Sparse knowledge sharing (SKS) for privacy-preserving domain incremental seizure detection

作者：Jiayu An, Ruimin Peng, Zhenbang Du, Heng Liu, Feng Hu, Kai Shu, Dongrui Wu · 发表于：Journal of Neural Engineering · 年份：2025 · DOI：10.1088/1741-2552/adb998 · 被引用次数：4 · 研究领域：EEG and Brain-Computer Interfaces、Brain Tumor Detection and Classification、Advanced Neural Network Applications

Abstract Objective . Epilepsy is a neurological disorder that affects millions of patients worldwide. Electroencephalogram-based seizure detection plays a crucial role in its timely diagnosis and effective monitoring. However, due to distribution shifts in patient data, existing seizure detection approaches are often patient-specific, which requires customized models for different patients. This paper considers privacy-preserving domain incremental learning (PP-DIL), where the model learns sequentially from each domain (patient) while only accessing the current domain data and previously trained models. This scenario has three main challenges: (1) catastrophic forgetting of previous domains, (2) privacy protection of previous domains, and (3) distribution shifts among domains. Approach . We propose a sparse knowledge sharing (SKS) approach. First, Euclidean alignment is employed to align data from different domains. Then, we propose an adaptive pruning approach for SKS to allocate subnet for each domain adaptively, allowing specific parameters to learn domain-specific knowledge while shared parameters to preserve knowledge from previous domains. Additionally, supervised contrastive learning is employed to enhance the model’s ability to distinguish relevant features. Main Results . Experiments on two public seizure datasets demonstrated that SKS achieved superior performance in PP-DIL. Significance . SKS is a rehearsal-free privacy-preserving approach that effectively learns n...