Combining template-based and feature-based classification to detect atrial fibrillation from a short single lead ECG recording

Matthieu Da Silva-Filarder, Faezeh Marzbanrad

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

1 Citation (Scopus)


Automated diagnosis of Atrial fibrillation (AF) has remained imperfect despite the threat it represents to millions of people. The main issues which can lead to a misdiagnosis of AF include its episodic nature, disease diversity and noise. The aim of 2017 PhysioNet/CinC Challenge is to classify short single lead ECG recordings as normal sinus rhythm, atrial fibrillation, other rhythm, or noisy. We present a method using heart rate variability features and noise detection features coupled with template-based wave morphology features. The method originality lies in the use of special templates sensitive to the heart rate variability as well as wave morphology. These special templates showed significant results in AF detection performances. Based on Cross-validation, an F1 score of 0.84 on AF classification, and a general classification score of 0.76 were obtained on the training set.

Original languageEnglish
Title of host publication2017 Computing in Cardiology (CinC 2017)
EditorsChristine Pickett, Cristiana Corsi, Pablo Laguna, Rob MacLeod
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)9781538666302
ISBN (Print)9781538645550
Publication statusPublished - 1 Jan 2017
EventComputing in Cardiology Conference 2017 - Rennes, France
Duration: 24 Sep 201727 Sep 2017
Conference number: 44th

Publication series

NameComputing in Cardiology
ISSN (Print)2325-8861


ConferenceComputing in Cardiology Conference 2017
Abbreviated titleCINC 2017

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