Novel Real-Time Algorithms:Quantifying Wake-Sleep States

新颖的实时算法：量化唤醒睡眠状态

• 批准号: 6838292
• 负责人: MO MODARRES
• 金额: $ 17.68万
• 依托单位: CLEVELAND MEDICAL DEVICES, INC.
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-27 至 2007-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6838292
• 关键词: arousal; artificial intelligence; bioengineering /biomedical engineering; clinical research; computer program /software; computer system design /evaluation; electroencephalography; human subject; mathematics; polysomnography; sleep; wakefulness

The overall goal of this project is to develop and clinically validate a novel class of computerized EEG-based algorithms and sophisticated signal processing methods designed for high temporal resolution characterization of the wake-sleep transition and fluctuations within sleep stages. In addition to their capability of profiling wakefulness and various sleep stages as a continuous process, these algorithms will be developed in a format such that they can be eventually implemented for real-time detection of wake-to sleep transitions. The new approach of this proposal is based on an innovative interaction of classically independent signal processing methods for quantifying EEG, including traditional Short-Term Fourier Transform, Recursive Auto-regressive Parameter Estimation, and Wavelet analysis. This new approach takes advantage of each method's relative strength and "fuses" the information obtained from each technique to provide a means of quantifying the entire wake-sleep transition with high temporal resolution (e.g., early detection of sleep onset) while being robust to factors that normally degrade EEG processing. The developed algorithms will be validated with an extensive set of clinical data including nocturnal polysomnography, and day-time tests of Sleepiness, MSLT (Multiple Sleep Latency Test) and MWT (Maintenance of Wakefulness Test). The dynamic events detected by these algorithms (e.g., micro-arousals and micro-sleeps) will be evaluated against standard R&K scoring of the same EEG data. Furthermore, the profile and characteristics of the events detected in PSG will be correlated with the daytime objective and subjective tests of sleepiness.

该项目的总体目标是开发和临床验证一种新型的计算机化的 基于EEG的算法和复杂的信号处理方法， 睡眠阶段内的觉醒-睡眠过渡和波动的分辨率表征。除了将清醒和各种睡眠阶段作为一个连续过程进行分析的能力之外，这些算法还将以一种格式进行开发，使得它们最终可以实现对清醒到睡眠过渡的实时检测。该建议的新方法是基于一个创新的相互作用的经典独立的信号处理方法量化EEG，包括传统的短期傅立叶变换，递归自回归参数估计，小波分析。 这种新方法利用了每种方法的相对强度，并“融合”了从每种技术获得的信息，以提供一种以高时间分辨率量化整个唤醒-睡眠过渡的手段（例如，睡眠开始的早期检测），同时对通常使EEG处理降级的因素是鲁棒的。开发的算法将通过一组广泛的临床数据进行验证，包括夜间多导睡眠图，以及白天的嗜睡测试，MPERT（多次睡眠潜伏期测试）和MWT（保持清醒测试）。由这些算法检测到的动态事件（例如，微觉醒和微睡眠）将针对相同EEG数据的标准R&K评分进行评估。此外，PSG中检测到的事件的轮廓和特征将被 与白天的客观和主观测试嗜睡。