Compressed Sensing Based on the Characteristic Correlation of ECG in Hybrid Wireless Sensor Network. (11th October 2015)

• Record Type: Journal Article
• Authors: Li, Weiwei; Jiang, Ting; Wang, Ning
• Other Names: Samn Sherwood W. Academic Editor.
• Is Part Of: International journal of distributed sensor networks. (2015)
• Publication Date: 2015-10-11
• Subjects: Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1155/2015/325103
• Languages: English
• ISSNs: 1550-1329

Abstract

Abstract : Hybrid wireless sensor network made up of wireless body area networks (WBANs) and cellular network provides support for telemedicine. In order to facilitate early diagnosis and treatment, WBANs collect and transmit crucial biomedical data to provide a continuous health monitoring by using various biomedical wireless sensors attached on or implanted in the human body. And then, collected signals are sent to a remote data center via cellular network. One of the features of WBAN is that its power consumption and sampling rate should be restricted to a minimum. Compressed sensing (CS) is an emerging signal acquisition/compression methodology which offers a prominent alternative to traditional signal acquisition. It has been proved that the successful recovery rate of multiple measurement vectors (MMV) model is higher than the single measurement vector (SMV) case. In this paper, we propose a simple algorithm of transforming the SMV model into MMV model based on the correlation of electrocardiogram (ECG), such that the MMV model can be used for general ECG signals rather than only several special signals. Experimental results show that its recovery quality is better than some existing CS-based ECG compression algorithms and sufficient for practical use.