Title
Matching Pursuit Decomposition on Electrocardiograms for Joint Compression and QRS Detection
Abstract
The electrocardiogram (ECG) is relevant for several medical purposes. In this work, a novel analysis-by-synthesis method to process ECG signals is presented. It is based on the matching pursuit algorithm, which is employed here to decompose the ECG in the time domain. The main features of the ECG are extracted through a dictionary of triangular functions, due to their good correlation with the typical electrocardiographic waveforms, especially the R wave. The individual elements of this signal representation can be further employed for different processing tasks, such as ECG compression and QRS detection. Compression is required to store and transmit signals in situations related to massive acquisitions, frequent monitoring, high-resolution data, real-time needs or narrow bandwidths. QRS detection is not only essential to study the heart rate variability, but also the basis of automatic systems for ECG applications such as heartbeat classification or anomaly identification. In this study, it is shown how to employ the proposed processing approach to perform ECG compression and beat detection jointly. The resulting algorithm is tested over the whole MIT-BIH Arrhythmia Database, with a wide variety of ECG records, yielding both high compression and efficient QRS detection.
Year
DOI
Venue
2019
10.1007/s00034-018-0986-2
Circuits, Systems, and Signal Processing
Keywords
Field
DocType
ECG decomposition,ECG compression,QRS detection,Matching pursuit (MP),Triangular atoms
Time domain,Compression (physics),Heartbeat,Matching pursuit decomposition,Pattern recognition,Control theory,Waveform,QRS complex,Beat detection,Artificial intelligence,Mathematics,Matching pursuit algorithms
Journal
Volume
Issue
ISSN
38
6
1531-5878
Citations 
PageRank 
References 
0
0.34
21
Authors
4
Name
Order
Citations
PageRank
Carlos Hernando-Ramiro100.34
Lisandro Lovisolo2378.82
Fernando Cruz-Roldán311119.33
Manuel Blanco-Velasco431430.57