This paper is in the following e-collection/theme issue:

Machine Learning (1501) Digital Health Reviews (1326) Decision Support for Health Professionals (1210) Electronic Health Records (1068)

Published on in Vol 10, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38454, first published .
State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Authors of this article:

Georgios Petmezas1 Author Orcid Image ;   Leandros Stefanopoulos1 Author Orcid Image ;   Vassilis Kilintzis1 Author Orcid Image ;   Andreas Tzavelis2 Author Orcid Image ;   John A Rogers3 Author Orcid Image ;   Aggelos K Katsaggelos4 Author Orcid Image ;   Nicos Maglaveras1 Author Orcid Image
Article Authors Cited by (24) Tweetations (4) Metrics

Journals

  1. 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 2023;23(4):2288 View
  2. Mehri M, Calmon G, Odille F, Oster J, Lalande A. A Generative Adversarial Network to Synthesize 3D Magnetohydrodynamic Distortions for Electrocardiogram Analyses Applied to Cardiac Magnetic Resonance Imaging. Sensors 2023;23(21):8691 View
  3. Farhad M, Masud M, Beg A, Ahmad A, Ahmed L. A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques. Applied Sciences 2023;13(11):6582 View
  4. Zama M, Schwenker F. ECG Synthesis via Diffusion-Based State Space Augmented Transformer. Sensors 2023;23(19):8328 View
  5. Mhamdi L, Dammak O, Cottin F, Ben Dhaou I. Deep learning for COVID‐19 contamination analysis and prediction using ECG images on Raspberry Pi 4. International Journal of Imaging Systems and Technology 2023;33(6):1858 View
  6. Mehari T, Strodthoff N. Towards Quantitative Precision for ECG Analysis: Leveraging State Space Models, Self-Supervision and Patient Metadata. IEEE Journal of Biomedical and Health Informatics 2023;27(11):5326 View
  7. Dathe H, Krefting D, Spicher N. Completing the Cabrera Circle: deriving adaptable leads from ECG limb leads by combining constraints with a correction factor. Physiological Measurement 2023;44(10):105005 View
  8. Nyström A, Olsson de Capretz P, Björkelund A, Lundager Forberg J, Ohlsson M, Björk J, Ekelund U. Prior electrocardiograms not useful for machine learning predictions of major adverse cardiac events in emergency department chest pain patients. Journal of Electrocardiology 2024;82:42 View
  9. Zhang W, Di X, Wei G, Geng S, Fu Z, Hong S. Cardiac arrhythmia classification with rejection of ECG recordings based on uncertainty estimation from deep neural networks. Neural Computing and Applications 2024;36(8):4047 View
  10. Duong S, Vaid A, My V, Butler L, Lampert J, Pass R, Charney A, Narula J, Khera R, Sakhuja A, Greenspan H, Gelb B, Do R, Nadkarni G. Quantitative Prediction of Right Ventricular Size and Function From the ECG. Journal of the American Heart Association 2024;13(1) View
  11. Yoon G, Joo S. Classification feasibility test on multi-lead electrocardiography signals generated from single-lead electrocardiography signals. Scientific Reports 2024;14(1) View
  12. Jain D, Ranjan R, Sharma A, Sharma S, Jain A. Fast and accurate ECG signal peaks detection using symbolic aggregate approximation. Multimedia Tools and Applications 2024;83(30):75033 View
  13. Chopannejad S, Roshanpoor A, Sadoughi F. Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE–Tomek method to detect cardiac arrhythmia based on 12-lead electrocardiogram signals. DIGITAL HEALTH 2024;10 View
  14. Yenurkar G, Mal S, Wakulkar A, Umbarkar K, Bhat A, Bhasharkar A, Pathade A. Future prediction for precautionary measures associated with heart-related issues based on IoT prototype. Multimedia Tools and Applications 2024;83(23):63723 View
  15. Petmezas G, Papageorgiou V, Vassilikos V, Pagourelias E, Tsaklidis G, Katsaggelos A, Maglaveras N. Recent advancements and applications of deep learning in heart failure: Α systematic review. Computers in Biology and Medicine 2024;176:108557 View
  16. Hadida Barzilai D, Cohen-Shelly M, Sorin V, Zimlichman E, Massalha E, Allison T, Klang E. Machine learning in cardiac stress test interpretation: a systematic review. European Heart Journal - Digital Health 2024;5(4):401 View
  17. Nolin-Lapalme A, Corbin D, Tastet O, Avram R, Hussin J. Advancing Fairness in Cardiac Care: Strategies for Mitigating Bias in Artificial Intelligence Models Within Cardiology. Canadian Journal of Cardiology 2024;40(10):1907 View
  18. Kumar P, S. V, Awasthi H, Khan I, Singh R, Sharma V, Pramanik C, Goel S. Shellac-mediated laser-induced reduced graphene oxide film on paper and fabric: exceptional performance in flexible fuel cell, supercapacitor and electrocardiography applications. Materials Advances 2024;5(14):5932 View
  19. Bontinck L, Fonteyn K, Dhaene T, Deschrijver D. ECGencode: Compact and computationally efficient deep learning feature encoder for ECG signals. Expert Systems with Applications 2024;255:124775 View
  20. Jimenez-Perez G, Acosta J, Alcaine A, Camara O. Generalising electrocardiogram detection and delineation: training convolutional neural networks with synthetic data augmentation. Frontiers in Cardiovascular Medicine 2024;11 View
  21. Chen S, Guo Z, Ding C, Hu X, Rudin C. Sparse learned kernels for interpretable and efficient medical time series processing. Nature Machine Intelligence 2024;6(10):1132 View
  22. Cacciari I, Ranfagni A. Hands-On Fundamentals of 1D Convolutional Neural Networks—A Tutorial for Beginner Users. Applied Sciences 2024;14(18):8500 View
  23. Choshi H, Miyoshi K, Tanioka M, Arai H, Tanaka S, Shien K, Suzawa K, Okazaki M, Sugimoto S, Toyooka S. Long Short-Term Memory Algorithm for Personalized Tacrolimus Dosing: A Simple and Effective Time Series Forecasting Approach Post-Lung Transplantation. The Journal of Heart and Lung Transplantation 2024 View