Neural networks and nonlinear regression modelling and control of depth of anaesthesia for spontaneously breathing and ventilated patients

Evaluation of inherent performance of intelligent medical decision support systems: utilising neural networks as an example

Researchers who design intelligent systems for medical decision support, are aware of the need for response to real clinical issues, in particular the need to address the specific ethical problems that the medical domain has in using black boxes. This means such intelligent systems have to be thoroughly evaluated, for acceptability. Attempts at compliance, however, are hampered by lack of guidelines. This paper addresses the issue of inherent performance evaluation, which researchers have addressed in part, but a Medline search, using neural networks as an example of intelligent systems, indicated that only about 12.5% evaluated inherent performance adequately. This paper aims to address this issue by concentrating on the possible evaluation methodology, giving a framework and specific suggestions for each type of classification problem. This should allow the developers of intelligent systems to produce evidence of a sufficiency of output performance evaluation.

Stochastic complexity measures for physiological signal analysis

Traditional feature extraction methods describe signals in terms of amplitude and frequency. This paper takes a paradigm shift and investigates four stochastic-complexity features. Their advantages are demonstrated on synthetic and physiological signals; the latter recorded during periods of Cheyne-Stokes respiration, anesthesia, sleep, and motor-cortex investigation.

Synthesis of an anaesthetic agent administration system using fuzzy inductive reasoning

Control of the depth of anaesthesia is a difficult undertaking. Progress has been made during recent years by use of different methodologies and monitoring systems that suggest the safe amount of an anaesthetic drug, considering the condition of an individual patient. Despite these improvements, anaesthetists still rely heavily on personal experience when suggesting the anaesthetic dosage during surgical operations. The purposes of this paper are twofold. One is a description of the design of an anaesthetic agent control system using a qualitative modelling and simulation methodology called Fuzzy Inductive Reasoning (FIR). A comparison with a system developed for the same application using a neural network approach is also presented. The second purpose is a discussion of the problem of separating system-generic from patient-specific behaviour in the context of inductive modeling using the FIR methodology. In order to be useful, the model generated by FIR should reflect upon system-generic behavioural characteristics exclusively, while suppressing patient-specific behavioural patterns. A technique based on combining knowledge obtained from different patients is designed that makes it possible to derive a single model characterizing a specific class of similar patients undergoing similar operations, preserving the common characteristics of all these patients while filtering out the specific behavioural patterns of any one of the individual patients from whom the data were obtained.