Weighted mixed-norm minimization based joint compressed sensing recovery of multi-channel electrocardiogram signals. (July 2016)

• Record Type: Journal Article
• Authors: Singh, Anurag; Dandapat, S.
• Is Part Of: Computers & electrical engineering. Volume 53(2016)
• Page Start: 203
• Page End: 218
• Publication Date: 2016-07
• Subjects: Multi-channel electrocardiogram -- Compressed sensing -- Weighted mixed-norm minimization -- Discrete wavelet transform -- Joint sparsity -- Data compression; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2016.01.027
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A weighted mixed norm minimization based joint sparse recovery algorithm is proposed for multichannel ECG signals. Algorithm aims to improve the joint signal recovery in compressed sensing based WBAN applications. It is based on a weighing rule that emphasizes the clinically important ECG features. Required measurements could be reduced significantly without compromise with the reconstruction accuracy. The approach leads to higher compression efficiency with reduced on-node computations and power consumptions in resource constrained WBANs. Graphical abstract: Abstract: Computational complexity and power consumption are prominent issues in wireless telemonitoring applications involving physiological signals. Because of its energy-efficient data reduction procedure, compressed sensing (CS) emerged as a promising framework to address these challenges. In this work, a multi-channel CS framework is explored for multi-channel electrocardiogram (MECG) signals. The work focuses on the successful joint recovery of the MECG signals using a low number of measurements by exploiting the correlated information across the channels. A CS recovery algorithm based on weighted mixed-norm minimization (WMNM) is proposed that exploits the joint sparsity of MECG signals in the wavelet domain and recovers signals from all the channels simultaneously. The proposed WMNM algorithm follows a weighting strategy to emphasize the diagnostically important MECG features. Experimental results on various MECG databases show that the proposed method can achieve superior reconstruction quality with high compression efficiency as compared to its non-weighted counterpart and other existing CS-based ECG compression techniques.