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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.
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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
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2
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Jin Y, Li Z, Wang M, Liu J, Tian Y, Liu Y, Wei X, Zhao L, Liu C. Cardiologist-level interpretable knowledge-fused deep neural network for automatic arrhythmia diagnosis. COMMUNICATIONS MEDICINE 2024; 4:31. [PMID: 38418628 PMCID: PMC10901870 DOI: 10.1038/s43856-024-00464-4] [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: 02/14/2023] [Accepted: 02/19/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Long-term monitoring of Electrocardiogram (ECG) recordings is crucial to diagnose arrhythmias. Clinicians can find it challenging to diagnose arrhythmias, and this is a particular issue in more remote and underdeveloped areas. The development of digital ECG and AI methods could assist clinicians who need to diagnose arrhythmias outside of the hospital setting. METHODS We constructed a large-scale Chinese ECG benchmark dataset using data from 272,753 patients collected from January 2017 to December 2021. The dataset contains ECG recordings from all common arrhythmias present in the Chinese population. Several experienced cardiologists from Shanghai First People's Hospital labeled the dataset. We then developed a deep learning-based multi-label interpretable diagnostic model from the ECG recordings. We utilized Accuracy, F1 score and AUC-ROC to compare the performance of our model with that of the cardiologists, as well as with six comparison models, using testing and hidden data sets. RESULTS The results show that our approach achieves an F1 score of 83.51%, an average AUC ROC score of 0.977, and 93.74% mean accuracy for 6 common arrhythmias. Results from the hidden dataset demonstrate the performance of our approach exceeds that of cardiologists. Our approach also highlights the diagnostic process. CONCLUSIONS Our diagnosis system has superior diagnostic performance over that of clinicians. It also has the potential to help clinicians rapidly identify abnormal regions on ECG recordings, thus improving efficiency and accuracy of clinical ECG diagnosis in China. This approach could therefore potentially improve the productivity of out-of-hospital ECG diagnosis and provides a promising prospect for telemedicine.
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Affiliation(s)
- Yanrui Jin
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
- MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Zhiyuan Li
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
- MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Mengxiao Wang
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
- MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Jinlei Liu
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
- MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Yuanyuan Tian
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
- MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Yunqing Liu
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
- MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaoyang Wei
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
- MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Liqun Zhao
- Department of cardiology, Shanghai First People's Hospital Affiliated to Shanghai Jiao Tong University, Shanghai, China.
| | - Chengliang Liu
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.
- MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China.
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3
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Zhang P, Ma C, Song F, Sun Y, Feng Y, He Y, Zhang T, Zhang G. D2AFNet: A dual-domain attention cascade network for accurate and interpretable atrial fibrillation detection. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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4
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Bui TH, Hoang VM, Pham MT. Automatic varied-length ECG classification using a lightweight DenseNet model. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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5
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Wang WK, Chen I, Hershkovich L, Yang J, Shetty A, Singh G, Jiang Y, Kotla A, Shang JZ, Yerrabelli R, Roghanizad AR, Shandhi MMH, Dunn J. A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications. SENSORS (BASEL, SWITZERLAND) 2022; 22:8016. [PMID: 36298367 PMCID: PMC9611376 DOI: 10.3390/s22208016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/23/2022] [Accepted: 10/17/2022] [Indexed: 05/06/2023]
Abstract
Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used for modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges. This review presents the non-deep learning models that are commonly used for time series classification in biomedical applications that can achieve high performance. Objective: We performed a systematic review to characterize the techniques that are used in time series classification of digital clinical measures throughout all the stages of data processing and model building. Methods: We conducted a literature search on PubMed, as well as the Institute of Electrical and Electronics Engineers (IEEE), Web of Science, and SCOPUS databases using a range of search terms to retrieve peer-reviewed articles that report on the academic research about digital clinical measures from a five-year period between June 2016 and June 2021. We identified and categorized the research studies based on the types of classification algorithms and sensor input types. Results: We found 452 papers in total from four different databases: PubMed, IEEE, Web of Science Database, and SCOPUS. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely used machine learning classifiers were the most commonly used technique, and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also the second-best classification technique. Conclusions: In this review paper, summaries of the time series classification models and interpretation methods for biomedical applications are summarized and categorized. While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation, however many different methods have proven successful.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | - Jessilyn Dunn
- Biomedical Engineering Department, Duke University, Durham, NC 27708, USA
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6
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Alsaleem MN, Islam MS, Al-Ahmadi S, Soudani A. Multiscale Encoding of Electrocardiogram Signals with a Residual Network for the Detection of Atrial Fibrillation. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9090480. [PMID: 36135025 PMCID: PMC9495512 DOI: 10.3390/bioengineering9090480] [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: 08/30/2022] [Revised: 09/09/2022] [Accepted: 09/14/2022] [Indexed: 11/16/2022]
Abstract
Atrial fibrillation (AF) is one of the most common cardiac arrhythmias, and it is an indication of high-risk factors for stroke, myocardial ischemia, and other malignant cardiovascular diseases. Most of the existing AF detection methods typically convert one-dimensional time-series electrocardiogram (ECG) signals into two-dimensional representations to train a deep and complex AF detection system, which results in heavy training computation and high implementation costs. In this paper, a multiscale signal encoding scheme is proposed to improve feature representation and detection performance without the need for using any transformation or handcrafted feature engineering techniques. The proposed scheme uses different kernel sizes to produce the encoded signal by using multiple streams that are passed into a one-dimensional sequence of blocks of a residual convolutional neural network (ResNet) to extract representative features from the input ECG signal. This also allows networks to grow in breadth rather than in depth, thus reducing the computing time by using the parallel processing capability of deep learning networks. We investigated the effects of the use of a different number of streams with different kernel sizes on the performance. Experiments were carried out for a performance evaluation using the publicly available PhysioNet CinC Challenge 2017 dataset. The proposed multiscale encoding scheme outperformed existing deep learning-based methods with an average F1 score of 98.54%, but with a lower network complexity.
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7
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Automatic electrocardiogram detection and classification using bidirectional long short-term memory network improved by Bayesian optimization. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103424] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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8
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Bahrami Rad A, Galloway C, Treiman D, Xue J, Li Q, Sameni R, Albert D, Clifford GD. Atrial fibrillation detection in outpatient electrocardiogram monitoring: An algorithmic crowdsourcing approach. PLoS One 2021; 16:e0259916. [PMID: 34784378 PMCID: PMC8594842 DOI: 10.1371/journal.pone.0259916] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 10/28/2021] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Atrial fibrillation (AFib) is the most common cardiac arrhythmia associated with stroke, blood clots, heart failure, coronary artery disease, and/or death. Multiple methods have been proposed for AFib detection, with varying performances, but no single approach appears to be optimal. We hypothesized that each state-of-the-art algorithm is appropriate for different subsets of patients and provides some independent information. Therefore, a set of suitably chosen algorithms, combined in a weighted voting framework, will provide a superior performance to any single algorithm. METHODS We investigate and modify 38 state-of-the-art AFib classification algorithms for a single-lead ambulatory electrocardiogram (ECG) monitoring device. All algorithms are ranked using a random forest classifier and an expert-labeled training dataset of 2,532 recordings. The seven top-ranked algorithms are combined by using an optimized weighting approach. RESULTS The proposed fusion algorithm, when validated on a separate test dataset consisting of 4,644 recordings, resulted in an area under the receiver operating characteristic (ROC) curve of 0.99. The sensitivity, specificity, positive-predictive-value (PPV), negative-predictive-value (NPV), and F1-score of the proposed algorithm were 0.93, 0.97, 0.87, 0.99, and 0.90, respectively, which were all superior to any single algorithm or any previously published. CONCLUSION This study demonstrates how a set of well-chosen independent algorithms and a voting mechanism to fuse the outputs of the algorithms, outperforms any single state-of-the-art algorithm for AFib detection. The proposed framework is a case study for the general notion of crowdsourcing between open-source algorithms in healthcare applications. The extension of this framework to similar applications may significantly save time, effort, and resources, by combining readily existing algorithms. It is also a step toward the democratization of artificial intelligence and its application in healthcare.
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Affiliation(s)
- Ali Bahrami Rad
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America
| | | | - Daniel Treiman
- AliveCor Inc., Mountain View, CA, United States of America
| | - Joel Xue
- AliveCor Inc., Mountain View, CA, United States of America
| | - Qiao Li
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America
| | - Reza Sameni
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America
| | - Dave Albert
- AliveCor Inc., Mountain View, CA, United States of America
| | - Gari D. Clifford
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
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9
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Oudkerk Pool MD, de Vos BD, Winter MM, Isgum I. Deep Learning-Based Data-Point Precise R-Peak Detection in Single-Lead Electrocardiograms. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:718-721. [PMID: 34891392 DOI: 10.1109/embc46164.2021.9630062] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Low-cost wearables with capability to record electrocardiograms (ECG) are becoming increasingly available. These wearables typically acquire single-lead ECGs that are mainly used for screening of cardiac arrhythmias such as atrial fibrillation. Most arrhythmias are characteruzed by changes in the RR-interval, hence automatic methods to diagnose arrythmia may utilize R-peak detection. Existing R-peak detection methods are fairly accurate but have limited precision. To enable data-point precise detection of R-peaks, we propose a method that uses a fully convolutional dilated neural network. The network is trained and evaluated with manually annotated R-peaks in a heterogeneous set of ECGs that contain a wide range of cardiac rhythms and acquisition noise. 700 randomly chosen ECGs from the PhysioNet/CinC challenge 2017 were used for training (n=500), validation (n=100) and testing (n=100). The network achieves a precision of 0.910, recall of 0.926, and an F1-score of 0.918 on the test set. Our data-point precise R-peak detector may be important step towards fully automatic cardiac arrhythmia detection.Clinical relevance- This method enables data-point precise detection of R-peaks that provides a basis for detection and characterization of arrhythmias.
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10
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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%.
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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
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11
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Krasteva V, Christov I, Naydenov S, Stoyanov T, Jekova I. Application of Dense Neural Networks for Detection of Atrial Fibrillation and Ranking of Augmented ECG Feature Set. SENSORS (BASEL, SWITZERLAND) 2021; 21:6848. [PMID: 34696061 PMCID: PMC8538849 DOI: 10.3390/s21206848] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/05/2021] [Accepted: 10/13/2021] [Indexed: 11/16/2022]
Abstract
Considering the significant burden to patients and healthcare systems globally related to atrial fibrillation (AF) complications, the early AF diagnosis is of crucial importance. In the view of prominent perspectives for fast and accurate point-of-care arrhythmia detection, our study optimizes an artificial neural network (NN) classifier and ranks the importance of enhanced 137 diagnostic ECG features computed from time and frequency ECG signal representations of short single-lead strips available in 2017 Physionet/CinC Challenge database. Based on hyperparameters' grid search of densely connected NN layers, we derive the optimal topology with three layers and 128, 32, 4 neurons per layer (DenseNet-3@128-32-4), which presents maximal F1-scores for classification of Normal rhythms (0.883, 5076 strips), AF (0.825, 758 strips), Other rhythms (0.705, 2415 strips), Noise (0.618, 279 strips) and total F1 relevant to the CinC Challenge of 0.804, derived by five-fold cross-validation. DenseNet-3@128-32-4 performs equally well with 137 to 32 features and presents tolerable reduction by about 0.03 to 0.06 points for limited input sets, including 8 and 16 features, respectively. The feature reduction is linked to effective application of a comprehensive method for computation of the feature map importance based on the weights of the activated neurons through the total path from input to specific output in DenseNet. The detailed analysis of 20 top-ranked ECG features with greatest importance to the detection of each rhythm and overall of all rhythms reveals DenseNet decision-making process, noticeably corresponding to the cardiologists' diagnostic point of view.
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Affiliation(s)
- Vessela Krasteva
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl 105, 1113 Sofia, Bulgaria; (V.K.); (I.C.); (T.S.)
| | - Ivaylo Christov
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl 105, 1113 Sofia, Bulgaria; (V.K.); (I.C.); (T.S.)
| | - Stefan Naydenov
- Department of Internal Diseases “Prof. St. Kirkovich”, Medical University of Sofia, 1431 Sofia, Bulgaria;
| | - Todor Stoyanov
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl 105, 1113 Sofia, Bulgaria; (V.K.); (I.C.); (T.S.)
| | - Irena Jekova
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl 105, 1113 Sofia, Bulgaria; (V.K.); (I.C.); (T.S.)
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12
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Yue Y, Chen C, Liu P, Xing Y, Zhou X. Automatic Detection of Short-Term Atrial Fibrillation Segments Based on Frequency Slice Wavelet Transform and Machine Learning Techniques. SENSORS (BASEL, SWITZERLAND) 2021; 21:5302. [PMID: 34450743 PMCID: PMC8399370 DOI: 10.3390/s21165302] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 07/25/2021] [Accepted: 08/02/2021] [Indexed: 01/22/2023]
Abstract
Atrial fibrillation (AF) is the most frequently encountered cardiac arrhythmia and is often associated with other cardiovascular and cerebrovascular diseases, such as ischemic heart disease, chronic heart failure, and stroke. Automatic detection of AF by analyzing electrocardiogram (ECG) signals has an important application value. Using the contaminated and actual ECG signals, it is not enough to only analyze the atrial activity of disappeared P wave and appeared F wave in the TQ segment. Moreover, the best analysis method is to combine nonlinear features analyzing ventricular activity based on the detection of R peak. In this paper, to utilize the information of the P-QRS-T waveform generated by atrial and ventricular activity, frequency slice wavelet transform (FSWT) is adopted to conduct time-frequency analysis on short-term ECG segments from the MIT-BIH Atrial Fibrillation Database. The two-dimensional time-frequency matrices are obtained. Furthermore, an average sliding window is used to convert the two-dimensional time-frequency matrices to the one-dimensional feature vectors, which are classified using five machine learning (ML) techniques. The experimental results show that the classification performance of the Gaussian-kernel support vector machine (GKSVM) based on the Bayesian optimizer is better. The accuracy of the training set and validation set are 100% and 93.4%. The accuracy, sensitivity, and specificity of the test set without training are 98.15%, 96.43%, and 100%, respectively. Compared with previous research results, our proposed FSWT-GKSVM model shows stability and robustness, and it could achieve the purpose of automatic detection of AF.
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Affiliation(s)
- Yaru Yue
- School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China; (Y.Y.); (P.L.)
| | - Chengdong Chen
- School of Economics and Management, Minjiang University, Fuzhou 350108, China;
| | - Pengkun Liu
- School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China; (Y.Y.); (P.L.)
| | - Ying Xing
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China;
| | - Xiaoguang Zhou
- School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China; (Y.Y.); (P.L.)
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13
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Jiang M, Gu J, Li Y, Wei B, Zhang J, Wang Z, Xia L. HADLN: Hybrid Attention-Based Deep Learning Network for Automated Arrhythmia Classification. Front Physiol 2021; 12:683025. [PMID: 34290619 PMCID: PMC8289344 DOI: 10.3389/fphys.2021.683025] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 05/27/2021] [Indexed: 11/13/2022] Open
Abstract
In recent years, with the development of artificial intelligence, deep learning model has achieved initial success in ECG data analysis, especially the detection of atrial fibrillation. In order to solve the problems of ignoring the correlation between contexts and gradient dispersion in traditional deep convolution neural network model, the hybrid attention-based deep learning network (HADLN) method is proposed to implement arrhythmia classification. The HADLN can make full use of the advantages of residual network (ResNet) and bidirectional long–short-term memory (Bi-LSTM) architecture to obtain fusion features containing local and global information and improve the interpretability of the model through the attention mechanism. The method is trained and verified by using the PhysioNet 2017 challenge dataset. Without loss of generality, the ECG signal is classified into four categories, including atrial fibrillation, noise, other, and normal signals. By combining the fusion features and the attention mechanism, the learned model has a great improvement in classification performance and certain interpretability. The experimental results show that the proposed HADLN method can achieve precision of 0.866, recall of 0.859, accuracy of 0.867, and F1-score of 0.880 on 10-fold cross-validation.
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Affiliation(s)
- Mingfeng Jiang
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China
| | - Jiayan Gu
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China
| | - Yang Li
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China
| | - Bo Wei
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China
| | - Jucheng Zhang
- Department of Clinical Engineering, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Zhikang Wang
- Department of Clinical Engineering, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Ling Xia
- Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
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14
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Wang H, Dai H, Zhou Y, Zhou B, Lu P, Zhang H, Wang Z. An effective feature extraction method based on GDS for atrial fibrillation detection. J Biomed Inform 2021; 119:103819. [PMID: 34029749 DOI: 10.1016/j.jbi.2021.103819] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 04/29/2021] [Accepted: 05/16/2021] [Indexed: 10/21/2022]
Abstract
Atrial fibrillation (AF) is a common and extremely harmful arrhythmia disease. Automatic detection of AF based on ECG helps accurate and timely detection of the condition. However, the existing AF detection methods are mostly based on complex signal transformation or precise waveform localization. This is a big challenge for complex, variable, and susceptible ECG signals. Therefore, we propose a simple feature extraction method based on gradient set (GDS) for AF detection. The method first calculates the GDS of the ECG segment and then calculates the statistical distribution feature and the information quantity feature of the GDS as the input of the classifier. Experiments on four databases include 146 subjects show that the feature extraction method for detecting AF proposed in this paper has the characteristics of simple calculation, noise tolerance, and high adaptability to all kinds of classifiers, and got the best performance on the DNN classifier we designed. Therefore, it is a good choice for feature extraction in AF detection.
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Affiliation(s)
- Haiyan Wang
- State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450003, China; Simulation Experiment Centre, Zhengzhou University of Aeronautics, Zhengzhou 450046, China; Collaborative Innovation Centre for Internet Healthcare, Zhengzhou University, Zhengzhou 450052, China
| | - Honghua Dai
- Collaborative Innovation Centre for Internet Healthcare, Zhengzhou University, Zhengzhou 450052, China; Institute of Intelligent Systems, Deakin University, Burwood, VIC 3125, Australia
| | - Yanjie Zhou
- School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China.
| | - Bing Zhou
- Collaborative Innovation Centre for Internet Healthcare, Zhengzhou University, Zhengzhou 450052, China; School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Peng Lu
- Collaborative Innovation Centre for Internet Healthcare, Zhengzhou University, Zhengzhou 450052, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Hongpo Zhang
- State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450003, China; Collaborative Innovation Centre for Internet Healthcare, Zhengzhou University, Zhengzhou 450052, China
| | - Zongmin Wang
- State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450003, China; Collaborative Innovation Centre for Internet Healthcare, Zhengzhou University, Zhengzhou 450052, China
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Olier I, Ortega-Martorell S, Pieroni M, Lip GYH. How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management. Cardiovasc Res 2021; 117:1700-1717. [PMID: 33982064 PMCID: PMC8477792 DOI: 10.1093/cvr/cvab169] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 05/11/2021] [Indexed: 02/01/2023] Open
Abstract
There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centred on applying ML for dvsetecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modelling; however, we did not observe a significant advantage in following this approach. We also found a fraction of studies using other data modalities, and others centred in aims, such as risk prediction, AF management, and others. From the clinical perspective, AI/ML can help expand the utility of AF detection and risk prediction, especially for patients with additional comorbidities. The use of AI/ML for detection and risk prediction into applications and smart mobile health (mHealth) technology would enable ‘real time’ dynamic assessments. AI/ML could also adapt to treatment changes over time, as well as incident risk factors. Incorporation of a dynamic AI/ML model into mHealth technology would facilitate ‘real time’ assessment of stroke risk, facilitating mitigation of modifiable risk factors (e.g. blood pressure control). Overall, this would lead to an improvement in clinical care for patients with AF.
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Affiliation(s)
- Ivan Olier
- School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.,Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Sandra Ortega-Martorell
- School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.,Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Mark Pieroni
- School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.,Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK.,Liverpool Heart and Chest Hospital, Liverpool, UK
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16
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Tao X, Dang H, Xu X, Zhou X, Xiong D. Multi-feature Fusion of Deep Neural Network for Screening Atrial Fibrillation Using ECG Signals. J Imaging Sci Technol 2021. [DOI: 10.2352/j.imagingsci.technol.2021.65.3.030412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
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17
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Wesselius FJ, van Schie MS, De Groot NMS, Hendriks RC. Digital biomarkers and algorithms for detection of atrial fibrillation using surface electrocardiograms: A systematic review. Comput Biol Med 2021; 133:104404. [PMID: 33951551 DOI: 10.1016/j.compbiomed.2021.104404] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/12/2021] [Accepted: 04/12/2021] [Indexed: 10/21/2022]
Abstract
AIMS Automated detection of atrial fibrillation (AF) in continuous rhythm registrations is essential in order to prevent complications and optimize treatment of AF. Many algorithms have been developed to detect AF in surface electrocardiograms (ECGs) during the past few years. The aim of this systematic review is to gain more insight into these available classification methods by discussing previously used digital biomarkers and algorithms and make recommendations for future research. METHODS On the 14th of September 2020, the PubMed database was searched for articles focusing on algorithms for AF detection in ECGs using the MeSH terms Atrial Fibrillation, Electrocardiography and Algorithms. Articles which solely focused on differentiation of types of rhythm disorders or prediction of AF termination were excluded. RESULTS The search resulted in 451 articles, of which 130 remained after full-text screening. Not only did the amount of research on methods for AF detection increase over the past years, but a trend towards more complex classification methods is observed. Furthermore, three different types of features can be distinguished: atrial features, ventricular features, and signal features. Although AF is an atrial disease, only 22% of the described methods use atrial features. CONCLUSION More and more studies focus on improving accuracy of classification methods for AF in ECGs. As a result, algorithms become increasingly complex and less well interpretable. Only a few studies focus on detecting atrial activity in the ECG. Developing innovative methods focusing on detection of atrial activity might provide accurate classifiers without compromising on transparency.
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Affiliation(s)
- Fons J Wesselius
- Department of Cardiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Mathijs S van Schie
- Department of Cardiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | | | - Richard C Hendriks
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, the Netherlands
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18
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Wu X, Zheng Y, Chu CH, He Z. Extracting deep features from short ECG signals for early atrial fibrillation detection. Artif Intell Med 2020; 109:101896. [DOI: 10.1016/j.artmed.2020.101896] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 05/18/2020] [Accepted: 05/29/2020] [Indexed: 02/02/2023]
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19
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A novel data augmentation method to enhance deep neural networks for detection of atrial fibrillation. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101675] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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