Dual-Cross Tri-Level Routing Transformer Based Metric Learning Network for Epileptic Seizure Prediction Using a Single-Channel iEEG.

Abstract

With the development of deep brain stimulation technique, single-channel intracranial electroencephalography (iEEG) based seizure prediction is a necessary and urgent needed tool for epilepsy closed-loop neuromodulation. However, previous prediction methods based on multi-channel scalp signals heavily relied on the spatial information, failing to fully exploit the interdependencies between temporal scales and spectral rhythms of single-channel iEEG. Additionally, current contrastive learning strategies can lead to model overfitting by excessively learning the feature distances in small samples, limiting the precision of seizure prediction. To tackle above issues, based on a single-channel iEEG, we propose a novel dual-cross tri-level routing transformer based metric learning network (DC-TRT-MLNet) for epileptic seizure prediction. First, a scale-rhythm dual-cross (DC) graph attention network is introduced to construct the dependent relationships across multi-scale temporal and multi-rhythm spectral features. Second, we design a tri-level routing transformer (TRT) network to comprehensively refine the most seizure-potential routing features while eliminating redundant information. Finally, a hard triplet optimization based metric learning (ML) strategy is developed to iteratively optimize the intra-class and inter-class distances of inter-ictal and pre-ictal routing features. Competitive experimental results on a private Xuanwu Single-Channel iEEG dataset validate the effectiveness of our proposed method, demonstrating the superior prediction performance of our DC-TRT-MLNet compared with the state-of-the-art methods. Our study may offer a new solution for intracranial single-channel seizure prediction.