Key result
Temporal Convolutional Network classifies ECG signals with ~99% accuracy, outperforming classical SVM despite lower interpretability.
Why the study?
Accurate and efficient automatic classification of ECG signals for ARR, CHF, and NSR is essential, but the trade-offs between classical feature engineering and TCN-based deep features remain unclear.
Does a TCN-based deep feature extractor improve classification accuracy of ARR, CHF, and NSR from ECG signals compared to a classical feature-based pipeline?
Does a TCN-based deep feature extractor improve classification accuracy of ARR, CHF, and NSR from ECG signals compared to a classical feature-based pipeline?
While TCNs deliver higher accuracy for ECG classification when computational budgets permit, well-engineered classical descriptors remain competitive for low-resource deployment.
Accurate and efficient automatic classification of electrocardiogram (ECG) signals is essential for scalable cardiac monitoring and timely clinical decision making. Herein, we focus on classifying Arrhythmia (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR). In particular, this study compares a classical feature-based pipeline that includes time-domain, frequency-domain, and time-scale features with a modern deep feature extractor employing the Temporal Convolutional Network (TCN). For this, MATLAB simulations were conducted on ECG signals from the MIT-BIH database to evaluate the classification performance and quantify practical trade-offs by reporting wall-clock training times, inference latency, and hardware requirements. The results showed that the Support Vector Machine (SVM) achieved classification accuracies of 88%, 91%, and 100% for ARR vs NSR, ARR vs CHF, and NSR vs CHF, respectively, when trained and tested on the classical features. On the other hand, the TCN achieved an overall accuracy of 99.4% to distinguish between the three classes. Furthermore, the TCN yielded superior raw predictive performance but offered reduced interpretability compared with the classical feature-based pipeline, at the expense of requiring substantially greater computational resources. These results demonstrated that well-engineered classical descriptors remain competitive for low-resource deployment, while data-driven TCNs deliver higher accuracy when computational budgets permit.
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El-Razek et al. (2026) studied Arrhythmia, Congestive Heart Failure, and Normal Sinus Rhythm. Temporal Convolutional Network (TCN) vs. Classical feature-based pipeline (Support Vector Machine) was evaluated on Classification accuracy. The Temporal Convolutional Network achieved an overall accuracy of 99.4% for classifying ECG signals, offering superior predictive performance but reduced interpretability compared to classical SVM.
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