Automated staging of sleep in cats using neural networks

Manual staging of sleep based on visual EEG criteria is a laborious and time-consuming task. In an effort to automate sleep staging, we have developed a neural network that 'learns' to stage sleep on the basis of wave band count data alone, in the cat. Wave band count data are collected on a microcomputer, using period-amplitude analysis. Delta waves, spindle bursts, ponto-geniculo-occipital (PGO) waves, electro-oculogram (EOG), basal electromyogram (EMG) amplitude, and movement artifact amplitude are collected, and used to 'train' the network to score sleep. These wave count data serve as the input patterns to the net, and the corresponding manually scored sleep stages serve as a 'teacher.' We demonstrate that, when used to score the states of wake, slow wave sleep (SWS), desynchronized sleep (D), and the transition period from SWS to D (SP), these neural networks agree with manual scoring an average of 93.3% for all epochs scored. Neural network programs can learn both rules and exceptions, and since the nets teach themselves these rules automatically, a minimum of human effort is required. Because programming requirements are small for neural nets, this approach is readily adaptable to microcomputer-based systems and is widely applicable to both animal and human EEG analyses. The utility of this approach for the detection and classification of a variety of clinical neurophysiological disorders is discussed.