Profiling continuous sleep representations for better understanding of the dynamic character of normal sleep

Publication date: Available online 29 December 2018Source: Artificial Intelligence in MedicineAuthor(s): Zuzana Roštáková, Roman RosipalAbstractThe amount and quality of sleep substantially influence health, daily behaviour and overall quality of life. The main goal of this study was to investigate to what extent sleep structure derived from polysomnographic (PSG) recordings of nocturnal human sleep can provide information about sleep quality in terms of correlation with a set of variables representing daytime subjective, neurophysiological and cognitive states of a healthy population without serious sleep problems. We focused on a continuous sleep representation derived from the probabilistic sleep model (PSM) and which describes the microstructure of sleep by a set of sleep probabilistic curves representing a finite number of sleep microstates. This contrasts approaches where sleep is characterised by a set of one-dimensional sleep measures derived from the standard discrete sleep staging. Considering this continuous sleep representation we aimed to identify typical sleep profiles representing the dynamic aspect of sleep changes during the whole night and being associated with a set of studied daily life quality measures.Cluster analysis of sleep probabilistic curves has proven to be a helpful tool when identifying specific sleep temporal profiles, but it faces problems when curves are complex and time misalignment is present. To overcome these problems we proposed and v...
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research