Matching Pursuit Decomposition on Electrocardiograms for Joint Compression and QRS Detection


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.

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Corresponding author

Correspondence to Carlos Hernando-Ramiro.

Additional information

This work has been partially supported by the Spanish Ministry of Economy and Competitiveness through Project TEC2015-64835-C3-1-R and by CNPq (Brazil) through Project 302829/2017-2.

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Hernando-Ramiro, C., Lovisolo, L., Cruz-Roldán, F. et al. Matching Pursuit Decomposition on Electrocardiograms for Joint Compression and QRS Detection. Circuits Syst Signal Process 38, 2653–2676 (2019).

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