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A knowledge-based approach to automated sleep EEG (electroencephalogram) analysis is described. In this system, an object-oriented approach is followed in which specific waveforms and sleep stages (objects) are represented in terms of frames. The latter capture the morphological and spatiotemporal information for each object. An object detection module (frame matcher), operating on the frames, is used to identify what features used to be extracted from the EEG and to trigger the appropriate specialist-specialized signal processing modules-to obtain values for these features. This leads to an opportunistic approach to EEG interpretation with quantitative information theory being extracted from the signal only when needed by the reasoning processes. The system has been tested on the detection of K complexes and sleep spindles. Its performance indicates that the approach is feasible.
Date of Publication: May 1989