http://iet.metastore.ingenta.com
1887

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

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

For access to this article, please select a purchase option:

Buy eFirst article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Signal Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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.

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2018.5076
Loading

Related content

content/journals/10.1049/iet-spr.2018.5076
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address