An automated sleep-state classification algorithm for quantifying sleep timing and sleep-dependent dynamics of electroencephalographic and cerebral metabolic parameters.
An automated sleep-state classification algorithm for quantifying sleep timing and sleep-dependent dynamics of electroencephalographic and cerebral metabolic parameters.
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DOI：
10.2147/nss.s84548
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发表时间：
2015
影响因子：
3.4
通讯作者：
Wisor JP
中科院分区：
文献类型：
--
作者：
Rempe MJ;Clegern WC;Wisor JP
啮齿动物睡眠研究使用脑电图（EEG）和肌电图（EMG）来确定动物在任何给定时间的睡眠状态。脑电图和肌电图信号，通常在bb0 - 100hz采样，被任意分割成等持续时间的时间段（通常为2-10秒），每个时间段在视觉检查的基础上被分为清醒、慢波睡眠（SWS）或快速眼动睡眠（REMS）。自动状态评分可以最大限度地减少与状态相关的负担，从而促进使用更短的epoch持续时间。我们开发了一种半自动状态评分程序，该程序使用主成分分析和naïve贝叶斯分类的组合，以脑电图和肌电图作为输入。我们用C57BL/6J和BALB/CJ小鼠的睡眠状态评分数据验证了该算法。然后，我们应用了一个通用的稳态模型来表征睡眠慢波活动和脑糖酵解通量的状态依赖动力学，以乳酸浓度测量。无论是2秒还是10秒，超过89%的人类被评为尾流或SWS的时段被机器评为相同的状态。大多数被人类标记为REMS的时代也被机器标记为REMS。然而，在人类被评为REMS的时段中，超过10%被机器评为SWS， 18（10秒）至28%（2秒）被评为尾迹。这些偏差不是应变特异性的，因为与光/暗周期、脑电图功率谱剖面、慢波和乳酸的稳态动力学相关的睡眠状态时间的应变差异，用自动方法和手动评分方法都能同样有效地检测到。与EEG慢波活动和脑乳酸时间动态的数学建模相关的误差，要么在使用自动状态评分与视觉评分进行状态评分时没有显著差异，要么在使用自动状态评分相对于手动分类时减少。在啮齿动物睡眠研究中，机器评分与人类评分一样有效。在研究睡眠中的应变差异和睡眠相关生理参数的时间动态方面，自动评分是一种有效的替代目测方法。
Rodent sleep research uses electroencephalography (EEG) and electromyography (EMG) to determine the sleep state of an animal at any given time. EEG and EMG signals, typically sampled at >100 Hz, are segmented arbitrarily into epochs of equal duration (usually 2–10 seconds), and each epoch is scored as wake, slow-wave sleep (SWS), or rapid-eye-movement sleep (REMS), on the basis of visual inspection. Automated state scoring can minimize the burden associated with state and thereby facilitate the use of shorter epoch durations. We developed a semiautomated state-scoring procedure that uses a combination of principal component analysis and naïve Bayes classification, with the EEG and EMG as inputs. We validated this algorithm against human-scored sleep-state scoring of data from C57BL/6J and BALB/CJ mice. We then applied a general homeostatic model to characterize the state-dependent dynamics of sleep slow-wave activity and cerebral glycolytic flux, measured as lactate concentration. More than 89% of epochs scored as wake or SWS by the human were scored as the same state by the machine, whether scoring in 2-second or 10-second epochs. The majority of epochs scored as REMS by the human were also scored as REMS by the machine. However, of epochs scored as REMS by the human, more than 10% were scored as SWS by the machine and 18 (10-second epochs) to 28% (2-second epochs) were scored as wake. These biases were not strain-specific, as strain differences in sleep-state timing relative to the light/dark cycle, EEG power spectral profiles, and the homeostatic dynamics of both slow waves and lactate were detected equally effectively with the automated method or the manual scoring method. Error associated with mathematical modeling of temporal dynamics of both EEG slow-wave activity and cerebral lactate either did not differ significantly when state scoring was done with automated versus visual scoring, or was reduced with automated state scoring relative to manual classification. Machine scoring is as effective as human scoring in detecting experimental effects in rodent sleep studies. Automated scoring is an efficient alternative to visual inspection in studies of strain differences in sleep and the temporal dynamics of sleep-related physiological parameters.