Mathematical Modelling of Heart Rate Changes in the Mouse

TL;DR

This paper models the complex blood pressure waveform in mice using coupled oscillators, combining experimental data analysis with linear and nonlinear mathematical models to understand cardiovascular variability.

Contribution

It introduces a minimalistic coupled oscillator model for blood pressure dynamics, integrating spectral, Hilbert, and phase plane analyses with experimental data.

Findings

Chaotic blood pressure waveform characterized by oscillatory interactions

Linear and nonlinear models capture key features of cardiovascular variability

Spectral and phase analysis reveal underlying oscillatory structures

Abstract

The CVS is composed of numerous interacting and dynamically regulated physiological subsystems which each generate measurable periodic components such that the CVS can itself be presented as a system of weakly coupled oscillators. The interactions between these oscillators generate a chaotic blood pressure waveform signal, where periods of apparent rhythmicity are punctuated by asynchronous behaviour. It is this variability which seems to characterise the normal state. We used a standard experimental data set for the purposes of analysis and modelling. Arterial blood pressure waveform data was collected from conscious mice instrumented with radiotelemetry devices over $24$ hours, at a $100$ Hz and $1$ kHz time base. During a $24$ hour period, these mice display diurnal variation leading to changes in the cardiovascular waveform. We undertook preliminary analysis of our data using Fourier transforms and subsequently applied a series of both linear and nonlinear mathematical approaches in parallel. We provide a minimalistic linear and nonlinear coupled oscillator model and employed spectral and Hilbert analysis as well as a phase plane analysis. This provides a route to a three way synergistic investigation of the original blood pressure data by a combination of physiological experiments, data analysis viz. Fourier and Hilbert transforms and attractor reconstructions, and numerical solutions of linear and nonlinear coupled oscillator models. We believe that a minimal model of coupled oscillator models that quantitatively describes the complex physiological data could be developed via such a method. Further investigations of each of these techniques will be explored in separate publications.