Light weight neural network for ECG and EEG anomaly detection in IOT edge sensors
E and TC Department, GCOE Yavatmal, Maharashtra, India.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 11(02), 269–280.
Article DOI: 10.30574/wjaets.2024.11.2.0111
Publication history:
Received on 15 February 2024; revised on 27 March 2024; accepted on 29 March 2024
Abstract:
Lightweight neural network designed for detecting anomalies in Electrocardiogram (ECG) and Electroencephalogram (EEG) signals at IoT edge sensors. By optimizing neural network architectures, we achieve high accuracy in anomaly detection while minimizing computational demands and memory usage. Experimental results validate the effectiveness of our approach in real-world scenarios, promising improved healthcare monitoring with early detection of abnormal ECG and EEG patterns at the edge.
Keywords:
Electrocardiagram (ECG); Electroencephalogram (EEG); Anomaly detection; Lightweight neural network.
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0