QRS detection in ECG signal with convolutional network

Pedro Silva, Eduardo Luz, Elizabeth Wanner, David Menotti, Gladston Moreira*

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputConference publication


The QRS complex is a very important part of a heartbeat in the electrocardiogram signal, and it provides useful information for physicians to diagnose heart diseases. Accurately detecting the fiducial points that compose the QRS complex is a challenging task. Another issue concerning the QRS detection is its computational costs since the algorithm should have a fast and real-time response. In this context, there is a trade-off between computational cost and precision. Convolutional networks are a deep learning approach, and it has achieved impressive results in several computer vision and pattern recognition problems. Nowadays there is hardware that fully embeds convolutional network models, significantly reducing computational cost for real-world and real-time applications. In this direction, this work proposes a deep learning approach, based on convolutional network, aiming to detect heartbeat pattern. We tested two different architectures with two different proposes, one very deep and that has small receptive fields, and the other that has larger receptive fields. Preliminary experiments on the MIT-BIH arrhythmia database showed that the studied convolutional network presents promising results for QRS detection which are comparable with state-of-the-art methods.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 23rd Iberoamerican Congress, CIARP 2018, Proceedings
EditorsRuben Vera-Rodriguez, Julian Fierrez, Aythami Morales
Number of pages8
ISBN (Print)9783030134686
Publication statusPublished - 3 Mar 2019
Event23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018 - Madrid, Spain
Duration: 19 Nov 201822 Nov 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11401 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018


  • Deep learning
  • Pattern recognition
  • Signal process


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