Iranian Journal of Electrical and Electronic Engineering
Iran University of Science and Technology
Mon, Jul 15, 2024
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Volume 20, Issue 2 (June 2024)
IJEEE 2024, 20(2): 3235-3235 |
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Sen B, Kashyap M, Tamang J S, Sharma S, Dey R. Comparative Analysis of Machine Learning Techniques for Arrhythmia Detection. IJEEE 2024; 20 (2) :3235-3235
URL: http://ijeee.iust.ac.ir/article-1-3235-en.html
URL: http://ijeee.iust.ac.ir/article-1-3235-en.html
Abstract: (252 Views)
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.
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.
Keywords: Arrhythmia, Electrocardiography (ECG), Machine Learning, Support Vector Machine (SVM), Logistic Regression (LG), Random Forest (RF).
Type of Study: Research Paper |
Subject:
Biomedical Signal Processing
Received: 2024/03/11 | Revised: 2024/06/25 | Accepted: 2024/06/10
Received: 2024/03/11 | Revised: 2024/06/25 | Accepted: 2024/06/10
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.