Matrix-Inversion-Free Compressed Sensing with Variable Orthogonal Multi-Matching Pursuit Based on Prior Information for ECG Signals

Yih Chun Cheng, Pei Yun Tsai, Ming Hao Huang

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Low-complexity compressed sensing (CS) techniques for monitoring electrocardiogram (ECG) signals in wireless body sensor network (WBSN) are presented. The prior probability of ECG sparsity in the wavelet domain is first exploited. Then, variable orthogonal multi-matching pursuit (vOMMP) algorithm that consists of two phases is proposed. In the first phase, orthogonal matching pursuit (OMP) algorithm is adopted to effectively augment the support set with reliable indices and in the second phase, the orthogonal multi-matching pursuit (OMMP) is employed to rescue the missing indices. The reconstruction performance is thus enhanced with the prior information and the vOMMP algorithm. Furthermore, the computation-intensive pseudo-inverse operation is simplified by the matrix-inversion-free (MIF) technique based on QR decomposition. The vOMMP-MIF CS decoder is then implemented in 90 nm CMOS technology. The QR decomposition is accomplished by two systolic arrays working in parallel. The implementation supports three settings for obtaining 40, 44, and 48 coefficients in the sparse vector. From the measurement result, the power consumption is 11.7 mW at 0.9 V and 12 MHz. Compared to prior chip implementations, our design shows good hardware efficiency and is suitable for low-energy applications.

Original languageEnglish
Article number7473850
Pages (from-to)864-873
Number of pages10
JournalIEEE Transactions on Biomedical Circuits and Systems
Volume10
Issue number4
DOIs
StatePublished - Aug 2016

Keywords

  • Compressed sensing (CS)
  • digital wavelet transform (DWT)
  • electrocardiogram (ECG)
  • orthogonal matching pursuit (OMP)
  • orthogonal multi-matching pursuit (OMMP)

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