Research on Multiscale Information Storage of MEG of Depression Based on ARFI Model

Abstract

As a noninvasive brain function detection technique, Magnetoencephalography (MEG) has been widely used in the research of depression. By analyzing the amount of information storage, the difference of MEG information storage between patients with depression and healthy people was studied. Our analysis was carried out in the popular multiscale entropy framework, in which the time series were first "coarse-grained" on the selected time scale by low-pass filtering and down-sampling, and then its complexity was evaluated ac-cording to conditional entropy. Within this framework, we used the linear fractional integral autoregressive (ARFI) model to derive the analytical expression of information storage calculated at multiple time scales. We used the information storage expression derived from the ARFI model and then collected the information storage of MEG through positive, negative and neutral stimuli and finally calculate it. The experimental results showed that it was best to distinguish between patients with depression and healthy people through the information storage of MEG through positive stimuli, and it was best to distinguish healthy people from patients with depression at a higher frequency if it was negative or neutral stimuli.