Investigation of the Evolution of Wavelet Higher-Order Dynamics in Atrial Fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with significant morbidity and mortality. Owing to the advances in sensor technology and the emergence of wearable devices that enable daily self-monitoring, ECG signal processing methods for the automatic detection of...

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Published in2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) pp. 363 - 366
Main Authors Zisou, Charilaos A., Apostolidis, Georgios K., Hadjileontiadis, Leontios J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 11.07.2022
Subjects
Atrial fibrillation
Electrocardiography
Evolution (biology)
Time series analysis
Time-frequency analysis
Wavelet analysis
Wearable computers
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Summary:Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with significant morbidity and mortality. Owing to the advances in sensor technology and the emergence of wearable devices that enable daily self-monitoring, ECG signal processing methods for the automatic detection of AF are more pertinent than ever. In this paper, we investigate the use of wavelet higher-order statistics (WHOS) for feature extraction and differentiation between normal sinus rhythm and AF. The proposed approach captures the evolution of the WHOS dynamics and quantifies the changes in the time-varying characteristics of the frequency couplings caused by AF. Results obtained from the statistical analysis of a dataset of 5834 single-lead ECG recordings, reveal 46/50 statistically significant features and provide insight into the complexity of the evolution of the ECG non-linearities during AF.
ISSN:2694-0604
DOI:10.1109/EMBC48229.2022.9871948