Automated Classification and Identification of Slow Wave Propagation Patterns in Gastric Dysrhythmia

Abstract

The advent of high-resolution electrical mapping of slow wave activity has significantly improved the understanding of gastric slow wave activity in normal and dysrhythmic states. One of the current limitations of this technique is it generates a vast amount of data, making manual analysis a tedious task for research and clinical development. In this study we present new automated methods to classify, identify and locate patterns of interest in gastric slow wave propagation. The classification method uses a similarity metric to classify slow wave propagations, while the identification algorithm uses the divergence and mean curvature of the slow wave propagation to identify and regionalize patterns of interest. The methods were applied to synthetic and experimental data sets and were also compared to manual analysis. The methods classified and identified patterns of slow wave propagation in less than 1 s, compared to manual analysis which took up to 40 minutes. The automated methods achieved 96% accuracy in classifying AT maps, and 95% accuracy in identifying the propagation pattern with a mean spatial error of 1.5 mm in comparison to manual methods. These new methods will facilitate the efficient translation of gastrointestinal high-resolution mapping techniques to clinical practice.

A system for automated quantification of cutaneous electrogastrograms

“…Application of the methods to a larger patient population will enable us to assess the clinical utility of cutaneous EGG. Similar to multi-electrode, real-time automated analysis in serosal slow wave recordings [10], [11], advances in recording techniques, along with mathematical modeling and signal processing algorithms may enable cutaneous EGG to discriminate dysrhythmic slow wave patterns and aid as a diagnostic.…”

“…The amplitude of the slow wave signal is another parameter that is seldom used as a discriminating factor, but has been shown to be vital for detecting dysrhythmic activity from serosal recordings [9]. Analysis of serosal signals have largely been automated, motivated via multi-electrode mapping, and techniques been implemented in real-time [10], [11]. However, the analysis of cutaneous EGG in most research centers is performed manually which is prone to bias and subjectivity.…”

Experimental evidence and mathematical modeling of thermal effects on human colonic smooth muscle contractility

“…It is well known that the contractile rhythm of intestinal smooth muscle is due to the pivotal role of interstitial cells of Cajal (ICCs), which form a network of cells between the circular and longitudinal muscle layers making contact with surrounding smooth muscle cells and acting as a pacemaker of spontaneous motility in the gut (33). Advanced experimental tools have been developed in this direction (24,48,49,52), leading to a fine characterization of electrical propagating fronts. Noninvasive measurements are currently being investigated to extend the diagnostic analysis to a wider clinical audience (13,62).…”

Detection of the Recovery Phase of in vivo gastric slow wave recordings

“…Currently, automated and real time techniques exist to analyze and define the activation phase of the slow wave [11], [12], [8]. In this study, we have developed a novel approach to detect the recovery phase of the slow wave using the wavelet transform to calculate the ARi of the slow wave event.…”