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Circuits, Systems, and Signal Processing

, Volume 38, Issue 6, pp 2653–2676 | Cite as

Matching Pursuit Decomposition on Electrocardiograms for Joint Compression and QRS Detection

  • Carlos Hernando-RamiroEmail author
  • Lisandro Lovisolo
  • Fernando Cruz-Roldán
  • Manuel Blanco-Velasco
Article
  • 93 Downloads

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.

Keywords

ECG decomposition ECG compression QRS detection Matching pursuit (MP) Triangular atoms 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Teoría de la Señal y ComunicacionesUniversidad de AlcaláAlcalá de HenaresSpain
  2. 2.PROSAICO-DETEL/PELState University of Rio de JaneiroRio de JaneiroBrazil

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