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ECG Image Classification Using Deep Learning Approach

ECG Image Classification Using Deep Learning Approach

Pratik Kanani, Mamta Chandraprakash Padole
Copyright: © 2021 |Pages: 15
ISBN13: 9781799827429|ISBN10: 1799827429|EISBN13: 9781799827436
DOI: 10.4018/978-1-7998-2742-9.ch016
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MLA

Kanani, Pratik, and Mamta Chandraprakash Padole. "ECG Image Classification Using Deep Learning Approach." Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning, edited by Geeta Rani and Pradeep Kumar Tiwari, IGI Global, 2021, pp. 343-357. https://doi.org/10.4018/978-1-7998-2742-9.ch016

APA

Kanani, P. & Padole, M. C. (2021). ECG Image Classification Using Deep Learning Approach. In G. Rani & P. Tiwari (Eds.), Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning (pp. 343-357). IGI Global. https://doi.org/10.4018/978-1-7998-2742-9.ch016

Chicago

Kanani, Pratik, and Mamta Chandraprakash Padole. "ECG Image Classification Using Deep Learning Approach." In Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning, edited by Geeta Rani and Pradeep Kumar Tiwari, 343-357. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-2742-9.ch016

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Abstract

Cardiovascular diseases are a major cause of death worldwide. Cardiologists detect arrhythmias (i.e., abnormal heart beat) with the help of an ECG graph, which serves as an important tool to recognize and detect any erratic heart activity along with important insights like skipping a beat, a flutter in a wave, and a fast beat. The proposed methodology does ECG arrhythmias classification by CNN, trained on grayscale images of R-R interval of ECG signals. Outputs are strictly in the terms of a label that classify the beat as normal or abnormal with which abnormality. For training purpose, around one lakh ECG signals are plotted for different categories, and out of these signal images, noisy signal images are removed, then deep learning model is trained. An image-based classification is done which makes the ECG arrhythmia system independent of recording device types and sampling frequency. A novel idea is proposed that helps cardiologists worldwide, although a lot of improvements can be done which would foster a “wearable ECG Arrhythmia Detection device” and can be used by a common man.

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