Detecting the Functional Gastrointestinal Disorder based on Wavelet Tra

In recent years, researchers have developed respectable wavelet techniques for the multi-scale representation and analysis of directs 12345. Wavelets localize the information in the time- frequence plane6. unrivalled of the aras where these properties have been applied is diagnosis. Due to the wide 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 galvanizing oscillation in the stomach. With the effect of the whole digestive process of the stomach, from mixing, stirring, and agitating, to propelling and elimination, a spatiotemporal prototype is formed 11. The stomach has a complex physiology, where physical, biological and psychological parameters issuance part in, becoming difficult to understand its behavior and function. It is presented the initial concepts of a mechanical prototype of thestomach, it uses to describe mechanical functions of storing, m ixing and food emptying 1213.The nature of gastric electrical activity in health and ailment 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 high-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 responsible for mixing and transp... ...ls from their wavelet coefficients, originally they are applied to a static skittish network for encourage classification. The design of neural network is simple because only interesting features of GEA types are presented. The exper imental results show that its possible to classify GEA types by victimisation this simple neural network computer architecture. We present the results from a network which is proficient on sample types.The approach of classifying the output of a feature sensor offers greater computational efficiency and accuracy than that of attempting to use a neural network directly upon a GEA signal, and yet preserves the ability to see to it and flexibility of a neural network.Section 3 of this paper describes the architecture of a network to classify the GEA types for detecting abnormalities. Experimental results of training and examen a network are presented in section 6.