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Alzheimer's disease diagnosis using rhythmic power changes and phase differences: a low-density EEG study.

Juan Wang1,2, Jiamei Zhao1, Xiaoling Chen1,2

  • 1Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China.

Frontiers in Aging Neuroscience
|February 3, 2025
PubMed
Summary

Low-density electroencephalography (EEG) can help diagnose Alzheimer's disease (AD). Combining eye-closed and eye-open EEG data improves classification accuracy for AD patients.

Keywords:
Alzheimer’s diseaseSupport vector machineelectroencephalographylow-densityresting state

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Area of Science:

  • Neuroscience
  • Medical Technology

Background:

  • Alzheimer's disease (AD) diagnosis needs accessible tools as disease-modifying treatments emerge.
  • Electroencephalography (EEG) is a cost-effective, non-invasive neuroimaging technique.
  • EEG biomarkers for AD are not fully understood with limited electrodes.

Purpose of the Study:

  • To investigate Alzheimer's disease (AD) pathological characteristics using low-density EEG.
  • To assess EEG alterations in AD patients compared to healthy controls (HC) under resting conditions.
  • To evaluate the utility of combining eye-closed (EC) and eye-open (EO) EEG data for AD classification.

Main Methods:

  • Collected low-density EEG data from 26 AD patients and 29 HC during EC and EO resting states.
  • Analyzed power spectrum, phase lock value (PLV), weighted lag phase index (wPLI), and theta/beta frequency coupling.
  • Applied machine learning (Support Vector Machine - SVM) for inter-group classification.

Main Results:

  • AD patients showed decreased alpha power and altered theta band connectivity in EC state.
  • Increased frontal and central theta/beta frequency coupling observed in AD during EC.
  • No significant group differences found in the EO condition.
  • Combined EC and EO EEG features significantly improved AD classification accuracy using SVM.

Conclusions:

  • Low-density EEG data from resting-state paradigms show potential for AD identification.
  • Combining EC and EO conditions provides complementary information for differentiating AD cohorts.
  • Machine learning techniques enhance the classification of Alzheimer's disease using EEG data.