BME Special Seminar: Sandya Subramanian

Description

Sandya Subramanian ('15), a data science postdoctoral fellow at Stanford University, will give a talk titled "Monitoring and Modeling the Autonomic Nervous System: The Bridge Between the Brain and the Body" for the Department of Biomedical Engineering. Associate Professor Sri Sarma will host.

This is a hybrid event; to attend virtually, please use the Zoom link.

Abstract:

In both complex clinical settings and chronic disease, the nervous system coordinates across many systems of the body. This role is carried out by the autonomic nervous system (ANS), the body's hidden control system that is responsible for our unconscious functioning. However, as a complex and distributed network of nerves deep within the body, the ANS is a poorly understood part of basic neurophysiology. Traditional approaches to tracking the ANS, for instance with wristwatch heart rate variability, have shown inconsistent findings and lack sufficient rigor for clinical use. In this talk, I will provide examples from both the operating room and from at-home chronic disease monitoring where I use three strategies to overcome challenges in precise, dynamic monitoring of the ANS. First, I will demonstrate how I used a multimodal approach to track unconscious pain in the operating room for more robust inference when a unimodal approach was subject to confounds. Second, I will give an example of how I constructed the first physiology-based interpretable statistical model for sweat gland activity that allowed me to precisely and accurately estimate underlying phenomena we cannot directly measure. Finally, I will illustrate how I engineered solutions for continuous, at-home monitoring in chronic disease settings using wearable sensors to shed light on patient-specific complexity and variability and suggest hypotheses about individual disease etiology. Together, I will provide a vision of how engineering new technological solutions for the ANS will enable the health care community to understand individual complexity and variability in disease, track disease progression and symptoms even in patients who cannot self-report, personalize disease management, and provide objective measures for factors like pain and stress that are the subjects of harmful stereotypes in health care.