This site has been permanently archived. This is a static copy provided by the University of Southampton.

TY - GEN ID - cogprints3227 UR - http://cogprints.org/3227/ A1 - Sinha, Mr Rakesh Kumar A1 - Agrawal, Mr Navin Kumar A1 - Ray, Dr Amit Kumar Y1 - 2003/04// N2 - Three layered feed-forward backpropagation artificial neural network architecture is designed to classify sleep-wake stages in rats. Continuous three channel polygraphic signals such as electroencephalogram, electrooculogram and 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 slow wave sleep, rapid eye movement sleep and 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. PB - Kakkilaya BS KW - Artificial neural network KW - Power spectrum KW - Sleep-wake states TI - A power spectrum based backpropagation artificial neural network model for classification of sleep-wake stages in rats AV - public ER -