Automated Techniques for Classification and Quantification of Colonic Manometry

Abstract

Novel diagnostics and therapies are emerging in the field of colonic motility. In order to evaluate the effectiveness of these novel therapies, colonic manometry is used to measure the pressure of the colon. Recently, however, clinicians have utilised high-resolution manometry (HRM) due to its capability to record the pressure from all segments of the colon. The advent of HRM has improved experimental and clinical understanding of colonic motility. HRM data has been useful in depicting two notable colonic motor patterns: the cyclic propagation motor pattern (CMP) and high amplitude propagation sequence (HAPS). However, one of the current limitations of HRM is the excessive amount of data, making manual analysis a tedious and time-consuming task. More importantly, manual analysis is prone to observer bias and error, and could result in misconstrued experimental observations. In this study, automated methods have been developed to detect and classify colonic motor patterns. The first step was to pre-process the signal, and a three-step filtering was designed to this end. Several methods such as root mean square and moving average were tested to remove the baseline and smooth the data. The secant method was selected to remove the baseline, the median technique was used to remove the synchronous noise, and Savitzky-Golay filter to smooth the data. Then, an algorithm was developed to detect the HAPS and CMP by analysing the amplitude in each channel to find the peaks and compare them regarding time with the peaks in other channels to form propagation. The precision of the algorithm in detecting the HAPS and CMP was 87% and 91% respectively. Finally, a novel and reliable quantitative method was developed to estimate the velocity, amplitude and the extent of propagation of the colonic motor pattern and to automatically classify the HAPS and CMP into retrograde or antegrade. The classification precision was 91% for HAPS and 72% for CMP. This thesis presents a path forward in the colonic field for the use of quantitative HRM data in clinical diagnosis and prognosis, enabling practitioners to direct treatment strategies accordingly and improve patient care outcomes.