Journal
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
Volume 15, Issue 7, Pages -Publisher
HINDAWI LTD
DOI: 10.1177/1550147719864884
Keywords
Compressed sensing; electrocardiogram; wavelet basis; dictionary learning
Funding
- National Natural Science Foundation of China [61802055, 61702221, 61771121]
- Fundamental Research Funds for the Central Universities [N171903003]
- Natural Science Foundation of LiaoNing Province [20170520180]
- Postdoctoral Science Foundation of Northeastern University [20180101]
- China Postdoctoral Science Foundation [2018M630301]
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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.
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