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Related Experiment Video

Updated: Dec 13, 2025

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Iterative Filtering Decomposition Based Early Dementia Diagnosis Using EEG With Cognitive Tests.

Neelam Sharma, Maheshkumar H Kolekar, Kamlesh Jha

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |August 4, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study developed a novel approach using electroencephalogram (EEG) signals and cognitive tests to diagnose dementia. The k-nearest neighbor (k NN) algorithm achieved high accuracy in identifying dementia and mild cognitive impairment.

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    Area of Science:

    • Neuroscience
    • Medical Technology
    • Computational Biology

    Background:

    • Rising life expectancy correlates with increased dementia prevalence, necessitating early detection methods.
    • Mild cognitive impairment (MCI) signifies a high risk for developing dementia, underscoring the need for timely diagnosis.
    • Healthcare expenditure on dementia is escalating, making cost-effective diagnostic tools crucial.

    Purpose of the Study:

    • To introduce a novel approach for dementia diagnosis by integrating cognitive task performance with electroencephalogram (EEG) signal processing.
    • To evaluate the efficacy of iterative filtering (IF) as a decomposition technique for EEG analysis in dementia detection.
    • To compare the diagnostic performance of various machine learning classifiers, including k-nearest neighbor (k NN), decision trees, support vector machines, and ensemble methods.

    Main Methods:

    • Continuous EEG data were recorded from dementia patients, early dementia patients, and healthy individuals under resting and cognitive task conditions.
    • Cognitive tasks included the finger tapping test (FTT) and the continuous performance test (CPT).
    • EEG signals were processed using iterative filtering (IF) and analyzed for key features: power spectral density, variance, fractal dimension, and Tsallis entropy.

    Main Results:

    • The k-nearest neighbor (k NN) classifier demonstrated superior performance, achieving accuracies of 92.00% for dementia, 91.67% for early dementia, and 91.87% for healthy subjects with 10-fold cross-validation.
    • The continuous performance test (CPT) emerged as the most effective predictor for distinguishing healthy individuals.
    • The finger tapping test (FTT) proved to be a significant indicator for diagnosing substantial dementia.

    Conclusions:

    • The k-nearest neighbor (k NN) algorithm is highly effective and superior to other tested classifiers for dementia diagnosis.
    • The iterative filtering (IF) decomposition technique significantly enhances diagnostic accuracy, even with limited datasets.
    • Cognitive tasks like FTT and CPT, when combined with EEG analysis, offer valuable insights for early dementia detection and diagnosis.