TY - GEN
T1 - Low-complexity compressed sensing with variable orthogonal multi-matching pursuit and partially known support for ECG signals
AU - Cheng, Yih Chun
AU - Tsai, Pei Yun
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/27
Y1 - 2015/7/27
N2 - In this paper, we present low-complexity compressed sensing (CS) techniques for monitoring electrocardiogram (ECG) signals in wireless body sensor network (WBSN). We first exploit ECG properties in the wavelet domain to extend the partially known support set (PKS) so as to reduce the support augmentation and estimation efforts in the iterative recovery algorithm. Then, variable orthogonal multi-matching pursuit (vOMMP) algorithm is proposed, which uses orthogonal matching pursuit (OMP) algorithm in the first phase to effectively augment the support set with reliable supports and adopts the orthogonal multi-matching pursuit (OMMP) in the second phase to rescue the missing supports. The reconstruction performance is thus enhanced. Furthermore, the computation-intensive pseudo-inverse operation for signal reconstruction is simplified by the matrix-inversion-free technique based on QR decomposition. The performance and complexity comparisons manifest the advantages of our proposed techniques.
AB - In this paper, we present low-complexity compressed sensing (CS) techniques for monitoring electrocardiogram (ECG) signals in wireless body sensor network (WBSN). We first exploit ECG properties in the wavelet domain to extend the partially known support set (PKS) so as to reduce the support augmentation and estimation efforts in the iterative recovery algorithm. Then, variable orthogonal multi-matching pursuit (vOMMP) algorithm is proposed, which uses orthogonal matching pursuit (OMP) algorithm in the first phase to effectively augment the support set with reliable supports and adopts the orthogonal multi-matching pursuit (OMMP) in the second phase to rescue the missing supports. The reconstruction performance is thus enhanced. Furthermore, the computation-intensive pseudo-inverse operation for signal reconstruction is simplified by the matrix-inversion-free technique based on QR decomposition. The performance and complexity comparisons manifest the advantages of our proposed techniques.
KW - Compressed Sensing (CS)
KW - digital wavelet transform (DWT)
KW - Electrocardiogram (ECG)
KW - orthogonal matching pursuit (OMP)
KW - orthogonal multi-matching pursuit (OMMP)
UR - http://www.scopus.com/inward/record.url?scp=84946229219&partnerID=8YFLogxK
U2 - 10.1109/ISCAS.2015.7168803
DO - 10.1109/ISCAS.2015.7168803
M3 - 會議論文篇章
AN - SCOPUS:84946229219
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
SP - 994
EP - 997
BT - 2015 IEEE International Symposium on Circuits and Systems, ISCAS 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Symposium on Circuits and Systems, ISCAS 2015
Y2 - 24 May 2015 through 27 May 2015
ER -