Application of deep learning algorithms to neural function assessment following cardiac arrest

Accurate assessment of neural function for individuals in a coma following cardiac arrest is challenging and currently relies on subjective scoring of physiological signals.
Using convolutional neural networks to model EEG responses to standardized auditory stimuli demonstrated positive predictive power when predicting awakening for both individuals undergoing therapeutic hyperthermia and normothermia (0.83 ±0.04 and 0.81 ±0.05, respectively).
The authors concluded that deep learning algorithms such as convolutional neural networks, in combination with auditory stimulation, have potential to standardize neural function assessment and likelihood of awakening from coma.