Summary
Objectives:
Signal analysis has played an important role in cardiac diagnosis, both as a separate
entity and in conjunction with clinical parameters. Hybrid systems are an effective
method for developing higher-order decision models in which biomedical signal data
can be incorporated.
Methods:
The hybrid system components include a knowledge-based system that utilizes approximate
reasoning techniques, a neural network model based on a potential function approach
to supervised learning that uses the general class of Cohen orthogonal functions as
potential functions, and a signal analysis component that relies on continuous chaotic
modeling to produce a degree of variability in the time series. The hybrid system
is illustrated in an application for differentiation among different types of dementia.
Results:
Application of this method to cardiac diagnosis shows that chaotic parameters alone
contribute significantly to correct classification while the addition of clinical
parameters increases the sensitivity, specificity, and accuracy. Applications to electroencephalogram
analysis indicate that the second-order difference plots display significant differences
for the different types of EEG waves identifiable by frequency, both in shape and
degree of dispersion. Hence the identification of these waves, and the duration of
their occurrence, may provide suitable variables for chaotic analysis.
Conclusions:
Results from studies in cardiology demonstrate that using chaotic measures for ECG
analysis provide useful information for classification. Sensitivity, specificity,
and accuracy are increased if these methods are combined with other clinical parameters
in a hybrid system. This approach has been extended to new applications based on EEG
analysis combined with other relevant information.
Keywords
Hybrid systems - electroencephalograms - chaos theory - medical decision support
