动态选择难样本作为原型，提升心电图心律失常检测的持续学习能力。
Dynamic Prototype Rehearsal for Continual ECG Arrhythmia Detection
- 基于学习行为聚类，动态筛选难样本作为记忆原型。
- 在Chapman和PTB-XL数据集上优于现有最先进方法。
- 适合长期心电监测中需持续更新模型的医疗场景。
持续学习（CL）旨在从一系列任务中学习，同时避免遗忘旧知识。我们提出DREAM-CL，一种用于心电图心律失常检测的新颖持续学习方法，引入动态原型重放记忆。DREAM-CL通过聚类训练过程中的学习行为，选择代表性原型。在每个簇内，采用平滑排序操作按训练难度对样本排序，压缩极端值并剔除异常值。更具挑战性的样本被选为重放记忆中的原型，以确保跨会话的有效知识保留。我们在时间增量、类别增量和导联增量三种场景下，使用Chapman和PTB-XL两个广泛使用的心电图心律失常数据集评估该方法。结果表明，DREAM-CL在心电图心律失常检测的持续学习中优于现有最先进方法。通过详细的消融实验和敏感性分析，验证了方法各项设计选择的有效性。
原文摘要 · Abstract (English)
Continual Learning (CL) methods aim to learn from a sequence of tasks while avoiding the challenge of forgetting previous knowledge. We present DREAM-CL, a novel CL method for ECG arrhythmia detection that introduces dynamic prototype rehearsal memory. DREAM-CL selects representative prototypes by clustering data based on learning behavior during each training session. Within each cluster, we apply a smooth sorting operation that ranks samples by training difficulty, compressing extreme values and removing outliers. The more challenging samples are then chosen as prototypes for the rehearsal memory, ensuring effective knowledge retention across sessions. We evaluate our method on time-incremental, class-incremental, and lead-incremental scenarios using two widely used ECG arrhythmia datasets, Chapman and PTB-XL. The results demonstrate that DREAM-CL outperforms the state-of-the-art in CL for ECG arrhythmia detection. Detailed ablation and sensitivity studies are performed to validate the different design choices of our method.
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