Wavelet Decomposition for the Analysis of Esophageal Manometric Data in the Study of Gastroesophageal Reflux Disease

Wavelet decomposition is gaining attention as a novel signal processing tool for analyzing nonlinear time-series. Compared to traditional Fourier transform, wavelet transform better represents functions exhibiting discontinuities and sudden changes. As such, wavelet-based techniques are strong candidates for the analysis of bio-signals (e.g. gastric and esophageal signals), in which, sudden changes and sharp peaks are likely. For the first time, this paper applies wavelet decomposition to the analysis of esophageal manometric data, which is critical in the diagnosis of gastroesophageal reflux disease. Simulation results of wavelet decomposition are compared with those of a recent approach based on empirical mode decomposition. Such comparison shows that wavelet decomposition leads to better results in terms of number of decomposition coefficients (15 versus 17), CPU-time (0.5 s versus 75 s), and signal-to-background ratio (0.97 versus 0.85).