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.

翻译：持续学习（CL）方法旨在从一系列任务中学习，同时避免遗忘先前知识的挑战。我们提出DREAM-CL，一种用于心电图心律失常检测的新型CL方法，引入了动态原型回放记忆。DREAM-CL通过基于每个训练阶段的学习行为对数据进行聚类来选择代表性原型。在每个聚类内，我们应用平滑排序操作，根据训练难度对样本进行排序，压缩极端值并移除异常值。随后选择更具挑战性的样本作为回放记忆的原型，确保跨阶段的有效知识保留。我们在时间增量、类别增量和导联增量三种场景下，使用两个广泛使用的心电图心律失常数据集（Chapman和PTB-XL）评估了我们的方法。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。我们进行了详细的消融和敏感性研究，以验证方法中不同设计选择的有效性。