Block Sparse Multi-lead ECG Compression Exploiting between-lead Collaboration

TL;DR

This paper introduces a block sparse multi-lead ECG compression method that leverages between-lead correlations and uses a raised cosine kernel for improved sparsity, achieving significant compression efficiency improvements.

Contribution

It proposes a novel block sparse ECG compression technique exploiting inter-lead correlations and demonstrates the effectiveness of raised cosine kernels for better sparsity and compression.

Findings

37% average improvement in reconstruction error with raised cosine kernel

88% improvement using Gaussian kernel in collaborative setting

90-97% improvement with Daubechies wavelet kernels

Abstract

Multilead ECG compression (MlEC) has attracted tremendous attention in long-term monitoring of the patients heart behavior. This paper proposes a method denoted by block sparse MlEC (BlS MlEC) in order to exploit between-lead correlations to compress the signals in a more efficient way. This is due to the fact that multi-lead ECG signals are multiple observations of the same source (heart) from different locations. Consequently, they have high correlation in terms of the support set of their sparse models which leads them to share dominant common structure. In order to obtain the block sparse model, the collaborative version of lasso estimator is applied. In addition, we have shown that raised cosine kernel has advantages over conventional Gaussian and wavelet (Daubechies family) due to its specific properties. It is demonstrated that using raised cosine kernel in constructing the sparsifying basis matrix gives a sparser model which results in higher compression ratio and lower reconstruction error. The simulation results show the average improvement of 37%, 88% and 90-97% for BlS M-lEC compared to the non-collaborative case with raised cosine kernel, Gaussian kernel and collaborative case with Daubechies wavelet kernels, respectively, in terms of reconstruction error while the compression ratio is considered fixed.