Hummel Richard, Bradley T Douglas, Fernie Geoff R, Chang S J Isaac, Alshaer Hisham
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:5416-9. doi: 10.1109/EMBC.2015.7319616.
Polysomnography is a comprehensive modality for diagnosing sleep apnea (SA), but it is expensive and not widely available. Several technologies have been developed for portable diagnosis of SA in the home, most of which lack the ability to detect sleep status. Wrist actigraphy (accelerometry) has been adopted to cover this limitation. However, head actigraphy has not been systematically evaluated for this purpose. Therefore, the aim of this study was to evaluate the ability of head actigraphy to detect sleep/wake status. We obtained full overnight 3-axis head accelerometry data from 75 sleep apnea patient recordings. These were split into training and validation groups (2:1). Data were preprocessed and 5 features were extracted. Different feature combinations were fed into 3 different classifiers, namely support vector machine, logistic regression, and random forests, each of which was trained and validated on a different subgroup. The random forest algorithm yielded the highest performance, with an area under the receiver operating characteristic (ROC) curve of 0.81 for detection of sleep status. This shows that this technique has a very good performance in detecting sleep status in SA patients despite the specificities in this population, such as respiration related movements.
多导睡眠图是诊断睡眠呼吸暂停（SA）的一种综合方法，但它成本高昂且尚未广泛应用。已经开发了几种用于在家中对SA进行便携式诊断的技术，其中大多数缺乏检测睡眠状态的能力。手腕活动记录仪（加速度计）已被采用来弥补这一局限性。然而，头部活动记录仪尚未针对此目的进行系统评估。因此，本研究的目的是评估头部活动记录仪检测睡眠/觉醒状态的能力。我们从75例睡眠呼吸暂停患者的记录中获得了完整的夜间三轴头部加速度计数据。这些数据被分为训练组和验证组（2:1）。对数据进行预处理并提取了5个特征。将不同的特征组合输入到3种不同的分类器中，即支持向量机、逻辑回归和随机森林，每个分类器在不同的子组上进行训练和验证。随机森林算法表现最佳，检测睡眠状态时受试者工作特征（ROC）曲线下面积为0.81。这表明，尽管该人群存在诸如呼吸相关运动等特殊性，但该技术在检测SA患者的睡眠状态方面具有非常好的性能。
