PDF(Pubmed)
Abstract：
BACKGROUND: Sedatives are commonly used to promote sleep in intensive care unit patients. However, it is not clear whether sedation-induced states are similar to the biological sleep. We explored if sedative-induced states resemble biological sleep using multichannel electroencephalogram (EEG) recordings.
METHODS: Multichannel EEG datasets from two different sources were used in this study: (1) sedation dataset consisting of 102 healthy volunteers receiving propofol (N = 36), sevoflurane (N = 36), or dexmedetomidine (N = 30), and (2) publicly available sleep EEG dataset (N = 994). Forty-four quantitative time, frequency and entropy features were extracted from EEG recordings and were used to train the machine learning algorithms on sleep dataset to predict sleep stages in the sedation dataset. The predicted sleep states were then compared with the Modified Observer\'s Assessment of Alertness/ Sedation (MOAA/S) scores.
RESULTS: The performance of the model was poor (AUC = 0.55-0.58) in differentiating sleep stages during propofol and sevoflurane sedation. In the case of dexmedetomidine, the AUC of the model increased in a sedation-dependent manner with NREM stages 2 and 3 highly correlating with deep sedation state reaching an AUC of 0.80.
CONCLUSIONS: We addressed an important clinical question to identify biological sleep promoting sedatives using EEG signals. We demonstrate that propofol and sevoflurane do not promote EEG patterns resembling natural sleep while dexmedetomidine promotes states resembling NREM stages 2 and 3 sleep, based on current sleep staging standards.
METHODS: Multichannel EEG datasets from two different sources were used in this study: (1) sedation dataset consisting of 102 healthy volunteers receiving propofol (N = 36), sevoflurane (N = 36), or dexmedetomidine (N = 30), and (2) publicly available sleep EEG dataset (N = 994). Forty-four quantitative time, frequency and entropy features were extracted from EEG recordings and were used to train the machine learning algorithms on sleep dataset to predict sleep stages in the sedation dataset. The predicted sleep states were then compared with the Modified Observer\'s Assessment of Alertness/ Sedation (MOAA/S) scores.
RESULTS: The performance of the model was poor (AUC = 0.55-0.58) in differentiating sleep stages during propofol and sevoflurane sedation. In the case of dexmedetomidine, the AUC of the model increased in a sedation-dependent manner with NREM stages 2 and 3 highly correlating with deep sedation state reaching an AUC of 0.80.
CONCLUSIONS: We addressed an important clinical question to identify biological sleep promoting sedatives using EEG signals. We demonstrate that propofol and sevoflurane do not promote EEG patterns resembling natural sleep while dexmedetomidine promotes states resembling NREM stages 2 and 3 sleep, based on current sleep staging standards.
摘要：
背景：镇静剂通常用于促进重症监护病房患者的睡眠。然而,目前尚不清楚镇静诱导的状态是否与生物睡眠相似。我们使用多通道脑电图（EEG）记录探索了镇静诱导的状态是否类似于生物睡眠。
方法：本研究使用来自两个不同来源的多通道脑电图数据集：（1）由102名接受异丙酚的健康志愿者（N=36）组成的镇静数据集，七氟醚（N=36），或右美托咪定（N=30），和(2)公开可用的睡眠EEG数据集(N=994)。四十四个定量时间，从脑电图记录中提取频率和熵特征，并将其用于在睡眠数据集上训练机器学习算法，以预测镇静数据集中的睡眠阶段。然后将预测的睡眠状态与改良观察者的警觉/镇静评估(MOAA/S)评分进行比较。
结果：该模型在区分异丙酚和七氟醚镇静期间的睡眠阶段方面的表现较差（AUC=0.55-0.58）。在右美托咪定的情况下，模型的AUC以镇静依赖的方式增加,其中NREM阶段2和3与深度镇静状态高度相关,AUC达到0.80.
结论：我们解决了一个重要的临床问题，以使用EEG信号识别生物睡眠促进镇静剂。我们证明异丙酚和七氟醚不促进类似于自然睡眠的脑电图模式，而右美托咪定促进类似于NREM阶段2和3睡眠的状态。基于当前的睡眠分期标准。
方法：本研究使用来自两个不同来源的多通道脑电图数据集：（1）由102名接受异丙酚的健康志愿者（N=36）组成的镇静数据集，七氟醚（N=36），或右美托咪定（N=30），和(2)公开可用的睡眠EEG数据集(N=994)。四十四个定量时间，从脑电图记录中提取频率和熵特征，并将其用于在睡眠数据集上训练机器学习算法，以预测镇静数据集中的睡眠阶段。然后将预测的睡眠状态与改良观察者的警觉/镇静评估(MOAA/S)评分进行比较。
结果：该模型在区分异丙酚和七氟醚镇静期间的睡眠阶段方面的表现较差（AUC=0.55-0.58）。在右美托咪定的情况下，模型的AUC以镇静依赖的方式增加,其中NREM阶段2和3与深度镇静状态高度相关,AUC达到0.80.
结论：我们解决了一个重要的临床问题，以使用EEG信号识别生物睡眠促进镇静剂。我们证明异丙酚和七氟醚不促进类似于自然睡眠的脑电图模式，而右美托咪定促进类似于NREM阶段2和3睡眠的状态。基于当前的睡眠分期标准。
