Expert-system classification of sleep/waking states in infants.

This work is part of a project to develop an expert system for automated classification of the sleep/waking states in human infants; i.e. active or rapid-eye-movement sleep (REM), quiet or non-REM sleep (NREM), including its four stages, indeterminate sleep (IS) and wakefulness (WA). A model to identify these states, introducing an objective formalisation in terms of the state variables characterising the recorded patterns, is presented. The following digitally recorded physiological events are taken into account to classify the sleep/waking states: predominant background activity and the existence of sleep spindles in the electro-encephalogram; existence of rapid eye movements in the electro-oculogram; and chin muscle tone in the electromyogram. Methods to detect several of these parameters are described. An expert system based on artificial ganglionar lattices is used to classify the sleep/waking states, on an off-line minute-by-minute basis. Algorithms to detect patterns automatically and an expert system to recognise sleep/waking states are introduced, and several adjustments and tests using various real patients are carried out. Results show an overall performance of 96.4% agreement with the expert on validation data without artefacts, and 84.9% agreement on validation data with artefacts. Moreover, results show a significant improvement in the classification agreement due to the application of the expert system, and a discussion is carried out to justify the difficulties of matching the expert's criteria for the interpretation of characterising patterns.