HumanTotalCare, Data Science Department, Utrecht, the Netherlands.
Jheronimous Academy of Data Science, 'S-Hertogenbosch, the Netherlands.
Comput Biol Med. 2021 May;132:104322. doi: 10.1016/j.compbiomed.2021.104322. Epub 2021 Mar 10.
Nighttime symptoms are important indicators of impairment for many diseases and particularly for respiratory diseases such as chronic obstructive pulmonary disease (COPD). The use of wearable sensors to assess sleep in COPD has mainly been limited to the monitoring of limb motions or the duration and continuity of sleep. In this paper we present an approach to concisely describe sleep patterns in subjects with and without COPD. The methodology converts multimodal sleep data into a text representation and uses topic modeling to identify patterns across the dataset composed of more than 6000 assessed nights. This approach enables the discovery of higher level features resembling unique sleep characteristics that are then used to discriminate between healthy subjects and those with COPD and to evaluate patients' disease severity and dyspnea level. Compared to standard features, the discovered latent structures in nighttime data seem to capture important aspects of subjects sleeping behavior related to the effects of COPD and dyspnea.
夜间症状是许多疾病（尤其是慢性阻塞性肺疾病（COPD）等呼吸系统疾病）受损的重要指标。使用可穿戴传感器评估 COPD 患者的睡眠主要限于监测肢体运动或睡眠的持续时间和连续性。在本文中，我们提出了一种简洁描述 COPD 患者和非 COPD 患者睡眠模式的方法。该方法将多模态睡眠数据转换为文本表示形式，并使用主题建模来识别由 6000 多个评估夜组成的数据集内的模式。这种方法可以发现类似于独特睡眠特征的更高层次的特征，然后用于区分健康受试者和 COPD 患者，并评估患者的疾病严重程度和呼吸困难水平。与标准特征相比，夜间数据中发现的潜在结构似乎捕捉到了与 COPD 和呼吸困难影响有关的受试者睡眠行为的重要方面。
