Anurag Singh
Indian Institute of Technology Guwahati, India
Title: Improving joint recovery of multi-channel ECG signals in compressed sensing-based telemonitoring systems through multiscale weighting
Biography
Biography: Anurag Singh
Abstract
Computational complexity and power consumption are prominent issues in wireless telemonitoring applications involving physiological signals. Compressed sensing (CS) has emerged as a promising framework to address these challenges because of its energy-efficient data reduction procedure. In this work, a CS-based approach is studied for joint compression/reconstruction of multichannel electrocardiogram (MECG) signals. Weighted mixed-norm minimization (WMNM)-based joint sparse recovery algorithm is proposed, which can successfully recover the signals from all the channels simultaneously by exploiting the inter-channel correlations. The proposed algorithm is based on a multi-scale weighting approach, which utilizes multi-scale signal information. Under this strategy, weights are designed based on the diagnostic information contents of each wavelet subband/scale. Such a weighting approach emphasizes wavelet subbands having high diagnostic importance during joint CS reconstruction. Coefficients in non-diagnostic subbands are deemphasized simultaneously, resulting in a sparser solution. The proposed method helps achieve superior reconstruction quality with a lower number of measurements. Reduction in the required number of measurements directly translates into higher compression efficiency, resulting in low energy consumption in CS-based remote ECG monitoring systems.