Key result
SVM models outperform ANN in classifying ECG heartbeats with 98.7% overall accuracy.
Why the study?
The study was conducted to compare ANN and SVM models and identify the most effective feature extraction and classification methodology for ECG-signal-based heartbeat classification.
Does an SVM classifier perform better than an ANN classifier for heartbeat classification using ECG signals?
Does an SVM classifier perform better than an ANN classifier for heartbeat classification using ECG signals?
Absolute Event Rate: 98.7% vs 98.4%
Support Vector Machine models slightly outperform Artificial Neural Networks (98.7% vs 98.4% accuracy) in classifying ECG heartbeats from the MIT-BIH database.
SVM may marginally aid automated ECG classification; leaves open prospective clinical validation before any practice change.
This research conducts a comparative performance of artificial neural networks (ANNs) and support vector machines (SVMs) models for heartbeat classification using Electrocardiogram (ECG) signals, which diagnose various cardiovascular diseases and abnormalities. The study scrutinizes various combinations of feature extraction and classification algorithms to identify the most effective methodology in terms of accuracy and other performance metrics. The experiment focuses on classifying five classes of ECG signals using the MIT-BIH database as the data source. Each signal is initially preprocessed into beats using Discrete Wavelet Transform (DWT), followed by Independent Component Analysis (ICA) for enhanced feature extraction. Results show that the SVM performs better than ANN with an overall accuracy of 98.7% to 98.4%. However, there are nuanced differences in their performance across different classes and configurations.
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Ugwu et al. (2024) studied Cardiovascular diseases and abnormalities (ECG heartbeat classification). Support Vector Machine (SVM) vs. Artificial Neural Network (ANN) was evaluated on Overall accuracy of heartbeat classification. Support Vector Machine (SVM) models achieved a higher overall accuracy of 98.7% compared to 98.4% for Artificial Neural Networks (ANN) in classifying five classes of ECG heartbeats.
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