Intracranial pressure processing with artificial neural networks: classification of signal properties.

Intracranial pressure (ICP) is commonly used by neurosurgeons as a source of valuable information about the current condition of the neurosurgical patient. Nevertheless, despite years of effort, extracting clinically valuable information from the ICP signal is still problematical. Approaches, using current values of ICP, may fail to disclose imminent risk, because unpredictable factors can rapidly change the properties of the signal. An alternative approach is to determine some global characteristics of the signal within a longer time interval and such statistical analyses have been proposed by several authors. A further, rarely considered, problem is assessment of the results obtained from the point of view of their practical utility and/or such classification of the obtained properties of the signal that they correspond to certain clinical states of the patient. While this might be a typical task for discriminant analysis, we approached the analysis using an alternative methodology, that of computational intelligence, implemented in artificial neural networks (ANN). We tested two variants of the ANN algorithms for classification and discrimination of global properties of the ICP signal. In a "dynamic pattern classification" the network was presented with several sections of ICP records together with information from the expert-neurosurgeon, classifying 4 risk groups. In this mode no data pre-processing was carried out, in contrast to our second approach, in which the signal had been pre-processed using published statistical analyses and only these intermediate coefficients were fed into the ANN classifier. The results obtained with both classification methods at their current stage of training were similar and approximated to a 70% rate of judgements consistent with the expert scoring. Nevertheless, the method based on the assessment of global parameters from the ICP record looks more promising, because it leaves the possibility for modification of the set of parameters analysed. The new parameters may include information extracted not only from the ICP signal, but also from other diagnostic modalities, like colour coded Doppler ultrasonography. The ultimate goal of this work is to build up a pseudo-intelligent computer expert system, which would be able to reason from a reduced set of input information, available from a standard monitoring modality, because it had been taught salient links between these data and higher-order data, upon which expert scoring was based.