EEG power spectrum and neural network based sleep-hypnogram analysis for a model of heat stress

Abstract

An effective application of back- propagation artificial neural network (ANN) in preparation of sleep-hypnogram based on electroencephalogram (EEG) power spectra under acute as well as chronic heat stress has been presented. Rats were divided in three groups (i) acute heat stress-subjected to a single exposure for four hours at 38 degrees C; (ii) chronic heat stress-exposed for 21 days daily for one hour at 38 degrees C, and (iii) handling control groups. The preprocessed EEG signals were fragmented in two-second artifact free epochs for calculation of power spectra, training and testing of ANN. The power spectrum analyses of EEG show that changes in higher frequency components (beta(2)) were significant in all sleep-wake states following both acute and chronic heat stress conditions. The power of beta(2) activity after acute heat exposure was significantly decreased during SWS (slow wave sleep) (P < 0.05) and REM (rapid eye movement) sleep (P < 0.05), while reverse was observed in AWA (awake state) (P < 0.05). Following chronic heat exposure, beta(2) activity was found increased in all three sleep-wake stages (P < 0.05). The ANN used for sleep-hypnogram preparation contains 64 nodes in input layer, weighted from power spectrum data from 0 to 32 Hz, 14 nodes in hidden layer and 3 output nodes. The results obtained from the study, suggest increased sleep efficiency following acute exposure to heat stress while fragmented sleep with decreased sleep efficiency following chronic heat stress. The ANN can be used for the analysis of stressful events by calculating the sleep-EEG alterations.