Compressed sensing for electrocardiogram acquisition in wireless body sensor network: A comparative analysis

Junxin Chen, Jiazhu Xing, Leo Yu Zhang and Lin Qi

International Journal of Distributed Sensor Networks, 2019, vol. 15, issue 7, 1550147719864884

Abstract: In the past decades, compressed sensing emerges as a promising technique for signal acquisition in low-cost sensor networks. For prolonging the monitoring duration of biosignals, compressed sensing is also exploited for simultaneous sampling and compression of electrocardiogram signals in the wireless body sensor network. This article presents a comprehensive analysis of compressed sensing for electrocardiogram acquisition. The performances of involved important factors, such as wavelet basis, overcomplete dictionaries, and the reconstruction algorithms, are comparatively illustrated, with the purpose to give data reference for practical applications. Drawn from a bulk of comparative experiments, the potential of compressed sensing in electrocardiogram acquisition is evaluated in different compression levels, while preferred sparsifying basis and reconstruction algorithm are also suggested. Relative perspectives and discussions are also given.

Keywords: Compressed sensing; electrocardiogram; wavelet basis; dictionary learning (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:15:y:2019:i:7:p:1550147719864884

DOI: 10.1177/1550147719864884

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