Analysis of regularity in the EEG background activity of Alzheimer's disease patients with Approximate Entropy
- PMID: 15979403
- DOI: 10.1016/j.clinph.2005.04.001
Analysis of regularity in the EEG background activity of Alzheimer's disease patients with Approximate Entropy
Abstract
Objective: The aim of this study was to analyse the regularity of the EEG background activity of Alzheimer's disease (AD) patients to test the hypothesis that the irregularity of the AD patients' EEG is lower than that of age-matched controls.
Methods: We recorded the EEG from 19 scalp electrodes in 10 AD patients and 8 age-matched controls and estimated the Approximate Entropy (ApEn). ApEn is a non-linear statistic that can be used to quantify the irregularity of a time series. Larger values correspond to more complexity or irregularity. A spectral analysis was also performed.
Results: ApEn was significantly lower in the AD patients at electrodes P3 and P4 (P < 0.01), indicating a decrease of irregularity. We obtained 70% sensitivity and 100% specificity at P3, and 80% sensitivity and 75% specificity at P4. Results seemed to be complementary to spectral analysis.
Conclusions: The decreased irregularity found in the EEG of AD patients in the parietal region leads us to think that EEG analysis with ApEn could be a useful tool to increase our insight into brain dysfunction in AD. However, caution should be applied due to the small sample size.
Significance: This article represents a first step in demonstrating the feasibility of ApEn for recognition of EEG changes in AD.
Similar articles
-
Approximate entropy as a measure of irregularity for psychiatric serial metrics.Bipolar Disord. 2006 Oct;8(5 Pt 1):430-40. doi: 10.1111/j.1399-5618.2006.00375.x. Bipolar Disord. 2006. PMID: 17042881 Review.
-
Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy.Physiol Meas. 2006 Nov;27(11):1091-106. doi: 10.1088/0967-3334/27/11/004. Epub 2006 Sep 12. Physiol Meas. 2006. PMID: 17028404
-
Entropy analysis of the EEG background activity in Alzheimer's disease patients.Physiol Meas. 2006 Mar;27(3):241-53. doi: 10.1088/0967-3334/27/3/003. Epub 2006 Jan 13. Physiol Meas. 2006. PMID: 16462011 Clinical Trial.
-
Analysis of EEG background activity in Alzheimer's disease patients with Lempel-Ziv complexity and central tendency measure.Med Eng Phys. 2006 May;28(4):315-22. doi: 10.1016/j.medengphy.2005.07.004. Epub 2005 Aug 24. Med Eng Phys. 2006. PMID: 16122963 Clinical Trial.
-
EEG dynamics in patients with Alzheimer's disease.Clin Neurophysiol. 2004 Jul;115(7):1490-505. doi: 10.1016/j.clinph.2004.01.001. Clin Neurophysiol. 2004. PMID: 15203050 Review.
Cited by
-
Brain complexity in stroke recovery after bihemispheric transcranial direct current stimulation in mice.Brain Commun. 2024 May 13;6(3):fcae137. doi: 10.1093/braincomms/fcae137. eCollection 2024. Brain Commun. 2024. PMID: 38741663 Free PMC article.
-
Advanced Overview of Biomarkers and Techniques for Early Diagnosis of Alzheimer's Disease.Cell Mol Neurobiol. 2023 Aug;43(6):2491-2523. doi: 10.1007/s10571-023-01330-y. Epub 2023 Feb 27. Cell Mol Neurobiol. 2023. PMID: 36847930 Review.
-
An Explainable Statistical Method for Seizure Prediction Using Brain Functional Connectivity from EEG.Comput Intell Neurosci. 2022 Dec 8;2022:2183562. doi: 10.1155/2022/2183562. eCollection 2022. Comput Intell Neurosci. 2022. PMID: 36531925 Free PMC article.
-
Assessing the Effects of Alzheimer Disease on EEG Signals Using the Entropy Measure: A Meta-analysis.Basic Clin Neurosci. 2022 Mar-Apr;13(2):153-164. doi: 10.32598/bcn.2021.1144.3. Epub 2022 Mar 1. Basic Clin Neurosci. 2022. PMID: 36425952 Free PMC article. Review.
-
Development of a Machine Learning Model to Discriminate Mild Cognitive Impairment Subjects from Normal Controls in Community Screening.Brain Sci. 2022 Aug 28;12(9):1149. doi: 10.3390/brainsci12091149. Brain Sci. 2022. PMID: 36138886 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Medical
Research Materials