IRANIAN JOURNAL OF ELECTRICAL AND ELECTRONIC ENGINEERING

en Comparative Analysis of Machine Learning Techniques for Arrhythmia Detection 4-Biomedical Signal Processing Biomedical Signal Processing Research Paper Research Paper Cardiovascular arrhythmia is indeed one of the most prevalent cardiac issues globally. In this paper, the primary objective was to develop and evaluate an automated classification system. This system utilizes a comprehensive database of electro- cardiogram (ECG) data, with a particular focus on improving the detection of minority arrhythmia classes. In this study, the focus was on investigating the performance of three different supervised machine learning models in the context of arrhythmia detection. These models included Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF). An analysis was conducted using real inter-patient electrocardiogram (ECG) records, which is a more realistic scenario in a clinical environment where ECG data comes from various patients. The study evaluated the models’ performances based on four important metrics: accuracy, precision, recall, and f1-score. After thorough experimentation, the results highlighted that the Random Forest (RF) classifier outperformed the other methods in all of the metrics used in the experiments. This classifier achieved an impressive accuracy of 0.94, indicating its effectiveness in accurately detecting arrhythmia in diverse ECG signals collected from different patients. Arrhythmia, Electrocardiography (ECG), Machine Learning, Support Vector Machine (SVM), Logistic Regression (LG), Random Forest (RF). 85 96 http://ijeee.iust.ac.ir/browse.php?a_code=A-10-5105-1&slc_lang=en&sid=1 Biswapriyo Sen sen.biswapriyo01@gmail.com 1800319475328460014085 1800319475328460014085 No Department of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University Maharishi Kashyap maharishi.kashyap1311@gmail.com 1800319475328460014086 1800319475328460014086 No Department of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University Jitendra Singh Tamang jstamang@gmail.com 1800319475328460014087 1800319475328460014087 No Department of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University Sital Sharma sitalneo@gmail.com 1800319475328460014088 1800319475328460014088 No Department of Artificial Intelligence and Data Science, Sikkim Manipal Institute of Technology, Sikkim Manipal University Rijhi Dey rijhi.dey88@gmail.com 1800319475328460014089 1800319475328460014089 Yes Department of Electronics and Communication Engineering, IEM New Town Campus, University of Engineering and Management, Kolkata