Design and validation of a computer-based sleep-scoring algorithm
Design and validation of a computer-based sleep-scoring algorithm
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DOI：
10.1016/j.jneumeth.2003.09.025
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发表时间：
2004-02-15
影响因子：
3
通讯作者：
Stephenson, R
中科院分区：
文献类型：
--
作者：
Louis, RP;Lee, J;Stephenson, R
设计了一种计算机睡眠评分算法，用于对Wistar大鼠的睡眠-觉醒状态进行真实的时间评分。在以下频带测量脑电图（EEG）振幅（μ V（rms））：δ（δ; 1.5-6 Hz）、θ（θ; 6-10 Hz）、α（α; 10.5-15 Hz）、β（β; 22-30 Hz）和γ（γ; 35-45 Hz）。从耳长提肌（颈部）记录肌电图（EMG）信号（muV（rms）），因为这产生了比脊髓三角肌（肩部）或颞肌（头部）EMG显著更高的算法准确性（ANOVA; P = 0.009）。使用系绳（n = 10）或遥测（n = 4）获得数据。我们开发了一种简单的三步算法，根据手动评分的90分钟初步记录期间设置的阈值，将行为状态分类为清醒、非快速眼动（NREM）睡眠、快速眼动（REM）睡眠。行为状态被分配在5秒的时期。测量每只动物的EMG振幅和EEG频带振幅的比率，并与经验阈值进行比较。是：“活跃”唤醒，否：睡眠或“安静”唤醒。第2步：EEG振幅比（δ x α）/（β x γ）大于阈值？是：NREM，否：REM或“安静”清醒。步骤3：EEG振幅比Theta（2）/（delta x alpha）大于阈值？是：REM，否：“安静”唤醒。算法通过一步、两步和三步验证。发现单独使用第一步区分清醒和睡眠（NREM和REM组合）的总体准确度为90.1%。发现使用前两步对清醒、NREM和REM睡眠评分的总体准确度为87.5%。当使用所有三个步骤时，清醒、NREM和REM睡眠评分的总体准确率为87.9%。所有准确性均来自与由经验丰富的人类评分员定义的四个90分钟记录的明确评分时期的比较。这些算法与三个人类评分员之间的一致性（88%）一样可靠。(C)2003 Elsevier B. V.保留所有权利。
A computer-based sleep scoring algorithm was devised for the real time scoring of sleep-wake state in Wistar rats. Electroencephalogram (EEG) amplitude (muV(rms)) was measured in the following frequency bands: delta (delta; 1.5-6 Hz), theta (Theta; 6-10 Hz), alpha (alpha; 10.5-15 Hz), beta (beta; 22-30 Hz), and gamma (gamma; 35-45 Hz). Electromyographic (EMG) signals (muV(rms)) were recorded from the levator auris longus (neck) muscle, as this yielded a significantly higher algorithm accuracy than the spinodeltoid (shoulder) or temporalis (head) muscle EMGs (ANOVA; P = 0.009). Data were obtained using either tethers (n = 10) or telemetry (n = 4). We developed a simple three-step algorithm that categorizes behavioural state as wake, non-rapid eye movement (NREM) sleep, rapid eye movement (REM) sleep, based on thresholds set during a manually-scored 90-min preliminary recording. Behavioural state was assigned in 5-s epochs. EMG amplitude and ratios of EEG frequency band amplitudes were measured, and compared with empirical thresholds in each animal.STEP 1: EMG amplitude greater than threshold? Yes: "active" wake, no: sleep or "quiet" wake.STEP 2: EEG amplitude ratio (delta x alpha)/(beta x gamma) greater than threshold? Yes: NREM, no: REM or "quiet" wake.STEP 3: EEG amplitude ratio Theta(2)/(delta x alpha) greater than threshold? Yes: REM, no: "quiet" wake.The algorithm was validated with one, two and three steps. The overall accuracy in discriminating wake and sleep (NREM and REM combined) using step one alone was found to be 90.1% Overall accuracy using the first two steps was found to be 87.5% in scoring wake, NREM and REM sleep. When all three steps were used, overall accuracy in scoring wake, NREM and REM sleep was determined to be 87.9%. All accuracies were derived from comparisons with unequivocally-scored epochs from four 90-min recordings as defined by an experienced human rater. The algorithms were as reliable as the agreement between three human scorers (88%). (C) 2003 Elsevier B.V. All rights reserved.