Study objectives: Unobtrusive monitoring of sleep and sleep disorders in children presents challenges. We investigated the possibility of using Ultra-Wide band (UWB) radar to measure sleep in children. Methods: Thirty-two children scheduled to undergo a clinical polysomnography participated; their ages ranged from 2 months to 14 years. During the polysomnography, the children's body movements and breathing rate were measured by an UWB-radar. A total of 38 features were calculated from the motion signals and breathing rate obtained from the raw radar signals. Adaptive boosting was used as machine learning classifier to estimate sleep stages, with polysomnography as gold standard method for comparison. Results: Data of all participants combined, this study achieved a Cohen's Kappa coefficient of 0.67 and an overall accuracy of 89.8% for wake and sleep classification, a Kappa of 0.47 and an accuracy of 72.9% for wake, rapid-eye-movement (REM) sleep, and non-REM sleep classification, and a Kappa of 0.43 and an accuracy of 58.0% for wake, REM sleep, light sleep and deep sleep classification. Conclusion: Although the current performance is not sufficient for clinical use yet, UWB radar is a promising method for non-contact sleep analysis in children.@en

Recently, there has been an increasing awareness of the critical role of sleep for the brain development of young infants. During the early neonatal stages of human development, the basic activity of the brain is to develop itself during sleep. Prolonged sleep is required in infants for further development of the nervous system. Sleep also plays an important role in body temperature regulation and energy saving. Neonatal sleep is divided into two main different sleep stages: Rapid EyeMovement (REM) and Non-Rapid Eye Movement (NREM). As the infant develops, sleep stages vary in maturity, length and distribution, thus underlining the importance of the quantification of these stages, which could eventually lead to new biomarkers of neonatal brain development. Nowadays, the golden standard in sleep monitoring is Polysomnography (PSG), in which vital signs as well as EEG and muscle activity are recorded during a whole-night study and subsequently sleep stages are classified by an expert. However, the high obtrusiveness of the multiple electrodes involved in PSG and its high associated costs make it impossible to use PSG as a routine monitor system. The SOMNUS project had two main targets: (i) to accurately measure respiration signals from patients using an ultra-wideband radar module, and (ii) to detect differences in respiration between REM and NREM phases in order to unobtrusively and automatically score sleep states of infants without the need of any electrode attached to the patient. The system has been developed using a training dataset of 23 patients from 3 months to 14 years old. It is for the first time UWB radar technology is used to monitor sleep in young patients. Moreover, this work provides a new data analysis algorithm to suppress motion artifacts from radar signals and increase the robustness of respiration monitoring. The results from the breathing detection algorithm developed in the present work provided an average mean absolute error of 3.25 Respirations per Minute in respiration rate. Amount of movement was employed to estimate Sleep/Awake events, with an average error in percentage of Total Sleep Time of 7.48%. Regarding sleep-state classification, several classification analyses were performed in order to study the best classification variable to detect REM/NREM, along with analyses regarding the best classification technique. Respiration variability was the main feature determining REM/NREMstate, with an overall sleep classification accuracy of around 80% when using linear classifiers such as Support Vector Machines and Fisher Linear Discrimination.