The University of Queensland, Brisbane, Australia.
Med Biol Eng Comput. 2010 Dec;48(12):1203-13. doi: 10.1007/s11517-010-0715-x. Epub 2010 Nov 25.
Chronic sleepiness is a common symptom in the sleep disorders, such as, Obstructive Sleep Apnea, Periodic leg movement disorder, narcolepsy, etc. It affects 8% of the adult population and is associated with significant morbidity and increased risk to individual and society. MSLT and MWT are the existing tests for measuring sleepiness. Sleep Latency (SL) is the main measures of sleepiness computed in these tests. These are the laboratory-based tests and require services of an expert sleep technician. There are no tests available to detect inadvertent sleep onset in real time and which can be performed in any professional work environment to measure sleepiness level. In this article, we propose a fully automated, objective sleepiness analysis technique based on the single channel of EEG. The method uses a one-dimensional slice of the EEG Bispectrum representing a nonlinear transformation of the underlying EEG generator to compute a novel index called Sleepiness Index. The SL is then computed from the SI. Working on the patient's database of 42 subjects we computed SI and estimated SL. A strong significant correlation (r ≥ 0.70, s < 0.001) was found between technician scored SL and that computed via SI. The proposed technology holds promise in the automation of the MSLT and MWT tests. It can also be developed into a sleep management system, wherein the SI is incorporated into a sleepiness index alert unit to alarm the user when sleepiness level crosses the predetermined threshold.
慢性嗜睡是睡眠障碍的常见症状,如阻塞性睡眠呼吸暂停、周期性肢体运动障碍、发作性睡病等。它影响 8%的成年人口,与显著的发病率和个体及社会的风险增加有关。MSLT 和 MWT 是测量嗜睡的现有测试。睡眠潜伏期 (SL) 是这些测试中计算嗜睡的主要指标。这些都是基于实验室的测试,需要专业睡眠技师的服务。目前还没有可以实时检测非自愿入睡的测试,也没有可以在任何专业工作环境中进行的测试来测量嗜睡程度。在本文中,我们提出了一种基于单通道 EEG 的全自动、客观的嗜睡分析技术。该方法使用 EEG 双谱的一维切片来表示潜在 EEG 发生器的非线性变换,以计算一个称为嗜睡指数 (Sleepiness Index, SI) 的新指标。然后从 SI 中计算 SL。在 42 名患者的数据库上进行工作,我们计算了 SI 并估计了 SL。发现通过技术人员评分的 SL 与通过 SI 计算的 SL 之间存在很强的显著相关性 (r≥0.70,s<0.001)。该技术有望实现 MSLT 和 MWT 测试的自动化。它还可以开发成睡眠管理系统,其中 SI 被纳入嗜睡指数警报单元,当嗜睡水平超过预定阈值时向用户发出警报。