Montage-guided fusion network for long-term scalp electroencephalography seizure detection

Abstract

Automatic seizure detection from long-term scalp electroencephalography (EEG) can reduce the burden of manual EEG review. However, multi-derivation EEG is commonly processed as a collection of input channels without explicitly modeling the structured relations defined by the bipolar montage. In this study, we propose MGF-Net, a montage-guided fusion network for seizure detection from long-term scalp EEG. MGF-Net represents 18 fixed bipolar derivations as nodes in a relation-labeled graph. The graph encodes same-chain adjacency, left-right correspondence, and shared-electrode relations derived from the bipolar montage. After epoch-wise robust normalization and wavelet-based signal reconstruction are applied independently to each epoch, a shared one-dimensional convolutional neural network extracts temporal features from each derivation. A relation-aware graph Transformer then performs feature fusion across derivations using relation-specific key and value projections. Attention-based graph pooling generates an epoch representation for ictal probability estimation, and post-processing converts epoch-wise probabilities into seizure-event detections. Experiments on the CHB-MIT dataset show that MGF-Net achieves a segment-level accuracy of 98.38%, sensitivity of 93.18%, and specificity of 98.41%. At the event level, the proposed method detected all 65 annotated test events, achieving a sensitivity of 100.00%, a false detection rate of 0.82/h, and a latency of 2.03 s. These results demonstrate MGF-Net's effectiveness for seizure detection from long-term scalp EEG.