Adami Adriana M, Adami André G, Hayes Tamara L, Pavel Misha, Beattie Zachary T
University of Caxias do Sul, Caxias do Sul, RS, Brazil.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:2263-6. doi: 10.1109/EMBC.2012.6346413.
Quality of sleep is an important attribute of an individual's health state and its assessment is therefore a useful diagnostic feature. Changes in the patterns of mobility in bed during sleep can be a disease marker or can reflect various abnormal physiological and neurological conditions. This paper describes a method for detection of movement in bed that is evaluated on data collected from patients admitted for regular polysomnography. The system is based on load cells installed at the supports of a bed. Since the load cell signal varies the most during movement, the approach uses a weighted combination of the short-term mean-square differences of each load cell signal to capture the variations in the signal caused by movement. We use a single univariate Gaussian model to represent each class: movement versus non-movement. We assess the performance of the method against manual annotation performed by a sleep clinic technician from seventeen patients. The proposed detection method achieved an overall sensitivity of 97.9% and specificity of 98.7%.
睡眠质量是个体健康状况的一个重要属性，因此对其进行评估是一项有用的诊断特征。睡眠期间床上活动模式的变化可能是疾病标志物，也可能反映各种异常的生理和神经状况。本文描述了一种用于检测床上活动的方法，该方法基于从接受常规多导睡眠图检查的患者收集的数据进行评估。该系统基于安装在床支架上的称重传感器。由于称重传感器信号在运动期间变化最大，该方法使用每个称重传感器信号的短期均方差差异的加权组合来捕获由运动引起的信号变化。我们使用单个单变量高斯模型来表示每个类别：运动与非运动。我们根据睡眠诊所技术人员对17名患者进行的手动标注来评估该方法的性能。所提出的检测方法总体灵敏度达到97.9%，特异性达到98.7%。
