A new approach for automatic sleep scoring: Combining Taguchi based complex-valued neural network and complex wavelet transform. Issue 129 (June 2016) - Peker, Musa
Highlights: In this study, a new complex classifier-based approach is presented for automatic sleep scoring using EEG signals. The effect of complex-valued classifiers shown to have a positive impact on classification accuracy of EEG signal data. One of the interesting parts of the study is the parameter optimization that significantly affects system performance. Proposed method can be used to design a computer support system for rapid and accurate sleep stage scoring. Abstract: Automatic classification of sleep stages is one of the most important methods used for diagnostic procedures in psychiatry and neurology. This method, which has been developed by sleep specialists, is a time-consuming and difficult process. Generally, electroencephalogram (EEG) signals are used in sleep scoring. In this study, a new complex classifier-based approach is presented for automatic sleep scoring using EEG signals. In this context, complex-valued methods were utilized in the feature selection and classification stages. In the feature selection stage, features of EEG data were extracted with the help of a dual tree complex wavelet transform (DTCWT). In the next phase, five statistical features were obtained. These features are classified using complex-valued neural network (CVANN) algorithm. The Taguchi method was used in order to determine the effective parameter values in this CVANN. The aim was to develop a stable model involving parameter optimization. Different statistical parameters were utilized in the evaluation phase. Also, results were obtained in terms of two different sleep standards. In the study in which a 2nd level DTCWT and CVANN hybrid model was used, 93.84% accuracy rate was obtained according to the Rechtschaffen & Kales (R&K) standard, while a 95.42% accuracy rate was obtained according to the American Academy of Sleep Medicine (AASM) standard. Complex-valued classifiers were found to be promising in terms of the automatic sleep scoring and EEG data.