Deep learning Methods for ECG-Based Heart Disease Detection
Abstract
Cardiovascular disease (CVD) continues to be a primary cause of death globally, and early detection plays a critical role in improving patient outcomes. This research introduces the development of a deep learning model designed to automatically categorize heart diseases using Electrocardiogram (ECG) data. The model utilizes a 1D Convolutional Neural Network (CNN) structure and makes use of the MIT-BIH Arrhythmia dataset from Physionet. The dataset was split into training, validation, and testing subsets. Our proposed design incorporates convolutional layers, max-pooling, ReLU activation functions, and dropout layers to prevent overfitting. Comparative assessment against conventional methods such as logistic regression and Support Vector Machines (SVM) shows superior performance, achieving an accuracy of 98.29%, recall of 87.60%, precision of 93.75%, and F1 score of 90.37%. The potential of deep learning to enhance the accuracy and efficiency of diagnosing CVD from ECG data is highlighted in this study, introducing a reliable tool for clinical application.
Downloads
References
WHO, “Cardiovascular diseases.” https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1 (accessed Jun. 30, 2024).
M. Brida et al., “Acquired cardiovascular disease in adults with congenital heart disease A call to action for timely preventive measures—a clinical consensus statement of the European Society of Cardiology Working Group on Adult Congenital Heart Disease in collaboration w,” European Heart Journal, vol. 44, no. 43, pp. 4533–4548, Nov. 2023, doi: 10.1093/eurheartj/ehad570.
A. Di Costanzo, C. A. M. Spaccarotella, G. Esposito, and C. Indolfi, “An Artificial Intelligence Analysis of Electrocardiograms for the Clinical Diagnosis of Cardiovascular Diseases: A Narrative Review,” Journal of Clinical Medicine, vol. 13, no. 4, 2024, doi: 10.3390/jcm13041033.
A. Irsyad, H. Tjandrasa, and S. C. Hidayati, “Segmentation of COVID-19 Chest CT Images Based on SwishUnet,” International Journal of Intelligent Engineering and Systems, vol. 16, no. 3, pp. 565–578, 2023, doi: 10.22266/ijies2023.0630.45.
M. Z. Alom, M. M. S. Rahman, M. S. Nasrin, T. M. Taha, and V. K. Asari, “COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches,” 2020, [Online]. Available: http://arxiv.org/abs/2004.03747.
E. Hussain, M. Hasan, M. A. Rahman, I. Lee, T. Tamanna, and M. Z. Parvez, “CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images,” Chaos, Solitons and Fractals, vol. 142, p. 110495, 2021, doi: 10.1016/j.chaos.2020.110495.
A. Kurani, P. Doshi, A. Vakharia, and M. Shah, “A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting,” Annals of Data Science, vol. 10, no. 1, pp. 183–208, 2023, doi: 10.1007/s40745-021-00344-x.
R. Hertel and R. Benlamri, “A deep learning segmentation-classification pipeline for X-ray-based COVID-19 diagnosis,” Biomedical Engineering Advances, vol. 3, no. November 2021, p. 100041, 2022, doi: 10.1016/j.bea.2022.100041.
A. Victor Ikechukwu, S. Murali, R. Deepu, and R. C. Shivamurthy, “ResNet-50 vs VGG-19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images,” Global Transitions Proceedings, vol. 2, no. 2, pp. 375–381, 2021, doi: 10.1016/j.gltp.2021.08.027.
H. Lu, Y. She, J. Tie, and S. Xu, “Half-UNet: A Simplified U-Net Architecture for Medical Image Segmentation,” Frontiers in Neuroinformatics, vol. 16, p. 54, Jun. 2022, doi: 10.3389/FNINF.2022.911679/BIBTEX.
D. Maji, P. Sigedar, and M. Singh, “Attention Res-UNet with Guided Decoder for semantic segmentation of brain tumors,” Biomedical Signal Processing and Control, vol. 71, no. PA, p. 103077, 2022, doi: 10.1016/j.bspc.2021.103077.
J. Zhang, Z. Jiang, J. Dong, Y. Hou, and B. Liu, “Attention Gate ResU-Net for Automatic MRI Brain Tumor Segmentation,” IEEE Access, vol. 8, pp. 58533–58545, 2020, doi: 10.1109/ACCESS.2020.2983075.
L. S. Lehmann, V. Natarajan, and L. Peng, “Artificial intelligence in healthcare: a perspective from Google,” Artificial Intelligence in Clinical Practice: How AI Technologies Impact Medical Research and Clinics, pp. 341–344, Jan. 2024, doi: 10.1016/B978-0-443-15688-5.00037-1.
Z. He et al., “The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis,” JMIR Public Health Surveill 2021;7(1):e20495 https://publichealth.jmir.org/2021/1/e20495, vol. 7, no. 1, p. e20495, Jan. 2021, doi: 10.2196/20495.
S. Jadon, “A survey of loss functions for semantic segmentation,” 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2020, 2020, doi: 10.1109/CIBCB48159.2020.9277638.
P. Solainayagi, “Predictive Modeling in the Cloud with Logistic Regression for High-Accuracy Biomedical Signal Classification,” in 2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT), Jun. 2024, pp. 409–412, doi: 10.1109/incacct61598.2024.10551022.
C. M. Ugwu, C. Pierrette Mukamakuza, and E. Tuyishimire, “ECG-Signals-based Heartbeat Classification: A Comparative Study of Artificial Neural Network and Support Vector Machine Classifiers,” 2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics, SAMI 2024 - Proceedings, pp. 217–222, 2024, doi: 10.1109/SAMI60510.2024.10432834.
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, doi: 10.1109/51.932724.
D. P. Singh, A. P. Pasupulla, A. Shanko, P. Nithya, Neha, and R. M. Singh, “Performance Analysis of ECG Disease Classification using SVM classifier,” 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024, 2024, doi: 10.1109/IATMSI60426.2024.10502948.
A. Das, “Logistic Regression,” Encyclopedia of Quality of Life and Well-Being Research, pp. 3985–3986, 2023, doi: 10.1007/978-3-031-17299-1_1689.
S. Dhyani, A. Kumar, and S. Choudhury, “Analysis of ECG-based arrhythmia detection system using machine learning,” MethodsX, vol. 10, p. 102195, Jan. 2023, doi: 10.1016/J.MEX.2023.102195.
F. G. Altin, İ. Budak, and F. Özcan, “Predicting the amount of medical waste using kernel-based SVM and deep learning methods for a private hospital in Turkey,” Sustainable Chemistry and Pharmacy, vol. 33, p. 101060, Jun. 2023, doi: 10.1016/J.SCP.2023.101060.
H. Moosaei and M. Hladík, “A lagrangian-based approach for universum twin bounded support vector machine with its applications,” Annals of Mathematics and Artificial Intelligence, vol. 91, no. 2–3, pp. 109–131, Jun. 2023, doi: 10.1007/S10472-022-09783-5/METRICS.
T. Sharma, N. K. Verma, and S. Masood, “Mixed fuzzy pooling in convolutional neural networks for image classification,” Multimedia Tools and Applications, vol. 82, no. 6, pp. 8405–8421, Mar. 2023, doi: 10.1007/S11042-022-13553-0/TABLES/8.
M. M. Taye, “Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions,” Computation 2023, Vol. 11, Page 52, vol. 11, no. 3, p. 52, Mar. 2023, doi: 10.3390/COMPUTATION11030052.
M. Sun, Z. Song, X. Jiang, J. Pan, and Y. Pang, “Learning Pooling for Convolutional Neural Network,” Neurocomputing, vol. 224, pp. 96–104, Feb. 2017, doi: 10.1016/j.neucom.2016.10.049.
P. Ramachandran, B. Zoph, and Q. V Le, “SEARCHING FOR ACTIVATION FUNCTIONS.”
I. Salehin and D. K. Kang, “A Review on Dropout Regularization Approaches for Deep Neural Networks within the Scholarly Domain,” Electronics (Switzerland), vol. 12, no. 14, 2023, doi: 10.3390/electronics12143106.
A. F. Agarap, “Deep Learning using Rectified Linear Units (ReLU),” 2018, Accessed: May 15, 2022. [Online]. Available: http://arxiv.org/abs/1803.08375.
A. Irsyad and H. Tjandrasa, “Detection of COVID-19 from Chest CT Images Using Deep Transfer Learning,” International Conference On Information & Communication Technology And System (ICTS), 2021.
Copyright (c) 2024 Akhmad Irsyad, Putut Pamilih widagdo, Putut Pamilih widagdo, Reza Wardhana
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).