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    Area of Science:

    • Otolaryngology
    • Biomedical Engineering
    • Speech-Language Pathology

    Background:

    • Voice disorders, often stemming from vocal hyperfunction (abuse/misuse), are challenging to diagnose accurately with standard clinical methods.
    • Hyperfunctional vocal behaviors vary daily and may not manifest during brief assessments, limiting the understanding of their prevalence and impact.
    • Developing noninvasive, ambulatory measures is crucial for reliably distinguishing vocal hyperfunction from normal vocal patterns.

    Purpose of the Study:

    • To investigate the potential of neck-surface accelerometry for detecting vocal hyperfunction.
    • To establish if acceleration signals can differentiate vocal behaviors associated with vocal fold nodules from those of healthy controls.
    • To lay the groundwork for ambulatory monitoring of vocal behaviors in hyperfunctional voice disorders.

    Main Methods:

    • Utilized neck-surface accelerometers to capture continuous, noninvasive acceleration signals during daily activities.
    • Collected weeklong acceleration data from 12 female adults with vocal fold nodules and 12 matched controls.
    • Derived and normalized acoustic features (sound pressure level, fundamental frequency) from 5-min windows of acceleration data.
    • Employed supervised machine learning algorithms to analyze the derived features and classify subjects.

    Main Results:

    • Supervised machine learning successfully differentiated vocal behaviors between patients with vocal fold nodules and control speakers.
    • The acceleration signal analysis achieved a 91.7% classification accuracy (22 out of 24 subjects correctly identified).
    • Distinct vocal behavior patterns were detectable using neck-surface acceleration recordings.

    Conclusions:

    • Neck-surface accelerometry shows promise as a noninvasive tool for detecting aberrant vocal behaviors linked to hyperfunctional voice disorders.
    • Ambulatory monitoring via accelerometry could enable more accurate diagnosis and management of conditions like vocal fold nodules.
    • This approach offers a potential future method for identifying individuals with vocal hyperfunction in real-world settings.