Matching a wavelet to class of signals can be of interest in feature detection and classification based on wavelet representation. The aim of this work is to provide a quantitative approach to the problem of matching a wavelet to electrogastrographic (EGG) signals. Visually inspected EGG recordings from sixteen dogs and six volunteers were submitted to wavelet analysis. Approximated wavelet-based versions of EGG signals were calculated using Pollen parameterization of 6-tap wavelet filters and wavelet compression techniques. Wavelet parameterization values that minimize the approximation error of compressed EGG signals were sought and considered optimal. The wavelets generated from the optimal parameterization values were remarkably similar to the standard Daubechies-3 wavelet.

Although the principles of electrogastrography (EGG) have been known for years, the clinical utility of EGG has not been clearly demonstrated, and EGG recording and analysis techniques have not been fully standardized. The aim of this study was to develop a multichannel EGG technique for detecting abnormal gastric motility using an EGG database specifically designed for a particular testing centre, maximizing the sensitivity and the specificity of the test. Eight healthy volunteers formed a reference group to which 4 study groups (17 gastro-oesophageal reflux disease (GORD) patients, 7 functional dyspepsia patients, 8 post-fundoplication patients and 12 healthy volunteers) were compared. Eight-channel EGG was recorded in the postprandial and fasting states for 30 min each. The recorded signals were wavelet compressed and the resulting error (per cent root mean square difference (PRD)) after the compression was utilized to compare the study groups to the reference group. A threshold in the number of channels with significantly different PRD values was introduced. Sensitivity (SE), specificity (SP) and correct classification rate (CC) of the test in recognizing each clinical condition in the study groups for several channel thresholds and compressions were calculated, and were maximized. Increasing the compression and channel threshold levels improved the specificity, but decreased the sensitivity of the multichannel EGG test. An optimal combination region was identified based on a centre-specific adjustment of the channel threshold and the wavelet compression. The achieved maximum sensitivity, specificity and correct classification for this region in our test centre were as follows: GORD--SE 82.4%, SP 83.3%, CC 82.8%; functional dyspepsia--SE 100%, SP 75%, CC 84.2%; post-fundoplication--SE 75.0%, SP 83.3%, CC 80.0%. The utilization of a wavelet-based decomposition technique to process multichannel EGG signals can be a very effective method for enhancing the clinical utility of EGG, provided it is specifically developed for a given testing centre.

Abnormal gastric motility function could be related to gastric electrical uncoupling, the lack of electrical, and respectively mechanical, synchronization in different regions of the stomach. Therefore, non-invasive detection of the onset of gastric electrical uncoupling can be important for diagnosing associated gastric motility disorders. The aim of this study is to provide a wavelet-based analysis of electrogastrograms (EGG, the cutaneous recordings of gastric electric activity), to detect gastric electric uncoupling. Eight-channel EGG recordings were acquired from 16 dogs in basal state and after each of two circular gastric myotomies. These myotomies simulated mild and severe gastric electrical uncoupling, while keeping the separated gastric sections electrophysiologically active by preserving their blood supply. After visual inspection, manually selected 10 min EGG segments were submitted to wavelet analysis. Quantitative methodology to choose an optimal wavelet was derived. This 'matching' wavelet was determined using the Pollen parametrization for 6-tap wavelet filters and error minimization criteria. After a wavelet-based compression, the distortion of the approximated EGG signals was computed. Statistical analysis on the distortion values allowed us to significantly (p< 0.05) distinguish basal state from mild and severe gastric electrical uncoupling groups in particular EGG channels.