A fuzzy neural network approach for automatic K-complex detection in sleep EEG signal

摘要

The study of sleep stages and the associated signals have emerged as a very important parameter to identify the neurological disorders and test of mental activities nowadays. Electroencephalogram (EEG) is an electrophysiological method for monitoring, managing, and diagnosing the mental disorders or neurological problems. The EEG signals are highly transient and nonlinear in nature. It varies with the mental conditions. In the sleep state, a non-stationary wave generates with comparatively higher peaks is known as K-complex. The K-complex is a kind of transient wave which can be seen in the NREM stage II sleep. The main difficulty behind the design of the automated K-complex detection system is a nonlinear and dynamic characterization of it. The other difficulty for the system design is the very much similar behaviour of K-complex to other EEG wave. To overcome these problems, in this paper we are giving the detailed description for developing an automatic K-complex detector using fuzzy neural network approach. In this method, fuzzy C-means algorithm is utilized for the rough and rapid recognition of K-complex and the neural network classifier does the exact evaluation on the detected K-complex. One more fast computing Back Proportion algorithm is used for train the network in this work. This technique of detection of K-complex with a well-known pattern present in sleep EEG is a fuzzy neural based software solution in the field of biomedical signal processing. (C) 2018 Elsevier B.V. All rights reserved.