Development of GUI and Computational Algorithms for Real‐Time Physiological Autonomic Activity Tracking

Abstract

Many modern diseases involve some form of autonomic disturbance/dysfunction, including diabetes, gastroparesis, and autoimmune inflammatory diseases. For some of these autonomic disruptive diseases, few studies have been able to quantify the autonomic dysfunction. Previous attempts usually resort to invasive means that carry additional risk to the patient. In addition, to properly stimulate the autonomic nervous system (ANS) artificially for treatment, quantification of dysfunction is needed for personalized medical treatment. This research has led to the development of a non-invasive, data-driven diagnostic tool that may be able to identify the type and degree of autonomic dysfunction by simply recording signals from the heart, stomach, and other vitals data, and feeding them into data-driven software. Here, BioCom Lab under Dr. Ward has developed a MATLAB-based research software that can filter and analyze several forms of biological signals for tailored viewing in a graphical user interface (GUI). The software includes several custom heart rate variability (HRV) analysis methods, several custom electrogastrogram analysis methods, and additional skin sympathetic nerve activity (SKNA) analysis implemented all into the GUI (see Fig. 1) for non-invasive physiological monitoring of potential autonomic activity. The software has been developed for real time and post hoc analysis of large data sets, enabling new insights into precise physiological monitoring by incorporating averaged data points with reference to an event or timepoint. For example, the GUI has been used on several large sets of patient data to discover the temporal interplay between autonomic activity and the cardiovascular and gastrointestinal systems where patients were monitored before and after water and Ensure drink consumption (Fig. 2). In this study's data, the GUI provided new visuals and insight from the capability to average 31 patients’ data with the same time base relative to the stimulus while also providing comparison capabilities between organ systems analysis. This research tool provides a new way to understand parasympathetic and sympathetic influence on organs, also enabling the ability to help identify timepoints of parasympathetic activity, as opposed to sympathetic activity, by monitoring organ activity. The research tool at hand has the potential to create better, less-invasive diagnostics that can help physicians to select the best course of treatment for their patients, as well as provide new analytical and visual tools for researchers to track effects on the ANS from both non-electrical and electrical stimuli.