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

Abstract

Because anesthetic drugs exhibit sleep-like patterns during deep hypnosis, can we predict hypnosis level from sleep brain rhythms? • Findings: Deep learning algorithms when trained on nonrapid eye movement stage 3 sleep electroencephalogram can predict dexmedetomidine-induced deep hypnotic level. • Meaning: Anesthetic-induced hypnosis levels can be predicted using sleep electroencephalogram and artificial intelligence techniques, eliminating the need for clinical trials to develop hypnotic level monitors. 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.

Analysis of the Clinical Effect of Music Combined with Hypnosis on Labor Analgesia Based on Data Mining

“…Hypnotic analgesia is effective in childbirth, headache, etc. e key is not the type of pain, but the different hypnotic methods used according to the pain [6,7]. Pain will cause some stress reactions, such as fear, anxiety, and irritability; these reactions stimulate the sympathetic nerve to make it excited, and maternal body will release catecholamine, causing uterine vasoconstriction, which is not conducive to childbirth.…”

An Open Source Patient Simulator for Design and Evaluation of Computer Based Multiple Drug Dosing Control for Anesthetic and Hemodynamic Variables

“…Recently, a novel PKPD model for Propofol has been proposed in [62], yet to be seen if applicable for optimizing closed loop control objectives. Furthermore, new evidence that brain activity modulates differently to noxious stimuli than to hypnotic states [63], enable artificial intelligence tools to predict sedation state of patients [64], [65]. The management of anesthesia moves from single drug to multi-drug co-administration, making small but essential steps forward towards a fully computerized regulatory paradigm of personalised patient services.…”

Artificial intelligence and anesthesia: A narrative review

“…Research in AI may eliminate the need to perform clinical trials on hypnosis level monitors by the use of deep learning models. [ 43 ]…”