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Artificial Intelligence in Health A fuzzy system for heartbeat classification

Conflict of interest classification approach using LDA with an enhanced SVM
method for ECG signals in cloud computing. Multimedia
The authors declare they have no competing interests. Tools Applic. 2018;77:10195-10215.
Author contributions doi: 10.1007/s11042-017-5318-1

Conceptualization: All authors 8. Sharma P, Ray KC. Efficient methodology for
Investigation: Roghayeh Rafieisangari electrocardiogram beat classification. IET Signal Process.
2016;10(7):825-832.
Methodology: Nabiollah Shiri
Writing – original draft: Roghayeh Rafieisangari doi: 10.1049/iet-spr.2015.0274
Writing – review & editing: Nabiollah Shiri 9. Übeyli ED. Adaptive neuro-fuzzy inference system for
classification of ECG signals using Lyapunov exponents.
Ethics approval and consent to participate Comput Methods Programs Biomed. 2009;93:313-321.

Not applicable. doi: 10.1016/j.cmpb.2008.10.012
Consent for publication 10. Zhang L, Peng H, Yu C. An approach for ECG Classification
Based on Wavelet Feature Extraction and Decision Tree. In:
Not applicable. 2010 International Conference on Wireless Communications
and Signal Processing. 2010. p. 1-4.
Availability of data
doi: 10.1109/WCSP.2010.5633782
The dataset used in this study, the MIT-BIH Arrhythmia 11. Karpagachelvi S, Arthanari M, Sivakumar M. Classification
Database, was expanded on February 24, 2005, and is of electrocardiogram signals with support vector machines
now freely available on PhysioNet. The dataset can be and extreme learning machine. Neural Comput Applic.
accessed through the following link: MIT-BIH Arrhythmia 2012;21:1331-1339.
Database.
doi: 10.1007/s00521-011-0572-z
References 12. El-Saadawy H, Tantawi M, Shedeed HA, Tolba MF. Hybrid
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Volume 1 Issue 4 (2024) 59 doi: 10.36922/aih.3367