Vanneau Théo, Quiquempoix Michael, Trignol Aurélie, Verdonk Charles, Van Beers Pascal, Sauvet Fabien, Gomez-Merino Danielle, Chennaoui Mounir
Unité Fatigue et Vigilance, Institut de Recherche Biomédicale des Armées (IRBA), Brétigny-sur-Orge, France.
EA7330 VIFASOM, Université de Paris, Paris, France.
J Sleep Res. 2021 Dec;30(6):e13373. doi: 10.1111/jsr.13373. Epub 2021 May 3.
The piezoelectric cage-floor sensors have been used to successfully dissect sleep patterns in mice based on signal features related to respiration and body movements. We studied performance of the piezoelectric system to quantify the sleep-wake pattern in the rat over 7 days of recording compared with a visual electroencephalogram/electromyogram scoring, and under two light/dark (LD12:12 and LD16:8) photoperiods leading to change in the 24-hr sleep characteristics (N = 7 per group). The total sleep time (%/24 hr) over the 7 days recording and hourly sleep time over the last 24-hr recording were not statistically different between methods under the two photoperiods. Both methods detected higher total sleep time with the LD16:8 photoperiod compared with LD12:12 (p < .05), and correlated significantly (p < .001) at light and dark periods during each photoperiod. The accuracies for discrimination of sleep-wake patterns between methods were 81.9% and 84.9% for LD12:12 and LD16:8, respectively. In addition, spectral analysis of the respiratory signal given by piezo during all 10-s periods of the corresponding non-rapid eye movement and rapid eye movement sleep periods recorded by electroencephalogram/electromyogram resulted in selection of 36 features that could be inserted in an automated non-rapid eye movement sleep and rapid eye movement sleep classification, with 90% accuracy with the electroencephalogram/electromyogram visual scoring. The piezo system proved to be a reliable non-invasive alternative to electroencephalogram recording to study total sleep time in rat, with feasibility to discriminate between non-rapid eye movement and rapid eye movement sleep stages. This will be interesting in pharmacological or bio-behavioural studies evaluating sleep patterns or the restorative functions of sleep in the body and the brain.
基于与呼吸和身体运动相关的信号特征,压电笼底传感器已成功用于剖析小鼠的睡眠模式。我们研究了压电系统在7天记录期间量化大鼠睡眠-觉醒模式的性能,并与视觉脑电图/肌电图评分进行比较,且在两种明暗(LD12:12和LD16:8)光周期下进行研究,这两种光周期会导致24小时睡眠特征发生变化(每组n = 7)。在两种光周期下,两种方法在7天记录期间的总睡眠时间(%/24小时)以及最后24小时记录中的每小时睡眠时间在统计学上无差异。与LD12:12相比,两种方法在LD16:8光周期下均检测到总睡眠时间更长(p < 0.05),并且在每个光周期的光照和黑暗阶段均具有显著相关性(p < 0.001)。对于LD12:12和LD16:8,两种方法区分睡眠-觉醒模式的准确率分别为81.9%和84.9%。此外,对脑电图/肌电图记录的相应非快速眼动和快速眼动睡眠期所有10秒时间段内压电给出的呼吸信号进行频谱分析,结果选出了36个特征,可用于自动非快速眼动睡眠和快速眼动睡眠分类,脑电图/肌电图视觉评分的准确率为90%。事实证明,压电系统是一种可靠的非侵入性替代方法,可用于研究大鼠的总睡眠时间,并且有能力区分非快速眼动和快速眼动睡眠阶段。这对于评估睡眠模式或身体及大脑中睡眠恢复功能的药理学或生物行为学研究将具有重要意义。