Multi-Scale Signal-Image Fusion Model Based On ECoGfor Automatic Detection of Early-stage Traumatic Brain Injury

Spreading depolarizations (SDs) are key drivers of secondary brain injury, yet existing bedside monitoring methods that use electrocorticography (ECoG) analyze electrodes and frequency bands separately, thereby obscuring the joint spatiotemporal patterns of SDs. Therefore, this paper introduces a multi-scale signal-image fusion framework that for the first time enables SDmonitoring as a joint multi-modal multi-band spectral image-based analysis. The ECoG signal is converted into a persistent spectral de-weighted spectrogram (PSd-Spec) and joined with multi-band features, through Transformer-CNN jointly empowered blocks: Multi-Channel and Band Transformer Block (MCBTB) and Multi-Scale Adaptive Fusion (MSAF). The network extracts short- and long-range dynamics in a multi-scale time window, while an attention-driven channel weighting module adaptively models the spatial propagation of the electrode strips. On 500h of neuro-ICU recordings, the proposed approach achieved 92.6% accuracy, 84.9% sensitivity. Relative to the best single-modality base line, performance improved by at least 18%, and SD onset was identified on average of 8 min before expert observation. The results suggest that multi-scale fusion of spectral images with ECoG signals yields a clinically actionable early-warning approach and extends quantitative imaging methods to intracranial electrophysiology.