1
|
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.
Collapse
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
| |
Collapse
|
2
|
Benchaira K, Bitam S. Enhancing ECG signal classification through pre-trained stacked-CNN embeddings: a transfer learning approach. Biomed Phys Eng Express 2024; 10:045010. [PMID: 38640904 DOI: 10.1088/2057-1976/ad40b0] [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] [Accepted: 04/19/2024] [Indexed: 04/21/2024]
Abstract
Rapid and accurate electrocardiogram (ECG) signal classification is crucial in high-stakes healthcare settings. However, existing computational models often struggle to balance high performance with computational efficiency. This study introduces an innovative computational framework that combines transfer learning with traditional machine learning to optimize ECG classification. We use a pre-trained Stacked Convolutional Neural Network (SCNN) to generate high-dimensional feature embeddings, which are then evaluated by an array of machine learning classifiers. Our models demonstrate exceptional performance, particularly when utilizing embeddings from SCNNs trained on diverse datasets. This underscores the importance of data diversity in improving classifier discrimination. Notably, Multilayer Perceptrons (MLPs) stand out for their ability to balance computational efficiency with strong performance, achieving test F1-scores of 0.94 and 1.00 in multi-class and binary tasks on the CinC2017 dataset, and 0.85 and 0.99 on the CPSC2018 dataset. Our approach consistently outperforms existing methods, setting new benchmarks in ECG classification. The synergy between deep learning-based feature extraction and traditional machine learning through transfer learning offers a robust, efficient, and adaptable strategy for ECG classification, addressing a critical research gap and laying the groundwork for future advancements in this crucial healthcare field.
Collapse
Affiliation(s)
- Khadidja Benchaira
- Department of Computer Science, University of Biskra, BP 145 RP, 07000, Algeria
| | - Salim Bitam
- Department of Computer Science, University of Biskra, BP 145 RP, 07000, Algeria
| |
Collapse
|
3
|
K M, Syed K. Arrhythmia classification for non-experts using infinite impulse response (IIR)-filter-based machine learning and deep learning models of the electrocardiogram. PeerJ Comput Sci 2024; 10:e1774. [PMID: 38435599 PMCID: PMC10909216 DOI: 10.7717/peerj-cs.1774] [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: 08/18/2023] [Accepted: 12/04/2023] [Indexed: 03/05/2024]
Abstract
Arrhythmias are a leading cause of cardiovascular morbidity and mortality. Portable electrocardiogram (ECG) monitors have been used for decades to monitor patients with arrhythmias. These monitors provide real-time data on cardiac activity to identify irregular heartbeats. However, rhythm monitoring and wave detection, especially in the 12-lead ECG, make it difficult to interpret the ECG analysis by correlating it with the condition of the patient. Moreover, even experienced practitioners find ECG analysis challenging. All of this is due to the noise in ECG readings and the frequencies at which the noise occurs. The primary objective of this research is to remove noise and extract features from ECG signals using the proposed infinite impulse response (IIR) filter to improve ECG quality, which can be better understood by non-experts. For this purpose, this study used ECG signal data from the Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) database. This allows the acquired data to be easily evaluated using machine learning (ML) and deep learning (DL) models and classified as rhythms. To achieve accurate results, we applied hyperparameter (HP)-tuning for ML classifiers and fine-tuning (FT) for DL models. This study also examined the categorization of arrhythmias using different filters and the changes in accuracy. As a result, when all models were evaluated, DenseNet-121 without FT achieved 99% accuracy, while FT showed better results with 99.97% accuracy.
Collapse
Affiliation(s)
- Mallikarjunamallu K
- School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India
| | - Khasim Syed
- School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India
| |
Collapse
|
4
|
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.
Collapse
|
5
|
Zhou C, Li X, Feng F, Zhang J, Lyu H, Wu W, Tang X, Luo B, Li D, Xiang W, Yao D. Inter-patient ECG heartbeat classification for arrhythmia classification: a new approach of multi-layer perceptron with weight capsule and sequence-to-sequence combination. Front Physiol 2023; 14:1247587. [PMID: 37841320 PMCID: PMC10569428 DOI: 10.3389/fphys.2023.1247587] [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: 06/26/2023] [Accepted: 09/07/2023] [Indexed: 10/17/2023] Open
Abstract
Objective: The objective of this research is to construct a method to alleviate the problem of sample imbalance in classification, especially for arrhythmia classification. This approach can improve the performance of the model without using data enhancement. Methods: In this study, we have developed a new Multi-layer Perceptron (MLP) block and have used a Weight Capsule (WCapsule) network with MLP combined with sequence-to-sequence (Seq2Seq) network to classify arrhythmias. Our work is based on the MIT-BIH arrhythmia database, the original electrocardiogram (ECG) data is classified according to the criteria recommended by the American Association for Medical Instrumentation (AAMI). Also, our method's performance is further evaluated. Results: The proposed model is evaluated using the inter-patient paradigm. Our proposed method shows an accuracy (ACC) of 99.88% under sample imbalance. For Class N, sensitivity (SEN) is 99.79%, positive predictive value (PPV) is 99.90%, and specificity (SPEC) is 99.19%. For Class S, SEN is 97.66%, PPV is 96.14%, and SPEC is 99.85%. For Class V, SEN is 99.97%, PPV is 99.07%, and SPEC is 99.94%. For Class F, SEN is 97.94%, PPV is 98.70%, and SPEC is 99.99%. When using only half of the training sample, our method shows that the SEN of Class N and V is 0.97% and 5.27% higher than the traditional machine learning algorithm. Conclusion: The proposed method combines MLP, weight capsule network with Seq2seq network, effectively addresses the problem of sample imbalance in arrhythmia classification, and produces good performance. Our method also shows promising potential in less samples.
Collapse
Affiliation(s)
- Chenchen Zhou
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
- Guangxi Key Laboratory of Digital Infrastructure, Guangxi Information Center, Nanning, China
| | - Xiangkui Li
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Fan Feng
- Guangxi Key Laboratory of Digital Infrastructure, Guangxi Information Center, Nanning, China
| | - Jian Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - He Lyu
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
| | - Weixuan Wu
- 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
| | - Bin Luo
- Sichuan Huhui Software Co., Ltd., Mianyang, China
| | - Dong Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Wei Xiang
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China
| | - Dengju Yao
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| |
Collapse
|
6
|
Ansari Y, Mourad O, Qaraqe K, Serpedin E. Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017-2023. Front Physiol 2023; 14:1246746. [PMID: 37791347 PMCID: PMC10542398 DOI: 10.3389/fphys.2023.1246746] [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: 06/26/2023] [Accepted: 08/28/2023] [Indexed: 10/05/2023] Open
Abstract
Cardiovascular diseases are a leading cause of mortality globally. Electrocardiography (ECG) still represents the benchmark approach for identifying cardiac irregularities. Automatic detection of abnormalities from the ECG can aid in the early detection, diagnosis, and prevention of cardiovascular diseases. Deep Learning (DL) architectures have been successfully employed for arrhythmia detection and classification and offered superior performance to traditional shallow Machine Learning (ML) approaches. This survey categorizes and compares the DL architectures used in ECG arrhythmia detection from 2017-2023 that have exhibited superior performance. Different DL models such as Convolutional Neural Networks (CNNs), Multilayer Perceptrons (MLPs), Transformers, and Recurrent Neural Networks (RNNs) are reviewed, and a summary of their effectiveness is provided. This survey provides a comprehensive roadmap to expedite the acclimation process for emerging researchers willing to develop efficient algorithms for detecting ECG anomalies using DL models. Our tailored guidelines bridge the knowledge gap allowing newcomers to align smoothly with the prevailing research trends in ECG arrhythmia detection. We shed light on potential areas for future research and refinement in model development and optimization, intending to stimulate advancement in ECG arrhythmia detection and classification.
Collapse
Affiliation(s)
- Yaqoob Ansari
- ECEN Program, Texas A&M University at Qatar, Doha, Qatar
| | | | - Khalid Qaraqe
- ECEN Program, Texas A&M University at Qatar, Doha, Qatar
| | - Erchin Serpedin
- ECEN Department, Texas A&M University, College Station, TX, United States
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Jang KJ, Dutta S, Park J, Weimer J, Lee I. Memory Classifiers for Robust ECG Classification against Physiological Noise. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38083527 DOI: 10.1109/embc40787.2023.10339980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The development of sophisticated machine learning algorithms has made it possible to detect critical health conditions like cardiac arrhythmia, directly from electrocardiogram (ECG) recordings. Large-scale machine learning models, like deep neural networks, are well known to underperform when subjected to small perturbations which would not pose a challenge to physicians. This is a hurdle that needs to be removed to facilitate wide-scale adoption. We find this to be true even for models trained using data-augmentation schemes.In this paper, we show that using memory classifiers it is possible to attain a boost in robustness using expert-informed features. Memory classifiers combine standard deep neural network training with a domain knowledge-guided similarity metric to boost the robustness of classifiers. We evaluate the performance of the models against naturally occurring physiological perturbations, specifically electrode movement, muscle artifact, and baseline wander noise. Our approach demonstrates improved robustness across all evaluated noises for an average improvement in F1 score of 3.13% compared to models using data augmentation techniques.Clinical relevance- This approach improves the robustness of deep learning methods in safety-critical medical applications.
Collapse
|
9
|
Ryu JS, Lee S, Chu Y, Ahn MS, Park YJ, Yang S. CoAt-Mixer: Self-attention deep learning framework for left ventricular hypertrophy using electrocardiography. PLoS One 2023; 18:e0286916. [PMID: 37289800 PMCID: PMC10249819 DOI: 10.1371/journal.pone.0286916] [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: 12/08/2022] [Accepted: 05/25/2023] [Indexed: 06/10/2023] Open
Abstract
Left ventricular hypertrophy is a significant independent risk factor for all-cause mortality and morbidity, and an accurate diagnosis at an early stage of heart change is clinically significant. Electrocardiography is the most convenient, economical, and non-invasive method for screening in primary care. However, the coincidence rate of the actual left ventricular hypertrophy and diagnostic findings was low, consequently increasing the interest in algorithms using big data and deep learning. We attempted to diagnose left ventricular hypertrophy using big data and deep learning algorithms, and aimed to confirm its diagnostic power according to the differences between males and females. This retrospective study used electrocardiographs obtained at Yonsei University Wonju Severance Christian Hospital, Wonju, Korea, from October 2010 to February 2020. Binary classification was performed for primary screening for left ventricular hypertrophy. Three datasets were used for the experiment: the male, female, and entire dataset. A cutoff for binary classification was defined as the meaningful as a screening test (<132 g/m2 vs. ≥132 g/m2, <109 g/m2 vs. ≥109 g/m2). Six types of input were used for the classification tasks. We attempted to determine whether electrocardiography had predictive power for left ventricular hypertrophy diagnosis. For the entire dataset, the model achieved an area under the receiver operating characteristic (AUROC) curve of 0.836 (95% CI, 0.833-838) with a sensitivity of 78.37% (95% CI, 76.79-79.95). For the male dataset, the AUROC was 0.826 (95% CI, 0.822-830) with a sensitivity of 76.73% (95% CI, 75.14-78.33). For the female dataset, the AUROC was 0.772 (95% CI, 0.769-775) with a sensitivity of 72.90% (95% CI, 70.33-75.46). Our model confirmed that left ventricular hypertrophy can be classified to some extent using electrocardiography, demographics, and electrocardiography features. In particular, a learning environment that considered gender differences was constructed. Consequently, the difference in diagnostic power between men and women was confirmed. Our model will help patients with suspected left ventricular hypertrophy to undergo screening tests at a low cost. In addition, our research and attempts will show the expected effect that gender-consideration approaches can help with various currently proposed diagnostic methods.
Collapse
Affiliation(s)
- Ji Seung Ryu
- Department of Precision Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Solam Lee
- Department of Preventive Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
- Department of Dermatology, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Yuseong Chu
- Department of Biomedical Engineering, Yonsei University, Wonju, Korea
| | - Min-Soo Ahn
- Division of Cardiology, Department of Internal Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Young Jun Park
- Division of Cardiology, Department of Internal Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Sejung Yang
- Department of Precision Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
| |
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|
11
|
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.
Collapse
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
| |
Collapse
|
12
|
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.
Collapse
|
13
|
Fki Z, Ammar B, Ayed MB. Towards Automated Optimization of Residual Convolutional Neural Networks for Electrocardiogram Classification. Cognit Comput 2023:1-11. [PMID: 36819737 PMCID: PMC9930020 DOI: 10.1007/s12559-022-10103-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 12/22/2022] [Indexed: 02/19/2023]
Abstract
The interpretation of biological data such as the ElectroCardioGram (ECG) signal gives clinical information and helps to assess the heart function. There are distinct ECG patterns associated with a specific class of arrhythmia. The convolutional neural network, inspired by findings in the study of biological vision, is currently one of the most commonly employed deep neural network algorithms for ECG processing. However, deep neural network models require many hyperparameters to tune. Selecting the optimal or the best hyperparameter for the convolutional neural network algorithm is a highly challenging task. Often, we end up tuning the model manually with different possible ranges of values until a best fit model is obtained. Automatic hyperparameters tuning using Bayesian Optimization (BO) and evolutionary algorithms can provide an effective solution to current labour-intensive manual configuration approaches. In this paper, we propose to optimize the Residual one Dimensional Convolutional Neural Network model (R-1D-CNN) at two levels. At the first level, a residual convolutional layer and one-dimensional convolutional neural layers are trained to learn patient-specific ECG features over which multilayer perceptron layers can learn to produce the final class vectors of each input. This level is manual and aims to limit the search space and select the most important hyperparameters to optimize. The second level is automatic and based on our proposed BO-based algorithm. Our optimized proposed architecture (BO-R-1D-CNN) is evaluated on two publicly available ECG datasets. Comparative experimental results demonstrate that our BO-based algorithm achieves an optimal rate of 99.95% for the MIT-BIH database to discriminate between five kinds of heartbeats, including normal heartbeats, left bundle branch block, atrial premature, right bundle branch block, and premature ventricular contraction. Moreover, experiments demonstrate that the proposed architecture fine-tuned with BO achieves a higher accuracy tested on the 10,000 ECG patients dataset compared to the other proposed architectures. Our optimized architecture achieves excellent results compared to previous works on the two benchmark datasets.
Collapse
Affiliation(s)
- Zeineb Fki
- REGIM-Lab.: REsearch Groups in Intelligent Machines, National Engineering School of Sfax (ENIS), University of Sfax, BP 1173, Sfax, 3038 Tunisia
| | - Boudour Ammar
- REGIM-Lab.: REsearch Groups in Intelligent Machines, National Engineering School of Sfax (ENIS), University of Sfax, BP 1173, Sfax, 3038 Tunisia
| | - Mounir Ben Ayed
- REGIM-Lab.: REsearch Groups in Intelligent Machines, National Engineering School of Sfax (ENIS), University of Sfax, BP 1173, Sfax, 3038 Tunisia
- Faculty of Science of Sfax (FSS), University of Sfax, Road of Soukra km 4, Sfax, 3038 Tunisia
| |
Collapse
|
14
|
Boulif A, Ananou B, Ouladsine M, Delliaux S. A Literature Review: ECG-Based Models for Arrhythmia Diagnosis Using Artificial Intelligence Techniques. Bioinform Biol Insights 2023; 17:11779322221149600. [PMID: 36798080 PMCID: PMC9926384 DOI: 10.1177/11779322221149600] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 12/18/2022] [Indexed: 02/12/2023] Open
Abstract
In the health care and medical domain, it has been proven challenging to diagnose correctly many diseases with complicated and interferential symptoms, including arrhythmia. However, with the evolution of artificial intelligence (AI) techniques, the diagnosis and prognosis of arrhythmia became easier for the physicians and practitioners using only an electrocardiogram (ECG) examination. This review presents a synthesis of the studies conducted in the last 12 years to predict arrhythmia's occurrence by classifying automatically different heartbeat rhythms. From a variety of research academic databases, 40 studies were selected to analyze, among which 29 of them applied deep learning methods (72.5%), 9 of them addressed the problem with machine learning methods (22.5%), and 2 of them combined both deep learning and machine learning to predict arrhythmia (5%). Indeed, the use of AI for arrhythmia diagnosis is emerging in literature, although there are some challenging issues, such as the explicability of the Deep Learning methods and the computational resources needed to achieve high performance. However, with the continuous development of cloud platforms and quantum calculation for AI, we can achieve a breakthrough in arrhythmia diagnosis.
Collapse
Affiliation(s)
- Abir Boulif
- Aix-Marseille University, CNRS, LIS, Marseille, France,Abir Boulif, Aix-Marseille University, CNRS, LIS, 13397 Marseille, France.
| | | | | | | |
Collapse
|
15
|
Xia Y, Xu Y, Chen P, Zhang J, Zhang Y. Generative adversarial network with transformer generator for boosting ECG classification. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
16
|
Farag MM. A Tiny Matched Filter-Based CNN for Inter-Patient ECG Classification and Arrhythmia Detection at the Edge. SENSORS (BASEL, SWITZERLAND) 2023; 23:1365. [PMID: 36772404 PMCID: PMC9919183 DOI: 10.3390/s23031365] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/12/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
Automated electrocardiogram (ECG) classification using machine learning (ML) is extensively utilized for arrhythmia detection. Contemporary ML algorithms are typically deployed on the cloud, which may not always meet the availability and privacy requirements of ECG monitoring. Edge inference is an emerging alternative that overcomes the concerns of cloud inference; however, it poses new challenges due to the demanding computational requirements of modern ML algorithms and the tight constraints of edge devices. In this work, we propose a tiny convolutional neural network (CNN) classifier for real-time monitoring of ECG at the edge with the aid of the matched filter (MF) theory. The MIT-BIH dataset with inter-patient division is used for model training and testing. The model generalization capability is validated on the INCART, QT, and PTB diagnostic databases, and the model performance in the presence of noise is experimentally analyzed. The proposed classifier can achieve average accuracy, sensitivity, and F1 scores of 98.18%, 91.90%, and 92.17%, respectively. The sensitivity of detecting supraventricular and ventricular ectopic beats (SVEB and VEB) is 85.3% and 96.34%, respectively. The model is 15 KB in size, with an average inference time of less than 1 ms. The proposed model achieves superior classification and real-time performance results compared to the state-of-the-art ECG classifiers while minimizing the model complexity. The proposed classifier can be readily deployed on a wide range of resource-constrained edge devices for arrhythmia monitoring, which can save millions of cardiovascular disease patients.
Collapse
Affiliation(s)
- Mohammed M. Farag
- Electrical Engineering Department, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia;
- Engineering Department, Faculty of Engineering, Alexandria University, Alexandria 5424041, Egypt;
| |
Collapse
|
17
|
Cosoli G, Antognoli L, Scalise L. Wearable Electrocardiography for Physical Activity Monitoring: Definition of Validation Protocol and Automatic Classification. BIOSENSORS 2023; 13:154. [PMID: 36831919 PMCID: PMC9953541 DOI: 10.3390/bios13020154] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 01/16/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
Wearable devices are rapidly spreading thanks to multiple advantages. Their use is expanding in several fields, from medicine to personal assessment and sport applications. At present, more and more wearable devices acquire an electrocardiographic (ECG) signal (in correspondence to the wrist), providing potentially useful information from a diagnostic point of view, particularly in sport medicine and in rehabilitation fields. They are remarkably relevant, being perceived as a common watch and, hence, considered neither intrusive nor a cause of the so-called "white coat effect". Their validation and metrological characterization are fundamental; hence, this work aims at defining a validation protocol tested on a commercial smartwatch (Samsung Galaxy Watch3, Samsung Electronics Italia S.p.A., Milan, Italy) with respect to a gold standard device (Zephyr BioHarness 3.0, Zephyr Technology Corporation, Annapolis, MD, USA, accuracy of ±1 bpm), reporting results on 30 subjects. The metrological performance is provided, supporting final users to properly interpret the results. Moreover, machine learning and deep learning models are used to discriminate between resting and activity-related ECG signals. The results confirm the possibility of using heart rate data from wearable sensors for activity identification (best results obtained by Random Forest, with accuracy of 0.81, recall of 0.80, and precision of 0.81, even using ECG signals of limited duration, i.e., 30 s). Moreover, the effectiveness of the proposed validation protocol to evaluate measurement accuracy and precision in a wide measurement range is verified. A bias of -1 bpm and an experimental standard deviation of 11 bpm (corresponding to an experimental standard deviation of the mean of ≈0 bpm) were found for the Samsung Galaxy Watch3, indicating a good performance from a metrological point of view.
Collapse
|
18
|
Khan F, Yu X, Yuan Z, Rehman AU. ECG classification using 1-D convolutional deep residual neural network. PLoS One 2023; 18:e0284791. [PMID: 37098024 PMCID: PMC10128986 DOI: 10.1371/journal.pone.0284791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 04/07/2023] [Indexed: 04/26/2023] Open
Abstract
An electrocardiograph (ECG) is widely used in diagnosis and prediction of cardiovascular diseases (CVDs). The traditional ECG classification methods have complex signal processing phases that leads to expensive designs. This paper provides a deep learning (DL) based system that employs the convolutional neural networks (CNNs) for classification of ECG signals present in PhysioNet MIT-BIH Arrhythmia database. The proposed system implements 1-D convolutional deep residual neural network (ResNet) model that performs feature extraction by directly using the input heartbeats. We have used synthetic minority oversampling technique (SMOTE) that process class-imbalance problem in the training dataset and effectively classifies the five heartbeat types in the test dataset. The classifier's performance is evaluated with ten-fold cross validation (CV) using accuracy, precision, sensitivity, F1-score, and kappa. We have obtained an average accuracy of 98.63%, precision of 92.86%, sensitivity of 92.41%, and specificity of 99.06%. The average F1-score and Kappa obtained were 92.63% and 95.5% respectively. The study shows that proposed ResNet performs well with deep layers compared to other 1-D CNNs.
Collapse
Affiliation(s)
- Fahad Khan
- School of Automation, Northwestern Polytechnical University, Xi'an, China
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Pakistan
| | - Xiaojun Yu
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Zhaohui Yuan
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Atiq Ur Rehman
- Artificial Intelligence and Intelligent Systems Research Group, School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden
- Department of Electrical and Computer Engineering, Pak-Austria Fachhochschule Institute of Applied Sciences and Technology, Haripur, Pakistan
| |
Collapse
|
19
|
Chiu IM, Cheng JY, Chen TY, Wang YM, Cheng CY, Kung CT, Cheng FJ, Yau FFF, Lin CHR. Using Deep Transfer Learning to Detect Hyperkalemia From Ambulatory Electrocardiogram Monitors in Intensive Care Units: Personalized Medicine Approach. J Med Internet Res 2022; 24:e41163. [PMID: 36469396 DOI: 10.2196/41163] [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: 07/17/2022] [Revised: 11/07/2022] [Accepted: 11/17/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Hyperkalemia is a critical condition, especially in intensive care units. So far, there have been no accurate and noninvasive methods for recognizing hyperkalemia events on ambulatory electrocardiogram monitors. OBJECTIVE This study aimed to improve the accuracy of hyperkalemia predictions from ambulatory electrocardiogram (ECG) monitors using a personalized transfer learning method; this would be done by training a generic model and refining it with personal data. METHODS This retrospective cohort study used open source data from the Waveform Database Matched Subset of the Medical Information Mart From Intensive Care III (MIMIC-III). We included patients with multiple serum potassium test results and matched ECG data from the MIMIC-III database. A 1D convolutional neural network-based deep learning model was first developed to predict hyperkalemia in a generic population. Once the model achieved a state-of-the-art performance, it was used in an active transfer learning process to perform patient-adaptive heartbeat classification tasks. RESULTS The results show that by acquiring data from each new patient, the personalized model can improve the accuracy of hyperkalemia detection significantly, from an average of 0.604 (SD 0.211) to 0.980 (SD 0.078), when compared with the generic model. Moreover, the area under the receiver operating characteristic curve level improved from 0.729 (SD 0.240) to 0.945 (SD 0.094). CONCLUSIONS By using the deep transfer learning method, we were able to build a clinical standard model for hyperkalemia detection using ambulatory ECG monitors. These findings could potentially be extended to applications that continuously monitor one's ECGs for early alerts of hyperkalemia and help avoid unnecessary blood tests.
Collapse
Affiliation(s)
- I-Min Chiu
- Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung City, Taiwan.,Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan
| | - Jhu-Yin Cheng
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan
| | - Tien-Yu Chen
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan
| | - Yi-Min Wang
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan
| | - Chi-Yung Cheng
- Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung City, Taiwan.,Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan
| | - Chia-Te Kung
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan
| | - Fu-Jen Cheng
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan
| | - Fei-Fei Flora Yau
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan
| | - Chun-Hung Richard Lin
- Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung City, Taiwan
| |
Collapse
|
20
|
Papageorgiou VE, Zegkos T, Efthimiadis G, Tsaklidis G. Analysis of digitalized ECG signals based on artificial intelligence and spectral analysis methods specialized in ARVC. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3644. [PMID: 36053812 DOI: 10.1002/cnm.3644] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 04/13/2022] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart muscle disease that appears between the second and forth decade of a patient's life, being responsible for 20% of sudden cardiac deaths before the age of 35. The effective and punctual diagnosis of this disease based on electrocardiograms (ECGs) could have a vital role in reducing premature cardiovascular mortality. In our analysis, we first outline the digitalization process of paper-based ECG signals enhanced by a spatial filter aiming to eliminate dark regions in the dataset's images that do not correspond to ECG waveform, producing undesirable noise. Next, we propose the utilization of a low-complexity convolutional neural network for the detection of an arrhythmogenic heart disease, that has not been studied through the usage of deep learning methodology to date, achieving high classification accuracy, namely 99.98% training and 98.6% testing accuracy, on a disease the major identification criterion of which are infinitesimal millivolt variations in the ECG's morphology, in contrast with other arrhythmogenic abnormalities. Finally, by performing spectral analysis we investigate significant differentiations in the field of frequencies between normal ECGs and ECGs corresponding to patients suffering from ARVC. In 16 out of the 18 frequencies where we encounter statistically significant differentiations, the normal ECGs are characterized by greater normalized amplitudes compared to the abnormal ones. The overall research carried out in this article highlights the importance of integrating mathematical methods into the examination and effective diagnosis of various diseases, aiming to a substantial contribution to their successful treatment.
Collapse
Affiliation(s)
| | - Thomas Zegkos
- 1st Cardiology Department, AHEPA University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Georgios Efthimiadis
- 1st Cardiology Department, AHEPA University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - George Tsaklidis
- Department of Mathematics, Aristotle University of Thessaloniki, Thessaloniki, Greece
| |
Collapse
|
21
|
Heartprint: A Dataset of Multisession ECG Signal with Long Interval Captured from Fingers for Biometric Recognition. DATA 2022. [DOI: 10.3390/data7100141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
The electrocardiogram (ECG) signal produced by the human heart is an emerging biometric modality that can play an important role in the future generation’s identity recognition with the support of machine learning techniques. One of the major obstacles in the progress of this modality is the lack of public datasets with a long interval between sessions of data acquisition to verify the uniqueness and permanence of the biometric signature of the heart of a subject. To address this issue, we put forward Heartprint, a large biometric database of multisession ECG signals comprising 1539 records captured from the fingers of 199 healthy subjects. The capturing time for each record was 15 s, and recordings were made in resting and reading conditions. They were collected in multiple sessions over ten years, and the average interval between first session (S1) and third session (S3L) was 1572.2 days. The dataset also covers several demographic classes such as genders, ethnicities, and age groups. The combination of raw ECG signals and demographic information turns the Heartprint dataset, which is made publicly available online, into a valuable resource for the development and evaluation of biometric recognition algorithms.
Collapse
|
22
|
Kumar M. A, Chakrapani A. Classification of ECG signal using FFT based improved Alexnet classifier. PLoS One 2022; 17:e0274225. [PMID: 36166430 PMCID: PMC9514660 DOI: 10.1371/journal.pone.0274225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/23/2022] [Indexed: 11/26/2022] Open
Abstract
Electrocardiograms (ECG) are extensively used for the diagnosis of cardiac arrhythmias. This paper investigates the use of machine learning classification algorithms for ECG analysis and arrhythmia detection. This is a crucial component of a conventional electronic health system, and it frequently necessitates ECG signal reduction for long-term data storage and remote transmission. Signal processing methods must be used to extract the function of the morphological properties of the ECG signal changing with time, which is difficult to discern in the typical visual depiction of the ECG signal. In biomedical research, signal processing and data analysis are commonly employed methodologies. This work proposes the use of an ECG arrhythmia classification method based on Fast Fourier Transform (FFT) for feature extraction and an improved AlexNet classifier to distinguish the difference between four types of arrhythmia conditions that were collected from records. The Convolutional Neural Network (CNN) algorithm’s results are compared to those of other algorithms, and the simulation results prove that the proposed technique is more effective for various parameters. The final results of the proposed system show that its ability to find deviations is 20% better than that of traditional systems.
Collapse
Affiliation(s)
- Arun Kumar M.
- Department of ECE, Karpagam Academy of Higher Education, Coimbatore, India
- * E-mail:
| | - Arvind Chakrapani
- Department of ECE, Karpagam College of Engineering, Coimbatore, India
| |
Collapse
|
23
|
Sinha N, Kumar Tripathy R, Das A. ECG beat classification based on discriminative multilevel feature analysis and deep learning approach. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
24
|
ELEKTRA: ELEKTRokardiomatrix application to biometric identification with convolutional neural networks. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.07.059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
25
|
Zhong M, Li F, Chen W. Automatic arrhythmia detection with multi-lead ECG signals based on heterogeneous graph attention networks. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:12448-12471. [PMID: 36654006 DOI: 10.3934/mbe.2022581] [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: 06/17/2023]
Abstract
Automatic arrhythmia detection is very important for cardiovascular health. It is generally performed by measuring the electrocardiogram (ECG) signals of standard multiple leads. However, the correlations of multiple leads are often ignored. In addition, an extensive and complex feature extraction process is usually needed in most existing studies. Therefore, these challenges will not only lead to the loss of overall lead information, but also cause the detection performance to depend on the quality of features. To solve these challenges, a novel multi-lead arrhythmia detection model based on a heterogeneous graph attention network is proposed in this paper. We have modeled the multi-lead data as a heterogeneous graph to integrate diverse information and construct intra-lead and inter-lead correlations in multi-lead data, providing a reasonable and effective the data model. A heterogeneous graph network with a dual-level attention strategy has been utilized to capture the interactions among diverse information and information types. At the same time, our model does not require any feature extraction process for the ECG signals, which avoids out complex feature engineering. Extensive experimental results show that multi-lead information and complex correlations can be well captured, thus confirming that the proposed model results in significant improvements in multi-lead arrhythmia detection.
Collapse
Affiliation(s)
- MingHao Zhong
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
| | - Fenghuan Li
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
| | - Weihong Chen
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
| |
Collapse
|
26
|
Gallego Vázquez C, Breuss A, Gnarra O, Portmann J, Madaffari A, Da Poian G. Label noise and self-learning label correction in cardiac abnormalities classification. Physiol Meas 2022; 43. [PMID: 35970176 DOI: 10.1088/1361-6579/ac89cb] [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: 01/11/2022] [Accepted: 08/15/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Learning to classify cardiac abnormalities requires large and high-quality labeled datasets, which is a challenge in medical applications. Small datasets from various sources are often aggregated to meet this requirement, resulting in a final dataset prone to label noise owing to inter- and intra-observer variability, and different expertise. It is well known that label noise can affect the performance and generalizability of the trained models. In this work, we explore the impact of label noise and self-learning label correction on the classification of cardiac abnormalities on large heterogeneous datasets of electrocardiogram (ECG) signals. APPROACH A state-of-the-art self-learning multi-class label correction method for image classification is adapted to learn a multi-label classifier for electrocardiogram signals. We evaluated our performance using 5-fold cross-validation on the publicly available PhysioNet/Computing in Cardiology (CinC) 2021 Challenge data, with full and reduced sets of leads. Due to the unknown label noise in the testing set, we tested our approach on the MNIST dataset. We investigated the performance under different levels of structured label noise for both datasets. MAIN RESULTS Under high levels of noise, the cross-validation results of self-learning label correction showed an improvement of approximately 3% in the Challenge score for the PhysioNet/CinC 2021 Challenge dataset and, an improvement in accuracy of 5$\%$ and reduction of the expected calibration error of 0.03 for the MNIST dataset. We demonstrate that self-learning label correction can be used to effectively deal with the presence of unknown label noise, also when using a reduced number of ECG leads.
Collapse
Affiliation(s)
- Cristina Gallego Vázquez
- Health Sciences and Technology, ETH Zürich D-HEST, Sonneggstrasse 3, Zurich, Zürich, 8092, SWITZERLAND
| | - Alexander Breuss
- Health Sciences and Technology, ETH Zurich Institute of Robotics and Intelligent Systems, Sonnegstrasse 3, Zurich, 8092, SWITZERLAND
| | - Oriella Gnarra
- Health Sciences and Technology, ETH Zürich D-HEST, Sonnegstrasse 3, Zurich, Zürich, 8092, SWITZERLAND
| | - Julian Portmann
- Computer Science, ETH Zürich, Universitätstrasse 6, Zurich, Zürich, 8092, SWITZERLAND
| | - Antonio Madaffari
- Inselspital Universitätsspital Bern Universitätsklinik für Kardiologie, Freiburgstrasse 18, Bern, Bern, 3010, SWITZERLAND
| | - Giulia Da Poian
- Health Sciences and Technologie, ETH Zürich D-HEST, Sonnegstrasse 3, Zurich, Zürich, 8092, SWITZERLAND
| |
Collapse
|
27
|
Kowalczewski A, Sakolish C, Hoang P, Liu X, Jacquir S, Rusyn I, Ma Z. Integrating nonlinear analysis and machine learning for human induced pluripotent stem cell-based drug cardiotoxicity testing. J Tissue Eng Regen Med 2022; 16:732-743. [PMID: 35621199 PMCID: PMC9719611 DOI: 10.1002/term.3325] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 05/13/2022] [Accepted: 05/17/2022] [Indexed: 01/16/2023]
Abstract
Utilizing recent advances in human induced pluripotent stem cell (hiPSC) technology, nonlinear analysis and machine learning we can create novel tools to evaluate drug-induced cardiotoxicity on human cardiomyocytes. With cardiovascular disease remaining the leading cause of death globally it has become imperative to create effective and modern tools to test the efficacy and toxicity of drugs to combat heart disease. The calcium transient signals recorded from hiPSC-derived cardiomyocytes (hiPSC-CMs) are highly complex and dynamic with great degrees of response characteristics to various drug treatments. However, traditional linear methods often fail to capture the subtle variation in these signals generated by hiPSC-CMs. In this work, we integrated nonlinear analysis, dimensionality reduction techniques and machine learning algorithms for better classifying the contractile signals from hiPSC-CMs in response to different drug exposure. By utilizing extracted parameters from a commercially available high-throughput testing platform, we were able to distinguish the groups with drug treatment from baseline controls, determine the drug exposure relative to IC50 values, and classify the drugs by its unique cardiac responses. By incorporating nonlinear parameters computed by phase space reconstruction, we were able to improve our machine learning algorithm's ability to predict cardiotoxic levels and drug classifications. We also visualized the effects of drug treatment and dosages with dimensionality reduction techniques, t-distributed stochastic neighbor embedding (t-SNE). We have shown that integration of nonlinear analysis and artificial intelligence has proven to be a powerful tool for analyzing cardiotoxicity and classifying toxic compounds through their mechanistic action.
Collapse
Affiliation(s)
- Andrew Kowalczewski
- Department of Biomedical & Chemical Engineering, Syracuse University, Syracuse NY, USA,BioInspired Syracuse Institute for Materials and Living Systems, Syracuse University, Syracuse NY, USA
| | - Courtney Sakolish
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA
| | - Plansky Hoang
- Department of Biomedical & Chemical Engineering, Syracuse University, Syracuse NY, USA,BioInspired Syracuse Institute for Materials and Living Systems, Syracuse University, Syracuse NY, USA
| | - Xiyuan Liu
- Department of Mechanical & Aerospace Engineering, Syracuse University, Syracuse NY, USA
| | - Sabir Jacquir
- Université Paris-Saclay, CNRS, Institut des Neurosciences Paris Saclay, Gif-sur-Yvette, France
| | - Ivan Rusyn
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA
| | - Zhen Ma
- Department of Biomedical & Chemical Engineering, Syracuse University, Syracuse NY, USA,BioInspired Syracuse Institute for Materials and Living Systems, Syracuse University, Syracuse NY, USA,Corresponding author Zhen Ma, PhD. Syracuse University ()
| |
Collapse
|
28
|
Development of a Convolutional Neural Network Model to Predict Coronary Artery Disease Based on Single-Lead and Twelve-Lead ECG Signals. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12157711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Coronary artery disease (CAD) is one of the most common causes of heart ailments; many patients with CAD do not exhibit initial symptoms. An electrocardiogram (ECG) is a diagnostic tool widely used to capture the abnormal activity of the heart and help with diagnoses. Assessing ECG signals may be challenging and time-consuming. Identifying abnormal ECG morphologies, especially in low amplitude curves, may be prone to error. Hence, a system that can automatically detect and assess the ECG and treadmill test ECG (TMT-ECG) signals will be helpful to the medical industry in detecting CAD. In the present work, we developed an intelligent system that can predict CAD, based on ECG and TMT signals more accurately than any other system developed thus far. The distinct convolutional neural network (CNN) architecture deals with single-lead and multi-lead (12-lead) ECG and TMT-ECG data effectively. While most artificial intelligence-based systems rely on the universal dataset, the current work used clinical lab data collected from a renowned hospital in the neighborhood. ECG and TMT-ECG graphs of normal and CAD patients were collected in the form of scanned reports. One-dimensional ECG data with all possible features were extracted from the scanned report with the help of a modified image processing method. This feature extraction procedure was integrated with the optimized architecture of the CNN model leading to a novel prediction system for CAD. The automated computer-assisted system helps in the detection and medication of CAD with a high prediction accuracy of 99%.
Collapse
|
29
|
Human-Assisted vs. Deep Learning Feature Extraction: An Evaluation of ECG Features Extraction Methods for Arrhythmia Classification Using Machine Learning. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12157404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The success of arrhythmia classification tasks with Machine Learning (ML) algorithms is based on the handcrafting extraction of features from Electrocardiography (ECG) signals. However, feature extraction is a time-consuming trial-and-error approach. Deep Neural Network (DNN) algorithms bypass the process of handcrafting feature extraction since the algorithm extracts the features automatically in their hidden layers. However, it is important to have access to a balanced dataset for algorithm training. In this exploratory research study, we will compare the evaluation metrics among Convolutional Neural Networks (1D-CNN) and Support Vector Machines (SVM) using a dataset based on the merged public ECG signals database TNMG and CINC17 databases. Results: Both algorithms showed good performance using the new, merged ECG database. For evaluation metrics, the 1D-CNN algorithm has a precision of 93.04%, an accuracy of 93.07%, a recall of 93.20%, and an F1-score of 93.05%. The SVM classifier (λ = 10, C = 10e9) achieved the best classification metrics with two combined, handcrafted feature extraction methods: Wavelet transforms and R-peak Interval features, which achieved an overall precision of 89.04%, accuracy of 92.00%, recall of 94.20%, and F1-score of 91.54%. As an unique input feature and SVM (λ=10,C=100), wavelet transforms achieved precision, accuracy, recall, and F1-score metrics of 86.15%, 85.33%, 81.16%, and 83.58%. Conclusion: Researchers face a challenge in finding a broad dataset to evaluate ML models. One way to solve this problem, especially for deep learning models, is to combine several public datasets to increase the amount of data. The SVM and 1D-CNN algorithms showed positive results with the merge of databases, showing similar F1-score, precision, and recall during arrhythmia classification. Despite the favorable results for both of them, it should be considered that in the SVM, feature selection is a time-consuming trial-and-error process; meanwhile, CNN algorithms can reduce the workload significantly. The disadvantage of CNN algorithms is that it has a higher computational processing cost; moreover, in the absence of access to powerful computational processing, the SVM can be a reliable solution.
Collapse
|
30
|
Pramukantoro ES, Gofuku A. A Heartbeat Classifier for Continuous Prediction Using a Wearable Device. SENSORS 2022; 22:s22145080. [PMID: 35890769 PMCID: PMC9320854 DOI: 10.3390/s22145080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 06/22/2022] [Accepted: 07/04/2022] [Indexed: 02/06/2023]
Abstract
Heartbeat monitoring may play an essential role in the early detection of cardiovascular disease. When using a traditional monitoring system, an abnormal heartbeat may not appear during a recording in a healthcare facility due to the limited time. Thus, continuous and long-term monitoring is needed. Moreover, the conventional equipment may not be portable and cannot be used at arbitrary times and locations. A wearable sensor device such as Polar H10 offers the same capability as an alternative. It has gold-standard heartbeat recording and communication ability but still lacks analytical processing of the recorded data. An automatic heartbeat classification system can play as an analyzer and is still an open problem in the development stage. This paper proposes a heartbeat classifier based on RR interval data for real-time and continuous heartbeat monitoring using the Polar H10 wearable device. Several machine learning and deep learning methods were used to train the classifier. In the training process, we also compare intra-patient and inter-patient paradigms on the original and oversampling datasets to achieve higher classification accuracy and the fastest computation speed. As a result, with a constrain in RR interval data as the feature, the random forest-based classifier implemented in the system achieved up to 99.67% for accuracy, precision, recall, and F1-score. We are also conducting experiments involving healthy people to evaluate the classifier in a real-time monitoring system.
Collapse
Affiliation(s)
- Eko Sakti Pramukantoro
- Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, 3-1-1 Tsushimanaka, Kita-Ku, Okayama 700-8530, Japan
- Faculty of Computer Science, Brawijaya University, Malang 65145, Indonesia
- Correspondence: (E.S.P.); (A.G.)
| | - Akio Gofuku
- Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, 3-1-1 Tsushimanaka, Kita-Ku, Okayama 700-8530, Japan
- Correspondence: (E.S.P.); (A.G.)
| |
Collapse
|
31
|
Transmission and Decryption of the Audio Signal Masked with ECG by FDM Method. IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY, TRANSACTIONS OF ELECTRICAL ENGINEERING 2022. [PMCID: PMC9250995 DOI: 10.1007/s40998-022-00517-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
Today, the use of these methods as hybrids has provided the motivation to be a solution to important problems, since the existing methods are insufficient at some points in ensuring the security of personal data. In data security, the inability to decrypt and decrypt the signal to be encrypted retrospectively has always been the subject of research in terms of privacy. At this point, it was preferred to use the electrocardiography (ECG) signal, which is a signal that shows the vital signs of the human body and is also difficult to copy. In the study, firstly, the emulator circuit was obtained by using the mathematical model of the ECG signal. With this obtained signal, the audio signals are masked. The audio signal masked on the transmitter side and the signals providing synchronization were transmitted to the receiver side over a single channel using the frequency division multiplexing (FDM) method. Then, the sliding mode control (SMC) method was chosen for the synchronization of the ECG emulator circuits on the receiver and transmitter side. Histogram, spectral, mean square error (MSE), peak signal to noise ratio (PSNR), key space and key sensitivity, NSCR (number of sample change rate), UACI (unified average changing intensity) and PESQ (perceptual evaluation of speech quality) analyses were used to check the accuracy of the system. These analyses showed that the ECG encoding method has faster unit change, reduces synchronization time, minimizes losses and improves the security of the masked signal compared to other methods sent from two channels. Finally, use of an arrhythmia ECG signal for the synchronization signal on both the transmitter and receiver sides, the synchronization of this signal with the SMC method and the testing of a live audio recording in addition to the conversation, distinguishes the study from other existing studies and reveals its originality.
Collapse
|
32
|
Kumar D, Puthusserypady S, Dominguez H, Sharma K, Bardram JE. CACHET-CADB: A Contextualized Ambulatory Electrocardiography Arrhythmia Dataset. Front Cardiovasc Med 2022; 9:893090. [PMID: 35845039 PMCID: PMC9283915 DOI: 10.3389/fcvm.2022.893090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/08/2022] [Indexed: 11/16/2022] Open
Abstract
ECG is a non-invasive tool for arrhythmia detection. In recent years, wearable ECG-based ambulatory arrhythmia monitoring has gained increasing attention. However, arrhythmia detection algorithms trained on existing public arrhythmia databases show higher FPR when applied to such ambulatory ECG recordings. It is primarily because the existing public databases are relatively clean as they are recorded using clinical-grade ECG devices in controlled clinical environments. They may not represent the signal quality and artifacts present in ambulatory patient-operated ECG. To help build and evaluate arrhythmia detection algorithms that can work on wearable ECG from free-living conditions, we present the design and development of the CACHET-CADB, a multi-site contextualized ECG database from free-living conditions. The CACHET-CADB is subpart of the REAFEL study, which aims at reaching the frail elderly patient to optimize the diagnosis of atrial fibrillation. In contrast to the existing databases, along with the ECG, CACHET-CADB also provides the continuous recording of patients' contextual data such as activities, body positions, movement accelerations, symptoms, stress level, and sleep quality. These contextual data can aid in improving the machine/deep learning-based automated arrhythmia detection algorithms on patient-operated wearable ECG. Currently, CACHET-CADB has 259 days of contextualized ECG recordings from 24 patients and 1,602 manually annotated 10 s heart-rhythm samples. The length of the ECG records in the CACHET-CADB varies from 24 h to 3 weeks. The patient's ambulatory context information (activities, movement acceleration, body position, etc.) is extracted for every 10 s interval cumulatively. From the analysis, nearly 11% of the ECG data in the database is found to be noisy. A software toolkit for the use of the CACHET-CADB is also provided.
Collapse
Affiliation(s)
- Devender Kumar
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Helena Dominguez
- Department of Cardiology, Bispebjerg-Frederiksberg Hospital, Copenhagen, Denmark
| | - Kamal Sharma
- U. N. Mehta Institute of Cardiology and Research Centre, Civil Hospital Campus, and SAL Hospital, Ahmedabad, India
| | - Jakob E. Bardram
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| |
Collapse
|
33
|
Sex Recognition through ECG Signals aiming toward Smartphone Authentication. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136573] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Physiological signals are strongly related to a person’s state of health and carry information about the human body. For example, by ECG, it is possible to obtain information about cardiac disease, emotions, personal identification, and the sex of a person, among others. This paper proposes the study of the heartbeat from a soft-biometric perspective to be applied to smartphone unlocking services. We employ the user heartbeat to classify the individual by sex (male, female) with the use of Deep Learning, reaching an accuracy of 94.4% ± 2.0%. This result was obtained with the RGB representation of the union of the time-frequency transformation from the pseudo-orthogonal X, Y, and Z bipolar signals. Evaluating each bipolar contribution, we found that the XYZ combination provides the best category distinction using GoogLeNet. The 24-h Holter database of the study contains 202 subjects with a female size of 49.5%. We propose an architecture for managing this signal that allows the use of a few samples to train the network. Due to the hidden nature of ECG, it does not present vulnerabilities like public trait exposition, light/noise sensibility, or learnability compared to fingerprint, facial, voice, or password verification methods. ECG may complement those gaps en route to a cooperative authentication ecosystem.
Collapse
|
34
|
A Micro Neural Network for Healthcare Sensor Data Stream Classification in Sustainable and Smart Cities. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4270295. [PMID: 35785086 PMCID: PMC9249444 DOI: 10.1155/2022/4270295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 06/06/2022] [Indexed: 11/17/2022]
Abstract
A smart city is an intelligent space, in which large amounts of data are collected and analyzed using low-cost sensors and automatic algorithms. The application of artificial intelligence and Internet of Things (IoT) technologies in electronic health (E-health) can efficiently promote the development of sustainable and smart cities. The IoT sensors and intelligent algorithms enable the remote monitoring and analyzing of the healthcare data of patients, which reduces the medical and travel expenses in cities. Existing deep learning-based methods for healthcare sensor data classification have made great achievements. However, these methods take much time and storage space for model training and inference. They are difficult to be deployed in small devices to classify the physiological signal of patients in real time. To solve the above problems, this paper proposes a micro time series classification model called the micro neural network (MicroNN). The proposed model is micro enough to be deployed on tiny edge devices. MicroNN can be applied to long-term physiological signal monitoring based on edge computing devices. We conduct comprehensive experiments to evaluate the classification accuracy and computation complexity of MicroNN. Experiment results show that MicroNN performs better than the state-of-the-art methods. The accuracies on the two datasets (MIT-BIH-AR and INCART) are 98.4% and 98.1%, respectively. Finally, we present an application to show how MicroNN can improve the development of sustainable and smart cities.
Collapse
|
35
|
Marzog HA, Abd HJ. ECG-signal Classification Using efficient Machine Learning Approach. 2022 INTERNATIONAL CONGRESS ON HUMAN-COMPUTER INTERACTION, OPTIMIZATION AND ROBOTIC APPLICATIONS (HORA) 2022. [DOI: 10.1109/hora55278.2022.9800092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Affiliation(s)
- Heyam A. Marzog
- College of Engineering, University of Babylon,Electrical Engineering Department,Hilla,Babil,Iraq
| | - Haider. J. Abd
- College of Engineering, University of Babylon,Electrical Engineering Department,Hilla,Babil,Iraq
| |
Collapse
|
36
|
Urtnasan E, Lee JH, Moon B, Lee HY, Lee K, Youk H. Noninvasive Screening Tool for Hyperkalemia Using a Single-Lead Electrocardiogram and Deep Learning: Development and Usability Study. JMIR Med Inform 2022; 10:e34724. [PMID: 35657658 PMCID: PMC9206199 DOI: 10.2196/34724] [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: 11/05/2021] [Revised: 03/21/2022] [Accepted: 04/11/2022] [Indexed: 11/29/2022] Open
Abstract
Background Hyperkalemia monitoring is very important in patients with chronic kidney disease (CKD) in emergency medicine. Currently, blood testing is regarded as the standard way to diagnose hyperkalemia (ie, using serum potassium levels). Therefore, an alternative and noninvasive method is required for real-time monitoring of hyperkalemia in the emergency medicine department. Objective This study aimed to propose a novel method for noninvasive screening of hyperkalemia using a single-lead electrocardiogram (ECG) based on a deep learning model. Methods For this study, 2958 patients with hyperkalemia events from July 2009 to June 2019 were enrolled at 1 regional emergency center, of which 1790 were diagnosed with chronic renal failure before hyperkalemic events. Patients who did not have biochemical electrolyte tests corresponding to the original 12-lead ECG signal were excluded. We used data from 855 patients (555 patients with CKD, and 300 patients without CKD). The 12-lead ECG signal was collected at the time of the hyperkalemic event, prior to the event, and after the event for each patient. All 12-lead ECG signals were matched with an electrolyte test within 2 hours of each ECG to form a data set. We then analyzed the ECG signals with a duration of 2 seconds and a segment composed of 1400 samples. The data set was randomly divided into the training set, validation set, and test set according to the ratio of 6:2:2 percent. The proposed noninvasive screening tool used a deep learning model that can express the complex and cyclic rhythm of cardiac activity. The deep learning model consists of convolutional and pooling layers for noninvasive screening of the serum potassium level from an ECG signal. To extract an optimal single-lead ECG, we evaluated the performances of the proposed deep learning model for each lead including lead I, II, and V1-V6. Results The proposed noninvasive screening tool using a single-lead ECG shows high performances with F1 scores of 100%, 96%, and 95% for the training set, validation set, and test set, respectively. The lead II signal was shown to have the highest performance among the ECG leads. Conclusions We developed a novel method for noninvasive screening of hyperkalemia using a single-lead ECG signal, and it can be used as a helpful tool in emergency medicine.
Collapse
Affiliation(s)
- Erdenebayar Urtnasan
- Artificial Intelligence Big Data Medical Center, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea.,Bigdata Platform Business Group, Yonsei Wonju Health System, Wonju, Republic of Korea
| | - Jung Hun Lee
- Department of Emergency Medicine, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea
| | - Byungjin Moon
- Bigdata Platform Business Group, Yonsei Wonju Health System, Wonju, Republic of Korea
| | - Hee Young Lee
- Bigdata Platform Business Group, Yonsei Wonju Health System, Wonju, Republic of Korea.,Department of Emergency Medicine, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea
| | - Kyuhee Lee
- Artificial Intelligence Big Data Medical Center, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea.,Bigdata Platform Business Group, Yonsei Wonju Health System, Wonju, Republic of Korea
| | - Hyun Youk
- Bigdata Platform Business Group, Yonsei Wonju Health System, Wonju, Republic of Korea.,Center of Regional Trauma, Wonju, Republic of Korea
| |
Collapse
|
37
|
Popescu DM, Shade JK, Lai C, Aronis KN, Ouyang D, Moorthy MV, Cook NR, Lee DC, Kadish A, Albert CM, Wu KC, Maggioni M, Trayanova NA. Arrhythmic sudden death survival prediction using deep learning analysis of scarring in the heart. NATURE CARDIOVASCULAR RESEARCH 2022; 1:334-343. [PMID: 35464150 DOI: 10.1038/s44161-022-00041-9] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Sudden cardiac death from arrhythmia is a major cause of mortality worldwide. Here, we develop a novel deep learning (DL) approach that blends neural networks and survival analysis to predict patient-specific survival curves from contrast-enhanced cardiac magnetic resonance images and clinical covariates for patients with ischemic heart disease. The DL-predicted survival curves offer accurate predictions at times up to 10 years and allow for estimation of uncertainty in predictions. The performance of this learning architecture was evaluated on multi-center internal validation data and tested on an independent test set, achieving concordance index of 0.83 and 0.74, and 10-year integrated Brier score of 0.12 and 0.14. We demonstrate that our DL approach with only raw cardiac images as input outperforms standard survival models constructed using clinical covariates. This technology has the potential to transform clinical decision-making by offering accurate and generalizable predictions of patient-specific survival probabilities of arrhythmic death over time.
Collapse
Affiliation(s)
- Dan M Popescu
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, 21224, USA
| | - Julie K Shade
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, 21224, USA
| | - Changxin Lai
- Johns Hopkins University School of Medicine, Department of Biomedical Engineering, Baltimore, 21224, USA
| | - Konstantinos N Aronis
- University of Pittsburgh Medical Center, Heart and Vascular Institute, Pittsburgh, 15237, USA
| | - David Ouyang
- Cedar-Sinai Medical Center, Department of Cardiology, Los Angeles, 90048, USA
| | - M Vinayaga Moorthy
- Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, USA
| | - Nancy R Cook
- Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, USA
| | - Daniel C Lee
- Northwestern University, Feinberg School of Medicine, Chicago, 60611, USA
| | - Alan Kadish
- Touro College and University System, Valhalla, 10595, USA
| | - Christine M Albert
- Cedar-Sinai Medical Center, Department of Cardiology, Los Angeles, 90048, USA
| | - Katherine C Wu
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, 21224, USA.,Johns Hopkins University School of Medicine, Department of Medicine, Division of Cardiology, Baltimore, 21224, USA
| | - Mauro Maggioni
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, 21224, USA.,Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, 21224, USA
| | - Natalia A Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, 21224, USA.,Johns Hopkins University School of Medicine, Department of Biomedical Engineering, Baltimore, 21224, USA
| |
Collapse
|
38
|
Conception and realization of an IoT-enabled deep CNN decision support system for automated arrhythmia classification. JOURNAL OF INTELLIGENT SYSTEMS 2022. [DOI: 10.1515/jisys-2022-0015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Arrhythmias are irregular heartbeats that may be life-threatening. Proper monitoring and the right care at the right time are necessary to keep the heart healthy. Monitoring electrocardiogram (ECG) patterns on continuous monitoring devices is time-consuming. An intense manual inspection by caregivers is not an option. In addition, such an inspection could result in errors and inter-variability. This article proposes an automated ECG beat classification method based on deep neural networks (DNN) to aid in the detection of cardiac arrhythmias. The data collected by an Internet of Things enabled ECG monitoring device are transferred to a server. They are analysed by a deep learning model, and the results are shared with the primary caregiver. The proposed model is trained using the MIT-BIH ECG arrhythmia database to classify into four classes: normal beat (N), left bundle branch block beat (L), right bundle branch block beat (R), and premature ventricular contraction (V). The received data are sampled with an overlapping sliding window and divided into an 80:20 ratio for training and testing, with tenfold cross-validation. The proposed method achieves higher accuracy with a simple model without any preprocessing when compared with previous works. For the train and test sets, we achieved accuracy rates of 99.09 and 99.03%, respectively. A precision, recall, and F1 scores of 0.99 is obtained. The proposed model achieves its goal of developing a simple and accurate ECG monitoring system with improved performance. This simple and efficient deep learning approach for heartbeat classification could be applied in real-time telehealth monitoring systems.
Collapse
|
39
|
Energy Efficient Framework for a AIoT Cardiac Arrhythmia Detection System Wearable during Sport. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12052716] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The growing market of wearables is expanding into different areas of application such as devices designed to improve and monitor sport activities. This in turn is pushing research on low-cost, very low-power wearable systems with increased analysis capabilities. This paper proposes integrated energy-aware techniques and a convolutional neural network (CNN) for a cardiac arrhythmia detection system that can be worn during sport training sessions. The dynamic power management strategy (DPMS) is programmed into an ultra-low-power microcontroller, and in combination with a photovoltaic (PV) energy harvesting (EH) circuit, achieves a battery-life extension towards a self-powered operation. The CNN-based analysis filters, scales the image, and using a bicubic technique, interpolates the measurements to subsequently classify the electrocardiogram (ECG) signal into normal and abnormal patterns. Experimental results show that the EH-DPMS achieves an extension in the battery charge for a total of 14.34% more energy available, which represents 12 consecutive workouts of 45 min without the need to manually recharge it. Furthermore, an arrhythmia detection precision of 98.6% is achieved among the experimental sessions using 55,222 images for training the system with the MIT-BIH, QT, and long-term ST databases, and 1320 implemented on a wearable system. Therefore, the proposed wearable system can be used to monitor an athlete’s condition, reducing the risk of abnormal heart conditions during sports activities.
Collapse
|
40
|
Pokaprakarn T, Kitzmiller RR, Moorman JR, Lake DE, Krishnamurthy AK, Kosorok MR. Sequence to Sequence ECG Cardiac Rhythm Classification Using Convolutional Recurrent Neural Networks. IEEE J Biomed Health Inform 2022; 26:572-580. [PMID: 34288883 PMCID: PMC9033271 DOI: 10.1109/jbhi.2021.3098662] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
This paper proposes a novel deep learning architecture involving combinations of Convolutional Neural Networks (CNN) layers and Recurrent neural networks (RNN) layers that can be used to perform segmentation and classification of 5 cardiac rhythms based on ECG recordings. The algorithm is developed in a sequence to sequence setting where the input is a sequence of five second ECG signal sliding windows and the output is a sequence of cardiac rhythm labels. The novel architecture processes as input both the spectrograms of the ECG signal as well as the heartbeats' signal waveform. Additionally, we are able to train the model in the presence of label noise. The model's performance and generalizability is verified on an external database different from the one we used to train. Experimental result shows this approach can achieve an average F1 scores of 0.89 (averaged across 5 classes). The proposed model also achieves comparable classification performance to existing state-of-the-art approach with considerably less number of training parameters.
Collapse
Affiliation(s)
- Teeranan Pokaprakarn
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27516 USA
| | | | - J. Randall Moorman
- Cardiology Division, Department of Internal Medicine, School of Medicine, University of Virginia, Charlottesville, VA 22903 USA and with AMP3D, Advanced Medical Predictive Devices, Diagnostics, and Displays, Inc, Charlottesville, VA 22902 USA. Conflict Statement: J. Randall Moorman owns stock in Medical Predictive Science Corporation and Advanced Medical Predictive Devices, Diagnostics, and Displays
| | - Doug E. Lake
- Department of Medicine, Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22903 USA
| | - Ashok K. Krishnamurthy
- Renaissance Computing Institute (RENCI) and the Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Michael R. Kosorok
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27516 USA
| |
Collapse
|
41
|
Alqudah AM, Alqudah A. Deep learning for single-lead ECG beat arrhythmia-type detection using novel iris spectrogram representation. Soft comput 2022. [DOI: 10.1007/s00500-021-06555-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
42
|
Guess M, Zavanelli N, Yeo WH. Recent Advances in Materials and Flexible Sensors for Arrhythmia Detection. MATERIALS 2022; 15:ma15030724. [PMID: 35160670 PMCID: PMC8836661 DOI: 10.3390/ma15030724] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/06/2022] [Accepted: 01/16/2022] [Indexed: 12/24/2022]
Abstract
Arrhythmias are one of the leading causes of death in the United States, and their early detection is essential for patient wellness. However, traditional arrhythmia diagnosis by expert evaluation from intermittent clinical examinations is time-consuming and often lacks quantitative data. Modern wearable sensors and machine learning algorithms have attempted to alleviate this problem by providing continuous monitoring and real-time arrhythmia detection. However, current devices are still largely limited by the fundamental mismatch between skin and sensor, giving way to motion artifacts. Additionally, the desirable qualities of flexibility, robustness, breathability, adhesiveness, stretchability, and durability cannot all be met at once. Flexible sensors have improved upon the current clinical arrhythmia detection methods by following the topography of skin and reducing the natural interface mismatch between cardiac monitoring sensors and human skin. Flexible bioelectric, optoelectronic, ultrasonic, and mechanoelectrical sensors have been demonstrated to provide essential information about heart-rate variability, which is crucial in detecting and classifying arrhythmias. In this review, we analyze the current trends in flexible wearable sensors for cardiac monitoring and the efficacy of these devices for arrhythmia detection.
Collapse
Affiliation(s)
- Matthew Guess
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.G.); (N.Z.)
- Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Nathan Zavanelli
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.G.); (N.Z.)
- Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Woon-Hong Yeo
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.G.); (N.Z.)
- Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Parker H. Petit Institute for Bioengineering and Biosciences, Neural Engineering Center, Institute for Materials, Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Correspondence: ; Tel.: +1-404-385-5710
| |
Collapse
|
43
|
Vaquerizo-Villar F, Álvarez D, Gutiérrez-Tobal GC, Arroyo-Domingo CA, del Campo F, Hornero R. Deep-Learning Model Based on Convolutional Neural Networks to Classify Apnea–Hypopnea Events from the Oximetry Signal. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1384:255-264. [PMID: 36217089 DOI: 10.1007/978-3-031-06413-5_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Automated analysis of the blood oxygen saturation (SpO2) signal from nocturnal oximetry has shown usefulness to simplify the diagnosis of obstructive sleep apnea (OSA), including the detection of respiratory events. However, the few preceding studies using SpO2 recordings have focused on the automated detection of respiratory events versus normal respiration, without making any distinction between apneas and hypopneas. In this sense, the characteristics of oxygen desaturations differ between obstructive apnea and hypopnea episodes. In this chapter, we use the SpO2 signal along with a convolutional neural network (CNN)-based deep-learning architecture for the automatic identification of apnea and hypopnea events. A total of 398 SpO2 signals from adult OSA patients were used for this purpose. A CNN architecture was trained using 30-s epochs from the SpO2 signal for the automatic classification of three classes: normal respiration, apnea, and hypopnea. Then, the apnea index (AI), the hypopnea index (HI), and the apnea-hypopnea index (AHI) were obtained by aggregating the outputs of the CNN for each subject (AICNN, HICNN, and AHICNN). This model showed a promising diagnostic performance in an independent test set, with 80.3% 3-class accuracy and 0.539 3-class Cohen's kappa for the classification of respiratory events. Furthermore, AICNN, HICNN, and AHICNN showed a high agreement with the values obtained from the standard PSG: 0.8023, 0.6774, and 0.8466 intra-class correlation coefficients (ICCs), respectively. This suggests that CNN can be used to analyze SpO2 recordings for the automated diagnosis of OSA in at-home oximetry tests.
Collapse
|
44
|
Alamgir A, Mousa O, Shah Z. Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review. JMIR Med Inform 2021; 9:e30798. [PMID: 34927595 PMCID: PMC8726033 DOI: 10.2196/30798] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 10/07/2021] [Accepted: 10/10/2021] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Cardiac arrest is a life-threatening cessation of activity in the heart. Early prediction of cardiac arrest is important, as it allows for the necessary measures to be taken to prevent or intervene during the onset. Artificial intelligence (AI) technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk. OBJECTIVE This study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature. METHODS A scoping review was conducted in line with the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. Scopus, ScienceDirect, Embase, the Institute of Electrical and Electronics Engineers, and Google Scholar were searched to identify relevant studies. Backward reference list checks of the included studies were also conducted. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. RESULTS Out of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. Of the 47 studies, we were able to classify the approaches taken by the studies into 3 different categories: 26 (55%) studies predicted cardiac arrest by analyzing specific parameters or variables of the patients, whereas 16 (34%) studies developed an AI-based warning system. The remaining 11% (5/47) of studies focused on distinguishing patients at high risk of cardiac arrest from patients who were not at risk. Two studies focused on the pediatric population, and the rest focused on adults (45/47, 96%). Most of the studies used data sets with a size of <10,000 samples (32/47, 68%). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (38/47, 81%), and the most used algorithm was the neural network (23/47, 49%). K-fold cross-validation was the most used algorithm evaluation tool reported in the studies (24/47, 51%). CONCLUSIONS AI is extensively used to predict cardiac arrest in different patient settings. Technology is expected to play an integral role in improving cardiac medicine. There is a need for more reviews to learn the obstacles to the implementation of AI technologies in clinical settings. Moreover, research focusing on how to best provide clinicians with support to understand, adapt, and implement this technology in their practice is also necessary.
Collapse
Affiliation(s)
- Asma Alamgir
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Osama Mousa
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Zubair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| |
Collapse
|
45
|
Bizzego A, Gabrieli G, Neoh MJY, Esposito G. Improving the Efficacy of Deep-Learning Models for Heart Beat Detection on Heterogeneous Datasets. Bioengineering (Basel) 2021; 8:bioengineering8120193. [PMID: 34940346 PMCID: PMC8698903 DOI: 10.3390/bioengineering8120193] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 11/05/2021] [Accepted: 11/24/2021] [Indexed: 11/16/2022] Open
Abstract
Deep learning (DL) has greatly contributed to bioelectric signal processing, in particular to extract physiological markers. However, the efficacy and applicability of the results proposed in the literature is often constrained to the population represented by the data used to train the models. In this study, we investigate the issues related to applying a DL model on heterogeneous datasets. In particular, by focusing on heart beat detection from electrocardiogram signals (ECG), we show that the performance of a model trained on data from healthy subjects decreases when applied to patients with cardiac conditions and to signals collected with different devices. We then evaluate the use of transfer learning (TL) to adapt the model to the different datasets. In particular, we show that the classification performance is improved, even with datasets with a small sample size. These results suggest that a greater effort should be made towards the generalizability of DL models applied on bioelectric signals, in particular, by retrieving more representative datasets.
Collapse
Affiliation(s)
- Andrea Bizzego
- Department of Psychology and Cognitive Science, University of Trento, 38068 Trento, Italy;
| | - Giulio Gabrieli
- Psychology Program, Nanyang Technological University, Singapore 639818, Singapore; (G.G.); (M.J.Y.N.)
| | - Michelle Jin Yee Neoh
- Psychology Program, Nanyang Technological University, Singapore 639818, Singapore; (G.G.); (M.J.Y.N.)
| | - Gianluca Esposito
- Department of Psychology and Cognitive Science, University of Trento, 38068 Trento, Italy;
- Psychology Program, Nanyang Technological University, Singapore 639818, Singapore; (G.G.); (M.J.Y.N.)
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore
- Correspondence: or
| |
Collapse
|
46
|
Ramesh J, Solatidehkordi Z, Aburukba R, Sagahyroon A. Atrial Fibrillation Classification with Smart Wearables Using Short-Term Heart Rate Variability and Deep Convolutional Neural Networks. SENSORS (BASEL, SWITZERLAND) 2021; 21:7233. [PMID: 34770543 PMCID: PMC8587743 DOI: 10.3390/s21217233] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 10/23/2021] [Accepted: 10/26/2021] [Indexed: 02/04/2023]
Abstract
Atrial fibrillation (AF) is a type of cardiac arrhythmia affecting millions of people every year. This disease increases the likelihood of strokes, heart failure, and even death. While dedicated medical-grade electrocardiogram (ECG) devices can enable gold-standard analysis, these devices are expensive and require clinical settings. Recent advances in the capabilities of general-purpose smartphones and wearable technology equipped with photoplethysmography (PPG) sensors increase diagnostic accessibility for most populations. This work aims to develop a single model that can generalize AF classification across the modalities of ECG and PPG with a unified knowledge representation. This is enabled by approximating the transformation of signals obtained from low-cost wearable PPG sensors in terms of Pulse Rate Variability (PRV) to temporal Heart Rate Variability (HRV) features extracted from medical-grade ECG. This paper proposes a one-dimensional deep convolutional neural network that uses HRV-derived features for classifying 30-s heart rhythms as normal sinus rhythm or atrial fibrillation from both ECG and PPG-based sensors. The model is trained with three MIT-BIH ECG databases and is assessed on a dataset of unseen PPG signals acquired from wrist-worn wearable devices through transfer learning. The model achieved the aggregate binary classification performance measures of accuracy: 95.50%, sensitivity: 94.50%, and specificity: 96.00% across a five-fold cross-validation strategy on the ECG datasets. It also achieved 95.10% accuracy, 94.60% sensitivity, 95.20% specificity on an unseen PPG dataset. The results show considerable promise towards seamless adaptation of gold-standard ECG trained models for non-ambulatory AF detection with consumer wearable devices through HRV-based knowledge transfer.
Collapse
Affiliation(s)
| | | | - Raafat Aburukba
- Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates; (J.R.); (Z.S.); (A.S.)
| | | |
Collapse
|
47
|
Classification of Arrhythmia in Heartbeat Detection Using Deep Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:2195922. [PMID: 34712316 PMCID: PMC8548158 DOI: 10.1155/2021/2195922] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 09/15/2021] [Accepted: 09/21/2021] [Indexed: 12/12/2022]
Abstract
The electrocardiogram (ECG) is one of the most widely used diagnostic instruments in medicine and healthcare. Deep learning methods have shown promise in healthcare prediction challenges involving ECG data. This paper aims to apply deep learning techniques on the publicly available dataset to classify arrhythmia. We have used two kinds of the dataset in our research paper. One dataset is the MIT-BIH arrhythmia database, with a sampling frequency of 125 Hz with 1,09,446 ECG beats. The classes included in this first dataset are N, S, V, F, and Q. The second database is PTB Diagnostic ECG Database. The second database has two classes. The techniques used in these two datasets are the CNN model, CNN + LSTM, and CNN + LSTM + Attention Model. 80% of the data is used for the training, and the remaining 20% is used for testing. The result achieved by using these three techniques shows the accuracy of 99.12% for the CNN model, 99.3% for CNN + LSTM, and 99.29% for CNN + LSTM + Attention Model.
Collapse
|
48
|
Li H, An Z, Zuo S, Zhu W, Cao L, Mu Y, Song W, Mao Q, Zhang Z, Li E, García JDP. Classification of electrocardiogram signals with waveform morphological analysis and support vector machines. Med Biol Eng Comput 2021; 60:109-119. [PMID: 34718933 DOI: 10.1007/s11517-021-02461-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 10/05/2021] [Indexed: 11/28/2022]
Abstract
Electrocardiogram (ECG) indicates the occurrence of various cardiac diseases, and the accurate classification of ECG signals is important for the automatic diagnosis of arrhythmia. This paper presents a novel classification method based on multiple features by combining waveform morphology and frequency domain statistical analysis, which offer improved classification accuracy and minimise the time spent for classifying signals. A wavelet packet is used to decompose a denoised ECG signal, and the singular value, maximum value, and standard deviation of the decomposed wavelet packet coefficients are calculated to obtain the frequency domain feature space. The slope threshold method is applied to detect R peak and calculate RR intervals, and the first two RR intervals are extracted as time-domain features. The fusion feature space is composed of time and frequency domain features. A combination of support vector machine (SVM) with the help of grid search and waveform morphological analysis is applied to complete nine types of ECG signal classification. Computer simulations show that the accuracy of the proposed algorithm on multiple types of arrhythmia databases can reach 96.67%.
Collapse
Affiliation(s)
- Hongqiang Li
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronics and Information Engineering, Tiangong University, Tianjin, China.
| | - Zhixuan An
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronics and Information Engineering, Tiangong University, Tianjin, China
| | - Shasha Zuo
- Textile Fiber Inspection Center, Tianjin Product Quality Inspection Technology Research Institute, Tianjin, China
| | - Wei Zhu
- Textile Fiber Inspection Center, Tianjin Product Quality Inspection Technology Research Institute, Tianjin, China
| | - Lu Cao
- Tianjin Chest Hospital, Tianjin, China
| | - Yuxin Mu
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronics and Information Engineering, Tiangong University, Tianjin, China
| | - Wenchao Song
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronics and Information Engineering, Tiangong University, Tianjin, China
| | - Quanhua Mao
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronics and Information Engineering, Tiangong University, Tianjin, China
| | - Zhen Zhang
- School of Computer Science and Technology, Tiangong University, Tianjin, China
| | - Enbang Li
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| | | |
Collapse
|
49
|
Abstract
The ambulatory ECG (AECG) is an important diagnostic tool for many heart electrophysiology-related cases. AECG covers a wide spectrum of devices and applications. At the core of these devices and applications are the algorithms responsible for signal conditioning, ECG beat detection and classification, and event detections. Over the years, there has been huge progress for algorithm development and implementation thanks to great efforts by researchers, engineers, and physicians, alongside the rapid development of electronics and signal processing, especially machine learning (ML). The current efforts and progress in machine learning fields are unprecedented, and many of these ML algorithms have also been successfully applied to AECG applications. This review covers some key AECG applications of ML algorithms. However, instead of doing a general review of ML algorithms, we are focusing on the central tasks of AECG and discussing what ML can bring to solve the key challenges AECG is facing. The center tasks of AECG signal processing listed in the review include signal preprocessing, beat detection and classification, event detection, and event prediction. Each AECG device/system might have different portions and forms of those signal components depending on its application and the target, but these are the topics most relevant and of greatest concern to the people working in this area.
Collapse
|
50
|
Haleem MS, Castaldo R, Pagliara SM, Petretta M, Salvatore M, Franzese M, Pecchia L. Time adaptive ECG driven cardiovascular disease detector. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|