Objective: 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.
Methods: 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.
Results: 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.
Conclusion: The ANN can be used for the analysis of stressful events by calculating the sleep-EEG alterations.