Predicting Deep Hypnotic State From Sleep Brain Rhythms Using Deep Learning:A Data-Repurposing Approach

BACKGROUND: Brain monitors tracking quantitative brain activities from electroencephalogram (EEG) to predict hypnotic levels have been proposed as a labor-saving alternative to behavioral assessments. Expensive clinical trials are required to validate any newly developed processed EEG monitor for every drug and combinations of drugs due to drug-specific EEG patterns. There is a need for an alternative, efficient, and economical method. METHODS: Using deep learning algorithms, we developed a novel data-repurposing framework to predict hypnotic levels from sleep brain rhythms. We used an online large sleep data set (5723 clinical EEGs) for training the deep learning algorithm and a clinical trial hypnotic data set (30 EEGs) for testing during dexmedetomidine infusion. Model performance was evaluated using accuracy and the area under the receiver operator characteristic curve (AUC). RESULTS: The deep learning model (a combination of a convolutional neural network and long short-term memory units) trained on sleep EEG predicted deep hypnotic level with an accuracy (95% confidence interval [CI]) = 81 (79.2-88.3)%, AUC (95% CI) = 0.89 (0.82-0.94) using dexmedetomidine as a prototype drug. We also demonstrate that EEG patterns during dexmedetomidine-induced deep hypnotic level are homologous to nonrapid eye movement stage 3 EEG sleep. CONCLUSIONS: We propose a novel method to develop hypnotic level monitors using large sleep EEG data, deep learning, and a data-repurposing approach, and for optimizing such a system for monitoring any given individual. We provide a novel data-repurposing framework to predict hypnosis levels using sleep EEG, eliminating the need for new clinical trials to develop hypnosis level monitors

Predicting Ordinal Level of Sedation from the Spectrogram of Electroencephalography

In Intensive Care Unit, the sedation level of patients is usually monitored by periodically assessing the behavioral response to stimuli. However, these clinical assessments are limited due to the disruption with patients' sleep and the noise of observing behaviors instead of the brain activity directly. Here we train a Gated Recurrent Unit using the spectrogram of electroencephalography (EEG) based on 166 mechanically ventilated patients to predict the Richmond Agitation-Sedation Score, scored as ordinal levels of -5, -4, ... up to 0. The model is able to predict 50% accurate with an error not larger than 1 level; and 80% accurate with an error not larger than 2 levels on hold-out testing patients. We show typical spectrograms in each sedation level and interpret the results based on the visualization of the gradient with respect to the spectrogram. Future improvements include utilizing the EEG waveforms since waveform patterns are clinically thought to be associated with sedation levels, as well as training patient-specific models