A Novel Deep Convolutional Neural Network based Classification of Arrhythmia

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Published Mar 31, 2023
https://doi.org/10.47164/ijngc.v14i2.1153
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Volume 14, Issue 2, March 2023

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Priyanka Rathee
National Institute of Technology Hamirpur
Mahesh Shirsath
Lalit Kumar Awasthi
Director, NIT Uttrakahnd
Naveen Chauhan
National Institute of Technology Hamirpur

Abstract


Holter monitors are used to record Electrocardiogram (ECG) data which is extremely hard to analyze manually. Convolutional Neural Network (CNN) are known to be efficient for classification of image data. Hence, in this study, we are using Deep Convolutional Neural Network to classify the ECG data into various types of Arrhythmias. Denoising, segmentation and data augmentation techniques are used for pre-processing of the data. The proposed model uses the MIT-BIH Arrhythmia Dataset for training and evaluation purpose this dataset has much imbalance which has been removed using data augmentation techniques. The proposed approach shows an overall accuracy 99.67% along with 99.68% precision and 99.66% recall. Further, we have also compared the state-of-the-art models like 2D CNN, genetic ensemble of classifiers, Long Short-Term Memory (LSTM) Networks, etc results with proposed model. And the introduced approach is outperforming when compared to these models.


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Author Biographies

Priyanka Rathee, National Institute of Technology Hamirpur

Priyanka currently works as an Assistant Professor in the Department of Computer Science & Engineering, National Institute of Technology Hamirpur (H.P). In the past she has worked in University of Delhi. She received her PhD degree in 2018, M.Tech. degree (Computer Engineering) in 2011, and B.Tech. degree (Honors) in Computer Science and Engineering in 2008. She has published many research papers and book chapters in reputed national and international journals and conferences, including papers in IEEE Xplore, and SCI paper in wireless personal communication. She had served as a Chairperson at IEEE Young Professional Delhi Section.

Lalit Kumar Awasthi, Director, NIT Uttrakahnd

Lalit Kumar Awasthi received his Ph.D. degree from the Indian Institute of Technology, Roorkee, in Computer Science and Engineering. He is working as Director, National Institute of Technology Uttarakhand. Before this, he was the Director/Principal of National Institute of Technology Hamirpur, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, and Government Engineering College Shimla. He has also served as Professor and Head of Department of Computer Science and Engineering, National Institute of Technology Hamirpur. His research interests are in distributed fault-tolerant computing, mobile computing, wireless sensor networks, and mobile ad hoc networks. He has published more than 180 research papers in various national and international journals and conferences and guided many Ph.D. scholars in these areas.

Naveen Chauhan, National Institute of Technology Hamirpur

Naveen Chauhan is an Associate Professor at Department of Computer Science and Engineering, NIT Hamirpur. He received his Ph.D. (Computer Science and Engineering) from NIT Hamirpur in 2012. His research interest includes Mobile Wireless Networks with particular emphasis on the Internet of Things and its Security Aspects. He has made excellent research contributions and published many research articles in SCI and Scopus-indexed journals. In addition, he is a reviewer for various national and international reputed journals and guides many Ph.D. students in these areas.

How to Cite
Priyanka Rathee, Mahesh Shirsath, Lalit Kumar Awasthi, & Naveen Chauhan. (2023). A Novel Deep Convolutional Neural Network based Classification of Arrhythmia. International Journal of Next-Generation Computing, 14(2). https://doi.org/10.47164/ijngc.v14i2.1153
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