Real-time ECG Analysis and Classification Using Neural Networks in IoT Devices
- Authors: Mohamed Osman Zaid K.B.1, Aayush Singh2, Sivaraj Chandrasekaran3, Sridhar Raj S.4
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View Affiliations Hide Affiliations1 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India 2 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India 3 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India 4 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
- Source: Advanced Computing Solutions for Healthcare , pp 380-393
- Publication Date: July 2025
- Language: English
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This paper presents the development of a simple, cost-efficient, and accessible ECG analyzer. The proposed prototype can acquire ECG signals from subjects and display them in real time through IoT devices such as mobile phones. It can diagnose conditions by classifying subjects as having arrhythmia, congestive heart failure, or normal sinus rhythm. The prototype includes a pre-trained deep learning model that helps with ECG signal categorization, delivering rapid insights about the subject's probable medical care needs.
Hardbound ISBN:
9789815274141
Ebook ISBN:
9789815274134
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