Deep Learning-Based Classification of Premature Ventricular Contractions and Supraventricular Tachycardia Using ECG Signals
Year: 2025
Type: conference-paper
Abstract: This study provides comparative evaluation of deep learning models for the classification of PVC and SVT from ECG signals.We evaluate ResNet, DenseNet, VGGNet, InceptionNet, and WaveNet performance on various measures like accuracy, precision, recall, F1 score, AUC, MCC and log loss.The findings are that ResNet performs the best for both PVC and SVT classification tasks with 94% for PVC and 98% for SVT.Whereas all the models perform best for PVC detection, DenseNet experiences a steep drop in performance for SVT classification, indicating that it requires optimization.The results show the effectiveness of deep learning models, ResNet and WaveNet, in the correct diagnosis of arrhythmias from ECG signals.
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4411094366