Adaptive compressive engine for real-time electrocardiogram monitoring under unreliable wireless channels

Wenbin Yu, Cailian Chen, Tian He, Bo Yang, Xinping Guan

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Traditional compressive sensing (CS) methods assume the data sparsity to be constant over time, which holds well in many long-term scenarios. However, the authors' recent study on electrocardiogram (ECG) monitoring reveals that data sparsity varies dramatically for real-time monitoring systems where the data latency must be bounded, due to limited data collected within the delay bound. The variation of data sparsity makes the reconstruction error (RE) unstable. Furthermore, the variation of wireless channel quality also impacts the reconstruction quality. To accommodate both variations, this study proposes a novel adaptive feedback architecture for real-time wireless ECG monitoring based on the CS technique, which can bound the REs in the presence of the variations of data sparsity and wireless channel. An experiment testbed has been built to evaluate the performance of the proposed system. The results show that the data latency can be limited to <300 ms and the RE can be controlled to 9%.

Original languageEnglish (US)
Pages (from-to)607-615
Number of pages9
JournalIET Communications
Volume10
Issue number6
DOIs
StatePublished - Apr 14 2016

Bibliographical note

Funding Information:
This work was supported in part by NSF of China under Grants U1405251, 61521063, and 61273181, in part by the Ministry of Education of China under Grants NCET 13-0358, and in part by the Science and Technology Commission of Shanghai Municipality (STCSM), China under Grant 13QA1401900 and 14511107903, sponsored by Shanghai Jiao Tong University under Grant YG2013ZD0504.

Publisher Copyright:
© 2016 The Institution of Engineering and Technology.

Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.

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