Modified Cardiac Arrhythmia Classification from Electrocardiography Signals Using a Convolutional Neural Network Model
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1007-1028
Digital Object Identifier:
10.58578/ajstea.v3i4.5905
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Authors:
Sabo Abdulhafiz1*, Abdulsalam Ya’u Gital2, Sani Sabo Mohammed3, D. M. Nazif4 
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Abstract
Manual classification of cardiac arrhythmias from electrocardiogram (ECG) signals is a labor-intensive and error-prone process due to the complex and variable nature of cardiac waveforms. Convolutional Neural Networks (ConvNets), widely recognized for their success in image classification, offer a promising solution for automating this task. This study proposes an enhanced ConvNet-based approach for the classification of cardiac arrhythmias, leveraging AlexNet as a feature extractor. The features obtained from the convolutional layers are input into a backpropagation neural network for final classification. The proposed model was evaluated on four distinct arrhythmia conditions using ECG waveforms from the MIT-BIH Arrhythmia Database. Comparative analysis against traditional models revealed the superior performance of the proposed ConvNet architecture, achieving high scores across multiple evaluation metrics, including accuracy, precision, recall, F1-score, and AUC-ROC. The feature extractor demonstrated robust performance, with classification accuracies of 1.00 and 0.99 on training and testing datasets, respectively. These findings underscore the potential of ConvNet-based models to serve as efficient, accurate, and fully automated tools for arrhythmia diagnosis, contributing significantly to advancements in cardiovascular disease detection and clinical decision support systems.
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References
Acharya, U.R.; Oh, S.L.; Hagiwara, Y.; Tan, J.H.; Adam, M.; Gertych, A.; San Tan, R. (2017). A deep convolutional neural network model to classify heartbeats. Comput. Biol. Med, 89, 389–396. [CrossRef]
Amanullah MA, Habeeb RAA, Nasaruddin FH, Gani A, Ahmed E, Nainar ASM, Imran M (2020). Deep learning and big data technologies for IoT security. Compute Commun. https://doi.org/10.1016/j. comcom.2020.01.016.
Ansari, M. Y., Abdalla, A., Ansari, M. Y., Ansari, M. I., Malluhi, B., Mohanty, S., et al. (2022a). Practical utility of liver segmentation methods in clinical surgeries and interventions. BMC Med. imaging 22, 97–17. doi:10.1186/s12880-022-00825-2
Banerjee R, Ghose A, Khandelwal S (2019). A Novel Recurrent Neural Network Architecture for Classification of Atrial Fibrillation Using Single-lead ECG. Paper presented on the 27th European Signal Processing Conference (EUSIPCO)
Boursalie, O.; Samavi, R.; Doyle, T.E. M4CVD (2015): Mobile machine learning model for monitoring cardiovascular disease. Procedia Comput. Sci., 63, 384–391. [CrossRef]
Butun, E.; Yildirim, O.; Talo, M.; Tan, R.S.; Acharya, U.R. (2019) 1D-CADCapsNet: One dimensional deep capsule networks for coronary artery disease detection using ECG signals. Phys. Med., 70, 39–48. [CrossRef]
Cai W, Chen Y, Guo J, Han B, Shi Y, Ji L, Luo J (2020). Accurate detection of atrial fibrillation from 12-lead ECG using deep neural net- work. Comput Biol Med 116:103378
Chandrasekar, V., Ansari, M. Y., Singh, A. V., Uddin, S., Prabhu, K. S., Dash, S., et al. (2023). Investigating the use of machine learning models to understand the drugs permeability across placenta. IEEE Access 11, 52726–52739. doi:10.1109/access.2023.3272987
Chang K-C, Hsieh P-H, Wu M-Y, Wang Y-C, Chen J-Y, Tsai F-J, Huang T-C (2020). Usefulness of Machine-Learning-Based Detection and Classification of Cardiac Arrhythmias with 12-Lead Electro cardiograms. Canadian Journal of Cardiology
Chan, W.; Park, D.; Lee, C.; Zhang, Y.; Le, Q.; Norouzi, M. SpeechStew: (2022) Simply Mix All Available Speech Recognition Data to Train One Large Neural Network.arXiv, arXiv:2104.02133. Available online: Http://arxiv.org/abs/2104.02133
Chou, Y.H.; Hong, S.; Zhou, Y.; Shang, J.; Song, M.; Li, H. Knowledge-shot learning (2020): An interpretable deep model for classifying imbalanced electrocardiography data. Neuro computing, 417, 64–73. [CrossRef]
Chuanqi Han, Peng Wang, RuoranHuang, and Li Cui, "HCTNet (2022): An experience-guided deep learning network for inter-patient arrhythmia classification on imbalanced dataset," Biomedical Signal Processing and Control, vol. 78, pp. 1039.
Ding, K.; Ma, K.; Wang, S.; Simoncelli, E.P. (2021) Comparison of Full-Reference Image Quality Models for Optimization of Image Processing Systems. Int. J. Comput. Vis., 129, 1258–1281. [CrossRef]
Diker A, Engin A (2019). Feature extraction of ECG signal by using deep feature. Paper presented at the 7th International Symposium on Digital Forensics and Security (ISDFS)
Elgendi, M, Eskofier, B, Dokos, S, Abbott, D (2014). Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems. PLoS ONE, e84018. [CrossRef]
E. Essa and X. and Xie (2021) "An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeats Arrhythmia Classification,” IEEE Access, vol. 9, pp. 103452-103464.
Feng, S.H.; Xu, J.Y.; Shen, H.B. (2022) Artificial intelligence in bioinformatics: Automated methodology development for protein residue contact map prediction. In Biomedical Information Technology; Academic Press: Cambridge, MA, USA, pp. 217–237. Available online: https://www.sciencedirect.com/science/article/pii/B9780128160343000079
Gai, N.D. (2023): ECG beat classification using machine learning and pre-trained convolutional neural networks. arXiv, arXiv: 2207.06408. Available online: Http://arxiv.org/abs/2207.
Gite, S.; Mishra, A.; Kotecha, K. (2022). Enhanced lung image segmentation using deep learning. Neural Comput. Appl., 1–15. [CrossRef]
Hassan, S.U.; Zahid, M.S.M.; Husain, K. Performance comparison of CNN and LSTM algorithms for arrhythmia classification. In Proceedings of the (2020). International Conference on Computational Intelligence (ICCI), Bandar Seri Iskandar, Malaysia, 8–9 October 2020; pp. 223–228. Available online: https://ieeexplore.ieee.org/abstract/document/9247636/.
Hu, H.; Zhang, Z.; Xie, Z.; Lin, S. Local relation networks for image recognition (2022). In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea, 27 October–2 November 2019; pp.3464–3473. Availableonline:http://openaccess.thecvf.com/content_ICCV_2019/
Hong, S., Zhou, Y., Shang, J., Xiao, C., and Sun, J. (2020). Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review. Comput. Biol. Med. 122, 103801. doi:10.1016/j.compbiomed.2020.103801
Hosgungor,E.“HowtoHandleImbalanceDataandSmallTrainingSetsinML.(2023)”TowardsDataScience,Medium, Available online: https://towardsdatascience.com/how-to-handle-imbalance-data-and-small-training-sets-in-ml-989f8053531d.
Jason, B. (2022). A Gentle Introduction to Batch Normalization for Deep Neural Networks—Machine Learning Master. Available online: https://machinelearningmastery.com/batch-normalization-for-training-of-deep-neural-networks.
Jianfeng Cui, Lixin Wang, Xiangmin He, Victor Hugo C. De Albuquerque, Salman A. AlQahtani and Mohammad Mehedi Hassan (2021): "Deep learning-based multidimensional feature fusion for classification of ECG arrhythmia," Neural Computing and Applications.
Kiranyaz, S.; Avci, O.; Abdeljaber, O.; Ince, T.; Gabbouj, M.; Inman, D.J. 1D (2021) convolutional neural networks and applications: A survey. Mech. Syst. Signal Process, 151, 107398. [CrossRef]
Kiranyaz,S.; Ince, T.; Gabbouj, M. (2022). Personalized monitoring and advance warning system for cardiac arrhythmias. Available online:https://www.nature.com/articles/s41598-017-09544-z [CrossRef] [PubMed]
Koppad, D. (2021). Arrhythmia classification using deep learning: A review. WSEAS Trans. Biol. Biomed. 18, 96–105. doi:10.37394/23208.2021.18.11
Latif, G.; al Anezi, F.Y.; Zikria, M.; Alghazo, J. (2020). EEG-ECG Signals Classification for Arrhythmia Detection using Decision Trees. In Proceedings of the 4th International Conference on Inventive Systems and Control, ICISC, TamilNadu, India, 8–10 January 2020; pp. 192–196. [CrossRef]
MAlkhodari, M.; Fraiwan, L. (2021). Convolutional and recurrent neural networks for the detection of valvular heart diseases in phonocardiogram recordings. Comput. Methods Programs Biomed. [CrossRef]
Midani, W., Ouarda, W., and Ayed, M. B. (2023). DeepArr: an investigative tool for arrhythmia detection using a contextual deep neural network from electrocardiograms (ECG) signals. Biomed. Signal Process. Control 85, 104954. doi:10.1016/j.bspc.2023.104954
NumPy. NumPy Array Objects. NumPy18 December (2023). Available online: https://numpy.org/doc/stable/reference/arrays.html.
Nurmaini, S., Tondas, A. E., Darmawahyuni, A., Rachmatullah, M. N., Partan, R. U., Firdaus, F., et al. (2020b). Robust detection of atrial fibrillation from short-term electrocardiogram using convolutional neural networks. FutureGener.Comput. Syst. 113, 304–317. doi:10.1016/j.future.2020.07.021
Ojha, M. K., Wadhwani, S., Wadhwani, A. K., and Shukla, A. (2022). Automatic detection of arrhythmias from an ECG signals using an auto-encoder and SVM classifier. Phys. Eng. Sci. Med. 45, 665–674. doi:10.1007/s13246-022-01119-1
P. Singh and A. Sharma, (2022): "Interpretation and Classification of Arrhythmia Using Deep Convolutional Network," IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 2518512.
Peimankar, A., and Puthusserypady, S. (2021).DENS-ECG: A deep learning approach for ECG signals delineation. Expert Syst. Appl. 165, 113911. doi:10.1016/j.eswa.2020.113911
Pedregosa,F.;Varoquaux,G.;Gramfort,A.;Michel,V.;Thirion,B.;Grisel,O.;Blondel,M.;Prettenhofer, P.; Weiss, R.; Dubourg, V. Scikit-learn: (2020) Machine learning in Python. J. Mach.Learn.Res2825–2830.Availableonline:https://www.jmlr.org/ papers/volume12/pedregosa11a/pedregosa11a.pdf? ref=https://githubhelp.com
Ribeiro AH, Ribeiro MH, Paixão GM, Oliveira DM, Gomes PR, Canaz- art JA, Wagner M Jr (2020). Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun 11(1):1–9
Shadhon Chandra Mohandas, Mohammod Abdul Motin, Dinesh Kant Kumar, (2022) “Electrocardiogram based arrhythmia classification using wavelet transform with deep learning model," Sensing and Bio-Sensing Research, vol. 37, pp. 100502.
Shaker AM, Tantawi M, Shedeed HA, Tolba MF (2020). Generalization of Convolutional Neural Networks for ECG Classification Using Generative Adversarial Networks. IEEE Access 8:35592–35605
Sun, J.-Y., Shen, H., Qu, Q., Sun, W., and Kong, X.-Q. (2021). The application of Deep Learning in electrocardiogram: where we came from and where we should go? Int. J. Cardiol. 337, 71–78. doi:10.1016/j.ijcard.2021.05.017
Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; Salakhutdinov, R. Dropout (2023): A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 2014, 15, 1929–1958. Available online: http://www.jmlr.org/papers/volume15/srivastava14a/ srivastava14a.pdf?utm_content=buffer79b43&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer.
Wani, A., Ahmad, F., Saduf, B., Asif, A., & Khan, I. (2020). Advances in Deep Learning, Singapore: Springer Nature
Wang, L.; Sun, W.; Chen, Y.; Li, P.; Zhao, L. (2018). Multimode Data Fusion Based Remote Healthcare Framework. In Proceedings of the International Conference on Big Data Engineering and Technology, Chengdu, China, 25–27, Association for Computing Machinery: New York, NY, USA.
Xu, X.; Jeong, S.; Li, J. (2020) Interpretation of Electrocardiogram (ECG) Rhythm by Combined CNN and BiLSTM. IEEE Access, 8, 125380–125388. [CrossRef]
Yu, Y.; Liang, S.; Samali, B.; Nguyen, T.N.; Zhai, C.; Li, J.; Xie, X. (2022) Torsional capacity evaluation of RC beams using an improved bird swarm algorithm optimised 2D convolutional neural network. Eng. Struct, 273, 115066. [CrossRef]
Yu, Y.; Samali, B.; Rashidi, M.; Mohammadi, M.; Nguyen, T.N.; Zhang, G. (2022) Vision-based concrete crack detection using a hybrid framework considering noise effect. J. Build. Eng., 61, 105246. [CrossRef]
Yuanlu Li, Renfei Qian, Kun Li, (2022). "Inter-patient arrhythmia classification with improved deep residual convolutional neural network," Computer Methods and Programs in Biomedicine, vol. 214, pp. 106582.
Yong-Yeon Jo, Joon-myoung Kwon, Ki-Hyun Jeon, Yong-Hyeon Cho, Jae-Hyun Shin, Yoon-Ji Lee, Min-Seung Jung, Jang-Hyeon Ban, Kyung-Hee Kim, Soo Youn Lee, Jinsik Park, Byung-Hee Oh (2021) "Detection and classification of arrhythmia using an explainable deep learning model," Journal of Electrocardiology, vol. 67, pp. 124-132, July–August.
Zheng, Z.; Chen, Z.; Hu, F.; Zhu, J.; Tang, Q.; Liang, Y. (2020): An automatic diagnosis of arrhythmias using a combination of CNN and LSTM technology. Electronics, [CrossRef]
Zhou, S.; Tian, B. (2020). Electrocardiogram soft computing using hybrid deep learning CNN-ELM.Appl.SoftComput.,86,105778.Availableonline:https://www.sciencedirect.com/ science/article/pii /S1568494619305599. [CrossRef]
Zhou Z, Zhai X, Tin C (2020). Fully Automatic Electrocardiogram Classification System based on Generative Adversarial Network with Auxiliary Classifier.arXiv preprint arXiv:2004.04894