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

Abstract

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.

Trial registration: ClinicalTrials.gov NCT03143972.

Conflict of interest statement

Conflicts of Interest: See Disclosures at the end of the article.

Figures

Figure 1.

Sample dexmedetomidine data. Illustration of (A) 15 s sample EEG at minute 5 and minute 108, (B) C4/A1 channel EEG spectrogram, and (C) MOAA/S score of a subject from UMCG data set and red-dotted line shows target-controlled infusion of dexmedetomidine in nanogram per milliliter. We can see the presence of spindle waves with an increase in the level of hypnosis. The following values were set to perform spectral estimation using multitaper spectral estimation via the chronux toolbox: length of the window T = 4 s with 0.1 s shift, time-bandwidth product TW = 3, number of tapers K = 5, and spectral resolution 2 W of 1.5 Hz. EEG indicates electroencephalogram; MOAA/S, Modified Observer’s Assessment of Alertness/Sedation Scale; TW, time-bandwidth product.

Figure 2.

The architecture of the LSTM-CNN model used in this study. The length of the input 1D EEG segment is 125 (samples) × 30 (seconds). The output of the model provides a probability score of a given EEG segment belonging to deep hypnotic state. “x4” refers to number of layers of residual network. In this architecture there are 4 + 4 + 4 = 12 layers of residual network. 1D indicates 1-dimensional; CNN, convolutional neural networks; EEG, electroencephalogram; LSTM, long short-term memory; ReLU, rectified linear unit.

Figure 3.

Illustration of the training testing experiment performed in this study. Because there are 4 sleep stages (N1, N2, N3, R) and a wake stage (W), we trained 4 separate DL models for binary classification: WN1, trained on W and N1; WN2, trained on W and N2; WN3, trained on W and N3; and WR, trained on W and R. Each model was then used to differentiate between awake (MOAA/S = 5) and individual dexmedetomidine-induced hypnotic levels. For example, WN1 was used to differentiate between MOAA/S = 5 and 4 (M54), MOAA/S = 5 and 3 (M53), and so on until MOAA/S = 5 and 0 (M50) to estimate the probability of hypnosis YpYp. This process was repeated until all sleep stage DL models were used for predicting hypnosis levels. DL indicates deep learning; MOAA/S, Modified Observer’s Assessment of Alertness/Sedation Scale; UMCG, University Medical Center Groningen.

Figure 4.

Hypnosis level prediction output. A, Illustration of mapping discrete MOAA/S score onto a continuous probability score via sigmoid transformation. Here probability score = 0 and 1 correspond to awake and deep hypnotic state, respectively. B, Illustration of correlation (ρ = 0.53 in this example) between the probability score predicted by the DL model (blue) and MOAA/S scores (red), and (C) box plot comparing the distribution of predicted probability scores across all MOAA/S scores. Here the probability score is obtained by the WN3 LSTM-CNN model tested on all MOAA/S scores. The predicted probability score tends toward zero with increase in level of consciousness. Here the DL model is trained on wake and NREM stage 3 EEG segments and is used to predict all levels of MOAA/S scores (MOAA/S 0, 1, 2, 3, 4, 5) to obtain continuous levels of hypnosis. CNN indicates convolutional neural networks; DL, deep learning; EEG, electroencephalogram; LSTM, long short-term memory; MOAA/S, Modified Observer’s Assessment of Alertness/Sedation Scale; NREM, nonrapid eye movement.

Figure 5.

Spectrogram comparison of deep hypnosis and N3 sleep stage. Comparison of 5-min EEG power spectrogram from 4 subjects during (A) N3 sleep state in SHHS and (B) dexmedetomidine deep hypnotic state in UMCG. We can clearly see large variability in the slow-wave delta band (0–4 Hz) and spindle band (11–16 Hz) across subjects in both SHHS and UMCG data set. The following values were set to perform spectral estimation using multitaper spectral estimation via the chronux toolbox: length of the window T = 4 s with 0.1 s shift, time-bandwidth product TW = 3, number of tapers K = 5, and spectral resolution 2 W of 1.5 Hz. EEG indicates electroencephalogram; SHHS, Sleep Heart Health Study; TW, time-bandwidth product; UMCG, University Medical Center Groningen.