An ubiquitous sleep quality monitoring and evaluation

In this paper, we propose a distance monitoring and evaluation model for sleep quality, which allows a doctor to be able to know users' sleep status of all night from their own home. The model focuses on a sleep-staging program running on an internet-based sleep-staging program running automatically on a remote server, in which we use the method of combining linear and non-linear parameters to extract feature from sleep EEG signals in the calculating modules and classify each 30 sleep fragments as different sleep stages by supervised learning in a BP neutral network in the classifying module. Our experiment shows that the sleep quality monitoring and evaluation scheme is effective.