Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment?

Abstract

So far, most articles using the multivariate multi-scale entropy algorithm mainly use algorithms to analyze the multivariable signal complexity without clearly describing what characteristics of signals these algorithms measure and what factors affect these algorithms. This paper analyzes six commonly used multivariate multi-scale entropy algorithms from a new perspective. It clarifies for the first time what characteristics of signals these algorithms measure and which factors affect them. It also studies which algorithm is more suitable for analyzing mild cognitive impairment (MCI) electroencephalograph (EEG) signals. The simulation results show that the multivariate multi-scale sample entropy (mvMSE), multivariate multi-scale fuzzy entropy (mvMFE), and refined composite multivariate multi-scale fuzzy entropy (RCmvMFE) algorithms can measure intra- and inter-channel correlation and multivariable signal complexity. In the joint analysis of coupling and complexity, they all decrease with the decrease in signal complexity and coupling strength, highlighting their advantages in processing related multi-channel signals, which is a discovery in the simulation. Among them, the RCmvMFE algorithm can better distinguish different complexity signals and correlations between channels. It also performs well in anti-noise and length analysis of multi-channel data simultaneously. Therefore, we use the RCmvMFE algorithm to analyze EEG signals from twenty subjects (eight control subjects and twelve MCI subjects). The results show that the MCI group had lower entropy than the control group on the short scale and the opposite on the long scale. Moreover, frontal entropy correlates significantly positively with the Montreal Cognitive Assessment score and Auditory Verbal Learning Test delayed recall score on the short scale.

A Novel Metric for Alzheimer’s Disease Detection Based on Brain Complexity Analysis via Multiscale Fuzzy Entropy

“…In particular, predictability focuses on the temporal-spatial evolution of a system's state and is measured with fractal metrics [20], while regularity analyzes the presence of repeated patterns in a time series. In the context of brain complexity, measures of regularity, such as entropy metrics, are frequently employed to assess the presence of repeated patterns in the time series [21].…”

“…This suggests that the complexity of EEG signals provides detailed insights into neural mechanisms that other measurement approaches may not capture, resulting in a high potential for diagnostics and early detection of AD. In the last few years, multiscale formulations of entropy showed good sensitivity to AD severity, reflecting a decline in complexity from moderate to severe stages and including cases of mild cognitive impairment (MCI) [21,22]. Various studies have utilized traditional formulations such as Shannon entropy, permutation entropy, and approximate entropy, with sample entropy currently standing as a widely adopted approach, especially in its multiscale formulation (MSE) [23].…”