Domingues A, Paiva Teresa, Sanches J M
Institute for Systems and Robotics / Instituto Superior Técnico, Lisbon, Portugal.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:2275-8. doi: 10.1109/EMBC.2012.6346416.
Wrist actigraphy is a well established procedure to monitor human activity. Among other areas, it has a special relevance in sleep studies where its lightweight and non-intrusive nature make it a valuable tool to access the circadian cycle. While there are several methods to extract information from the data, the differentiation between sleep and wakefulness states is still an open discussion. In this paper, the characteristics of the movements in the different states are assumed to be intrinsically different. These differences are not simply related with magnitude and movement counting, but due to real differences on the statistical distributions describing the actigraphy data. Thus it is possible to refine the discrimination level when detecting these states. The proposed methodology to characterize the actigraphy data is based on a mixture of three canonical distributions; i)Exponential, ii)Rayleigh and iii)Gaussian. It is shown that the weights and parameters estimated in each state are organized into almost separable clusters on the feature space. This suggests the ability of the method to discriminate these states based only on the movements recorded on actigraphy data.
手腕活动记录仪是一种成熟的监测人类活动的方法。在诸多领域中，它在睡眠研究中具有特殊的意义，其轻巧且非侵入性的特点使其成为研究昼夜节律周期的宝贵工具。虽然有多种从数据中提取信息的方法，但睡眠和清醒状态之间的区分仍然是一个有待深入探讨的问题。在本文中，假设不同状态下的运动特征本质上是不同的。这些差异不仅仅与幅度和运动计数有关，而是源于描述活动记录仪数据的统计分布的实际差异。因此，在检测这些状态时可以提高辨别水平。所提出的表征活动记录仪数据的方法基于三种典型分布的混合：i）指数分布，ii）瑞利分布和iii）高斯分布。结果表明，在每个状态下估计的权重和参数在特征空间上几乎形成可分离的聚类。这表明该方法仅根据活动记录仪数据记录的运动就能够区分这些状态。
