Dexmedetomidine-induced deep sedation mimics non-rapid eye movement stage 3 sleep: large-scale validation using machine learning

Abstract

Study objectives: Dexmedetomidine-induced electroencephalogram (EEG) patterns during deep sedation are comparable with natural sleep patterns. Using large-scale EEG recordings and machine learning techniques, we investigated whether dexmedetomidine-induced deep sedation indeed mimics natural sleep patterns.

Methods: We used EEG recordings from three sources in this study: 8,707 overnight sleep EEG and 30 dexmedetomidine clinical trial EEG. Dexmedetomidine-induced sedation levels were assessed using the Modified Observer's Assessment of Alertness/Sedation (MOAA/S) score. We extracted 22 spectral features from each EEG recording using a multitaper spectral estimation method. Elastic-net regularization method was used for feature selection. We compared the performance of several machine learning algorithms (logistic regression, support vector machine, and random forest), trained on individual sleep stages, to predict different levels of the MOAA/S sedation state.

Results: The random forest algorithm trained on non-rapid eye movement stage 3 (N3) predicted dexmedetomidine-induced deep sedation (MOAA/S = 0) with area under the receiver operator characteristics curve >0.8 outperforming other machine learning models. Power in the delta band (0-4 Hz) was selected as an important feature for prediction in addition to power in theta (4-8 Hz) and beta (16-30 Hz) bands.

Conclusions: Using a large-scale EEG data-driven approach and machine learning framework, we show that dexmedetomidine-induced deep sedation state mimics N3 sleep EEG patterns.

Clinical trials: Name-Pharmacodynamic Interaction of REMI and DMED (PIRAD), URL-https://ichgcp.net/clinical-trials-registry/NCT03143972, and registration-NCT03143972.

Keywords: dexmedetomidine; electroencephalogram; machine learning; sedation monitoring; sleep.

© Sleep Research Society 2020. Published by Oxford University Press on behalf of the Sleep Research Society.

Figures

Figure 1.

Example comparison of the multitaper EEG power spectrogram and their corresponding annotations from the SHHS and UMCG datasets. The spectrogram was obtained using multitaper spectral estimation via the chronux toolbox with the following parameters: time–bandwidth product TW = 3, window length T = 4 s (with 3.9 s overlap), number of tapers K = 5, and spectral resolution 2W of 1.5 Hz.

Figure 2.

Distribution of (A) 30 s epochs in different sleep stages in SHHS (outer ring), MrOS (inner ring), (B) 30 s epochs in different levels of sedation (MOAA/S scores) in the UMCG datasets used in this study.

Figure 3.

Illustration of the (A) proposed validation model used in this study and (B) different training and testing combinations performed in this study. Here, the machine learning model is trained in individual sleep states to predict different levels of sedation induced during dexmedetomidine infusion.

Figure 4.

Heatmap of features selected by the EN regularization algorithm during the different training process. Here, the features selected by the EN algorithm are represented by the dark blue color. Different features were selected in (A) SHHS and (B) MrOS datasets. The power in the spindle band (normalized and un-normalized) was not selected as an important feature in both datasets.

Figure 5.

Prediction performance of individual features to differentiate between wake (W) and individual sleep stages in SHHS (red) and MrOS (blue) datasets; between awake (MOAA/S = 5) and individual sedated states in UMCG (blue) datasets. The prediction performance of power in the spindle band (normalized and un-normalized) to differentiate between awake and different sedation levels was poor (AUCs ≤0.6) in dexmedetomidine when compared with other spectral features. N1 = features tested to differentiate between wake and NREM stage 1 sleep state; N2 = features tested to differentiate between wake and NREM stage 2 sleep state; N3 = features tested to differentiate between wake and NREM stage 3 sleep state; R = features tested to differentiate between wake and rapid eye movement sleep state; M4 = features tested to differentiate between awake (MOAA/S score 5) and MOAA/S score 4; M3 = features tested to differentiate between awake (MOAA/S score 5) and MOAA/S score 3; M2 = features tested to differentiate between awake (MOAA/S score 5) and MOAA/S score 2; M1 = features tested to differentiate between awake (MOAA/S score 5) and MOAA/S score 1; M0 = features tested to differentiate between awake (MOAA/S score 5) and MOAA/S score 0.

Figure 6.

Comparison of EEG power spectrogram from three participants during N3 sleep state in SHHS, MrOS and deep sedated state (MOAA/S = 0) in UMCG datasets. Here, the multitaper spectrogram was obtained using all 30 s EEG epochs from N3 and deep sedated state. Large variability can be seen in delta (0–4 Hz) and spindle (12–16 Hz) spectral regions in all three datasets. This suggests that the morphology of spindles seen during the biological NREM stage 3 sleep is different from spindles seen during dexmedetomidine-induced deep sedation. N3 = NREM stage 3 sleep state.