CL4CEA: A Clinical-Knowledge-Informed Augmentation for Contrastive Learning on Childhood Epilepsy Analysis

Although existing contrastive learning models utilizing conventional data augmentation achieve modest performance in EEG-based epilepsy analysis, such models risk disrupting EEG semantic consistency during pre-training and compromise the robustness of downstream tasks. To this end, we propose a clinical-knowledge-informed augmentation for contrastive learning on childhood epilepsy analysis (CL4CEA) in this paper. Our method comprises two core components: firstly, inspired by clinical knowledge, we introduce a montage conversion-based plug-and-play augmentation strategy for preserving EEG semantic consistency in contrastive learning. Secondly, a channel-aware adaptive fusion block is employed to integrate features from both temporal and frequency domains, enabling the model to capture discriminative representations of childhood EEG signals, and thus enhancing performance in downstream tasks. Through pre-training on an EEG dataset with more than 1,000 hours childhood EEG recording, and performance fine-tuning, the developed CL4CEA model can achieve promising performance on 3 downstream tasks from 3 medical centers in childhood epilepsy analysis, including onset detection, seizure type classification, and hypoxic-ischaemic encephalopathy (HIE) grading. Comparative experiments with state-of-the-art methods and systematic ablation studies demonstrate the superiority of our proposed model.