Detection of atrial fibrillation in ECG hand-held devices using a random forest classifier

Morteza Zabihi*, Ali Bahrami Rad, Aggelos K. Katsaggelos, Serkan Kiranyaz, Susanna Narkilahti, Moncef Gabbouj

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

121 Scopus citations


Atrial Fibrillation (AF) is characterized by chaotic electrical impulses in the atria, which leads to irregular heartbeats and can develop blood clots and stroke. Therefore, early detection of AF is crucial for increasing the success rate of the treatment. This study is focused on detection of AF rhythm using hand-held ECG monitoring devices, in addition to three other classes: normal or sinus rhythm, other rhythms, and too noisy to analyze. The pipeline of the proposed method consists of three major components: preprocessing and feature extraction, feature selection, and classification. In total, 491 hand-crafted features are extracted. Then, 150 features are selected in a feature ranking procedure. The selected features are from time, frequency, time-frequency domains, and phase space reconstruction of the ECG signals. In the final stage, a random forest classifier is used to classify the selected features into one of the four aforementioned ECG classes. Using the scoring mechanism provided by PhysioNet/Computing in Cardiology (CinC) Challenge 2017, the overall score (mean±std) of 81.9±2.6% is achieved over the training dataset in 10-fold cross-validation. The proposed algorithm tied for the first place in the PhysioNet/CinC Challenge 2017 with an overall score of 82.6% (rounded to 83%) on the unseen test dataset.

Original languageEnglish (US)
Pages (from-to)1-4
Number of pages4
JournalComputing in Cardiology
StatePublished - 2017
Event44th Computing in Cardiology Conference, CinC 2017 - Rennes, France
Duration: Sep 24 2017Sep 27 2017

ASJC Scopus subject areas

  • General Computer Science
  • Cardiology and Cardiovascular Medicine


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