动态原型回放用于连续心电图心律失常检测
Dynamic Prototype Rehearsal for Continual ECG Arrhythmia Detection
AI总结：
DREAM-CL通过动态原型回放机制提升连续心电图心律失常检测的持续学习性能
摘要：
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。我们提出了DREAM-CL，一种新的用于心电图心律失常检测的CL方法，引入了动态原型回放记忆。DREAM-CL通过基于每次训练会话中的学习行为对数据进行聚类来选择代表性原型。在每个聚类中，我们应用平滑排序操作，按训练难度对样本进行排序，压缩极端值并去除异常值。更具有挑战性的样本则被选为原型，用于回放记忆，以确保在不同会话中有效保留知识。我们使用两个广泛使用的心电图心律失常数据集，Chapman和PTB-XL，在时间递增、类别递增和导联递增场景中评估我们的方法。结果表明，DREAM-CL在心电图心律失常检测的CL中优于现有最先进方法。进行了详细的消融和敏感性研究，以验证我们方法的不同设计选择。
英文摘要：
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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