Classification of Premature Ventricular Contraction Using Deep Learning

Fabiola De Marco, D Finlay, RR Bond

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)
48 Downloads (Pure)


Electrocardiogram (ECG) analysis has been used to identify different heart problems and deep learning is emerging as a common tool to analyse ECGs. Premature ventricular contraction (PVC) is the most common cause of abnormal heartbeats; in most cases this is harmless but under specific conditions, it can lead to a life-threatening cardiac disease. Automated PVC detection in this scenario is a task of significant importance for relieving the heavy workloads of experts in the manual analysis of long-term ECGs. To identify PVCs, this research aims to use the MIT-BIH Arrhythmia Database to classify QRS complexes using five different deep neural networks: Long Short Term Memory, AlexNet, GoogleNet, Inception V3 and ResNet-50. The results showed high efficiency and reliability in the final diagnoses during two separate experiments (one with the entire dataset and the other with a balanced dataset). The ResNet-50 was the first experiment's best classifier (accuracy = 99.8%, F1-score = 99.2%), and the second experiment's best classifier was Inception V3 (accuracy = 98.8%, F1-score=98.8%). Relevant information, in this research, was extrapolated from a study of the confusion matrix to conduct a “failure analysis” to understand where and why the classifiers made incorrect classifications.
Original languageEnglish
Title of host publication2020 Computing in Cardiology, CinC 2020
Place of PublicationRimini, Italy
PublisherIEEE Xplore
Number of pages4
ISBN (Electronic)978-1-7281-7382-5
ISBN (Print)978-1-7281-1105-6
Publication statusPublished (in print/issue) - 10 Feb 2021
EventComputing in Cardiology 2020 - Palacongressi, Rimini, Italy
Duration: 13 Sep 202016 Sep 2020

Publication series

NameComputing in Cardiology
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X


ConferenceComputing in Cardiology 2020
Abbreviated titleCinC20

Bibliographical note

Funding Information:
This work was supported by the Eastern Corridor Medical Engineering Centre that is funded by the European Union’s INTERREG VA Programme and managed by the Special EU Programmes Body (SEUPB)

Publisher Copyright:
© 2020 Creative Commons; the authors hold their copyright.

Copyright 2021 Elsevier B.V., All rights reserved.


  • deep learning
  • Premature ventricular contraction
  • machine learning
  • ECG analysis


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