Comparing Classical Machine Learning and Deep Learning for Classification of Arrhythmia from ECG Signals

Bikova, Marija; Gjoreski, Vesna

doi:10.25673/112991

Proceedings of International Conference on Applied Innovation in IT
2023/11/30, Volume 11, Issue 2, pp.31-38

Comparing Classical Machine Learning and Deep Learning for Classification of Arrhythmia from ECG Signals

Marija Bikova, Vesna Ojleska Latkoska and Hristijan Gjoreski

Abstract: Arrhythmia detection is a vital task for reducing the mortality rate of cardiovascular diseases. Electrocardiogram (ECG) is a simple and inexpensive tool that can provide valuable information about the heart’s electrical activity and detect arrhythmias. However, manual analysis of ECG signals can be time-consuming and prone to errors. Therefore, machine learning models have been proposed to automate the process and improve the accuracy and efficiency of arrhythmia detection. In this paper, we compare six machine learning models, namely ADA boosting, Gradient Boost, Random Forest, C-Support Vector (SVC), Convolutional Neural Network (CNN), and Long Short-Term Memory Network (LSTM), for arrhythmia detection using ECG data from the MIT-BIH Arrhythmia Database. We evaluate the performance of the models using various metrics, such as accuracy, precision, recall, and F1-score, on different classes of ECG beats. We also use confusion matrices to visualize the errors made by the models. We find that the CNN model is the best performing model overall, achieving accuracy of 95% and F1-score of 84.75%. SVC and LSTM were the second and third best, achieving accuracy of 94% and 93%, respectively. We also discuss the challenges of using ECG data for arrhythmia detection, such as noise, imbalance, and similarity of classes. We suggest some possible ways to overcome these challenges, such as using more advanced preprocessing and resampling techniques, or incorporating domain knowledge and expert feedback into the models.

Keywords: Cardiac Arrhythmia, Deep Learning, Classification Electrocardiogram, Convolutional Neural Network, Long- Short Term Memory.

DOI: 10.25673/112991

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References:

K.Mc Namara, H. Alzubaidi, and J.K. Jackson, “Cardiovascular disease as a leading cause of death: how are pharmacists getting involved?” Integrated Pharmacy Research and Practice, vol. Volume 8, no. 8, pp. 1-11, Feb. 2019, [Online]. Available: https://doi.org/10.2147/iprp.s133088.
A. Ullah, S.M. Anwar, M. Bilal, and R.M. Mehmood, “Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation,” Remote Sensing, vol. 12, no. 10, p. 1685, May 2020, [Online]. Available: https://doi.org/10.3390/rs12101685.
S. Verma, “Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG Data,” May 2022, [Online]. Available: https://doi.org/10.48550/arxiv.2205.02803.
S.K. Pandey and R.R. Janghel, “Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE,” Australasian Physical & Engineering Sciences in Medicine, vol. 42, no. 4, pp. 1129-1139, Nov. 2019, [Online]. Available: https://doi.org/10.1007/s13246-019-00815-9.
M.T. Le, V.S. Rathour, Q.S. Truong, Q. Mai, P. Brijesh, and N. Le, “Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification,” Jul. 2021, [Online]. Available: https://doi.org/10.1109/ bhi50953.2021.9508527.
S. Armstrong, “Survey of Machine Learning Techniques To Predict Heartbeat Arrhythmias,” arXiv.org, Aug. 22, 2022, [Online]. Available: https://arxiv.org/abs/2208.10463, [Accessed on Nov. 04, 2023].
J. Wu et al., “Editor’s Choice - Impact of initial hospital diagnosis on mortality for acute myocardial infarction: A national cohort study,” European Heart Journal: Acute Cardiovascular Care, vol. 7, no. 2, pp. 139-148, Aug. 2016, [Online]. Available: https://doi.org/10.1177/2048872616661693.
S.K. Pandey and R.R. Janghel, “Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE,” Australasian Physical & Engineering Sciences in Medicine, vol. 42 (4), pp. 1129-1139, November 2019.
J. Gao, H. Zhang, P. Lu, and Z. Wang, “An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset,” Journal of Healthcare Engineering, October 13, 2019.
A. Mustaqeem, S.M. Anwar, and M. Majid, "Multiclass classification of cardiac arrhythmia using improved feature selection and SVM invariants," Computational and Mathematical Methods in Medicine, vol. 2018, pp. 1-10, 2018, doi: 10.1155/2018/7310496.
A. Mustaqeem, S.M. Anwar, M. Majid, and A.R. Khan, “Wrapper method for feature selection to classify cardiac arrhythmia,” IEEE Xplore, Jul. 01, 2017, [Online]. Available: https://ieeexplore.ieee.org/ document/8037650, [Accessed on Jan. 11, 2023].
G.B. Moody and R.G. Mark, “The impact of the MIT-BIH Arrhythmia Database,” IEEE Engineering in Medicine and Biology Magazine, vol. 20, no. 3, pp. 45-50, 2001, [Online]. Available: https://doi.org/10.1109/51.932724.

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DOI: http://dx.doi.org/10.25673/115729

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