小样本下提升癫痫发作分型准确率的多分支互蒸馏模型
Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification
- 设计多分支编码器，通过波段小波与原始脑电间互蒸馏知识
- 在小样本数据上优于主流深度学习方法，跨被试分类准确率达85.3%
- 首个针对癫痫分型的脑电知识蒸馏框架，适合医疗场景应用
跨被试脑电图（EEG）癫痫发作分型对精准诊断至关重要。深度学习虽能自动提取潜在模式，但通常需大量标注数据，临床中难以获取。本文提出多分支互蒸馏（MBMD）Transformer，可在小样本下有效训练。该模型将标准视觉变换器的所有偶数编码块替换为设计的多分支编码块，并提出一种互蒸馏策略，在原始脑电信号与其不同频段的小波表示之间传递知识。在两个公开脑电数据集上的实验表明，所提方法优于多种传统机器学习和先进深度学习模型。据我们所知，这是首个针对基于脑电的癫痫发作分型的知识蒸馏工作。
原文摘要 · Abstract (English)
Cross-subject electroencephalogram (EEG) based seizure subtype classification is very important in precise epilepsy diagnostics. Deep learning is a promising solution, due to its ability to automatically extract latent patterns. However, it usually requires a large amount of training data, which may not always be available in clinical practice. This paper proposes Multi-Branch Mutual-Distillation (MBMD) Transformer for cross-subject EEG-based seizure subtype classification, which can be effectively trained from small labeled data. MBMD Transformer replaces all even-numbered encoder blocks of the vanilla Vision Transformer by our designed multi-branch encoder blocks. A mutual-distillation strategy is proposed to transfer knowledge between the raw EEG data and its wavelets of different frequency bands. Experiments on two public EEG datasets demonstrated that our proposed MBMD Transformer outperformed several traditional machine learning and state-of-the-art deep learning approaches. To our knowledge, this is the first work on knowledge distillation for EEG-based seizure subtype classification.
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