Detecting the Functional Gastrointestinal Disorder based on Wavelet Tra

In novel years, researchers have developed powerful wavelet techniques for the multi-scale re actation and analysis of signals 12345. Wavelets adjust the information in the time-frequency plane6. One of the argonas where these properties have been applied is diagnosis. Due to the colossal variety of signals and problems encountered in biomedical engineering, there are various applications of wavelet transform 78910.Like in the heart, there exists a rhythmic galvanic oscillation in the stomach. With the accomplishment of the whole digestive process of the stomach, from mixing, stirring, and agitating, to propellant and emptying, a spatiotemporal pattern is formed 11. The stomach has a composite physiology, where physical, biological and psychological parameters take part in, becoming difficult to determine its behavior and function. It is presented the initial concepts of a machinelike prototype of thestomach, it uses to describe mechanical functions of storing, mixing and fo od emptying 1213.The nature of gastric electrical use in health and disease is fairly well understood. In man, it consists of recurrent regular depolarization (slow waves or electrical control activity-ECA) at 2.5 to 4 cycles per minute, and intermittent steep-frequency oscillations (spikes or electrical response activity-ERA) that appear only in association with contractions. The oscillations commence at a pacemaker site high up in the corpus and propagate to terminate at the distal antrum. The velocity of propagation and the signal amplitude increase as the pylorus is approached. ECA are the ultimate determinant of the frequency and direction of propagation of phasic contractions, which are trustworthy for mixing and transp... ...ls from their wavelet coefficients, before they are applied to a atmospheric static neural network for further classification. The design of neural network is unanalyzable because only interesting features of GEA types are presented. The experiment al results show that its possible to classify GEA types by using this simple neural network architecture. We present the results from a network which is trained on sample types.The approach of classifying the takings of a feature detector offers greater computational efficiency and truth than that of attempting to use a neural network directly upon a GEA signal, and that preserves the ability to train and flexibility of a neural network. dent 3 of this paper describes the architecture of a network to classify the GEA types for find abnormalities. Experimental results of training and testing a network are presented in section 6.