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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Automatic speech analysis to early detect functional cognitive decline in elderly population.

E Ambrosini, M Cid, C Galan de Isla

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
    PubMed
    Summary
    This summary is machine-generated.

    Acoustic features from spontaneous speech can distinguish normal cognitive function from mild cognitive impairment in older adults. Voice analysis shows promise for early detection of cognitive decline.

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    Area of Science:

    • Gerontology
    • Speech Science
    • Computational Linguistics

    Background:

    • Mild cognitive impairment (MCI) affects a significant portion of the aging population.
    • Early detection of MCI is crucial for timely intervention and management.
    • Current diagnostic methods can be resource-intensive and may not capture subtle changes.

    Purpose of the Study:

    • To evaluate the efficacy of acoustic features from spontaneous speech in discriminating between individuals with normal cognitive function and those with MCI.
    • To identify specific acoustic features that are significant predictors of cognitive status.
    • To develop a classifier for early detection of cognitive decline using voice analysis.

    Main Methods:

    • Voice recordings were collected from 90 Italian adults (>65 years) categorized into normal cognition (MMSE>26) and MCI (20≤MMSE≤26) groups.
    • Acoustic features were extracted using custom MATLAB software.
    • Linear mixed models were employed to select significant discriminating features, including % unvoiced segments, duration of unvoiced segments, % voice breaks, speech rate, and syllable duration.
    • A learning-based classifier was trained and tested using leave-one-out cross-validation.

    Main Results:

    • Acoustic features alone achieved a classification accuracy of 0.73.
    • Incorporating age and years of education improved accuracy to 0.80.
    • These results demonstrate the potential of voice analysis for cognitive assessment.

    Conclusions:

    • Acoustic features derived from spontaneous speech can effectively discriminate between normal cognition and mild cognitive impairment.
    • This approach offers a non-invasive and potentially scalable method for early detection of cognitive decline.
    • The findings support the development of mobile applications for on-the-fly voice analysis to aid in identifying early signs of cognitive impairment.