RESUMO

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.

Assuntos

Algoritmos , Diagnóstico por Computador/métodos , Eletromiografia/métodos , Gastroparesia/diagnóstico , Músculo Liso/fisiopatologia , Estômago/inervação , Estômago/fisiopatologia , Animais , Simulação por Computador , Cães , Eletrodiagnóstico/métodos , Feminino , Gastroparesia/fisiopatologia , Masculino , Modelos Biológicos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Processamento de Sinais Assistido por Computador

RESUMO

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.