title: A new approach for automatic sleep scoring: Combining Taguchi based complex-valued neural network and complex wavelet transform.
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.
authors.affiliation: Department of Information Systems Engineering, Faculty of Technology, Mugla Sitki Kocman University, 48000 Mugla, Turkey. Electronic address: musa@mu.edu.tr.
authors.forename: Musa
authors.initials: M
authors.lastname: Peker
mesh_terms: D000465:Algorithms; D001331:Automation; D006801:Humans; D016571:Neural Networks, Computer; D058067:Wavelet Analysis
keywords: Complex-valued neural networks; Dual-tree complex wavelet transform; EEG signals; Sleep stage scoring; Taguchi method