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Classifying Neurodegenerative Diseases from Selected Temporal EEG Electrodes: Towards Ear-EEG Applications.

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    Summary

    Early dementia detection using machine learning on temporal EEG shows promise. While distinguishing Alzheimer's Disease (AD) from healthy controls is feasible, differentiating between AD and Fronto-temporal Dementia (FTD) requires further model improvement.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Artificial Intelligence

    Background:

    • Dementia affects over 55 million globally, necessitating early detection due to the lack of a cure.
    • Machine learning (ML) applied to electroencephalography (EEG) shows potential for early dementia diagnosis.
    • Accessible tools like ear-EEG are being explored for widespread brain health monitoring.

    Purpose of the Study:

    • To investigate the classification of Alzheimer's Disease (AD) and Fronto-temporal Dementia (FTD) using temporal EEG data.
    • To assess the feasibility of a multiclass classification approach for differentiating between healthy individuals, AD, and FTD.
    • To evaluate the potential of ear-EEG as an accessible tool for dementia detection.

    Main Methods:

    • Utilized EEG recordings from 88 participants (29 control, 36 AD, 23 FTD) at rest with eyes closed.
    • Extracted features from temporal electrodes (T3, T4, T5, T6, F7, F8), including sub-band power and band power ratios.
    • Employed binary and multiclass classification models to differentiate between cognitive states.

    Main Results:

    • Achieved 81.8% accuracy in binary classification of healthy vs. AD.
    • Obtained 63.8% accuracy in multiclass classification of healthy, AD, and FTD.
    • Identified challenges in multiclass classification for differentiating dementia subtypes using the selected features.

    Conclusions:

    • Temporal EEG data holds potential for developing dementia classification models, supporting the use of accessible tools like ear-EEG.
    • Further advancements in multiclass classification models are needed to accurately differentiate between dementia types.
    • Early detection through improved EEG-based tools can facilitate timely interventions and enhance patient outcomes.