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A core aim of neurocritical care is to prevent secondary brain injury.Spreading depolarizations (SDs) have been identified as an importantindependent cause of secondary brain injury. SDs are usually detected usinginvasive electrocorticography recorded at high sampling frequency. Recent pilotstudies suggest a possible utility of scalp electrodes generatedelectroencephalogram (EEG) for non-invasive SD detection. However, noise andattenuation of EEG signals makes this detection task extremely challenging.Previous methods focus on detecting temporal power change of EEG over a fixedhigh-density map of scalp electrodes, which is not always clinically feasible.Having a specialized spectrogram as an input to the automatic SD detectionmodel, this study is the first to transform SD identification problem from adetection task on a 1-D time-series wave to a task on a sequential 2-D renderedimaging. This study presented a novel ultra-light-weight multi-modaldeep-learning network to fuse EEG spectrogram imaging and temporal powervectors to enhance SD identification accuracy over each single electrode,allowing flexible EEG map and paving the way for SD detection onultra-low-density EEG with variable electrode positioning. Our proposed modelhas an ultra-fast processing speed (<0.3 sec). Compared to the conventionalmethods (2 hours), this is a huge advancement towards early SD detection and tofacilitate instant brain injury prognosis. Seeing SDs with a new dimension -frequency on spectrograms, we demonstrated that such additional dimension couldimprove SD detection accuracy, providing preliminary evidence to support thehypothesis that SDs may show implicit features over the frequency profile.

Real-Time Non-Invasive Imaging and Detection of Spreading Depolarizations through EEG: An Ultra-Light Explainable Deep Learning Approach