Currently submitted to: JMIR mHealth and uHealth
Date Submitted: Jul 19, 2020
Open Peer Review Period: Jul 19, 2020 - Sep 13, 2020
(currently open for review)
A Wearable Real-time Non-Contact Electrocardiogram System for Arrhythmia Detection and Classification
Driven by the increasing demand for potential patients to monitor their own heart health, wearable technology is increasingly helping people to better monitor their heart health status at a medical level.
The aim of this study was to develop a flexible and non-contact wearable electrocardiogram system, which can achieve real-time monitoring and primary diagnosis.
A flexible electrocardiogram (ECG) acquisition device (wearable ECG) is designed based on flexible front-end circuit and textile capacitive electrodes, which are based on a conductive textile instead of rigid metal plates. The multi-domain feature space consists of time-domain features and frequency-domain statistical features, which can be used for classification via a back-propagation neural network (BPNN) and a support vector machine (SVM), both of which are optimized using a genetic algorithm.
The BPNN classifier exhibits good performance, with an accuracy of 98.33%, a sensitivity of 98.33%, a specificity of 99.63% and a positive predictive value of 97.85%. The SVM classifier achieves a higher classification accuracy of 98.89% and also performs better than the BPNN classifier in terms of the sensitivity, specificity and positive predictive value, achieving values of 98.89%, 99.81% and 98.89%, respectively.
The experimental results reveal that there is a better classification effect of SVM when classifying normal heart rhythms and 8 types of arrhythmia. The proposed wearable ECG monitoring can aid in the primary diagnosis of certain heart diseases.
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