Programs
- M. Tech. in Automotive Engineering -
- Fellowship In Interventional Pulmonology and Pulmonary Critical care - Fellowship
Publication Type : Conference Paper
Publisher : ACWR 2011 - Proceedings of the International Conference on Wireless Technologies for Humanitarian Relief
Source : ACWR 2011 - Proceedings of the International Conference on Wireless Technologies for Humanitarian Relief, Amritapuri, p.225-232 (2011)
Keywords : Distance metrics, Electrocardiography, Event detection, Gaussian derivatives, Health care, Peak-finding logic, QRS detection, Remote patient monitoring, Signal detection, Wavelet analysis, Wireless telecommunication systems
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2011
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.
Cite this Research Publication : Ba Baby, Manikandan, M. Sb, and Dr. Soman K. P., “Automated cardiac event change detection for continuous remote patient monitoring devices”, in ACWR 2011 - Proceedings of the International Conference on Wireless Technologies for Humanitarian Relief, Amritapuri, 2011, pp. 225-232.