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Self-Organising Feature Map (SOFM) along with learning vector quantizers (LVQ) have been designed to identify the alterations in brain electrical potentials due to exposure to high environmental heat in rats. Three groups of rats were considered—acute heat stressed, chronic heat stressed and control groups. After long EEG recordings following heat exposure, EEG data representing three different vigilance states such as slow wave sleep (SWS), rapid eye movement (REM) sleep and AWAKE were visually selected and further subdivided into 2 seconds long epoch. In order to evaluate the performance of artificial neural network (ANN) in recognizing chronic and acute effects of heat stress with respect to the control subjects, unsupervised learning algorithm was applied on EEG data. Mean performance of SOFM with quadratic taper function was found to be better (chronic-92.6%, acute-93.2%) over the other two tapers. The effect of LVQ after the initial SOFM training seems explicit giving rise to considerable improvements in performance in terms of selectivity and sensitivity. The percentage increase in selectivity with uniform taper function is maximum for chronic and its control group (4.01%) and minimum for acute group (1.29%) whereas, with Gaussian it is almost identical (chronic-2.57%, acute-2.03%, control- 2.33%). Quadratic taper function gives rise to an increase of 2.41% for chronic, 1.96% for acute and 2.91% for control patterns

Predicting heat-stressed EEG spectra by self-organising feature map and learning vector quantizers——SOFM and LVQ based stress prediction