Block sparse multi‐lead ECG compression exploiting between‐lead collaboration. Issue 1 (1st February 2019)

• Record Type: Journal Article
• Authors: Eftekharifar, Siavash; Rezaii, Tohid Yousefi; Beheshti, Soosan; Daneshvar, Sabalan
• Is Part Of: IET signal processing. Volume 13:Issue 1(2019)
• Page Start: 46
• Page End: 55
• Publication Date: 2019-02-01
• Subjects: medical signal processing -- electrocardiography -- wavelet transforms -- signal reconstruction -- data compression; block sparse multilead ECG compression -- between‐lead collaboration -- tremendous attention -- long‐term monitoring -- patient -- block sparse M‐lEC -- BlS M‐lEC -- between‐lead correlations -- multilead electrocardiography signals -- source -- high correlation -- sparse models -- block sparse model -- collaborative version -- raised cosine kernel -- higher compression ratio -- noncollaborative case -- collaborative case; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2018.5076
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract : Multi‐lead ECG compression (M‐lEC) has attracted tremendous attention in long‐term monitoring of the patient's heart behaviour. This study proposes a method denoted by block sparse M‐lEC (BlS M‐lEC) 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 electrocardiography signals are multiple observations of the same source (heart) from different locations. Consequently, they have a 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, it is 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.