An unbiased algorithm for objective separation of Alzheimer’s, Alzheimer’s mixed with cerebrovascular symptomology, and healthy controls from one another using electrovestibulography (EVestG)

Zeinab A. Dastgheib, Brian J. Lithgow, Zahra K. Moussavi

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3 Citations (Scopus)


Diagnosis of Alzheimer’s disease (AD) from AD with cerebrovascular disease pathology (AD-CVD) is a rising challenge. Using electrovestibulography (EVestG) measured signals, we develop an automated feature extraction and selection algorithm for an unbiased identification of AD and AD-CVD from healthy controls as well as their separation from each other. EVestG signals of 24 healthy controls, 16 individuals with AD, and 13 with AD-CVD were analyzed within two separate groupings: One-versus-One and One-versus-All. A multistage feature selection process was conducted over the training dataset using linear support vector machine (SVM) classification with 10-fold cross-validation, k nearest neighbors/averaging imputation, and exhaustive search. The most frequently selected features that achieved highest classification performance were selected. 10-fold cross-validation was applied via a linear SVM classification on the entire dataset. Multivariate analysis was run to test the between population differences while controlling for the covariates. Classification accuracies of ≥ 80% and 78% were achieved for the One-versus-All classification approach and AD versus AD-CVD separation, respectively. The results also held true after controlling for the effect of covariates. AD/AD-CVD participants showed smaller/larger EVestG averaged field potential signals compared to healthy controls and AD-CVD/AD participants. These characteristics are in line with our previous study results. Graphical abstract: [Figure not available: see fulltext.]

Original languageEnglish
Pages (from-to)797-810
Number of pages14
JournalMedical & Biological Engineering & Computing
Issue number3
Publication statusPublished - Mar 2022
Externally publishedYes


  • Alzheimer’s Disease
  • Cerebrovascular pathology
  • Classification
  • Electrovestibulography
  • Feature selection

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