Abstract
Body surface potential mapping (BSPM) is a valuable tool for research regarding electrocardiograms (ECG). However, the BSPM system is limited by its large number of electrodes and wires, long installation time, and high computational complexity. In this paper, we designed a wearable four-electrode electrocardiogram-sensor (WFEES) module that measures six-channel ECGs simultaneously for ECG investigation. To reduce the testing lead number and the measurement complexity, we further proposed a method, the layered (A, N) square-based (LANS) method, to optimize the ECG acquisition and analysis process using WFEES modules for different applications. Moreover, we presented a case study of electrode location optimization for wearable single-lead ECG monitoring devices using WFEES modules with the LANS method. In this study, 102 sets of single-lead ECG data from 19 healthy subjects were analyzed. The signal-to-noise ratio of ECG, as well as the mean and coefficient of variation of QRS amplitude, was derived among different channels to determine the optimal electrode locations. The results showed that a single-lead electrode pair should be placed on the left chest above the electrode location of standard precordial leads V1 to V4. Additionally, the best orientation was the principal diagonal as the direction of the heart’s electrical axis.
Highlights
Cardiovascular diseases (CVDs) have been a major cause of health loss with high mortality worldwide for a decade
To reduce the ECG investigation complexity using the wearable four-electrode electrocardiogram-sensor (WFEES) modules, we proposed a novel layered (A, N) square-based method, i.e., LANS method, which included two stages, i.e., the layered (A, N) square-based method, i.e., LANS method, which included two stages, i.e., the coarsecoarse-grained stage and the fine-grained stage
We introduced the coefficient of variance (CV) of QRS amplitude as an ECG evaluation metric to eliminate the impact of individual differences on optimized electrode location determination, a total study population of 19 was still relatively small
Summary
Cardiovascular diseases (CVDs) have been a major cause of health loss with high mortality worldwide for a decade. Among CVDs, heart diseases were the leading cause of death [1]. The progress in biomedical computing brought more possibilities for ECG-based cardiology analysis, e.g., myocardial ischemia and infarction detection, arrhythmia identification, and sudden cardiac death evaluation, in which the ECGs with detailed wave morphology features were required [3]. For those studies, the body surface potential map (BSPM) system [4] was a valuable tool recording high-resolution ECGs by measuring bio-potentials from 80 to 200 electrodes on the subject’s chest
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