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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)

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

A Wearable Real-time Non-Contact Electrocardiogram System for Arrhythmia Detection and Classification

  • Hongqiang Li; 
  • Sai Zhang; 
  • Shasha Zuo; 
  • Zhen Zhang; 
  • Binhua Wang; 
  • Lu Cao; 
  • Xuyi Chen; 
  • Runjie Wang; 
  • Zheng Gong; 
  • Joan Daniel Prades; 

ABSTRACT

Background:

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.

Objective:

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.

Methods:

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.

Results:

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.

Conclusions:

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.


 Citation

Please cite as:

Li H, Zhang S, Zuo S, Zhang Z, Wang B, Cao L, Chen X, Wang R, Gong Z, Prades JD

A Wearable Real-time Non-Contact Electrocardiogram System for Arrhythmia Detection and Classification

JMIR Preprints. 19/07/2020:22527

DOI: 10.2196/preprints.22527

URL: https://preprints.jmir.org/preprint/22527

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