Ecg Signal Classification With Deep Learning and Machine Learning: Representation of Qrs Complexes in Two-dimensional Frames and Data Balancing With Smote
Year: 2026
Type: article
Abstract: This study aims to support reliable ECG signal interpretation by reducing human-dependent variability through computer-aided analysis methods. Machine learning and deep learning methods were employed to examine 2D ECG representations and Synthetic Minority Over-Sampling Technique (SMOTE)-based balancing in ECG classification. Unlike existing ECG classification studies that typically address signal representation and class imbalance separately, this study jointly investigates the interaction between two-dimensional QRS representation and SMOTE-based data balancing within a unified experimental framework, thereby providing a systematic analysis of their combined impact on classification performance. Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and K-Nearest Neighbors (KNN) algorithms were implemented and comparatively analyzed. ECG beats from record 108 of the MIT-BIH Arrhythmia dataset were represented in a vision-based form for classification. To address severe class imbalance, SMOTE was applied only to the training data, and its effect on two-dimensional ECG representations was explicitly examined. Normal and Abnormal heartbeats were classified using a stratified 5-fold cross-validation strategy. Experimental results demonstrated that the CNN model achieved the most successful performance after applying SMOTE, reaching a weighted average F1-score of 99.82% ± 0.002, highlighting the combined effectiveness of two-dimensional QRS representation and data balancing in improving automated ECG classification.
Institution: Konya Technical University
Language: Turkish
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7134274641