Electroencephalogram Background Activity Characterization with Approximate Entropy and Auto Mutual Information in Alzheimer's Disease Patients

The aim of this study was to analyze the electroencephalogram (EEG) background activity in Alzheimer's disease (AD) with two non-linear methods: Approximate Entropy (ApEri) and Auto Mutual Information (AMI). ApEn quantifies the regularity in data, while AMI detects linear and non-linear dependencies in time series. EEGs were recorded from the 19 scalp loci of the international 10-20 system in 11 AD patients and 11 age-matched controls. ApEn was significantly lower in AD patients at electrodes O1, O2, P3 and P4 (p < 0.01). The AMI of the AD patients decreased significantly more slowly with time delays than the AMI of normal controls at electrodes T5, T6, O1, 02, P3 and P4 (p < 0.01). Furthermore, we observed a strong correlation between the results obtained with both non-linear methods, suggesting that the AMI rate of decrease can be used to estimate the regularity in time series. The decreased irregularity found in AD patients suggests that EEG analysis with ApEn and AMI could help to increase our insight into brain dysfunction in AD.