Classification of Premature Ventricular Contractions from ECG Signals using Multi-Domain Feature Extraction and Dimensional Reduction based on a Variational Autoencoder

Maryam Ebrahimpoor ¹ ( Department of Electrical and Computer Engineering, Kazerun Branch, Islamic Azad University,Kazerun,.Iran )
Mehdi Taghizadeh ² ( Department of Electrical and Computer Engineering, Kazerun Branch, Islamic Azad University,Kazerun,.Iran )
Mohammad Hosein Fatehi ³ ( Department of Electrical and Computer Engineering, Kazerun Branch, Islamic Azad University,Kazerun,.Iran )
Omid Mahdiyar ⁴ ( Department of Electrical and Computer Engineering, Kazerun Branch, Islamic Azad University,Kazerun,.Iran )
Jasem Jamali ⁵ ( Department of Electrical and Computer Engineering, Kazerun Branch, Islamic Azad University,Kazerun,.Iran )

Submited date : 2024-11-17 Accepted date : 2025-01-25

Keywords: Premature ventricular contraction (PVC), Electrocardiogram (ECG), Feature Extraction, Auto-Encoder, Feature Reduction, Machine Learning,

Abstract :

The most serious disease and the main causes of death worldwide are recognized to be cardiovascular ailments.the most common kinds of cardiac arrhythmia is premature ventricular contraction (PVC). the most common (and least invasive and Low costs) techniques for examining cardiac problems is the recording and analysis of electrocardiogram (ECG) data. Through a combination of manually extracted features from ECG signals and feature reduction based on deep learning, a novel supervised strategy for the automatic identification of PVC has been established in this work. The suggested technique extracted several properties from ECG signals using a variety of approaches, including statistical,chaotic analysis in the time domain and time-frequency domain, as well as morphological evaluation. Then, in order to minimize the amount of extracted features and acquire the most discriminating features, a variational autoencoder (VAE) network is constructed as a deep learning based feature reduction approach. In order to identify ECG data, support vector machines, k-nearest neighbors, and neural network classifiers with five-fold cross-validation are used. The suggested strategy is assessed using the MIT-BIH database. The obtained results showed that the proposed method using SVM classifier reached 99/85% accuracy, 99/95% sensitivity, 99/42% specificity, which has performed better compared to existing similar studies

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