@article{(Open Science Index):https://publications.waset.org/pdf/10013427,
	  title     = {ECG-Based Heartbeat Classification Using Convolutional Neural Networks},
	  author    = {Jacqueline R. T. Alipo-on and  Francesca I. F. Escobar and  Myles J. T. Tan and  Hezerul Abdul Karim and  Nouar AlDahoul},
	  country	= {},
	  institution	= {},
	  abstract     = {Electrocardiogram (ECG) signal analysis and processing are crucial in the diagnosis of cardiovascular diseases which are considered as one of the leading causes of mortality worldwide. However, the traditional rule-based analysis of large volumes of ECG data is time-consuming, labor-intensive, and prone to human errors. With the advancement of the programming paradigm, algorithms such as machine learning have been increasingly used to perform an analysis on the ECG signals. In this paper, various deep learning algorithms were adapted to classify five classes of heart beat types. The dataset used in this work is the synthetic MIT-Beth Israel Hospital (MIT-BIH) Arrhythmia dataset produced from generative adversarial networks (GANs). Various deep learning models such as ResNet-50 convolutional neural network (CNN), 1-D CNN, and long short-term memory (LSTM) were evaluated and compared. ResNet-50 was found to outperform other models in terms of recall and F1 score using a five-fold average score of 98.88% and 98.87%, respectively. 1-D CNN, on the other hand, was found to have the highest average precision of 98.93%.},
	    journal   = {International Journal of Biomedical and Biological Engineering},
	  volume    = {17},
	  number    = {12},
	  year      = {2023},
	  pages     = {344 - 351},
	  ee        = {https://publications.waset.org/pdf/10013427},
	  url   	= {https://publications.waset.org/vol/204},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 204, 2023},
	}