A new approach for automatic sleep scoring: Combining Taguchi based complex-valued neural network and complex wavelet transform

Computer Methods and Programs in Biomedicine
Musa Peker

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 ...Continue Reading

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Citations

Jul 28, 2016·Journal of Neuroscience Methods·Ahnaf Rashik Hassan, Mohammed Imamul Hassan Bhuiyan
Feb 20, 2020·Journal of Medical Systems·Ensar Arif SağbaşSerkan Balli
Oct 20, 2019·Computer Methods and Programs in Biomedicine·Mohammed DiykhShahab Abdulla

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