Characterisation of Sleep Quality in COPD patients using the chest-wearable Respeck

It is well understood that sleep disturbances impact general quality of life. The Respeck device worn as a plaster on the chest was designed to monitor the respiratory signal – respiratory rate (breaths/minute) and the respiratory flow/effort (amplitude), and the intensity of physical activity (in terms of equivalent volume of oxygen consumed (VO2) measure), and types of static and dynamic physical activity. Unsupervised machine learning methods considered for binary classification of sleep-wake states were k-means, hierarchical clustering, and anomaly detection applied to numerical features such as mean breathing rate, and activity levels derived from the Respeck data. K-Prototypes and Factor Analysis of Mixed Data (FAMD) which extended the k-means clustering with the activity type as a categorical feature, were the most effective in classifying the sleep-wake states. The FAMD with k-means method was applied to the SMILE dataset containing 904-days worth of Respeck data collected by a cohort of 17 patients with Chronic Obstructive Pulmonary Disease (COPD). For the first time night-time sleep quality features were quantified in COPD patients, such as sleep duration, sleep latency, sleep efficiency, wake-after-sleep-onset, and, number of positional changes using the Respeck sensor. It was estimated that 88% of the SMILE COPD cohort suffered from poor night-time sleep quality based on their satisfying threshold values for all three criteria: sleep duration, sleep efficiency and number of instances of wake- after-sleep-onset.