Nighttime features derived from topic models for classification of patients with COPD. (May 2021)

• Record Type: Journal Article
• Authors: Spina, Gabriele; Casale, Pierluigi; Albert, Paul S.; Alison, Jennifer; Garcia-Aymerich, Judith; Clarenbach, Christian F.; Costello, Richard W.; Hernandes, Nidia A.; Leuppi, Jörg D.; Mesquita, Rafael; Singh, Sally J.; Smeenk, Frank W.J.M.; Tal-Singer, Ruth; Wouters, Emiel F.M.; Spruit, Martijn A.; den Brinker, Albertus C.
• Is Part Of: Computers in biology and medicine. Volume 132(2021)
• Publication Date: 2021-05
• Subjects: COPD -- Topic models -- Sleep -- Classification; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104322
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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. Highlights: Nighttime symptoms are important indicators of impairment for respiratory diseases such as COPD. Data collection during sleeping hours may offer a better trade-off sensor unobtrusiveness and signal quality. Features derived from topic models capture important aspects of subjects sleeping behavior related to the effects of COPD.