Why the study?
In low-resource countries, inadequate ECG systems, lack of skilled physicians, high prices, and limited availability of continuous ECG monitoring devices lead to a higher CVD burden.
A newly developed low-cost wearable ECG system paired with a 1-D CNN deep learning model achieved 94.03% accuracy in detecting arrhythmias, offering a potential solution for continuous monitoring in low-resource settings.
Continuously monitoring the Electrocardiogram (ECG) is an essential tool for Cardiovascular Disease (CVD) patients. In low-resource countries, the hospitals and health centers do not have adequate ECG systems, and this unavailability exacerbates the patients’ health condition. Lack of skilled physicians, limited availability of continuous ECG monitoring devices, and their high prices, all lead to a higher CVD burden in the developing countries. To address these challenges, we present a low-cost, low-power, and wireless ECG monitoring system with deep learning-based automatic arrhythmia detection. Flexible fabric-based design and the wearable nature of the device enhances the patient’s comfort while facilitating continuous monitoring. An AD8232 chip is used for the ECG Analog Front-End (AFE) with two 450 mi-Ah Li-ion batteries for powering the device. The acquired ECG signal can be transmitted to a smart-device over Bluetooth and subsequently sent to a cloud server for analysis. A 1-D Convolutional Neural Network (CNN) based deep learning model is developed that provides an accuracy of 94.03% in classifying abnormal cardiac rhythm on the MIT-BIH Arrhythmia Database. Index Terms Wearable ECG, deep learning, arrhythmia detection.
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Huda et al. (2020) studied this question.
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