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Vol. 10, No. 3, August 2025

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Classification of Arrhythmia Electrocardiogram Signals Using Kernel Principal Component Analysis and Naive Bayes

https://doi.org/10.22219/kinetik.v10i3.2219
Melinda Melinda
Universitas Syiah Kuala
https://orcid.org/0000-0001-9082-6639
Farhan
Universitas Syiah Kuala
Muhammad Irhamsyah
Universitas Syiah Kuala
Rizka Miftahujjannah
Universitas Syiah Kuala
Donata D Acula
University of Santo Tomas
Yunidar Yunidar
Universitas Syiah Kuala

Corresponding Author(s) : Melinda Melinda

melinda@usk.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 10, No. 3, August 2025

Abstract

Arrhythmia is a cardiovascular disorder commonly detected through electrocardiogram (ECG) signal analysis. Classifying arrhythmias based on ECG signals remains challenging due to signal complexity and individual variability. This study aims to develop a more accurate and efficient method for arrhythmia classification. The proposed method utilizes Kernel Principal Component Analysis (KPCA) and the Naive Bayes algorithm for classifying arrhythmic ECG signals. KPCA is chosen because of its ability to reduce data dimensionality, which allows complex ECG signal processing and improves classification accuracy by minimizing noise. Naive Bayes algorithm is chosen because of its simplicity and computational speed, as well as its effective performance even with limited data. ECG signals are processed with KPCA to reduce data dimensionality and extract relevant features. The Naive Bayes algorithm is then applied to classify the ECG signals into four categories: Premature Atrial Contraction (PAC), Premature Ventricular Contraction (PVC), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB). Model performance evaluation employs metrics such as accuracy, sensitivity, specificity, precision, and F1-score. The Naive Bayes model achieves an overall accuracy of 97.67%, with the highest performance observed in the RB class at 99.33%. Additionally, the F1-scores for all classes range from 96.62% to 98.57%, demonstrating the model's capability to detect arrhythmias effectively. These results indicate that the combination of KPCA and Naive Bayes is effective for classifying arrhythmic ECG signals.

Keywords

ECG Signals KPCA Naive bayes Arrhythmia Classification
Melinda, M., Farhan, Irhamsyah, M. ., Miftahujjannah, R. ., D Acula, D., & Yunidar, Y. (2025). Classification of Arrhythmia Electrocardiogram Signals Using Kernel Principal Component Analysis and Naive Bayes . Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 10(3). https://doi.org/10.22219/kinetik.v10i3.2219
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