Alzheimer's disease (AD) is one of the fastest growing neurological diseases. Multiscale entropy with coarse-graining based on mean (MSEμ) has been widely used to characterize AD. Alternatively, multiscale entropy based on variance (MSEσ2) has been recently proposed to quantify the dynamics of volatility (variance) of univariate signals. Here, we extend the MSEσ2 to multivariate signals to take into account both the time and spatial domains for discrimination of resting-state magnetoencephalogram (MEG) recordings of 36 AD patients from those of 26 normal controls. We also consider the usefulness of the refined composite mvMSEσ2 (RCmvMSEσ2) to understand if the RCmvMSEσ2 can better discriminate AD group from control subjects in comparison with mvMSEσ2. The results show mvMSEσ2 and RCmvMSEσ2, unlike exiting multiscale-based methods, lead to significant differences between control and AD patients at all scale factors. The results obtained by the mvMSEσ2 and RCmvMSEσ2 are similar. Thus, refined composite technique might not enhance the detection of different pathological states, especially when signals are not too noisy and short. Finally, our findings show that the mvMSEσ2 and RCmvMSEσ2 can be useful tools for the analysis of real signals to characterize different kinds of dynamics.
|Title of host publication||IEEE International Conference for Students on Applied Engineering (ISCAE)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||6|
|Publication status||Published - 21 Oct 2016|