Evaluation of Scale-Invariance In Physiological Signals By Means Of Balanced Estimation Of Diffusion Entropy

TL;DR

This study uses balanced estimation of diffusion entropy to reliably evaluate scale-invariance in physiological signals across sleep stages and stride records, revealing state-dependent variability and methodological advantages over traditional diffusion entropy analysis.

Contribution

It introduces the BEDE method for precise, short-time scale-invariance evaluation in physiological signals, outperforming traditional DE especially in short or trend-affected data.

Findings

Scale-invariance varies significantly across sleep stages.

BEDE provides more accurate estimates than DE in short time series.

Physiological states exhibit abrupt changes detectable by local scaling analysis.

Abstract

By means of the concept of balanced estimation of diffusion entropy we evaluate reliable scale-invariance embedded in different sleep stages and stride records. Segments corresponding to Wake, light sleep, REM, and deep sleep stages are extracted from long-term EEG signals. For each stage the scaling value distributes in a considerable wide range, which tell us that the scaling behavior is subject- and sleep cycle- dependent. The average of the scaling exponent values for wake segments is almost the same with that for REM segments ($\sim 0.8$). Wake and REM stages have significant high value of average scaling exponent, compared with that for light sleep stages ($\sim 0.7$). For the stride series, the original diffusion entropy (DE) and balanced estimation of diffusion entropy (BEDE) give almost the same results for de-trended series. Evolutions of local scaling invariance show that the physiological states change abruptly, though in the experiments great efforts have been done to keep conditions unchanged. Global behaviors of a single physiological signal may lose rich information on physiological states. Methodologically, BEDE can evaluate with considerable precision scale-invariance in very short time series ($\sim 10^2$), while the original DE method sometimes may underestimate scale-invariance exponents or even fail in detecting scale-invariant behavior. The BEDE method is sensitive to trends in time series. Existence of trend may leads to a unreasonable high value of scaling exponent, and consequent mistake conclusions.