An approach to estimate EEG power spectrum as an index of heat stress using backpropagation artificial neural network

A method has been presented for an effective application of backpropagation artificial neural network (ANN) in establishment of electro-encephalogram (EEG) power spectra as an index of stress in hot environment. The power spectrum data for slow wave sleep (SWS), rapid eye movement (REM) sleep and awake (AWA) states in three groups of rats (acute heat stress, chronic heat stress and the normal) were tested by an ANN, containing 60 nodes in input layer, weighted from power spectrum data from 0 to 30 Hz, 18 nodes in hidden layer and an output node. The target output values for this network were determined with another five-layered neural network (with the structure of 3-12-1-12-3). The input and output of this network was assigned with the three well-established heat stress indices (body temperature, body weight and plasma corticosterone). The most important feature for acute stress, chronic stress and normal conditions were extracted from the third layer single neuron and used for the target value for the three-layered neural network. The ANN was found effective in recognising the EEG power spectra with an average of 96.67% for acute heat stress, 97.17% for chronic heat stress and 98.5% for normal subjects.