An efficient sleep scoring system based on EEG signal using complex-valued machine learning algorithms

• In this study, a new hybrid machine learning method is presented for automatic sleep scoring using EEG signals.
• In this study, a new complex-valued feature set was obtained for sleep scoring.
• Complex-valued methods gave good results in classification of EEG data and automatic sleep scoring.
• With a single channel EEG, this method can reach quite a similar performance with the sleep expert.
• As part of the study, the behaviour of non-linear features in different sleep stages has been examined.

Sleep staging is a significant step in the diagnosis and treatment of sleep disorders. Sleep scoring is a time-consuming and difficult process. Given that sleep scoring requires expert knowledge, it is generally undertaken by sleep experts. In this study, a new hybrid machine learning method consisting of complex-valued nonlinear features (CVNF) and a complex-valued neural network (CVANN) has been presented for automatic sleep scoring using single channel electroencephalography (EEG) signals. First of all, we should note that in this context, nine nonlinear features have been obtained as those are often preferred for the classification of EEG signals. These obtained features were then converted into a complex-valued number format using a phase encoding method. In this way, a new complex-valued feature set was obtained for sleep scoring. The obtained attributes have been presented as input to the CVANN algorithm. We have used a number of different statistical parameters during the evaluation process. The results that have been obtained are based on two sleep standards: Rechtschaffen & Kales (R&K) and American Academy of Sleep Medicine (AASM). Finally, a 91.57% accuracy rate was obtained according to R&K standard; a 93.84% accuracy rate was obtained according to the AASM standard using the proposed method. We therefore observed that the proposed method is promising in terms of the sleep scoring.