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Arrhythmia Classification Using Deep Learning Architecture

Arrhythmia Classification Using Deep Learning Architecture

Kuldeep Singh Chouhan, Jyoti Gajrani, Bhavna Sharma, Satya Narayan Tazi
Copyright: © 2022 |Pages: 25
ISBN13: 9781799893080|ISBN10: 1799893081|ISBN13 Softcover: 9781799893097|EISBN13: 9781799893103
DOI: 10.4018/978-1-7998-9308-0.ch010
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MLA

Chouhan, Kuldeep Singh, et al. "Arrhythmia Classification Using Deep Learning Architecture." Real-Time Applications of Machine Learning in Cyber-Physical Systems, edited by Balamurugan Easwaran, et al., IGI Global, 2022, pp. 148-172. https://doi.org/10.4018/978-1-7998-9308-0.ch010

APA

Chouhan, K. S., Gajrani, J., Sharma, B., & Tazi, S. N. (2022). Arrhythmia Classification Using Deep Learning Architecture. In B. Easwaran, K. Hiran, S. Krishnan, & R. Doshi (Eds.), Real-Time Applications of Machine Learning in Cyber-Physical Systems (pp. 148-172). IGI Global. https://doi.org/10.4018/978-1-7998-9308-0.ch010

Chicago

Chouhan, Kuldeep Singh, et al. "Arrhythmia Classification Using Deep Learning Architecture." In Real-Time Applications of Machine Learning in Cyber-Physical Systems, edited by Balamurugan Easwaran, et al., 148-172. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-9308-0.ch010

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Abstract

As cardiovascular diseases (CVDs) are a serious concern to modern medical science to diagnose at an early stage, it is vital to build a classification model that can effectively reduce mortality rates by treating millions of people in a timely manner. An electrocardiogram (ECG) is a specialized instrument that measures the heart's physiological responses. To accurately diagnose a patient's acute and chronic heart problems, an in-depth examination of these ECG signals is essential. The proposed model consists of a convolutional neural network having three convolutional, two pooling, and two dense layers. The proposed model is trained and evaluated on the MIT-BIH arrhythmia and PTB diagnostic datasets. The classification accuracy is 99.16%, which is higher than state-of-the-art studies on similar arrhythmias. Recall, precision, and F1 score of the proposed model are 96.53%, 95.15%, and 99.17%, respectively. The proposed model can aid doctors explicitly for the detection and classification of arrhythmias.

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