Model-based Data Processing, Validation, and Abstraction for Intelligent Real-time Patient Monitoring and Control

B. M. Dawant, E. J. Manders, and D. P. Lindstrom

Intelligent patient monitoring and control can be conceptually decomposed into four layers [Coi93]: (1) the signal level concerned with the acquisition and processing of the raw data, (2) the validation level that should eliminate noise and artifacts from the raw data, (3) the signal-to-symbol transformation level which maps features detected in the signals to various clinical conditions and (4) the inference level which integrates information from various sources to derive possible diagnoses and control actions or to predict the patient’s evolution. Although a fair amount of work has been dedicated over the years to addressing questions and solving problems associated with each of these layers, most of this work has been done in isolation and very little has been done to conceptualize and formalize the interaction between layers and to provide an integrated framework for intelligent monitoring tasks. The main reason may be that intelligent monitoring tasks require real-time data interpretation, a concept that lies at the intersection of two vastly different disciplines: artificial intelligence (AI) and signal processing.

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