y Lin
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York City, New York, USA
| | - Martin Hadamitzky
- Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany
| | - Yong-Jin Kim
- Division of Cardiology, Seoul National University College of Medicine, Cardiovascular Center, Seoul National University Hospital, Seoul, South Korea
| | | | | | | | - Matthew J Budoff
- Department of Medicine, Lundquist Institute at Harbor UCLA Medical Center, Torrance, California, USA
| | - Ilan Gottlieb
- Department of Radiology, Casa de Saude São Jose, Rio de Janeiro, Brazil
| | - Eun Ju Chun
- Seoul National University Bundang Hospital, Sungnam, South Korea
| | | | - Erica Maffei
- Department of Radiology, Fondazione Monasterio/CNR, Pisa, Italy
| | - Hugo Marques
- Unit of Cardiovascular Imaging, Hospital da Luz, Catolica Medical School, Lisbon, Portugal
| | - Pedro de A Gonçalves
- Unit of Cardiovascular Imaging, Hospital da Luz, Catolica Medical School, Lisbon, Portugal
- Nova Medical School, Lisbon, Portugal
| | - Jonathon A Leipsic
- Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sanghoon Shin
- Department of Cardiology, Ewha Womans University Seoul Hospital, Seoul, South Korea
| | - Jung H Choi
- Department of Cardiology, Pusan University Hospital, Busan, South Korea
| | - Renu Virmani
- Department of Pathology, CVPath Institute, Gaithersburg, Maryland, USA
| | - Habib Samady
- Department of Cardiology, Georgia Heart Institute, Northeast Georgia Health System, Georgia, USA
| | - Kavitha Chinnaiyan
- Department of Cardiology, William Beaumont Hospital, Royal Oak, Michigan, USA
| | - Peter H Stone
- Department of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Daniel S Berman
- Department of Imaging and Medicine, Cedars Sinai Medical Center, Los Angeles, California, USA
| | - Jagat Narula
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, Zena and Michael A. Wiener Cardiovascular Institute, and Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, New York City, New York, USA
| | - Leslee J Shaw
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York City, New York, USA
| | - Jeroen J Bax
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | - James K Min
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York City, New York, USA
| | - Woong Kook
- Department of Mathematical Sciences, Seoul National University, Seoul, South Korea
| | - Hyuk-Jae Chang
- CONNECT-AI Research Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea
- Department of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea
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Mehri M, Calmon G, Odille F, Oster J. A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision. SENSORS (BASEL, SWITZERLAND) 2023; 23:2288. [PMID: 36850889 PMCID: PMC9963088 DOI: 10.3390/s23042288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/14/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Providing reliable detection of QRS complexes is key in automated analyses of electrocardiograms (ECG). Accurate and timely R-peak detections provide a basis for ECG-based diagnoses and to synchronize radiologic, electrophysiologic, or other medical devices. Compared with classical algorithms, deep learning (DL) architectures have demonstrated superior accuracy and high generalization capacity. Furthermore, they can be embedded on edge devices for real-time inference. 3D vectorcardiograms (VCG) provide a unifying framework for detecting R-peaks regardless of the acquisition strategy or number of ECG leads. In this article, a DL architecture was demonstrated to provide enhanced precision when trained and applied on 3D VCG, with no pre-processing nor post-processing steps. Experiments were conducted on four different public databases. Using the proposed approach, high F1-scores of 99.80% and 99.64% were achieved in leave-one-out cross-validation and cross-database validation protocols, respectively. False detections, measured by a precision of 99.88% or more, were significantly reduced compared with recent state-of-the-art methods tested on the same databases, without penalty in the number of missed peaks, measured by a recall of 99.39% or more. This approach can provide new applications for devices where precision, or positive predictive value, is essential, for instance cardiac magnetic resonance imaging.
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Affiliation(s)
- Maroua Mehri
- Epsidy, 54000 Nancy, France
- Ecole Nationale d’Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems, Université de Sousse, Sousse 4023, Tunisia
| | | | - Freddy Odille
- Epsidy, 54000 Nancy, France
- IADI-Imagerie Adaptative Diagnostique et Interventionnelle, Inserm U1254, Université de Lorraine, 54000 Nancy, France
- CIC-IT 1433, Inserm, CHRU de Nancy, Université de Lorraine, 54000 Nancy, France
| | - Julien Oster
- IADI-Imagerie Adaptative Diagnostique et Interventionnelle, Inserm U1254, Université de Lorraine, 54000 Nancy, France
- CIC-IT 1433, Inserm, CHRU de Nancy, Université de Lorraine, 54000 Nancy, France
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43
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Bortolan G. 3D ECG display with deep learning approach for identification of cardiac abnormalities from a variable number of leads. Physiol Meas 2023; 44. [PMID: 36657171 DOI: 10.1088/1361-6579/acb4dc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 01/19/2023] [Indexed: 01/21/2023]
Abstract
Objective.The objective of this study is to explore new imaging techniques with the use of the deep learning method for the identification of cardiac abnormalities present in electrocardiogram (ECG) signals with 2, 3, 4, 6 and 12-lead in the framework of the PhysioNet/Computing in Cardiology Challenge 2021. The training set is a public database of 88,253 twelve-lead ECG recordings lasting from 6 s to 60 s. Each ECG recording has one or more diagnostic labels. The six-lead, four-lead, three-lead, and two-lead are reduced-lead versions of the original twelve-lead data.Approach.The deep learning method considers images that are built from raw ECG signals. This technique considers innovative 3D display of the entire ECG signal, observing the regional constraints of the leads, obtaining time-spatial images of the 12 leads, where the x-axis is the temporal evolution of ECG signal, the y-axis is the spatial location of the leads, and the z-axis (color) the amplitude. These images are used for training Convolutional Neural Networks with GoogleNet for ECG diagnostic classification.Main results.The official results of the classification accuracy of our team named 'Gio_new_img' received scores of 0.4, 0.4, 0.39, 0.4 and 0.4 (ranked 18th, 18th, 18th,18th, 18th out of 39 teams) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions of the hidden test set with the Challenge evaluation metric.Significance.The results indicated that all these algorithms have similar behaviour in the various lead groups, and the most surprising and interesting point is the fact that the 2-lead scores are similar to those obtained with the analysis of 12 leads. It permitted to test the diagnostic potential of the reduced-lead ECG recordings. These aspects can be related to the pattern recognition capacity and generalizability of the deep learning approach and/or to the fact that the characteristics of the considered cardiac abnormalities can be extracted also from a reduced set of leads.
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44
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Liu W, Guo Q, Chen S, Chang S, Wang H, He J, Huang Q. A fully-mapped and energy-efficient FPGA accelerator for dual-function AI-based analysis of ECG. Front Physiol 2023; 14:1079503. [PMID: 36814476 PMCID: PMC9939833 DOI: 10.3389/fphys.2023.1079503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 01/23/2023] [Indexed: 02/09/2023] Open
Abstract
In this paper, a fully-mapped field programmable gate array (FPGA) accelerator is proposed for artificial intelligence (AI)-based analysis of electrocardiogram (ECG). It consists of a fully-mapped 1-D convolutional neural network (CNN) and a fully-mapped heart rate estimator, which constitute a complementary dual-function analysis. The fully-mapped design projects each layer of the 1-D CNN to a hardware module on an Intel Cyclone V FPGA, and a virtual flatten layer is proposed to effectively bridge the feature extraction layers and fully-connected layer. Also, the fully-mapped design maximizes computational parallelism to accelerate CNN inference. For the fully-mapped heart rate estimator, it performs pipelined transformations, self-adaptive threshold calculation, and heartbeat count on the FPGA, without multiplexed usage of hardware resources. Furthermore, heart rate calculation is elaborately analyzed and optimized to remove division and acceleration, resulting in an efficient method suitable for hardware implementation. According to our experiments on 1-D CNN, the accelerator can achieve 43.08× and 8.38× speedup compared with the software implementations on ARM-Cortex A53 quad-core processor and Intel Core i7-8700 CPU, respectively. For the heart rate estimator, the hardware implementations are 25.48× and 1.55× faster than the software implementations on the two aforementioned platforms. Surprisingly, the accelerator achieves an energy efficiency of 63.48 GOPS/W, which obviously surpasses existing studies. Considering its power consumption is only 67.74 mW, it may be more suitable for resource-limited applications, such as wearable and portable devices for ECG monitoring.
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45
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Fernández–Calvillo MG, Goya–Esteban R, Cruz–Roldán F, Hernández–Madrid A, Blanco–Velasco M. Machine Learning approach for TWA detection relying on ensemble data design. Heliyon 2023; 9:e12947. [PMID: 36699267 PMCID: PMC9868537 DOI: 10.1016/j.heliyon.2023.e12947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/23/2022] [Accepted: 01/10/2023] [Indexed: 01/17/2023] Open
Abstract
Background and objective T-wave alternans (TWA) is a fluctuation of the ST-T complex of the surface electrocardiogram (ECG) on an every-other-beat basis. It has been shown to be clinically helpful for sudden cardiac death stratification, though the lack of a gold standard to benchmark detection methods limits its application and impairs the development of alternative techniques. In this work, a novel approach based on machine learning for TWA detection is proposed. Additionally, a complete experimental setup is presented for TWA detection methods benchmarking. Methods The proposed experimental setup is based on the use of open-source databases to enable experiment replication and the use of real ECG signals with added TWA episodes. Also, intra-patient overfitting and class imbalance have been carefully avoided. The Spectral Method (SM), the Modified Moving Average Method (MMA), and the Time Domain Method (TM) are used to obtain input features to the Machine Learning (ML) algorithms, namely, K Nearest Neighbor, Decision Trees, Random Forest, Support Vector Machine and Multi-Layer Perceptron. Results There were not found large differences in the performance of the different ML algorithms. Decision Trees showed the best overall performance (accuracy 0.88 ± 0.04 , precision 0.89 ± 0.05 , Recall 0.90 ± 0.05 , F1 score 0.89 ± 0.03 ). Compared to the SM (accuracy 0.79, precision 0.93, Recall 0.64, F1 score 0.76) there was an improvement in every metric except for the precision. Conclusions In this work, a realistic database to test the presence of TWA using ML algorithms was assembled. The ML algorithms overall outperformed the SM used as a gold standard. Learning from data to identify alternans elicits a substantial detection growth at the expense of a small increment of the false alarm.
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Affiliation(s)
| | - Rebeca Goya–Esteban
- Department of Teoría de la Señal y Comunicaciones, Universidad Rey Juan Carlos, Madrid, Spain
| | - Fernando Cruz–Roldán
- Department of Teoría de la Señal y Comunicaciones, Universidad de Alcalá, Madrid, Spain
| | | | - Manuel Blanco–Velasco
- Department of Teoría de la Señal y Comunicaciones, Universidad de Alcalá, Madrid, Spain,Corresponding author.
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Song Y, Chen J, Zhang R. Heart Rate Estimation from Incomplete Electrocardiography Signals. SENSORS (BASEL, SWITZERLAND) 2023; 23:597. [PMID: 36679394 PMCID: PMC9860828 DOI: 10.3390/s23020597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/23/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
As one of the most remarkable indicators of physiological health, heart rate (HR) has become an unfailing investigation for researchers. Unlike many existing methods, this article proposes an approach to implement short-time HR estimation from electrocardiography in time series missing patterns. Benefiting from the rapid development of deep learning, we adopted a bidirectional long short-term memory model (Bi-LSTM) and temporal convolution network (TCN) to recover complete heartbeat signals from those with durations are less than one cardiac cycle, and the estimated HR from recovered segment combining the input and the predicted output. We also compared the performance of Bi-LSTM and TCN in PhysioNet dataset. Validating the method over a resting heart rate range of 60−120 bpm in the database without significant arrhythmias and a corresponding range of 30−150 bpm in the database with arrhythmias, we found that networks provide an estimated approach for incomplete signals in a fixed format. These results are consistent with real heartbeats in the normal heartbeat dataset (γ > 0.7, RMSE < 10) and in the arrhythmia database (γ > 0.6, RMSE < 30), verifying that HR could be estimated by models in advance. We also discussed the short-time limits for the predictive model. It could be used for physiological purposes such as mobile sensing in time-constrained scenarios, and providing useful insights for better time series analyses in missing data patterns.
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Affiliation(s)
- Yawei Song
- School of Electronic Science and Engineering (National Model Microelectronics College), Xiamen University, Xiamen 361005, China
| | - Jia Chen
- School of Electronic Science and Engineering (National Model Microelectronics College), Xiamen University, Xiamen 361005, China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, China
| | - Rongxin Zhang
- Key Laboratory of Underwater Acoustic Communication and Marine Information Technology, Xiamen University, Ministry of Education, Xiamen 361005, China
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Feng P, Fu J, Wang N, Zhou Y, Zhou B, Wang Z. Semantic-aware alignment and label propagation for cross-domain arrhythmia classification. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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48
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Tutuko B, Darmawahyuni A, Nurmaini S, Tondas AE, Naufal Rachmatullah M, Teguh SBP, Firdaus F, Sapitri AI, Passarella R. DAE-ConvBiLSTM: End-to-end learning single-lead electrocardiogram signal for heart abnormalities detection. PLoS One 2022; 17:e0277932. [PMID: 36584187 PMCID: PMC9803308 DOI: 10.1371/journal.pone.0277932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 11/08/2022] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND The electrocardiogram (ECG) is a widely used diagnostic that observes the heart activities of patients to ascertain a heart abnormality diagnosis. The artifacts or noises are primarily associated with the problem of ECG signal processing. Conventional denoising techniques have been proposed in previous literature; however, some lacks, such as the determination of suitable wavelet basis function and threshold, can be a time-consuming process. This paper presents end-to-end learning using a denoising auto-encoder (DAE) for denoising algorithms and convolutional-bidirectional long short-term memory (ConvBiLSTM) for ECG delineation to classify ECG waveforms in terms of the PQRST-wave and isoelectric lines. The denoising reconstruction using unsupervised learning based on the encoder-decoder process can be proposed to improve the drawbacks. First, The ECG signals are reduced to a low-dimensional vector in the encoder. Second, the decoder reconstructed the signals. The last, the reconstructed signals of ECG can be processed to ConvBiLSTM. The proposed architecture of DAE-ConvBiLSTM is the end-to-end diagnosis of heart abnormality detection. RESULTS As a result, the performance of DAE-ConvBiLSTM has obtained an average of above 98.59% accuracy, sensitivity, specificity, precision, and F1 score from the existing studies. The DAE-ConvBiLSTM has also experimented with detecting T-wave (due to ventricular repolarisation) morphology abnormalities. CONCLUSION The development architecture for detecting heart abnormalities using an unsupervised learning DAE and supervised learning ConvBiLSTM can be proposed for an end-to-end learning algorithm. In the future, the precise accuracy of the ECG main waveform will affect heart abnormalities detection in clinical practice.
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Affiliation(s)
- Bambang Tutuko
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indonesia
| | - Annisa Darmawahyuni
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indonesia
- * E-mail: , .id (SN); , .id (AD)
| | - Siti Nurmaini
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indonesia
- * E-mail: , .id (SN); , .id (AD)
| | - Alexander Edo Tondas
- Department of Cardiology & Vascular Medicine, Dr. Mohammad Hoesin Hospital, Palembang, Indonesia
| | | | | | - Firdaus Firdaus
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indonesia
| | - Ade Iriani Sapitri
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indonesia
| | - Rossi Passarella
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indonesia
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Ayano YM, Schwenker F, Dufera BD, Debelee TG. Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review. Diagnostics (Basel) 2022; 13:111. [PMID: 36611403 PMCID: PMC9818170 DOI: 10.3390/diagnostics13010111] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/22/2022] [Accepted: 12/23/2022] [Indexed: 12/31/2022] Open
Abstract
Heart disease is one of the leading causes of mortality throughout the world. Among the different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non-invasive procedure. However, the following are challenges: the scarcity of medical experts, the complexity of ECG interpretations, the manifestation similarities of heart disease in ECG signals, and heart disease comorbidity. Machine learning algorithms are viable alternatives to the traditional diagnoses of heart disease from ECG signals. However, the black box nature of complex machine learning algorithms and the difficulty in explaining a model's outcomes are obstacles for medical practitioners in having confidence in machine learning models. This observation paves the way for interpretable machine learning (IML) models as diagnostic tools that can build a physician's trust and provide evidence-based diagnoses. Therefore, in this systematic literature review, we studied and analyzed the research landscape in interpretable machine learning techniques by focusing on heart disease diagnosis from an ECG signal. In this regard, the contribution of our work is manifold; first, we present an elaborate discussion on interpretable machine learning techniques. In addition, we identify and characterize ECG signal recording datasets that are readily available for machine learning-based tasks. Furthermore, we identify the progress that has been achieved in ECG signal interpretation using IML techniques. Finally, we discuss the limitations and challenges of IML techniques in interpreting ECG signals.
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Affiliation(s)
| | | | - Bisrat Derebssa Dufera
- Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa 11760, Ethiopia
| | - Taye Girma Debelee
- Ethiopian Artificial Intelligence Institute, Addis Ababa 40782, Ethiopia
- College of Electrical and Computer Engineering, Addis Ababa Science and Technology University, Addis Ababa 16417, Ethiopia
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50
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Hammad M, Meshoul S, Dziwiński P, Pławiak P, Elgendy IA. Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification. SENSORS (BASEL, SWITZERLAND) 2022; 22:9347. [PMID: 36502049 PMCID: PMC9736761 DOI: 10.3390/s22239347] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/29/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
An arrhythmia happens when the electrical signals that organize the heartbeat do not work accurately. Most cases of arrhythmias may increase the risk of stroke or cardiac arrest. As a result, early detection of arrhythmia reduces fatality rates. This research aims to provide a lightweight multimodel based on convolutional neural networks (CNNs) that can transfer knowledge from many lightweight deep learning models and decant it into one model to aid in the diagnosis of arrhythmia by using electrocardiogram (ECG) signals. Thus, we gained a multimodel able to classify arrhythmia from ECG signals. Our system's effectiveness is examined by using a publicly accessible database and a comparison to the current methodologies for arrhythmia classification. The results we achieved by using our multimodel are better than those obtained by using a single model and better than most of the previous detection methods. It is worth mentioning that this model produced accurate classification results on small collection of data. Experts in this field can use this model as a guide to help them make decisions and save time.
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Affiliation(s)
- Mohamed Hammad
- Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shibin El Kom 32511, Egypt
| | - Souham Meshoul
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Piotr Dziwiński
- Department of Intelligent Computer Systems, Czestochowa University of Technology, Armii Krajowej 36, 42-218 Czestochowa, Poland
| | - Paweł Pławiak
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Baltycka 5, 44-100 Gliwice, Poland
| | - Ibrahim A. Elgendy
- Department of Computer Science, Faculty of Computers and Information, Menoufia University, Shibin El Kom 32511, Egypt
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