BME Master's Defense Announcement for Ana K. Kirby (B. Duerstock, advisor) Everyone is invited to attend the public presentation beginning at 2:00pm EST. Title: Frequency and Time Domain Analysis of Physiological Features during Autonomic Dysreflexia after Spinal Cord Injury Date: July 11, 2022 Time: 2:00pm EST Location: Zoom https://purdue-edu.zoom.us/j/94919821296 Meeting ID: 949 1982 1296 One tap mobile +13017158592,,94919821296# US (Washington DC) +13126266799,,94919821296# US (Chicago) Dial by your location +1 301 715 8592 US (Washington DC) +1 312 626 6799 US (Chicago) +1 646 558 8656 US (New York) +1 253 215 8782 US (Tacoma) +1 346 248 7799 US (Houston) +1 669 900 6833 US (San Jose) Meeting ID: 949 1982 1296 Find your local number: https://purdue-edu.zoom.us/u/aciR81uypl Advisory Committee: Bradley S. Duerstock, Chair; Riyi Shi; Thomas H. Everett IV Abstract: Persons with a spinal cord injury (SCI) often suffer from secondary complications including the dysfunction of the autonomic nervous system below the level of injury. For persons with a SCI at or above T6, autonomic dysreflexia (AD) may be triggered by noxious stimulation below the level of injury causing rapid sympathetic hyperactivation, leading to paroxysmal hypertension. If AD is not recognized and managed promptly, this increase in blood pressure can lead to stroke, organ damage, and/or death. Currently, AD is only determined in clinical settings through intermittent blood pressure monitoring. Recent studies have revealed that accurate detection of the onset of AD is possible by using extracted features from electrocardiogram (ECG) data collected non-invasively to develop a machine learning model with a five-layer neural network. This project further characterizes physiological responses before and during AD to noninvasively measure the overreaction of sympathetic nerve activity prior to the detrimental increase in hypertension. Animals were implanted with ECG telemetry to compare changes in blood pressure with high temporal resolution as the gold standard for determining AD. A four-week acclimation protocol was developed to minimize noise and motion artifacts during data collection from noninvasive ECG sensors in conscious rats. Only a minimal increase in sympathetic activity occurred prior to experimentation. We analyzed skin nerve activity (SKNA) and heart rate variability parameters in the time and frequency domain post-SCI to improve the non-invasive detection of AD. Results indicated an increase in SKNA features occurred 18.1 seconds before the initial rise in blood pressure after the onset of AD. Additionally, integrated SKNA features in the frequency domain quantified nerve activity and low frequency components were found to be dominant during AD, providing another parameter that can be used to improve the responsiveness and accuracy of machine learning models to detect AD. In humans, noninvasive SKNA monitoring may be used to alert persons with SCI of the onset of AD prior to critical increases in blood pressure, improving management techniques and potentially decreasing the severe effects of AD.