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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Alzheimer's Disease Classification From Speech Pause Distributions With Context Information.

Geet Khatri, Reza Soleimani, Katarina L Haley

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    Summary

    This study introduces a new method for detecting Alzheimer's disease (AD) by analyzing speech pauses and their surrounding context. Incorporating context significantly improves accuracy, reaching up to 81% while preserving privacy.

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    Area of Science:

    • Computational linguistics
    • Neurodegenerative disease research
    • Biomedical signal processing

    Background:

    • Alzheimer's disease (AD) is known to alter speech patterns, specifically pause characteristics.
    • Prior research utilized pause lengths for AD classification, but context was overlooked.

    Purpose of the Study:

    • To develop and evaluate a novel Alzheimer's disease detection method.
    • To investigate the impact of incorporating speech pause context into classification features.

    Main Methods:

    • Utilized automatic speech recognition (ASR) and forced alignment to extract pause lengths and context from audio.
    • Extracted statistical summaries of pause lengths with contextual information from transcripts.
    • Employed these summaries as features for a classification model.

    Main Results:

    • Incorporating pause context significantly enhanced classification performance compared to using pause lengths alone.
    • Achieved a classification accuracy of up to 81% with the proposed method.
    • The developed features demonstrated a high degree of privacy preservation.

    Conclusions:

    • Speech pause context is a valuable feature for improving Alzheimer's disease detection.
    • The proposed method offers a promising, privacy-preserving approach for AD diagnosis.
    • Further research can explore the clinical utility of this context-aware speech analysis.