Light weight neural network for ECG and EEG anomaly detection in IOT edge sensors

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.