Shenda Hong1, Yuxi Zhou2, Junyuan Shang3, Cao Xiao4, Jimeng Sun5. 1. Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, USA. Electronic address: hongshenda@pku.edu.cn. 2. School of Electronics Engineering and Computer Science, Peking University, Beijing, China; Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China. Electronic address: joy_yuxi@pku.edu.cn. 3. School of Electronics Engineering and Computer Science, Peking University, Beijing, China; Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China. Electronic address: sjy1203@pku.edu.cn. 4. Analytics Center of Excellence, IQVIA, Cambridge, USA. Electronic address: cao.xiao@iqvia.com. 5. Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, USA. Electronic address: jimeng@illinois.edu.
Abstract
BACKGROUND: The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals. OBJECTIVE: This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives. METHODS: We extracted papers that applied deep learning (deep neural network) models to ECG data that were published between January 1st of 2010 and February 29th of 2020 from Google Scholar, PubMed, and the Digital Bibliography & Library Project. We then analyzed each article according to three factors: tasks, models, and data. Finally, we discuss open challenges and unsolved problems in this area. RESULTS: The total number of papers extracted was 191. Among these papers, 108 were published after 2019. Different deep learning architectures have been used in various ECG analytics tasks, such as disease detection/classification, annotation/localization, sleep staging, biometric human identification, and denoising. CONCLUSION: The number of works on deep learning for ECG data has grown explosively in recent years. Such works have achieved accuracy comparable to that of traditional feature-based approaches and ensembles of multiple approaches can achieve even better results. Specifically, we found that a hybrid architecture of a convolutional neural network and recurrent neural network ensemble using expert features yields the best results. However, there are some new challenges and problems related to interpretability, scalability, and efficiency that must be addressed. Furthermore, it is also worth investigating new applications from the perspectives of datasets and methods. SIGNIFICANCE: This paper summarizes existing deep learning research using ECG data from multiple perspectives and highlights existing challenges and problems to identify potential future research directions.
BACKGROUND: The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals. OBJECTIVE: This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives. METHODS: We extracted papers that applied deep learning (deep neural network) models to ECG data that were published between January 1st of 2010 and February 29th of 2020 from Google Scholar, PubMed, and the Digital Bibliography & Library Project. We then analyzed each article according to three factors: tasks, models, and data. Finally, we discuss open challenges and unsolved problems in this area. RESULTS: The total number of papers extracted was 191. Among these papers, 108 were published after 2019. Different deep learning architectures have been used in various ECG analytics tasks, such as disease detection/classification, annotation/localization, sleep staging, biometric human identification, and denoising. CONCLUSION: The number of works on deep learning for ECG data has grown explosively in recent years. Such works have achieved accuracy comparable to that of traditional feature-based approaches and ensembles of multiple approaches can achieve even better results. Specifically, we found that a hybrid architecture of a convolutional neural network and recurrent neural network ensemble using expert features yields the best results. However, there are some new challenges and problems related to interpretability, scalability, and efficiency that must be addressed. Furthermore, it is also worth investigating new applications from the perspectives of datasets and methods. SIGNIFICANCE: This paper summarizes existing deep learning research using ECG data from multiple perspectives and highlights existing challenges and problems to identify potential future research directions.
Authors: Nehemiah Musa; Abdulsalam Ya'u Gital; Nahla Aljojo; Haruna Chiroma; Kayode S Adewole; Hammed A Mojeed; Nasir Faruk; Abubakar Abdulkarim; Ifada Emmanuel; Yusuf Y Folawiyo; James A Ogunmodede; Abdukareem A Oloyede; Lukman A Olawoyin; Ismaeel A Sikiru; Ibrahim Katb Journal: J Ambient Intell Humaniz Comput Date: 2022-07-07
Authors: Ozal Yildirim; Muhammed Talo; Edward J Ciaccio; Ru San Tan; U Rajendra Acharya Journal: Comput Methods Programs Biomed Date: 2020-09-08 Impact factor: 5.428