提出可评估置信度的ECG心律失常检测模型，提升医疗场景可靠性。
UCTECG-Net: Uncertainty-aware Convolution Transformer ECG Network for Arrhythmia Detection
- 融合一维卷积与Transformer，联合处理原始心电图和频谱图
- 在MIT-BIH和PTB数据集上准确率达98.58%和99.14%
- 通过三种不确定性量化方法，实现更可靠的预测置信度评估
深度学习提升了心电图（ECG）自动分类性能，但对预测可靠性的理解不足限制了其在高风险场景的应用。本文提出UCTECG-Net，一种融合一维卷积与Transformer编码器的不确定性感知混合架构，用于联合处理原始ECG信号及其频谱图。在MIT-BIH Arrhythmia和PTB Diagnostic数据集上，该模型在准确率、精确率、召回率和F1分数上均优于LSTM、CNN1D和Transformer基线模型，最大准确率分别达到98.58%（MIT-BIH）和99.14%（PTB）。为评估预测可靠性，我们在所有模型中集成三种不确定性量化方法（蒙特卡洛丢弃、深度集成、集成蒙特卡洛丢弃），并通过不确定性感知混淆矩阵及衍生指标分析其表现。结果表明，特别是采用深度集成或集成蒙特卡洛丢弃时，UCTECG-Net能提供更可靠且与真实情况更一致的不确定性估计，为风险敏感的心电决策支持提供更强依据。
原文摘要 · Abstract (English)
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.
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