Regeneration of Stochastic Processes: An Inverse Method

TL;DR

This paper introduces an inverse method to reconstruct stochastic processes from data, demonstrated on heart rate fluctuations, offering a potential diagnostic tool for distinguishing healthy individuals from those with heart failure.

Contribution

The paper presents a new inverse technique to derive governing equations of stochastic processes directly from data, applicable to biomedical signals.

Findings

Differentiates healthy and heart failure subjects based on drift and diffusion coefficients.

Provides a novel diagnostic approach for early detection of heart failure.

Successfully reconstructs stochastic models from real-world heart rate data.

Abstract

We propose a novel inverse method that utilizes a set of data to construct a simple equation that governs the stochastic process for which the data have been measured, hence enabling us to reconstruct the stochastic process. As an example, we analyze the stochasticity in the beat-to-beat fluctuations in the heart rates of healthy subjects as well as those with congestive heart failure. The inverse method provides a novel technique for distinguishing the two classes of subjects in terms of a drift and a diffusion coefficients which behave completely differently for the two classes of subjects, hence potentially providing a novel diagnostic tool for distinguishing healthy subjects from those with congestive heart failure, even at the early stages of the disease development.