Neural network technique for detecting emergency states in neurosurgical patients.

Publication/Presentation Date

11-1-1998

Abstract

The problem of reliable detection of life-threatening situations in the neurosurgical patient undergoing treatment in the ICU is still far from reaching a satisfactory solution, although several methods of clinical and instrumental evaluation have recently been developed for the early detection of oncoming signs of danger. Continuous monitoring of intracranial pressure (ICP) provides neurosurgeons with valuable information about the current condition of the patient. However, it is increasingly felt that traditional methods of extracting information from the ICP signal have reached their natural limits, mostly because of difficulties in fitting the appropriate mathematical model to this non-linear and non-stationary process. Successful implementations of artificial neural networks in many medical tasks have encouraged the application of this method of ICP processing. Two problems are considered: the prediction of trends in ICP, and recognition of the configuration of unfavourable symptoms likely to signal danger for the neurosurgical patient. The construction of neural network predictors of ICP trends is based on wavelet pre-processing of the original signal. The approach to the second task involves pre-processing of the ICP with spectral and statistical methods and classification of the extracted features of the current signal on an arbitrarily selected scale of danger.

Volume

36

Issue

6

First Page

717

Last Page

722

ISSN

0140-0118

Disciplines

Business Administration, Management, and Operations | Health and Medical Administration | Management Sciences and Quantitative Methods

PubMedID

10367462

Department(s)

Administration and Leadership

Document Type

Article

Link to Full Text

Share

COinS