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https://dspace.ctu.edu.vn/jspui/handle/123456789/101285
Title: | Machine learning and ECG-based arrhythmia classification exploiting r-peak detection |
Authors: | Pham, Van Thinh Phung, Ngoc Anh Nguyen, Trong Trung Anh Le, Hai Chau |
Keywords: | ECG EEMD Hilbert transform Machine learning Arrhythmia classification |
Issue Date: | 2023 |
Series/Report no.: | Tạp chí Khoa học Công nghệ Thông tin và Truyền thông;Số 01 (CS.01) .- P.19-27 |
Abstract: | The increasing incidence of heart-related diseases has prompted the development of efficient techniques to identify irregular heart problems. It has proven to be challenging to promptly and accurately diagnose many complicated and interferential symptom diseases including arrhythmia. Thanks to the recent evolution of artificial intelligence (AI) and the advances in signal processing, automated arrhythmia classification has become more effective and widely applied for physicians and practitioners with machine learning (MI.) techniques and the use of electrocardiogram (ECG). In this work, we have investigated a machine learning-based arrhythmia classification problem based on ECGs and successfully proposed an efficient ECG-based machine learning solution employing R-peaks. In order to enhance the arrhythmia diagnosis performance, our developed approach exploits a Butterworth filter and utilizes the EEMD technique, Hilbert transformation, and a proper machine learning algorithm. The performance of the proposed method is evaluated with the most popular public dataset, MIT-BIH Arrhythmia. The numerical results, imply that the developed method outperforms the notable algorithms given in the conventional works and obtains better performance with an accuracy of 93.4%, a sensitivity of 95.4%, and an F1-score of 96.3%. The attained high F1-score proves that the proposed method can effectively deal with the data imbalance while detecting arrhythmia, or in other words, it can be suitable and proper to deploy in practical clinical environments. |
URI: | https://dspace.ctu.edu.vn/jspui/handle/123456789/101285 |
ISSN: | 2525-2224 |
Appears in Collections: | Khoa học Công nghệ Thông tin và Truyền thông |
Files in This Item:
File | Description | Size | Format | |
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_file_ Restricted Access | 4.61 MB | Adobe PDF | ||
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