Hamrahi H, Chan B, Horner R L
Department of Medicine, University of Toronto, Toronto, Ontario, Canada M5S 1A8.
J Appl Physiol (1985). 2001 Jun;90(6):2130-40. doi: 10.1152/jappl.2001.90.6.2130.
Sleep-disordered breathing is associated with adverse clinical consequences such as daytime sleepiness and hypertension. The mechanisms behind these associations have been studied in animal models, especially rats, but intermittent stimuli such as hypoxia have been applied without reference to sleep-wake states. To determine mechanisms underlying the adverse physiological consequences of stimuli associated with sleep-disordered breathing requires criteria for detection of sleep-wake states on-line to trigger stimuli only in sleep. This study aimed to develop such a system for freely behaving rats. Twelve rats with implanted electroencephalogram and neck electromyogram electrodes were studied in the light and dark phases. Electroencephalogram frequencies in the high (20-30 Hz) and low (2-4 Hz) frequency bands distinguished non-rapid eye movement (REM) sleep, whereas neck electromyogram distinguished REM. Using these parameters in a simple algorithm led to detection accuracies of 94.5 +/- 1.0 (SE) % for wakefulness, 96.2 +/- 0.8% for non-REM sleep, and 92.3 +/- 1.6% for REM compared with blinded human judgment. The algorithm was then used to trigger hypoxic stimuli only in sleep. Because frequency and amplitude analysis is readily performed using a variety of commercial systems, incorporation of these parameters into such an algorithm will facilitate studies investigating mechanisms underlying the physiological consequences of sleep-related respiratory stimuli in a fashion that more effectively models clinical disorders.
睡眠呼吸障碍与诸如日间嗜睡和高血压等不良临床后果相关。这些关联背后的机制已在动物模型(尤其是大鼠)中进行了研究,但诸如低氧等间歇性刺激的应用并未参考睡眠 - 觉醒状态。要确定与睡眠呼吸障碍相关的刺激所导致的不良生理后果的潜在机制,需要用于在线检测睡眠 - 觉醒状态的标准,以便仅在睡眠期间触发刺激。本研究旨在为自由活动的大鼠开发这样一种系统。对12只植入了脑电图和颈部肌电图电极的大鼠在明相和暗相进行了研究。高(20 - 30Hz)频段和低(2 - 4Hz)频段的脑电图频率可区分非快速眼动(REM)睡眠,而颈部肌电图可区分快速眼动睡眠。在一个简单算法中使用这些参数,与人工盲判相比,清醒状态的检测准确率为94.5±1.0(SE)%,非快速眼动睡眠为96.2±0.8%,快速眼动睡眠为92.3±1.6%。然后该算法被用于仅在睡眠期间触发低氧刺激。由于使用各种商业系统很容易进行频率和幅度分析,将这些参数纳入这样的算法将有助于以更有效地模拟临床疾病的方式研究与睡眠相关的呼吸刺激的生理后果的潜在机制的研究。