Deep Learning-Driven Heartbeat Classification Using Segmented and Preprocessed ECG Signals
DOI:
https://doi.org/10.24237/04.04.714Keywords:
Deep Learning, ECG Classification, Convolutional Neural Networks, Heartbeat Categorization Dataset, Heartbeat Classification, Cardiovascular DiseasesAbstract
Cardiovascular diseases are a leading cause of death worldwide, so accurate and timely diagnosis is essential for effective treatment. Electrocardiogram (ECG) signals offer valuable information for detecting various cardiac abnormalities. Traditional ECG analysis is time-consuming and prone to human error, particularly in distinguishing between different types of arrhythmias. Manual interpretation of large amounts of ECG data can lead to misdiagnosis or delayed treatment. There is an urgent need for a reliable solution capable of automatically classifying recorded heart rhythms with high accuracy. In this paper, a deep learning-based framework is presented for classifying ECG heartbeats into five distinct classes. The dataset is preprocessed through normalization and reconstructed into a format compatible with convolutional neural networks (CNNs), which are highly adept at learning spatial hierarchies from input data. The model is trained using optimized hyperparameters and evaluated based on several metrics. The trained model achieves a validation accuracy of 99% and test accuracy of 98% on the ECG Heartbeat Categorization Dataset. The proposed system significantly contributes to the advancement of automated heartbeat classification and has the potential to support clinical decision-making systems.
Downloads
References
[1]
K. Husain, M. S. Mohd Zahid, S. U Hassan, S. Hasbullah, S. Mandala, Advances of ECG sensors from hardware, software and format interoperability perspectives, Electronics, 10, 105 (2021), DOI:10.3390/electronics10020105
[2]
F. Sadikoglu, C. Kavalcioglu, B. Dagman, Electromyogram (EMG) signal detection, classification of EMG signals and diagnosis of neuropathy muscle disease, Procedia Computer Science, 120, 422-429 (2017), DOI: 10.1016/j.procs.2017.11.267
[3]
S. Irfan, N. Anjum, T. Althobaiti, A. A. Alotaibi, A. B. Siddiqui, N. Ramzan, Heartbeat classification and arrhythmia detection using a multi-model deep-learning technique, Sensors, 22, 5606 (2022), DOI: 10.3390/s22155606
[4]
E. E. Tripoliti, T. G. Papadopoulos, G. S. Karanasiou, K. K. Naka, D. I. Fotiadis, Heart failure: diagnosis, severity estimation and prediction of adverse events through machine learning techniques, Computational and Structural Biotechnology Journal, 15, 26-47 (2017), DOI: 10.1016/j.csbj.2016.11.001
[5]
B. S. Chandra, C. S. Sastry, S. Jana, Robust heartbeat detection from multimodal data via CNN-based generalizable information fusion, IEEE Transactions on Biomedical Engineering, 66, 710-717 (2019), DOI: 10.1109/TBME.2018.2854696
[6]
B. Feng, T. Yu, H. Wang, K. Liu, W. Wu, W. Long, Machine learning and deep learning in biomedical signal analysis, Frontiers in Human Neuroscience, 17, 1183840 (2023), DOI:10.3389/fnhum.2023.1183840
[7]
A. Khan, A. Sohail, U. Zahoora, A. S. Qureshi, A survey of the recent architectures of deep convolutional neural networks, Artificial Intelligence Review, 53, 5455-5516 (2020), DOI: 10.1007/s10462-020-09825-6
[8]
M. Kachuee, S. Fazeli, M. Sarrafzadeh, ECG heartbeat classification: A deep transferable representation, In 2018 IEEE International Conference on Healthcare Informatics (ICHI) (IEEE, 2018), pp. 443-444, DOI: 10.1109/ICHI.2018.00074
[9]
S. M. Mathews, C. Kambhamettu, K. E. Barner, A novel application of deep learning for single-lead ECG classification, Computers in Biology and Medicine, 99, 53-62 (2018), DOI: 10.1016/j.compbiomed.2018.06.008
[10]
M. Alfaras, M. C. Soriano, S. Ortín, A fast machine learning model for ECG-based heartbeat classification and arrhythmia detection, Frontiers in Physics, 7, 103 (2019), DOI: 10.3389/fphy.2019.00103
[11]
Y. Liang, S. Yin, Q. Tang, Z. Zheng, M. Elgendi, Z. Chen, Deep learning algorithm classifies heartbeat events based on electrocardiogram signals, Frontiers in Physiology, 11, 569050 (2020), DOI: 10.3389/fphys.2020.569050
[12]
K. Weimann, T. O. Conrad, Transfer learning for ECG classification, Scientific Reports, 11, 5251 (2021), DOI: https://doi.org/10.1038/s41598-021-84374-8
[13]
K. Tang, S. Ma, X. Sun, D. Guo, Optimizing machine learning for enhanced automated ECG analysis in cardiovascular healthcare, Egyptian Informatics Journal, 28, 100578 (2024), DOI: https://doi.org/10.1016/j.eij.2024.100578
[14]
S. Desmalia, A. M. Siregar, K. A. Baihaqi, T. Rohana, Comparison Model Optimal Machine Learning Model With Feature Extraction for Heart Attack Disease Classification, Scientific Journal of Informatics, 11, 485-492 (2024), DOI: https://doi.org/10.15294/sji.v13i3.48890
[15]
A. S. Yahya, N. M. Edan, ECG Signal Classification Using Hybrid and Non-Hybrid Learning Technologies, International Journal on Perceptive and Cognitive Computing, 11, 114-121 (2025), DOI: https://doi.org/10.31436/ijpcc.v11i1.503
[16]
J. Hu, L. Shen, G. Sun, Squeeze-and-Excitation Networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7132-7141 (2018), DOI: 10.48550/arXiv.1709.01507
[17]
D. Bahdanau, K. Cho, Y. Bengio, Neural Machine Translation by Jointly Learning to Align and Translate, arXiv preprint arXiv:1409.0473 (2014), DOI: 10.48550/arXiv.1409.0473
[18]
S. Woo, J. Park, J. Y. Lee, I. S. Kweon, CBAM: Convolutional Block Attention Module, Proceedings of the European Conference on Computer Vision (ECCV), 3-19 (2018), DOI: 10.48550/arXiv.1807.0652
[19]
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, I. Polosukhin, Attention Is All You Need, Advances in Neural Information Processing Systems, 30 (2017), DOI(10.48550/arXiv.1706.03762)
[20]
S. Fazeli, ECG Heartbeat Categorization Dataset [Data set], Kaggle (2018), Available online, Accessed 8 May 2025
Downloads
Published
Issue
Section
License
Copyright (c) 2026 CC BY 4.0
This work is licensed under a Creative Commons Attribution 4.0 International License.
