Neural network application in strange attractor investigation to detect a FGD

There is growing interest in modeling and processing nonlinear behavior in the biological systems. In this paper we applied such methods for detecting Functional Disorder in Gastric. Conventional tools for analyzing such data use information from the power spectral density of the time series, and hence are restricted to little information of data. This information does not provide a sufficient representation of a signal with strong nonlinear properties. In this work, we attempt to extract various nonlinear dynamical invariants of the underlying attractor from the signals. We show that these invariants can discriminate between normal and Functional Gastrointestinal Disorders (FGD) classes.