Fuzzy Neural Network for detecting nonlinear determinism in gastric electrical activity: Fractal dimension approach

A robust method of detecting determinism for short time series is proposed and applied to both healthy and Functional Gastrointestinal Activity of GEA signals. The method provides a robust measure of determinism through characterizing the trajectories of the signal components which are obtained through singular value decomposition. In order to automatically differentiate the gastric function, a fuzzy neural network to classify the types based on the knowledge of qualified knowledge in chaotic features differences in diagnosis was designed. The designed classifier can make hard decision and soft decision for identifying the chaotic patterns of GMA signal at the accuracy of 91%, which is better than the results that achieved by back-propagation neural network.