NDLI: Block sparse multi-lead ECG compression exploiting between-lead collaboration

Content Provider	IET Digital Library
Author	Eftekharifar, Siavash Rezaii, Tohid Yousefi Beheshti, Soosan Daneshvar, Sabalan
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.
Starting Page	46
Ending Page	55
Page Count	10
ISSN	17519675
Volume Number	13
e-ISSN	17519683
Issue Number	Issue 1, Feb (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/13/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2018.5076
Journal	IET Signal Processing
Publisher Date	2018-08-08
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Between-lead Collaboration Between-lead Correlations Bioelectric Signal Biology And Medical Computing Block Sparse M-lEC Block Sparse Model Block Sparse Multilead ECG Compression BlS M-lEC Collaborative Case Collaborative Version Data Compression Digital Signal Processing Electrocardiography Electrodiagnostics And Other Electrical Measurement Technique High Correlation Higher Compression Ratio Long-term Monitoring Medical Signal Processing Multilead Electrocardiography Signal Noncollaborative Case Patient Raised Cosine Kernel Signal Processing And Detection Signal Reconstruction Source Sparse Model Statistics Tremendous Attention Wavelet Transform
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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