A simple method for detecting chaos in nature

TL;DR

This paper introduces an automated, noise-robust method for detecting chaos in empirical data, demonstrating its effectiveness in biological signals like heart rate variability and providing a practical tool for researchers.

Contribution

The authors develop a combined chaos detection pipeline that overcomes noise sensitivity of classic methods, applicable to biological data and freely accessible.

Findings

The pipeline successfully detects chaos in noisy biological recordings.

Heart rate variability is shown to be stochastic, not chaotic.

The tool is user-friendly and publicly available.

Abstract

Chaos, or exponential sensitivity to small perturbations, appears everywhere in nature. Moreover, chaos is predicted to play diverse functional roles in living systems. A method for detecting chaos from empirical measurements should therefore be a key component of the biologist's toolkit. But, classic chaos-detection tools are highly sensitive to measurement noise and break down for common edge cases, making it difficult to detect chaos in domains, like biology, where measurements are noisy. However, newer tools promise to overcome these limitations. Here, we combine several such tools into an automated processing pipeline, and show that our pipeline can detect the presence (or absence) of chaos in noisy recordings, even for difficult edge cases. As a first-pass application of our pipeline, we show that heart rate variability is not chaotic as some have proposed, and instead reflects a stochastic process in both health and disease. Our tool is easy-to-use and freely available.