PVC arrhythmia classification based on fractional order system modeling
Author:
Affiliation:
1. Laboratoire de Traitement du Signal, Département d’Electronique , Université des Frères Mentouri , Constantine , Algeria
2. Université Saad Dahlab Blida 1 , Blida , Algeria
3. Département de Télécommunication , USTHB , Bab-Ezzouar , Algeria
Abstract
Publisher
Walter de Gruyter GmbH
Subject
Biomedical Engineering
Reference52 articles.
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3. Kandala, R, Dhuli, R, Pławiak, P, Naik, G, Moeinzadeh, H, Gargiulo, G, et al.. Towards real-time heartbeat classification: evaluation of nonlinear morphological features and voting method. Sensors 2019;19:1–27. https://doi.org/10.3390/s19235079.
4. Zhan, X, Zhang, L, Wang, K, Yu, C, Zhu, T, Tang, J. A rapid approach to assess cardiac contractility by ballistocardiogram and electrocardiogram. Biomed Eng-Biomed Tech 2016;63:113–22.
5. Pławiak, P, Acharya, UR. Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals. Neural Comput Appl 2020;32:11137–61.
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2. Biometric identification by mean of fractional modeling of the ECG signal;2023 International Conference on Fractional Differentiation and Its Applications (ICFDA);2023-03-14
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