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Communication dans un congrès

Automatic Detection of the Wolff:Parkinson:White (WPW) Syndrome from Electrocardiograms (ECGs)

Abstract : In this paper, a new method of automatic detection of the Wolff-Parkinson-White (WPW) syndrome is proposed based on electrocardiograms (ECGs) signals. Firstly, with the continuous wavelet transform (CWT), the P wave, the T wave and the QRS complex are identified. Then, their durations are also computed after determination of the boundaries (onsets and offsets of the P, T waves and the QRS complex). Secondly, the PR interval, the QRS complex interval and the area of the QRS complex are determined in order to detect the presence or not of the delta wave. This method has been tested on ECGs signals from patients affected by the WPW syndrome in order to evaluate its robustness. It can provide assistance to cardiologists during the interpretation of the ECG.
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01557505
Contributeur : Le2i - Université de Bourgogne <>
Soumis le : jeudi 6 juillet 2017 - 12:29:50
Dernière modification le : vendredi 17 juillet 2020 - 14:59:05

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Hassan Adam Mahamat, Sabir Jacquir, Khalil Cliff, Gabriel Laurent, Stéphane Binczak. Automatic Detection of the Wolff:Parkinson:White (WPW) Syndrome from Electrocardiograms (ECGs). Computing in Cardiology Conference (CinC), 2016, Sep 2016, Vancouver, BC, Canada. pp.417-420, ⟨10.22489/CinC.2016.120-278⟩. ⟨hal-01557505⟩

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