Predicting Deep Hypnotic State From Sleep Brain Rhythms Using Deep Learning
| Autor: | Michel Struys, Maud A S Weerink, Sowmya M. Ramaswamy, Sunil B. Nagaraj |
|---|---|
| Přispěvatelé: | Critical care, Anesthesiology, Peri-operative and Emergency medicine (CAPE) |
| Rok vydání: | 2020 |
| Předmět: |
Adult
Data Analysis Male Hypnosis medicine.medical_specialty medicine.drug_class Electroencephalography Audiology Convolutional neural network Hypnotic 03 medical and health sciences Deep Learning 0302 clinical medicine Brain Waves/drug effects Predictive Value of Tests 030202 anesthesiology Hypnotics and Sedatives/administration & dosage medicine Hypnotics and Sedatives Humans Electroencephalography/drug effects Original Clinical Research Report Aged Brain/drug effects Receiver operating characteristic medicine.diagnostic_test Featured Articles business.industry Deep learning Brain Eye movement Middle Aged Brain Waves Anesthesiology and Pain Medicine Sleep/drug effects Dexmedetomidine/administration & dosage Female Sleep (system call) Artificial intelligence Sleep business Dexmedetomidine 030217 neurology & neurosurgery |
| Zdroj: | Anesthesia and Analgesia, 130(5), 1211-1221. LIPPINCOTT WILLIAMS & WILKINS Anesthesia and Analgesia |
| ISSN: | 0003-2999 |
| DOI: | 10.1213/ane.0000000000004651 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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