Evaluating stimulus-based heart rate variability assessment in chronic constipation using a machine learning approach

Abstract

Autonomic dysfunction plays an important role in the pathophysiology of chronic constipation. However, this has been severely understudied due to the heterogenicity and complexity of the clinical data. The autonomic nervous system (ANS) is divided into sympathetic and parasympathetic branches, providing essential control of the gastrointestinal tract, and allowing for normal digestion and expulsion of stool. Heart rate variability (HRV) is a non-invasive method to assess autonomic function using a series of heartbeat intervals derived from electrocardiography data. In order to comprehensively assess autonomic function in patients, we developed a stimulus-based HRV protocol involving active standing, deep breathing, and stomach distention, separately stimulating the sympathetic and parasympathetic branches of the ANS. Due to the presence of multiple interventions and HRV variables, we employed logistic regression to determine the value of each test intervention in order to improve the understanding of pathophysiology in chronic constipation.

Jennifer Yu

PhD Student in Computational Biology and Medicine

My research interests include machine learning, large language models, precision oncology and clinical time series.