Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11030
Title: Detection of Premature Ventricular Contractions Using Machine Learning
Authors: Unlu, B.
Erogul, O.
Keywords: Arrhythmia Classification
Electrocardiogram (ECG)
Machine Learning
Premature Ventricular Contraction (PVC)
Biomedical signal processing
Classification (of information)
Decision trees
Diseases
Nearest neighbor search
Support vector machines
Arrhythmia classification
Electrocardiogram
Electrocardiogram signal
Machine-learning
Nearest-neighbour
Premature ventricular contraction
RR intervals
Support vectors machine
Ventricular arrhythmias
Electrocardiograms
Issue Date: 2023
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: Premature Ventricular Contractions (PVCs), a form of abnormal heartbeat that can be identified through electrocardiogram (ECG) signals, play a crucial role in detecting potentially life-threatening ventricular arrhythmias. In this study, three features (RR interval, QRS width, and R amplitude) are extracted from the MIT-BIH Arrhythmia Database and used Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) as classifiers. The classifiers achieved satisfactory results, with average accuracy rates of 94 % for KNN(K = 5) and 93% for KNN (K = 7), 87% for SVM, and 93% for DT. In addition, the classifiers were tested with the St. Petersburg Institute of Cardiological Technics 12-lead Arrhythmia database and obtained a convincing result of 74% accuracy for the SVM classifier, 70% for the KNN (K=5) and 68% KNN(K = 7) classifier, and 95% for the DT classifier. These results highlight the potential of feature selection and classification techniques in accurately identifying PVC beats from ECG signals, which is crucial for the early detection and effective treatment of ventricular arrhythmias. © 2023 IEEE.
Description: 2023 Medical Technologies Congress, TIPTEKNO 2023 -- 10 November 2023 through 12 November 2023 -- 195703
URI: https://doi.org/10.1109/TIPTEKNO59875.2023.10359234
https://hdl.handle.net/20.500.11851/11030
ISBN: 9798350328967
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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