Small-world networks propensity in spontaneous speech signals of Alzheimer's disease: visibility graph analysis.

Exploiting complex network methods to describe dynamical behavior based on speech time series can provide fundamental insights into the function of underlying dynamical processes in Alzheimer's disease (AD). This study scrutinizes the dynamic alterations in Alzheimer's speech through abstract concepts of small-world networks. The visibility graph (VG) of the time series of spontaneous speech is introduced as a quantitative method to differentiate between healthy individuals and those with Alzheimer's. The dynamic speech patterns across three AD and healthy subjects stages are analyzed by examining the small-world feature structure, characterized by a high clustering coefficient (C) and short average path length (L) in the VG. These characteristics are calculated based on degree K. The results demonstrate the practical utility of C and L in identifying the underlying pathological mechanisms of AD. Furthermore, all speech series exhibit small-world topology based on VG, with changes reflecting the brain system's pathology that impacts individuals' language skills.