Key result
Alzheimer's disease patients had significantly lower EEG approximate entropy and a slower decrease in auto mutual information compared to age-matched controls (p < 0.01).
Why the study?
Does nonlinear EEG analysis using approximate entropy and auto mutual information detect differences in brain signal regularity between Alzheimer's disease patients and controls?
Case-Control (n=22)
Does nonlinear EEG analysis using approximate entropy and auto mutual information detect differences in brain signal regularity between Alzheimer's disease patients and controls?
p-value: p=<0.01
Nonlinear EEG analysis using approximate entropy and auto mutual information can detect decreased regularity in brain signals of Alzheimer's disease patients.
Nonlinear EEG metrics may distinguish Alzheimer's-related signal changes; hypothesis-generating and requires prospective validation before clinical use.
We analysed the electroencephalogram (EEG) from Alzheimer's disease (AD) patients with two nonlinear methods: approximate entropy (ApEn) and auto mutual information (AMI). ApEn quantifies regularity in data, while AMI detects linear and nonlinear dependencies in time series. EEGs from 11 AD patients and 11 age-matched controls were analysed. ApEn was significantly lower in AD patients at electrodes O1, O2, P3 and P4 (p < 0.01). The EEG AMI decreased more slowly with time delays in patients than in controls, with significant differences at electrodes T5, T6, O1, O2, P3 and P4 (p < 0.01). The strong correlation between results from both methods shows that the AMI rate of decrease can be used to estimate the regularity in time series. Our work suggests that nonlinear EEG analysis may contribute to increase the insight into brain dysfunction in AD, especially when different time scales are inspected, as is the case with AMI.
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Abásolo et al. (2008) conducted a case-control in Alzheimer's disease (n=22). Alzheimer's disease vs. Age-matched controls was evaluated on Approximate entropy (ApEn) and auto mutual information (AMI) of EEG (p=<0.01). Alzheimer's disease patients had significantly lower EEG approximate entropy and a slower decrease in auto mutual information compared to age-matched controls (p < 0.01).
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