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Actigraphy-based sleep/wake detection for insomniacs

机译:基于Actirigraphy的睡眠/醒目检测,用于失眠

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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:本文提出了睡眠的基于活动记录的方法/唤醒检测失眠。由于其相对的不可见性,活动记录仪通常用于在临床实践中估计一夜的睡眠 - 觉醒模式。然而,它的表现已经显示出与睡眠投诉对象限于如失眠。 30-S划时代基础定量活动计数,如通常在常规活动记录仪进行,可能会导致唤醒时段,其中所述受试者表现出降低的身体运动的低估。因此,我们提出了一个新的actigraphic特性表征时代是的'可能性”睡着了(或者醒着)或之前以非常高的活性水平与其最接近的时期之后。预计到正确识别一些觉醒时代的时候都非常接近高活性的时期,虽然他们可能是一动不动。含有25名失眠受试者和线性判别分类器的数据组被用来测试我们在本研究中的方法。留一主题进行交叉验证结果表明,结合新的,并导致睡眠/唤醒检测出显着改善性能相比传统actigraphic的功能,仅使用传统的功能,从0.49到0.55增加科恩kappa 。

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