Coupling numerical and symbolic methods for signal interpretation

A framework for a general signal interpretation system is presented. The structure allows for a collaboration between domain-dependent knowledge (acquired by human experts through years of experience in a specific area) and signal-analysis knowledge (the expertise needed to select and use signal-processing techniques for automated extraction of meaningful features). The system has been built following an object-oriented approach and is organized around a blackboard. Control is handled by a global request-centered mechanism. Morphological and contextual (e.g spatial-temporal relationships) information about the events to be detected by the system is represented in terms of frames. A model interpreter is in charge of matching the events' attributes with the data. A first level of analysis is performed to detect possible candidates. Additional features are opportunistically retrieved from the signal whenever needed by the model interpreter. This permits a focusing on segments of data that are of significance to the reasoning process. The models and the model interpreter have been designed such that the depth of analysis can be adapted to the situation at hand (obvious events will require less feature extraction and computation than doubtful cases). Features needed by the model interpreter are extracted from the signal by independent specialists, which consist of a set of specific digital signal-processing routines and the knowledge required to select the one most appropriate for a given task. The system allows for extensive use of contextual information and adaptation of the event models on the basis of earlier detections. The system has been tested on the problem of automatic electroencephalogram interpretation.<
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