International Journal of Advances in Applied Computational Intelligence
Journal DOI
2833-5600ISSN (Online)
Volume 1 , Issue 2 , PP: 63-72, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Alber S. Aziz 1 * , Hoda K. Mohamed 2 , Ahmed Abdelhafeez 3
Doi: https://doi.org/10.54216/IJAACI.010205
Arrhythmias are a significant cause of morbidity and mortality worldwide, necessitating accurate and timely detection for effective clinical intervention. Electrocardiogram (ECG) signals serve as invaluable sources of information for diagnosing arrhythmias, but their analysis is complex and demanding. Recent advancements in computational intelligence, particularly Convolutional Networks (CNNs), have demonstrated remarkable capabilities in various signal-processing tasks. In this paper, we unveil the power of CNNs by applying computational intelligence techniques to detect arrhythmias from ECG signals. The proposed methodology involves preprocessing the ECG signals to enhance their quality and remove noise interference. Subsequently, CNN architectures are developed and trained using a large dataset of annotated ECG recordings. The network's structure is optimized to effectively capture the discriminative features present in the ECG signals that characterize diverse types of arrhythmias. Through an extensive evaluation process, the performance of the CNN models is assessed using confusion matrices. Experimental results demonstrate the effectiveness of the applied computational intelligence approach in arrhythmia detection. The CNN model achieves outstanding performance, exhibiting robustness against noise and variations in ECG recording conditions, highlighting its potential for real-world applications.
Computational Intelligence , Electrocardiogram , Arrhythmia Detection , Convolutional Networks.
[1] Alfaras, M., Soriano, M.C. and Ortín, S., 2019. A fast machine learning model for ECG-based heartbeat classification and arrhythmia detection. Frontiers in Physics, p.103.
[2] Sahoo, S., Dash, M., Behera, S. and Sabut, S., 2020. Machine learning approach to detect cardiac arrhythmias in ECG signals: A survey. Irbm, 41(4), pp.185-194.
[3] Parvaneh, S., Rubin, J., Babaeizadeh, S. and Xu-Wilson, M., 2019. Cardiac arrhythmia detection using deep learning: A review. Journal of electrocardiology, 57, pp. S70-S74.
[4] Subramanian, K., and Prakash, N.K., 2020, August. Machine learning-based cardiac arrhythmia detection from ecg signal. In 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 1137-1141). IEEE.
[5] Pandey, S.K., Sodum, V.R., Janghel, R.R. and Raj, A., 2020. ECG arrhythmia detection with machine learning algorithms. In Data Engineering and Communication Technology: Proceedings of 3rd ICDECT-2K19 (pp. 409-417). Springer Singapore.
[6] Scirè, A., Tropeano, F., Anagnostopoulos, A. and Chatzigiannakis, I., 2019. Fog-computing-based heartbeat detection and arrhythmia classification using machine learning. Algorithms, 12(2), p.32.
[7] Hammad, M., Iliyasu, A.M., Subasi, A., Ho, E.S. and Abd El-Latif, A.A., 2020. A multitier deep learning model for arrhythmia detection. IEEE Transactions on Instrumentation and Measurement, 70, pp.1-9.
[8] Hussain, L., Aziz, W., Saeed, S., Awan, I.A., Abbasi, A.A. and Maroof, N., 2020. Arrhythmia detection by extracting hybrid features based on refined Fuzzy entropy (FuzEn) approach and employing machine learning techniques. Waves in Random and Complex Media, 30(4), pp.656-686.
[9] Singh, V., Tewary, S., Sardana, V., and Sardana, H.K., 2019, March. Arrhythmia detection-a machine learning-based comparative analysis with mit-bih ecg data. In 2019 IEEE 5th International Conference for Convergence in Technology (I2CT) (pp. 1-5). IEEE.
[10] Devi, R.L. and Kalaivani, V., 2020. Machine learning and IoT-based cardiac arrhythmia diagnosis using statistical and dynamic features of ECG. The Journal of Supercomputing, 76(9), pp.6533-6544.
[11] Singh, N. and Singh, P., 2019. Cardiac arrhythmia classification using machine learning techniques. In Engineering Vibration, Communication, and Information Processing: ICoEVCI 2018, India (pp. 469-480). Springer Singapore.
[12] Nandhini Abirami, R. and Durai Raj Vincent, P.M., 2020. Cardiac arrhythmia detection using an ensemble of machine learning algorithms. In Soft Computing for Problem Solving: SocProS 2018, Volume 2 (pp. 475-487). Springer Singapore.
[13] Haddi, Z., Ananou, B., Trardi, Y., Pons, J.F., Delliaux, S., Deharo, J.C. and Ouladsine, M., 2020, July. Advanced machine learning coupled with heart-inter-beat derivatives for cardiac arrhythmia detection. In 2020 American Control Conference (ACC) (pp. 5433-5438). IEEE.
[14] Nandhini Abirami, R. and Durai Raj Vincent, P.M., 2020. Cardiac arrhythmia detection using an ensemble of machine learning algorithms. In Soft Computing for Problem Solving: SocProS 2018, Volume 2 (pp. 475-487). Springer Singapore.
[15] Rhif, Manel, et al. "Wavelet transform application for/in non-stationary time-series analysis: a review." Applied Sciences 9.7 (2019): 1345.
[16] Hershey, S., Chaudhuri, S., Ellis, D. P., Gemmeke, J. F., Jansen, A., Moore, R. C., ... & Wilson, K. (2017, March). CNN architectures for large-scale audio classification. In 2017 IEEE international conference on Acoustics, speech, and signal processing (icassp) (pp. 131-135). IEEE.
[17] Targ, S., Almeida, D., & Lyman, K. (2016). Resnet in Resnet: Generalizing residual architectures. arXiv preprint arXiv:1603.08029.
[18] Bottou, Léon, et al. "Comparison of classifier methods: a case study in handwritten digit recognition." Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3-Conference C: Signal Processing (Cat. No. 94CH3440-5). Vol. 2. IEEE, 1994.
