Why the study?
Existing automated ECG interpretation methods for CVD classification often rely on computationally complex hybrid or Transformer-based architectures and provide limited cross-dataset evaluation.
Does a demographic-assisted TCN-MLP deep learning framework improve automated cardiovascular disease classification accuracy from ECG signals compared to baseline models across multiple benchmark datasets?
Does a demographic-assisted TCN-MLP deep learning framework improve automated cardiovascular disease classification accuracy from ECG signals compared to baseline models across multiple benchmark datasets?
A Temporal Convolutional Network and Multi-Layer Perceptron (TCN-MLP) framework integrating age and gender metadata achieved 98.7% accuracy and 98.7% AUC-ROC for classifying six cardiac condition categories across three public ECG datasets.
Cardiovascular diseases (CVDs) remain a major global health burden, making accurate ECG-based classification important for automated cardiac assessment.Existing ECG interpretation methods often employ computationally complex hybrid or Transformer-based architectures and provide limited cross-dataset evaluation.To address these limitations, this study proposes a TCN-MLP framework that uses a Temporal Convolutional Network (TCN) with causal dilated convolutions and residual connections to extract temporal ECG representations, followed by a Multi-Layer Perceptron (MLP) for CVD classification.Age, gender, and their missingness indicators are incorporated as demographic metadata to examine their contribution to classification.The framework was evaluated on PTB-XL, MIT-BIH, and PhysioNet/CinC 2021 using a patient-independent 70% training, 10% validation, and 20% testing protocol, with additional five-fold cross-validation.The proposed framework achieved 98.7% accuracy, 98.5% precision, and 98.7% AUC-ROC, outperforming the evaluated baseline models.The results demonstrate effective demographic-assisted ECG classification and cross-dataset evaluation under retrospective patient-independent testing.
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Majeed et al. (2026) studied Cardiovascular diseases (Myocardial Infarction, ST/T Change, Conduction Disturbance, Hypertrophy, Arrhythmia) (n=109,848). Demographic-assisted Temporal Convolutional Network and Multi-Layer Perceptron (TCN-MLP) framework vs. Baseline deep learning models was evaluated on Multi-class cardiovascular disease classification accuracy on patient-independent test split.
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