Domingues Alexandre, Paiva Teresa, Sanches J Miguel
IEEE Trans Biomed Eng. 2014 Jun;61(6):1711-9. doi: 10.1109/TBME.2014.2301462.
The automatic computation of the hypnogram and sleep Parameters, from the data acquired with portable sensors, is a challenging problem with important clinical applications. In this paper, the hypnogram, the sleep efficiency (SE), rapid eye movement (REM), and nonREM (NREM) sleep percentages are automatically estimated from physiological (ECG and respiration) and behavioral (Actigraphy) nocturnal data. Two methods are described; the first deals with the problem of the hypnogram estimation and the second is specifically designed to compute the sleep parameters, outperforming the traditional estimation approach based on the hypnogram. Using an extended set of features the first method achieves an accuracy of 72.8%, 77.4%, and 80.3% in the detection of wakefulness, REM, and NREM states, respectively, and the second an estimation error of 4.3%, 9.8%, and 5.4% for the SE, REM, and NREM percentages, respectively.
根据便携式传感器采集的数据自动计算睡眠图和睡眠参数，是一个具有重要临床应用价值的挑战性问题。本文从生理（心电图和呼吸）和行为（活动记录仪）夜间数据中自动估计睡眠图、睡眠效率（SE）、快速眼动（REM）和非快速眼动（NREM）睡眠百分比。文中描述了两种方法；第一种方法解决睡眠图估计问题，第二种方法专门设计用于计算睡眠参数，优于基于睡眠图的传统估计方法。第一种方法使用一组扩展特征，在检测清醒、REM和NREM状态时的准确率分别达到72.8%、77.4%和80.3%，第二种方法对SE、REM和NREM百分比的估计误差分别为4.3%、9.8%和5.4%。
