Modelling techniques and their application for monitoring in high dependency environments--learning models.
This paper reviews the use of learning models including Bayesian classifiers and artificial neural networks in monitoring and interpreting biosignals. Generally learning models applied for analysis of biosignals are "black-box' types trained on the basis of measured signals. It is illustrated that the training and application of learning models more or less follow the same sequences. The main focus is the interpretation of electrical signals from the brain (electroencephalogram (EEG) and evoked potentials (EP)). Current analysis of these signals often reveals sudden changes in the EEG or evoked potentials to be the earliest discernible signs of inadequate perfusion of the brain. They may reflect problems such as systemic arterial oxygen desaturation or hypotension arising from other body system failures during critical illness. It is suggested that these brain signals should be recorded in the critical care unit, and that they should form part of the annotated database of biosignals established during the IMPROVE project. This would allow for the development of new methods for on-line warning of impending damage to the central nervous system, such that corrective actions could be taken before permanent damage occurred.
Department of Medical Informatics and Image Analysis, Aalborg University, Denmark. jga@miba.auc.dk
J
J
Gade
A
A
Rosenfalck
M
M
van Gils
P
P
Cluitmans
D001314:Auscultation; D001499:Bayes Theorem; D003422:Critical Care / Q000379:methods*; D004569:Electroencephalography; D004576:Electromyography; D005071:Evoked Potentials; D008962:Models, Theoretical; D008991:Monitoring, Physiologic; D009426:Netherlands; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted