A power spectrum based backpropagation artificial neural network model forclassification of sleep-wake stages in rats

Abstract

Three layered feedforward backpropagation ANN (Artificial neural network) architecture is designed to classify sleep-wake stages in rats. Continuous three channel polygraphic signals such as EEG (electroencephalogram), EOG (electrooculogram) and EMG (electromyogram) were recorded from conscious rats for eight hours during day time. Signals were also stored in computer hard disk with the help of analog to digital converter and its compatible data acquisition software. The power spectra (in dB scale) of the digitized signals in three sleep-wake stages were calculated. Selected power spectrum data of all three simultaneously recorded polygraphic signals were used for training the network and to classify SWS (slow wave sleep), REM (rapid eye movement) sleep and AWA (awake) stages. The ANN architecture used in present study shows a very good agreement with manual sleep stage scoring with an average of 94.83% for all the 1200 samples tested from SWS, REM and AWA stages. The high performance observed with the system based on ANN highlights the need of this computational tool into the field of sleep research.