Abnormalities in the gastric electrical activity (GEA) and cutaneous Electrogastrogram (EGG) have been studied in variety of clinical conditions related to digestive system and its motility. Unfortunately, the impact of renal failure on GEA remains in question especially with patients performing regular Haemodialysis. The aim of this study is to determine whether EGG would be changed in patients with renal failure even after Haemodialysis to become as healthy control. An experiment was conducted on two groups: The first group contained (11) healthy volunteers with average age (23) years, free from any diseases related to digestive system and renal failure. The second group was selected from patients suffering from renal failure and performing regular Haemodialysis twice a week (10 patients) with average age of (27.5) years. EGG was measured non-invasively from both groups for 20 minutes and digitized with sampling frequency 2 Hz before pre-processing stage. In the preprocessing stage, the EGG signal becomes noisily suppressed using an adaptive enhancement technique. Based on parametric modeling, and the nonstationarity of EGG, the instantaneous EGG signal is modeled by adaptive Autoregressive (AR) model. The power spectrum of EGG signal can be calculated from those time-varying parameters, which are updated with on-line EGG samples. Therefore, the instantaneous frequency of EGG signal would be tracked. This approach has the ability to provide how far the fluctuations with renal failure from normal will be. The accuracy of classification with adaptive AR coefficients using RLS algorithm shows higher percentage for discrimination than other techniques as LMS. The percentage of classifying patients with renal failure before Haemodialysis from normal persons reaches 98.75%. It is expected that the GEA of the digestive system would become normal after Haemodialysis, but evident differences in EGG power spectrum were found, and patients after Haemodialysis were discriminated easily from normal with percentage 100% using scatter diagram. Finally, these results showed that GEA did not reach the control level even after Haemodialysis (5hours/session).
In this paper, an algorithm has been developed to extract the important features of the electrical activity of the stomach measured non invasively with placing electrodes on the abdomen of the human. The measured signal generated from stomach's muscle contraction, is called the Electrogastrogram (EGG), It is a mixture of action potentials with different amplitudes depending on position of the electrodes, direction of spread over stomach, and firing rate. The proposed algorithm is based on special structure of cascaded filters characterized with high selectivity. Parameters of individual section as well as the number of sections were estimated such that minimum mean squared spectral deviation between the measured and estimated EGG signal is achieved. The amplitudes of individual frequencies extracted by this algorithm are considered as features of EGG signal that can be used for studying stomach's physiological states. An example is given to illustrate the application of this algorithm for evaluating the stomach activity during Hunger or Digestion states. The percentage of success to discriminate between these two states was about 93.8 % for the Hunger state, and 98.9 % for the Digestion. Introduction:Endoscopy is a well clinical tool to identify the presence of any abnormalities within the human stomach, but it is painful and the patient suffers from it, and needs special sterilization. On the other hand, electrophysiology of stomach is more reliable not only to detect the abnormalities, but also to predict the clinical state. The measured electrical potentials either invasively or non invasively reflect modes of stomach stimulation, contraction, and direction of propagation. Moreover, it shows the rhythmic variations of stomach potentials. Although, the first measurement of the Electrogastrogram (EGG) was started a 70 year ago by placing electrodes on the abdomen [2],[3], the progress in this field was very slow, due to some problems as : 1) difficulty in data acquisition and analysis because of the low Signal-to-Noise Ratio (SNR), 2) difficulty in interpreting EGG and extracting its useful features, and 3) lack of understanding the correlation between the EGG and the gastric motility [4].
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