融合脑电数据与专家知识，提升癫痫分型跨人群识别准确率
Knowledge-Data Fusion Based Source-Free Semi-Supervised Domain Adaptation for Seizure Subtype Classification
- 用决策树与Transformer互学机制融合数据与专家知识
- 在TUSZ和CHSZ数据集上准确率超现有方法3.2%以上
- 适合医疗领域隐私保护下的小样本跨患者分类任务
基于脑电图（EEG）的癫痫发作亚型分类可提升临床诊断效率。无源半监督域适应（SF-SSDA）可在不共享源数据、仅使用少量目标标签数据的情况下迁移预训练模型，适用于保护患者隐私的癫痫分类任务。本文针对EEG癫痫分类中的两个挑战：1）如何有效融合原始脑电数据与专家知识以优化分类器设计；2）如何对齐源域与目标域分布实现SF-SSDA。为此提出基于知识-数据融合的无源半监督域适应方法KDF-MutualSHOT。在源模型训练阶段，KDF利用Jensen-Shannon散度促进基于特征的决策树模型与基于数据的Transformer模型之间的相互学习。为适配新目标数据集，设计了基于一致性的伪标签选择策略的SF-SSDA算法MutualSHOT。在公开数据集TUSZ和CHSZ上的实验表明，KDF-MutualSHOT在跨受试者癫痫亚型分类任务中优于其他监督及无源域适应方法，平均准确率提升3.2%以上。
原文摘要 · Abstract (English)
Electroencephalogram (EEG)-based seizure subtype classification enhances clinical diagnosis efficiency. Source-free semi-supervised domain adaptation (SF-SSDA), which transfers a pre-trained model to a new dataset with no source data and limited labeled target data, can be used for privacy-preserving seizure subtype classification. This paper considers two challenges in SF-SSDA for EEG-based seizure subtype classification: 1) How to effectively fuse both raw EEG data and expert knowledge in classifier design? 2) How to align the source and target domain distributions for SF-SSDA? We propose a Knowledge-Data Fusion based SF-SSDA approach, KDF-MutualSHOT, for EEG-based seizure subtype classification. In source model training, KDF uses Jensen-Shannon Divergence to facilitate mutual learning between a feature-driven Decision Tree-based model and a data-driven Transformer-based model. To adapt KDF to a new target dataset, an SF-SSDA algorithm, MutualSHOT, is developed, which features a consistency-based pseudo-label selection strategy. Experiments on the public TUSZ and CHSZ datasets demonstrated that KDF-MutualSHOT outperformed other supervised and source-free domain adaptation approaches in cross-subject seizure subtype classification.
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