Nighttime features derived from topic models for classification of patients with COPD

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

Identifying Physical Activity Profiles in COPD Patients Using Topic Models

With the growing amount of physical activity (PA) measures, the need for methods and algorithms that automatically analyse and interpret unannotated data increases. In this paper PA is seen as a combination of multi-modal constructs that can co-occur in different ways and proportions during the day. The design of a methodology able to integrate and analyse them is discussed and its operation is illustrated by applying it to a data set comprising data from COPD patients and healthy subjects acquired in daily life. The method encompasses different stages. The first stage is a completely automated method of labelling low-level multi-modal PA measures. The information contained in the PA labels are further structured using topic modelling techniques, a machine learning method from the text processing community. The topic modelling discovers the main themes that pervade a large set of data. In our case, topic models discover PA routines that are active in the assessed days of the subjects under study. Applying the designed algorithm to our data provides new learnings and insights. As expected, the algorithm discovers that PA routines for COPD patients and healthy subjects are substantially different regarding their composition and moments in time in which transitions occur. Furthermore, it shows consistent trends relating to disease severity as measured by standard clinical practice