Expert-system classification of sleep/waking states in infants

This work is part of a project to develop an expert system for automated cl assification of the sleep/waking states in human infants; i.e. active or ra pid-eye-movement sleep (REM), quiet or non-REM sleep (NREM), including ifs four stages, indeterminate sleep (IS) and wakefulness (WA). A model to iden tify these states, introducing an objective formalisation in terms of the s tate variables characterising the recorded patterns, is presented. The foll owing digitally recorded physiological events are taken into account to cla ssify the sleep/waking states: predominant background activity and the exis tence of sleep spindles in the electro-encephalogram; existence of rapid ey e movements in the electro-oculogram; and chin muscle tone in the electromy ogram. Methods to detect several of these parameters are described. An expe rt system based an artificial ganglionar lattices is used to classify the s leep/waking states, on an off-line minute-by-minute basis. Algorithms to de tect 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% agre ement with the expert on validation data without artefacts, and 84.9% agree ment on validation data with artefacts. Moreover, results show a significan t 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.