Non-linear analysis of EEG and MEG in patients with Alzheimer’s disease

The aim of the present study is to show the utility of non-linear methods to analyse the electroencephalogram (EEG) and magnetoencephalogram (MEG) in patients with Alzheimer’s disease (AD). The following non-linear methods have been applied to study the EEG and MEG background activity in AD patients and control subjects: approximate entropy, sample entropy, multiscale entropy, auto-mutual information and Lempel-Ziv complexity. We discuss why these non-linear methods are appropriate to analyse EEG and MEG. Furthermore, the performance of all these methods has been compared when applied to the same databases of EEG and MEG recordings. Our results show that EEG and MEG background activities in AD patients are less complex and more regular than in healthy control subjects. In line with previous studies, our work suggests that non-linear analysis techniques could be useful in AD diagnosis.