@inproceedings{515b8b10467e4e8bb267537bc3e82050,
title = "Support Vector Machine for Heart Beats Classification Based on Robust Filtering",
abstract = "The Electrocardiogram (ECG) signal is by far the most intensive tool used to inspect the condition of the Heart and to detect early arrhythmia abnormalities, which is a life-saving process. The classification process highly depends on the quality of the ECG signal. Through this paper, we present a comparative study of two preprocessing techniques, namely high-pass derivative and robust neural net-work preprocessing filters. Our work involves de-veloping a Super Vector Machine (SVM) detector and assessing its performance by two preprocessing methods. We evaluated the detector's performance by using the MIT-BIH database under the AAMI EC57 standard and using Synthetic Minority Over-sampling Technique (SMOTE). The robust-based classifier shows higher performance with an overall accuracy of 99,51 \% for intra-patient detection and 82,23\% for inter-patient classification compared to the derivative-based one. that has an overall accuracy of 99,34\% for intra-patient and 73,51 \% for inter-patient detection.",
keywords = "Classification, ECG, Heart Beats, Robust Filtering, SVM",
author = "Khaled Arbateni and Mohamed Deriche",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022 ; Conference date: 06-05-2022 Through 10-05-2022",
year = "2022",
doi = "10.1109/SSD54932.2022.9955703",
language = "English",
series = "2022 19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "653--656",
booktitle = "2022 19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022",
address = "United States",
}