TY - GEN
T1 - Investigation of the Evolution of Wavelet Higher-Order Dynamics in Atrial Fibrillation
AU - Zisou, Charilaos A.
AU - Apostolidis, Georgios K.
AU - Hadjileontiadis, Leontios J.
N1 - Funding Information:
∗This research project was funded by the Abu Dhabi Department of Education and Knowledge (ADEK), UAE, under the Award for Research Excellence (AARE) 2018, ref. no: 29934.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85138128259&partnerID=8YFLogxK
U2 - 10.1109/EMBC48229.2022.9871948
DO - 10.1109/EMBC48229.2022.9871948
M3 - Conference contribution
C2 - 36085853
AN - SCOPUS:85138128259
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 363
EP - 366
BT - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Y2 - 11 July 2022 through 15 July 2022
ER -