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