Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy

Abstract

The aim of this study was to analyse the electroencephalogram (EEG) background activity of Alzheimer's disease (AD) patients using the Multiscale Entropy (MSE). The MSE is a recently developed method that quantifies the regularity of a signal on different time scales. These time scales are inspected by means of several coarsegrained sequences formed from the analysed signals. We recorded the EEGs from 19 scalp electrodes in 11 AD patients and 11 age-matched controls and estimated the MSE profile for each epoch of the EEG recordings. The shape of the MSE profiles reveals the EEG complexity, and it suggests that the EEG contains information in deeper scales than the smallest one. Moreover, the results showed that the EEG background activity is less complex in AD patients than control subjects. We found significant differences between both subject groups at electrodes F3, F7, Fp1, Fp2, T5, T6, P3, P4, O1 and O2 (p-value < 0.01, Student's t-test). These findings indicate that the EEG complexity analysis performed on deeper time scales by the MSE may be a useful tool in order to increase our knowledge of AD.

Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients

“…Accuracies reached 81.82% at 3 electrodes with LZ complexity, improving the results obtained with ApEn [3]. On the other hand, the inspection of EEG signals with MSE revealed their complex structure [13]. As the MSE profile values are higher in control subjects than in AD patients for most scales, it can be concluded that EEG background activity is less complex in patients, something that is also in agreement with our LZ complexity results [13].…”

“…The MSE was estimated with m = 1, r = 0.25 times the SD of the original time series and a maximum time scale ε MAX = 12. The analysis of our AD patients and control subjects' database with MSE showed important differences in the shape of the MSE profiles on the larger time scales, with significant differences at electrodes F3, F7, Fp1, Fp2, T5, T6, P3, P4, O1 and O2 (p < 0.01) [13]. All these techniques can be applied to relatively short and noisy time series, irrespective of whether their origin is stochastic or deterministic.…”

“…We have previously analysed the same EEG dataset with other non-linear techniques, such as sample entropy (SampEn) [2], Lempel-Ziv (LZ) complexity [3] or multiscale entropy (MSE) [13].…”

Analysis of MEG Background Activity in Alzheimer’s Disease Using Nonlinear Methods and ANFIS

“…33 Additionally, if r is too small, the entropy estimation might fail. 9 In addition to this, the accuracy and confidence of the SampEn estimate improve for low m values and large r values, since the number of matches of length m and m + 1 increases. 27 The existing rules lead to the use of r values between 0.1 and 0.25 times the standard deviation of the original time series and m values of 1 or 2, for signals from 100 to 5000 data points.…”

“…These results agree with other studies that showed a decreased complexity in the brain recordings from AD patients. For instance, Escudero et al 9 found significant differences in some EEG channels with multiscale entropy. Other EEG/MEG studies demonstrated that AD patients had lower LZC values than controls.…”