Using modeling and data analysis to explain heart rate variability

Faculty Mentor: Mette Olufsen

Prerequisites: Differential equations, interest in biology, programming experience.

Outline: Heart Rate Variability (HRV) refer to the variation in ms between heartbeats at rest. This measure is an easily obtained clinical metric that is useful for predicting the presence of disease (1), aging (2), and stress (3), but the underlying mechanism generating HRV is not completely understood. Many researchers have generated methods for calculating and analyzing HRV using statistics, signal processing, and machine learning (1), while these methods can detect how this quantity change they do not explain what causes HRV.  It is believed that HRV is generated by “sloppy” heart-brain interactions mediated by non-linear autonomic nervous system (ANS) processes. Recent studies have attempted to capture HRV via stochastic (4) and deterministic (5) differential equations models.

Research objectives: We will use various HRV methods to analyze data from healthy and diseased patients to find differences in the neurological response. Additionally, we will derive a mathematical model to explain HRV and its variation between patient groups.

Outcomes:  A structured analysis that, using machine learning, will be able to separate patient groups based on HRV metrics. Additionally, we will develop a mathematical model that can explain the observed differences between groups. 

References:

  1. ChuDuc H, NguyenPhan K, NguyenViet D (2013). A review of heart rate variability and its applications. APCBEE proc, 7:80-5.
  2. Stein PK, Barzila JI, Chaves PHM, Domitrovich PD, Gottdiener JS (2009). Heart rate variability and its changes over 5 years in older adults. Age Ageing, 38(2): 212-218. 
  3. Matthews S, Jelinek H, Vafaeiafraz S, McLachlan CS (2012). Heart rate stability and decreased parasympathetic heart rate variability in healthy young adults during perceived stress. Int J Cardiol, 156(3):337-338.
  4. West BJ, Turalska M (2019). Hypothetical control of heart rate variability. Frontiers Physiol, doi:10.3389/fphys.2019.01078.
  5. Geddes J, Ottesen JT, Mehlsen J, Olufsen MS (2019). Postural Orthostatic Tachycardia Syndrome explained using a baroreflex response model. arXiv.org: 2109.14558.