Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel EEG Signal

Among the essential body functions like breathing, eating or drinking, sleeping is probably the most problematic one nowadays. According to the US government through its Centers for Control of Disease and Prevention (CDC), about 9 million citizens have frequent problems to develop good quality sleep and end up resorting to sleeping pills (Ford et al. 2014). In parallel, recent studies (Stranges et al. 2012; Chong et al. 2013) have estimated that at least 15% of adult population might have some kind of sleeping problem or poor-quality sleep as a result of a number of issues. Moreover, the World Health Organization (WHO) (2015) claimed that a good quality sleep was one of the most important factors for good health while sleeping problems were directly related to other diseases, including depression, stress or early cardiac diseases. As a consequence, new units focused on the study and treatment of sleeping problems have been created in hospitals all over the world. The physicians in these units have as their main tool for their work the records obtained during their patients' sleep. These records, called polysomnography (PSG), may include a great variety of signals such as Electrocardiograms, Electroencephalograms, respiratory signals or movement records. Among these signals, the most important one is the Electroencephalogram (EEG) because it is the most reliable to determine the sleep stage a patient is in. The interpretation of an EEG is a highly time-consuming activity (Akben and Alkan 2016), which usually requires a specialist and it is deeply dependent on the expert's expertise.