Artificial neural network detects changes in electro-encephalogram power spectrum of different sleep-wake states in an animal model of heat stress.
An effective application is presented of a back-propagation artificial neural network (ANN) in differentiating electro-encephalogram (EEG) power spectra of stressed and normal rats in three sleep-wakefulness stages. The rats were divided into three groups, one subjected to acute heat stress, one subjected to chronic heat stress and one a handling control group. The polygraphic sleep recordings were performed by simultaneous recording of cortical EEG, electro-oculogram (EOG) and electromyogram (EMG) on paper and in digital form on a computer hard disk. The preprocessed EEG signals (after removal of DC components and reduction of baseline movement) were fragmented into 2 s artifact-free epochs for the calculation of power spectra. The slow-wave sleep (SWS), rapid eye movement (REM) sleep and awake (AWA) states were analysed separately. The power spectrum data for all three sleep-wake states in the three groups of rats were tested by a back-propagation ANN. The network contained 60 nodes in the input layer, weighted from power spectrum data from 0 to 30 Hz, 18 nodes in the hidden layer and an output node. The ANN was found effective in differentiating the EEG power spectra from stressed to normal spectral patterns following acute (92% in SWS, 85.5% in REM sleep, 91% in AWA state) as well as chronic heat exposure (95.5% in SWS, 93.8% in REM sleep, 98.5% in AWA state).
School of Biomedical Engineering, Institute of Technology, Banaras Hindu University, India. rksinha_res@rediffmail.com
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Sinha
D000818:Animals; D004195:Disease Models, Animal; D004569:Electroencephalography; D018882:Heat Stress Disorders / Q000503:physiopathology*; D008297:Male; D016571:Neural Networks, Computer; D051381:Rats; D012815:Signal Processing, Computer-Assisted; D012894:Sleep Stages