Deep Learning Image Embeddings for Ventricular Beat Classification
Journal
2025 33rd Telecommunications Forum (TELFOR)
Date Issued
2025-11-25
Author(s)
Tudjarski, Stojancho
Petrovski, Nikola
DOI
10.1109/telfor67910.2025.11314332
Abstract
Ventricular arrhythmias pose significant risks to cardiovascular health, necessitating accurate detection from electrocardiogram signals. This study investigates the effectiveness of vector embeddings derived from various convolutional neural networks and contrastive language-image pretraining-based models for classifying ventricular heartbeats presented as images. A comparative analysis revealed that the combination of the ResNet50 model and support vector machines achieved the top F1 score of 79.30%. The results provide insights into how deep neural network models can produce effective vector embeddings of heartbeats to train heartbeat classification models.