ANN model for detection and classification of sleep and non-sleep stages Online publication date: Thu, 07-Apr-2022
by Bollampally Anupama; Somayajulu Laxmi Narayana; K.S. Rao
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 18, No. 1/2, 2022
Abstract: Disorders related to sleep has become one of the prime issues in human life, which may become the major reasons for health consequences. This work proposes an efficient approach to discriminate sleep stage from non-sleep stage by analysing electroencephalogram (EEG) signals from frontal lobes of brain from a single channel. Second order FIR filter is designed to separate Delta and Theta waves from the collected EEG data, empirical mode decomposition decomposes the EEG signals into small definite components which extracts distinct features like Kurtosis, median absolute deviation (MAD) and inter quartile range (IQR). An artificial neural network model is developed to classify the features of sleep and non-sleep classes which combine the advantages of feed forward and backpropagation algorithms. The experimental results obtained determine that the features extracted proved to be good discriminators of sleep and non-sleep stage with an accuracy of 92%, sensitivity 100% and specificity 98%.
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