A sleep monitoring method with EEG signals

National audienceDiagnosis of sleep disorders is still a challenging issue for a large number of nerve diseases. In this sense, EEG is a powerful tool due to its non-invasive and real-time catacteristics. This modality is being more and more used for diagnosis such as for epilepsy. It is also becoming widely used for many redictive, Preventive and Personalized Medicine (PPPM) applications.To understand sleep disorders, we propose a method to classify EEG signals in order to detect abnormal behaviours that could refect a specificmodification of the sleep pattern. Our method consists of extracting the characteristics based on temporal and spectral analyses with different descriptors. A classifcation is then performed based on these features. Validation on a public available database show promizing results withhigh accuracy levels

Sleep detection using physiological signals from a wearable device

Internet of things for medical devices is revolutionizing healthcare industry by providing platforms for data collection via cloud gateways and analytics. In this paper, we propose a process for developing a proof of concept solution for sleep detection by observing a set of am- bulatory physiological parameters in a completely non-invasive manner. Observing and detecting the state of sleep and also its quality, in an objective way, has been a challenging problem that impacts many medical fields. With the solution presented here, we propose to collect physiological signals from wearable devices, which in our case consists of a smart wristband equipped with sensors and a protocol for communication with a mobile device. With machine learning based algorithms, that we developed, we are able to detect sleep from wakefulness in up to 93% of cases. The results from our study are promising with a potential for novel insights and effective methods to manage sleep disturbances and improve sleep quality