Block sparsity‐based joint compressed sensing recovery of multi‐channel ECG signals

Abstract

In recent years, compressed sensing (CS) has emerged as an effective alternative to conventional wavelet based data compression techniques. This is due to its simple and energy-efficient data reduction procedure, which makes it suitable for resource-constrained wireless body area network (WBAN)-enabled electrocardiogram (ECG) telemonitoring applications. Both spatial and temporal correlations exist simultaneously in multi-channel ECG (MECG) signals. Exploitation of both types of correlations is very important in CS-based ECG telemonitoring systems for better performance. However, most of the existing CS-based works exploit either of the correlations, which results in a suboptimal performance. In this work, within a CS framework, the authors propose to exploit both types of correlations simultaneously using a sparse Bayesian learning-based approach. A spatiotemporal sparse model is employed for joint compression/reconstruction of MECG signals. Discrete wavelets transform domain block sparsity of MECG signals is exploited for simultaneous reconstruction of all the channels. Performance evaluations using Physikalisch-Technische Bundesanstalt MECG diagnostic database show a significant gain in the diagnostic reconstruction quality of the MECG signals compared with the state-of-the art techniques at reduced number of measurements. Low measurement requirement may lead to significant savings in the energy-cost of the existing CS-based WBAN systems.

Time–frequency localization using three-tap biorthogonal wavelet filter bank for electrocardiogram compressions

“…Current literature uses two-band WFBs to analyze ECG signals [4][5][6][7][8][9][10]. Poor resolution (∆ω = π/2) of low and highfrequency bands during signal decomposition is the major drawback of two-band WFBs.…”

Quantum Leap in Cardiac Prognosis: EMIP-CardioPPG’s Pioneering Approach to Early Myocardial Infarction Prediction

“…Traditional methods [6], for diagnosing MI encompass clinical assessments [7], ECG signal analysis [8], and medical image-based analysis (MIA) [9], utilizing modalities like computed tomography (CT) [10], magnetic resonance imaging (MRI) [11], positron emission tomography (PET) [12], and ultrasonography [13]. However, a group of researchers working in this area [14], and traditional methods are working well but some research gaps are available, aiming to minimize the research gap from the traditional approach. Clinical assessments rely on subjective judgments, leading to variability.…”

A compressed-sensing-based compressor for ECG

“…As the ADC is more willing to consume more power, the focus of researchers have been centered on this module to reduce the power consumption with keeping the accuracy and resolution high [3]. Since the ECG signal is a good compressible signal and has redundant information, numerous ECG compression algorithms have been proposed [4][5][6][7][8][9][10]. There are a few factors that can help us to compare the compressors and their applied algorithms.…”