Convolutional neural network for detecting cardiac arrhythmias
https://doi.org/10.25881/18110193_2025_4_44
Abstract
The aim of the study is to develop and evaluate the architecture of a neural network for automated analysis of electrocardiographic (ECG) signals to detect cardiac arrhythmias.
Materials and methods. Open ECG databases (34,570 records) were used for training. The study was conducted using data from six standard ECG leads (I, II, III, aVR, aVL, aVF). Signal preprocessing included polynomial trend removal, wavelet filtering, median filtering, smoothing, and normalization. A convolutional neural network, which processes signals from each of the six leads separately, was used for classification. The final decision is formed using a weighted voting method with empirically determined coefficients reflecting the contribution of each channel based on a preliminary analysis of metrics. A clinical annotation performed by cardiologists was used as a reference standard.
Results. The proposed model demonstrates high classification performance: accuracy – 0.97 (95% CI 0.96-0.98), precision – 0.98 (95% CI 0.97-0.99), recall – 0.98 (95% CI 0.97-0.99), specificity – 0.94 (95% CI 0.92-0.96), F1-score – 0.98 (95% CI 0.97-0.99), ROC-AUC – 0.99 (95% CI 0.98-1.00), PR-AUC – 0.96 (95% CI 0.94-0.97).
Conclusion. The obtained results confirm the effectiveness of the proposed method for diagnosing cardiovascular diseases. The proposed method can be adapted for diagnosing a wider range of cardiac diseases, making it relevant for implementation in practical cardiology.
About the Authors
O. A. Novikova
I.M. Sechenov First Moscow State Medical University
Russian Federation
Russian Federation
PhD
Moscow
A. E. Ermolov
MIREA – Russian Technological University
Russian Federation
Russian Federation
Moscow
References
1. Zhang C, Li J, Guo P, et al. A configurable hardware-efficient ECG classification inference engine based on CNN for mobile healthcare applications. Microelectronics Journal. 2023; 141: 105969. doi: 10.1016/j.mejo.2023.105969.
2. Shutov DV, Drozdov DV, Gazashvili TM, et al. Methodology and regulatory implications of ECG database formation for testing automated algorithms and AI systems. Medical doctor and information technology. 2021; 4: 4-15. (In Russ.) doi: 1025881/18110193_2021_4_4.
3. Zhang Y, Liu S, He Z, et al. A CNN Model for Cardiac Arrhythmias Classification Based on Individual ECG Signals. Cardiovasc Eng Technol. 2022; 13(4): 548-447. doi: 10.1007/s13239-021-00599-8.
4. Cheng J, Zou Q, Zhao Y. ECG signal classification based on deep CNN and BiLSTM. BMC Med Inform Decis Mak. 2021; 21: 365. doi: 10.1186/s12911-021-01736-y.
5. Maweu BM, Dakshit S, Shamsuddin R, et al. CEFEs: A CNN Explainable Framework for ECG Signals. Artif Intell Med. 2021; 115: 102059. doi: 10.1016/j.artmed.2021.102059.
6. Andrikov DA, Kurbanov SV. Algorithmic support for spectral processing of cardiograms. RUDN Journal of Engineering Research. 2024; 25(2): 111-120. (In Russ.) doi: 10.22363/2312-8143-2024-25-2-111-120.
7. Pavlov AN, Filatova AE, Hramov AE. Time-frequency analysis of nonstationary processes: concepts of wavelets and empirical modes. Izvestiya vuzov. PND. 2011; 19(2): 141-157. (In Russ.) doi: 10.18500/0869-6632-2011-19-2-141-157.
8. Kaggle. Shaoxing and Ningbo Hospital ECG Database. Available at: https://www.kaggle.com/datasets/bjoernjostein/shaoxing-and-ningbo-first-hospital-database. Accessed January 2, 2025.
9. Kaggle. PTB-XL ECG dataset. Available at: https://www.kaggle.com/datasets/khyeh0719/ptb-xl-dataset.Accessed January 2, 2025.
10. Kaggle. Georgia 12-Lead ECG Challenge Database. Available at: https://www.kaggle.com/datasets/bjoernjostein/georgia-12lead-ecg-challenge-database. Accessed January 2, 2025.
11. Kaggle. China Physiological Signal Challenge in 2018. Available at: https://www.kaggle.com/datasets/bjoernjostein/china-physiological-signal-challenge-in-2018. Accessed January 2, 2025.
12. Kaggle. China 12-Lead ECG Challenge Database. Available at: https://www.kaggle.com/datasets/bjoernjostein/china-12lead-ecg-challenge-database. Accessed January 2, 2025.
13. Safdar M, Nowak R, Palka P. Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review. Computers in Biology and Medicine. 2024; 170: 107908. doi: 10.1016/j.compbiomed.2023.107908.
14. Lin M., Hong Y., Hong S., et al. Discrete Wavelet Transform based ECG classification using gcForest: A deep ensemble method. Technol Health Care. 2024; 32(S1): 95-105. doi: 10.3233/thc-248008.
15. Pal A, Rai H, Agarwal S, et al. Advanced Noise-Resistant Electrocardiography Classification Using Hybrid Wavelet-Median Denoising and a Convolutional Neural Network. Sensors. 2024; 24(21): 7033. doi: 10.3390/s24217033.
16. Mokhtari R, Belhaouari S, Kassoul K, et al. ECG Heartbeat classification using Progressive Moving Average Transform. Research Square. 2024; 10: 21203. doi: 10.1038/s41598-025-88119-9.
17. Yousuf A, Hafiz R, Riaz S, et al. Inferior Myocardial Infarction Detection from lead II of ECG: A Gramian Angular Field-based 2D-CNN Approach. Electrical Engineering and Systems Science. 2024; 2302: 13011. doi: 10.1109/lsens.2024.3450176.
18. Narotamo H, Dias M, Santos R, et al. Deep learning for ECG classification: A comparative study of 1D and 2D representations and multimodal fusion approaches. Biomedical Signal Processing and Control. 2024; 93: 106141. doi: 10.2139/ssrn.4555989.
19. Guo K, Wang S, Ma L, et al. ECG signals’ detection using a Dropout Deep Convolutional Neural Network. Reasearch Square. 2022; 10: 21203. doi: 10.21203/rs.3.rs-1591115/v1.
20. Liu C, Wan Z, Ouyang C, et al. Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement. Electrical Engineering and Systems Science. 2024; 2403: 06659. doi: 10.48550/arXiv.2403.06659.
21. Ebrahimi Z, Loni M, Daneshtalab M, et al. A review on deep learning methods for ECG arrhythmia classification. Expert Systems with Applications: X. 2020; 7: 100033. doi: 10.1016/j.eswax.2020.100033.
22. Xie C, Wang Z, Yang C, et al. Machine Learning for Detecting Atrial Fibrillation from ECGs: Systematic Review and Meta-Analysis. Rev Cardiovasc Med. 2024; 25(1): 8. doi: 10.31083/j.rcm2501008.
23. Fiorina L, Chemaly P, Cellier J, et al. Artificial intelligence–based electrocardiogram analysis improves atrial arrhythmia detection from a smartwatch electrocardiogram. Eur Heart J Digit Health. 2024; 5(5): 535-541. doi: 10.1093/ehjdh/ztae047.
24. Hygrell T, Viberg F, Dahlberg E, et al. An artificial intelligence–based model for prediction of atrial fibrillation from single-lead sinus rhythm electrocardiograms facilitating screening. EP Europace. 25(4): 1332-1338. doi: 10.1093/europace/euad036.
25. Barker J, Li X, Kotb A, et al. Artificial intelligence for ventricular arrhythmia capability using ambulatory electrocardiograms. European Heart Journal – Digital Health. 5(3): 384-388. doi: 10.1093/ehjdh/ztae004.
26. Gruwez H, Barthels M, Haemers P, et al. Detecting Paroxysmal Atrial Fibrillation From an Electrocardiogram in Sinus Rhythm: External Validation of the AI Approach. JACC: Clinical Electrophysiology. 9(8): 1771-1782. doi: 10.1016/j.jacep.2023.04.008.
27. Gadaleta M, Harrington, Barnhill E, et al. Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias. npj Digit. Med. 6: 229. doi: 10.1038/s41746-023-00966-w.
Review
For citations:
Novikova O.A., Ermolov A.E. Convolutional neural network for detecting cardiac arrhythmias. Medical Doctor and Information Technologies. 2025;(4):44-55. (In Russ.) https://doi.org/10.25881/18110193_2025_4_44
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