Abstract

Esophageal Motility Disorders (EGMDs) are a group of abnormalities characterized by the muscular dysfunction of the esophagus in the transportation of food from the oral cavity to the stomach. EGMDs typically cause chronic problems and affect a vast and ever-increasing number of the global population. The diagnosis of EGMDs mainly relies on a key test presently used to study the esophagus motility, known as esophageal manometry (EGM). EGM involves pressure measurements inside the esophagus, which provide information pertaining to its contractions. The diagnosis process is mainly based on visual inspection of the EGM test results to find certain characteristics of the manometric patterns. There are several factors that make such inspection tedious. For instance, manometry test results are often contaminated with a considerable amount of noise, (e.g. noise from external environment) and artifacts, (e.g. respiration artifacts) leading to a longer and more complex diagnosis process. As such, the diagnosis based on visual inspection is prone to human error and demands extensive amount of expert's time. This thesis introduces new signal processing approaches to provide an accurate means for the diagnosis of EGMDs as well as to reduce the amount of time spent on the diagnosis process. Specifically, a new technique known as wavelet decomposition (WD) is applied to the filtering of the EGM data. A nonlinear pulse detection technique (NPDT) is applied to the de-noised data leading to extraction of diagnostically important information i.e. esophageal pulses. Such information is used to generate a model using a statistical pulse modeling (SPM) technique, which can classify the EGM patterns. The proposed approaches are applied to the EGM data of 20 patients and compared with those from existing techniques. Such comparisons illustrate the advantages of the proposed approaches in terms of accuracy and efficiency. As part of this thesis, a new circuit-based approach is proposed for the treatment of Gastroesophageal Reflux Disease (GERD), i.e. the most prevalent disease caused by EGMDs. The objective is to provide a framework for further research towards the implementation of the proposed approach for GERD treatment.