UCTECG-Net: Uncertainty-aware Convolution Transformer ECG Network for Arrhythmia Detection
This addresses the need for uncertainty-aware models in safety-critical medical settings like ECG analysis, though it is incremental as it combines existing methods.
The paper tackled the problem of limited insight into prediction reliability in automated ECG classification for arrhythmia detection, proposing UCTECG-Net, which achieved up to 98.58% accuracy on MIT-BIH and 99.14% on PTB datasets, outperforming baselines and providing more reliable uncertainty estimates.
Deep learning has improved automated electrocardiogram (ECG) classification, but limited insight into prediction reliability hinders its use in safety-critical settings. This paper proposes UCTECG-Net, an uncertainty-aware hybrid architecture that combines one-dimensional convolutions and Transformer encoders to process raw ECG signals and their spectrograms jointly. Evaluated on the MIT-BIH Arrhythmia and PTB Diagnostic datasets, UCTECG-Net outperforms LSTM, CNN1D, and Transformer baselines in terms of accuracy, precision, recall and F1 score, achieving up to 98.58% accuracy on MIT-BIH and 99.14% on PTB. To assess predictive reliability, we integrate three uncertainty quantification methods (Monte Carlo Dropout, Deep Ensembles, and Ensemble Monte Carlo Dropout) into all models and analyze their behavior using an uncertainty-aware confusion matrix and derived metrics. The results show that UCTECG-Net, particularly with Ensemble or EMCD, provides more reliable and better-aligned uncertainty estimates than competing architectures, offering a stronger basis for risk-aware ECG decision support.