Automated cardiac event change detection for continuous remote patient monitoring devices

Abstract

Recently, wireless body area network (WBAN) plays an important role in remote cardiac patient monitoring, and mobile healthcare applications. Generally, the use of WBAN technology is restricted by size, power consumption, transmission capacity (bandwidth), and computational loads. In this paper, we therefore propose an automated cardiac event change detection for continuous remote patient monitoring devices. The proposed event change detection algorithm consists of two stages: i) ECG beat extraction; and ii) ECG beat similarity measure. In the first stage, the onset of each QRS complex is identified using the Gaussian derivative based QRS detector and the two heuristics rules. In the second stage, we employ the weighted wavelet distance (WWD) metric for finding the similarity between two ECG beats in wavelet domain. The WWD is the weighted normalized Euclidean wavelet distance between the wavelet subband coefficients vectors of the current and past ECG beats, where weights are equal to the relative wavelet subband energies of the corresponding subbands. The experimental results show that the weighted wavelet distance measure works substantially better than the conventional PRD and the wavelet based weighted PRD (WWPRD) measures under noisy environments. The proposed approach has been tested and yielded an accuracy of 99.76% on MIT-BIH Arrhythmia Database.