Smart Sensing for Advanced Sleep Analysis

Dear Colleagues,

Human sleep monitoring is essential for the diagnosis and management of various sleep-related disorders. Wearable sensors can be used to measure physiological parameters to predict the occurrence of such disorders and assess the quality of sleep. In addition, they can be used to monitor, treat, and manage medical conditions related to sleep. The most common sleep abnormalities include obstructive sleep apnoea, upper airway resistance syndrome, periodic limb movement disorders, restless leg movement disorder, narcolepsy, rapid eye movement (REM), sleep behaviour, insomnia, dyssomnias, parasomnias, hypersomnia, nocturia, sleep stage detection and categorisation (wake, N1, N2, N3, and REM Sleep), circadian rhythm disorders and consciousness disorders.

Currently, the use of wearable sensors is limited to professional installation in a clinical setting. However, recent research has focused on transforming sleep monitoring systems to allow their use inside the home and as online and mobile modalities. This involves a reduction of the number of sensors used while still maintaining an acceptable level of accuracy.

This Special Issue will present novel methods for sleep monitoring applications including the use of various body-worn sensors such as smart watches, smart phones, and wearable EEGs. In particular, it will focus on methods incorporating advanced analytics techniques powered by the latest machine intelligence methods, such as deep learning or other new artificial intelligence paradigms, to identify various sleep stages, classify common body positions and unobtrusively estimate the respiratory rate.

Dr. Javier Andreu-Perez
Dr. Delaram Jarchi
Guest Editors

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