A Diffusion Model and Few-Shot Learning Framework for EEG Abnormality Classification and Cerebral Infarction Assessment

Deep learning has shown great potential in assisting medical experts in diagnosing cerebral infarction through electroencephalogram (EEG) analysis. Classifying EEG abnormality levels is a critical component of this process. However, the inherent complexity of raw EEG signals makes reliable feature extraction challenging, while the scarcity of severe cases leads to substantial class imbalance. The limited size of available datasets further exacerbates these issues, as excessive dependence on generative data may cause classifier overfitting. To address these challenges, we propose a novel framework that integrates a diffusion-based generative model with a hybrid embedding-based few-shot learning strategy for EEG abnormality classification. The framework first extracts a comprehensive set of entropy-based and statistical features from EEG waveforms. The diffusion model incorporates class labels into the noise prediction mechanism, embedding label information into the timestep representation to generate high-fidelity, class-consistent synthetic feature vectors. The hybrid embedding-based few-shot classifier is trained using an episodic learning strategy that optimizes latent-space feature distances under limited data. During inference, the classifier employs a two-step decision procedure in which a preclassifier first estimates the two most probable candidate classes. The prototypical module then performs refined metric-based discrimination within this reduced candidate space, which improves both robustness and generalization. In addition, combining EEG abnormality levels with quantitative clinical scale scores enables more rapid and reliable auxiliary assessment of cerebral infarction. Experimental results demonstrate that the proposed framework yields substantial improvements in EEG abnormality classification accuracy and provides meaningful support for clinical decision-making.