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A Novel Computer-Aided Diagnostic System for Alzheimer’s Diagnosis Using Variational Mode Decomposition Method

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Abstract

Alzheimer’s disease (AD) is a significant neurological disorder with deficits in cognitive and behavioral brain functions. Although there is no cure for AD, early diagnosis is essential in slowing the disease and increasing the patient’s quality of life. In addition, the diagnosis of the disease includes costly tests and a complex process that an experienced specialist must evaluate. Therefore, this study presents a new computer-aided diagnosis system (CAD) allowing automatic AD diagnosis by EEG signals. The present study used EEG recordings of 24 healthy controls and 24 AD patients. The proposed algorithm includes a preprocessing step using multi-scale principal component analysis (MSPCA) for noise removal, decomposition of the signal into subcomponents with the variational mode decomposition (VMD) method, and extraction of statistical features from each subcomponent. The achievement of the recommended method in distinguishing between healthy individuals and AD patients was tested by applying various ensemble learning techniques and decomposition methods. As a result of the empirical studies, the maximum classification accuracy of AD diagnosis was obtained as 98.42 ± 0.06 using the Rotation Forest algorithm.

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Data Availability

The data used in this study are taken from the publicly available data set. The data set is available at "https://osf.io/s74qf/ "[25].

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Authors and Affiliations

  1. Technical Sciences Vocational School, Gaziantep University, 27310, Gaziantep, Turkey

    Zülfikar Aslan

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Aslan, Z. A Novel Computer-Aided Diagnostic System for Alzheimer’s Diagnosis Using Variational Mode Decomposition Method. Circuits Syst Signal Process 43, 615–633 (2024). https://doi.org/10.1007/s00034-023-02496-y

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