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Actigraphy-based sleep/wake detection for insomniacs

机译:基于行为的失眠症睡眠/唤醒检测

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This paper presents an actigraphy-based approach for sleep/wake detection for insomniacs. Due to its relative unobtrusiveness, actigraphy is often used to estimate overnight sleep-wake patterns in clinical practice. However, its performance has been shown to be limited in subjects with sleep complaints such as insomniacs. Quantifying activity counts on 30-s epoch basis, as usually done in regular actigraphy, may lead to an underestimation of wake periods where the subject shows reduced body movements. We therefore propose a new actigraphic feature to characterize the `possibility' of epochs being asleep (or awake) before or after its nearest epoch with a very high activity levels. It is expected to correctly identify some wake epochs when they are very close to the high activity epochs, although they can be motionless. A data set containing 25 insomnia subjects and a linear discriminant classifier were used to test our approach in this study. Leave-one-subject-out cross validation results show that combining the new and the traditional actigraphic features led to a markedly improved performance in sleep/wake detection compared to that using the traditional feature only, with an increase in Cohen's kappa from 0.49 to 0.55.
机译:本文介绍了一种基于行为学的失眠症睡眠/苏醒检测方法。由于其相对不引人注目,因此在临床实践中经常使用书法书来估计过夜的睡眠-觉醒模式。然而,它的表现已被证明在患有睡眠失调症(例如失眠)的受试者中受到限制。通常在定期的书法作品中,以30秒为周期对活动计数进行量化,可能会导致低估了醒觉时间,而受试者的身体运动减少了。因此,我们提出了一种新的书法特征,以表征在具有最高活动水平的最近时期之前或之后处于睡眠状态(或清醒状态)的时期的“可能性”。尽管它们可能是静止的,但是当它们非常接近高活动性时期时,可以期望正确地识别它们。包含25个失眠受试者和线性判别分类器的数据集用于测试本研究中的方法。留一法则的交叉验证结果表明,与仅使用传统功能相比,将新的和传统的书法功能相结合可显着改善睡眠/唤醒检测性能,科恩的kappa从0.49增加到0.55 。

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