ECA-ECGNet: An Efficient Channel Attention-Enhanced 1D CNN for ECG Arrhythmia Detection
Abstract
Cardiovascular diseases remain a major cause of mortality worldwide, with cardiac arrhythmias representing a potentially serious condition. Although deep learning has improved automated electrocardiogram (ECG) analysis, many recent models rely on recurrent, residual, transformer-based, or multi-input architectures, thereby increasing computational complexity. Moreover, reported classification performance can vary substantially according to the adopted data-partitioning strategy. This study proposes an Efficient Channel Attention ECG Network, termed ECA-ECGNet, for automated five-class ECG beat classification using the MIT-BIH Arrhythmia Database. ECG beats were segmented, normalized, and grouped into five AAMI categories: Normal (N), Supraventricular (S), Ventricular (V), Fusion (F), and Unknown (Q). The ECA-ECGNet integrates three Conv1D-based feature-extraction blocks with lightweight ECA modules to emphasize informative channel features. To mitigate the severe class imbalance inherent in clinical ECG data, categorical focal loss was implemented during model training to dynamically scale the loss based on prediction confidence. ECA-ECGNet was assessed under complementary intra-patient and inter-patient evaluation settings. The model achieved an accuracy of 99.20%, a macro F1-score of 0.953, and a weighted F1-score of 0.992. In the inter-patient setting, complete subject groups were separated between model development and testing, resulting in an accuracy of 97.96%, a macro F1-score of 0.9165, and a weighted F1-score of 0.9798. The implementation of focal loss substantially narrowed the performance gap between majority and minority classes, demonstrating enhanced generalization for Supraventricular and Ventricular arrhythmias. Ultimately, the principal novelty of ECA-ECGNet is the synergistic application of structural channel attention and dynamic focal loss. This framework shows that diagnostic accuracy on unseen patients is achievable without high computational overhead, supporting real-time arrhythmia screening on low-resource clinical devices
Downloads
References
Z. Tong, Y. Xie, K. Li, R. Yuan, and L. Zhang, “The global burden and risk factors of cardiovascular diseases in adolescent and young adults, 1990–2019,” BMC Public Health, vol. 24, no. 1, p. 1017, Apr. 2024, doi: 10.1186/s12889-024-18445-6.
World Health Organization, “Cardiovascular diseases (CVDs),” Fact sheet, Feb. 11, 2025. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds). [Accessed: July. 29, 2026]
M. R. Khan, Z. M. Haider, J. Hussain, F. H. Malik, I. Talib, and S. Abdullah, “Comprehensive Analysis of Cardiovascular Diseases: Symptoms, Diagnosis, and AI Innovations,” Bioengineering, vol. 11, no. 12, p. 1239, Dec. 2024, doi: 10.3390/bioengineering11121239.
G. Tse, “Mechanisms of cardiac arrhythmias,” J. Arrhythm., vol. 32, no. 2, pp. 75–81, 2016.
U. R. Acharya, H. Fujita, O. S. Lih, Y. Hagiwara, J. H. Tan, and M. Adam, “Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network,” Inf. Sci. (N. Y)., vol. 405, pp. 81–90, Sep. 2017, doi: 10.1016/j.ins.2017.04.012.
Maher Al-Asaady, T. N. Mohd Aris, and A. D. Khalaf, “Multi-Scale Inception 1D-CNN for Patient-Independent Congestive Heart Failure Detection from ECG Signals,” in International Conference on Cybersecurity, Data Science, and Machine Learning (ICCDM-2026), Kuala Lumpur: Springer - Accepted for publication, In press, 2026.
T. Liu et al., “Inter-Patient Congestive Heart Failure Detection Using ECG-Convolution-Vision Transformer Network,” Sensors, vol. 22, no. 9, p. 3283, Apr. 2022, doi: 10.3390/s22093283.
Y. Kutlu, G. Altan, and N. Allahverdi, “Arrhythmia classification using waveform ECG signals,” in Int. Conf. Advanced Technology & Sciences, Konya, Turkey, 2016.
A. Rajkumar, M. Ganesan, and R. Lavanya, “Arrhythmia classification on ECG using Deep Learning,” in 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), 2019, pp. 365–369. doi: 10.1109/ICACCS.2019.8728362.
Z. Ebrahimi, M. Loni, M. Daneshtalab, and A. Gharehbaghi, “A review on deep learning methods for ECG arrhythmia classification,” Expert Systems with Applications: X, vol. 7, p. 100033, 2020, doi: 10.1016/j.eswax.2020.100033.
U. R. Acharya et al., “A deep convolutional neural network model to classify heartbeats,” Comput. Biol. Med., vol. 89, pp. 389–396, Oct. 2017, doi: 10.1016/j.compbiomed.2017.08.022.
P. K. Tyagi, N. Rathore, and D. Agrawal, “A Review on Heartbeat Classification for Arrhythmia Detection Using ECG signal Processing,” in 2023 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), 2023, pp. 1–6. doi: 10.1109/SCEECS57921.2023.10063143.
I. Sayin et al., “CNN Based Detection of Cardiovascular Diseases from ECG Images,” 2024. [Online]. Available: https://arxiv.org/abs/2408.16800
M. Zubair, J. Kim, and C. Yoon, “An Automated ECG Beat Classification System Using Convolutional Neural Networks,” in 2016 6th International Conference on IT Convergence and Security (ICITCS), IEEE, Sep. 2016, pp. 1–5. doi: 10.1109/ICITCS.2016.7740310.
Association for the Advancement of Medical Instrumentation, ANSI/AAMI EC57:2012/(R)2020: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. Arlington, VA, USA: AAMI, 2020. [Online]. Available: https://aami.org/standard/ansi-aami-ec572012-r2020-pdf
X. Xu and H. Liu, “ECG heartbeat classification using convolutional neural networks,” IEEE access, vol. 8, pp. 8614–8619, 2020, doi: 10.1109/ACCESS.2020.2964749.
T. Liu et al., “Inter-Patient Congestive Heart Failure Detection Using ECG-Convolution-Vision Transformer Network,” Sensors, vol. 22, no. 9, p. 3283, Apr. 2022, doi: 10.3390/s22093283.
S. U. Hassan, M. S. Mohd Zahid, T. A. Abdullah, and K. Husain, “Classification of cardiac arrhythmia using a convolutional neural network and bi-directional long short-term memory,” Digit. Health, vol. 8, p. 205520762211027, Jan. 2022, doi: 10.1177/20552076221102766.
F. Khan, X. Yu, Z. Yuan, and A. ur Rehman, “ECG classification using 1-D convolutional deep residual neural network,” PLoS One, vol. 18, no. 4, p. e0284791, Apr. 2023, doi: 10.1371/journal.pone.0284791.
B. Zheng, W. Luo, M. Zhang, and H. Jin, “Arrhythmia classification based on multi-input convolutional neural network with attention mechanism,” PLoS One, vol. 20, no. 6, p. e0326079, Jun. 2025, doi: 10.1371/journal.pone.0326079.
M. Guhdar, A. O. Mohammed, and R. J. Mstafa, “Advanced deep learning framework for ECG arrhythmia classification using 1D-CNN with attention mechanism,” Knowl. Based. Syst., vol. 315, p. 113301, Apr. 2025, doi: 10.1016/j.knosys.2025.113301.
N. Rafie, A. H. Kashou, and P. A. Noseworthy, “ECG Interpretation: Clinical Relevance, Challenges, and Advances,” Hearts, vol. 2, no. 4, pp. 505–513, Nov. 2021, doi: 10.3390/hearts2040039.
M. T. Al-Asaady, T. N. M. Aris, N. M. Sharef, and H. binti Hamdan, “XCOA-MRFO: a hybrid meta-heuristic framework for simultaneous optimization of architecture and weight of neural networks in medical data classification,” Neural Computing and Applications, vol. 38, Art. no. 600, 2026, doi: 10.1007/s00521-026-12215-8.
M. T. Al-Asaady, T. N. Mohd Aris, N. M. Sharef, and H. Hamdan, “XCOA-MLP: Extended Coyote Optimization Algorithm for Training Neural Networks in Medical Data Classification,” International Journal of Intelligent Engineering and Systems, vol. 18, no. 11, pp. 746–762, Dec. 2025, doi: 10.22266/ijies2025.1231.46.
Md. A. Talukder, A. S. Talaat, N. J. Muna, A. Alazab, M. Kazi, and U. K. Das, “An explainable deep learning framework for trustworthy arrhythmia detection from ECG signals,” Sci. Rep., vol. 15, no. 1, p. 39496, Nov. 2025, doi: 10.1038/s41598-025-22986-0.
S. Lamba, S. Kumar, and M. Diwakar, “FADLEC: feature extraction and arrhythmia classification using deep learning from electrocardiograph signals,” Discover Artificial Intelligence, vol. 5, no. 1, p. 82, May 2025, doi: 10.1007/s44163-025-00290-0.
B. Saha Tchinda and D. Tchiotsop, “A lightweight 1D convolutional neural network model for arrhythmia diagnosis from electrocardiogram signal,” Phys. Eng. Sci. Med., vol. 48, no. 2, pp. 577–589, Jun. 2025, doi: 10.1007/s13246-025-01525-1.
E. H. Houssein, M. Kilany, and A. E. Hassanien, “ECG signals classification: a review,” International Journal of Intelligent Engineering Informatics, vol. 5, no. 4, pp. 376–396, 2017.
X. Xu and H. Liu, “ECG Heartbeat Classification Using Convolutional Neural Networks,” IEEE Access, vol. 8, pp. 8614–8619, 2020, doi: 10.1109/ACCESS.2020.2964749.
M. Sun, Y. Si, W. Yang, W. Fan, and L. Zhou, “Inter-patient congestive heart failure automatic recognition using attention-based multi-scale convolutional neural network,” Measurement, vol. 218, p. 113239, Aug. 2023, doi: 10.1016/j.measurement.2023.113239.
M. A. Raza et al., “Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network,” Computers, Materials & Continua, vol. 77, no. 3, pp. 3817–3834, 2023, doi: 10.32604/cmc.2023.032275.
M. Wu, Y. Lu, W. Yang, and S. Y. Wong, “A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network,” Front. Comput. Neurosci., vol. 14, Jan. 2021, doi: 10.3389/fncom.2020.564015.
M. Al-Asaady, T. N. Mohd Aris, N. Sharef, and H. Hamdan, “Swarm intelligence approach for optimizing neural network architectures in medical data classification,” Journal of Studies in Science and Engineering, vol. 6, no. 1, pp. 321–346, 2026, doi: 10.53898/josse2026624.
Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, and Q. Hu, “ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 11531–11539. doi: 10.1109/CVPR42600.2020.01155.
T. Rasti, Q. Zhu, and K. Zhou, “ECG-based arrhythmia detection by a shallow CNN model,” Series of Cardiology Research, vol. 6, no. 1, pp. 1–14, 2024.
D. Tenepalli and T. M. Navamani, “Design of an Explainable Deep Learning Framework for ECG-Based Arrhythmia Prediction,” IEEE Access, vol. 13, pp. 200754–200768, 2025, doi: 10.1109/ACCESS.2025.3636481.
Í. F. Di Paolo and A. R. G. Castro, “Intra- and Interpatient ECG Heartbeat Classification Based on Multimodal Convolutional Neural Networks with an Adaptive Attention Mechanism,” Applied Sciences, vol. 14, no. 20, p. 9307, Oct. 2024, doi: 10.3390/app14209307.
G. B. Moody and R. G. Mark, “The MIT-BIH arrhythmia database on CD-ROM and software for use with it,” in Computers in Cardiology 1990, Chicago, IL, USA, Sep. 23–26, 1990, pp. 185–188. Los Alamitos, CA, USA: IEEE Computer Society Press, doi: 10.1109/CIC.1990.144205.
E. Izci, M. A. Ozdemir, M. Degirmenci, and A. Akan, “Cardiac Arrhythmia Detection from 2D ECG Images by Using Deep Learning Technique,” in 2019 Medical Technologies Congress (TIPTEKNO), 2019, pp. 1–4. doi: 10.1109/TIPTEKNO.2019.8895011.
J. Cui, L. Wang, X. He, V. H. C. De Albuquerque, S. A. AlQahtani, and M. M. Hassan, “Deep learning-based multidimensional feature fusion for classification of ECG arrhythmia,” Neural Comput. Appl., vol. 35, no. 22, pp. 16073–16087, Aug. 2023, doi: 10.1007/s00521-021-06487-5.
D. P. Kingma, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
Copyright (c) 2026 Maher Al-Asaady, Mohanad Rashid, Mahmood Mahmood
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlikel 4.0 International (CC BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
.png)
.png)
.png)
.png)
.png)
