Learning to Detect Vocal Hyperfunction From Ambulatory Neck-Surface Acceleration Features: Initial Results for Vocal Fold Nodules
Name
Guttag_Learning to.pdf
Size
612.29 KB
Format
Adobe PDF
Checksum (MD5)
7c508255a834ec1c5fd281abb179b523
Author(s)
Ghassemi, Marzyeh
Van Stan, Jarrad H.
Mehta, Daryush D.
Zanartu, Matias
Cheyne, Harold A.
Hillman, Robert E.
Guttag, John V.
Date Issued
January 2014
Journal
IEEE Transactions on Biomedical Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Ghassemi, Marzyeh, Jarrad H. Van Stan, Daryush D. Mehta, Matias Zanartu, Harold A. Cheyne, Robert E. Hillman, and John V. Guttag. “Learning to Detect Vocal Hyperfunction From Ambulatory Neck-Surface Acceleration Features: Initial Results for Vocal Fold Nodules.” IEEE Trans. Biomed. Eng. 61, no. 6 (June 2014): 1668–1675.
Version
Author's final manuscript
Abstract
Voice disorders are medical conditions that often result from vocal abuse/misuse which is referred to generically as vocal hyperfunction. Standard voice assessment approaches cannot accurately determine the actual nature, prevalence, and pathological impact of hyperfunctional vocal behaviors because such behaviors can vary greatly across the course of an individual's typical day and may not be clearly demonstrated during a brief clinical encounter. Thus, it would be clinically valuable to develop noninvasive ambulatory measures that can reliably differentiate vocal hyperfunction from normal patterns of vocal behavior. As an initial step toward this goal we used an accelerometer taped to the neck surface to provide a continuous, noninvasive acceleration signal designed to capture some aspects of vocal behavior related to vocal cord nodules, a common manifestation of vocal hyperfunction. We gathered data from 12 female adult patients diagnosed with vocal fold nodules and 12 control speakers matched for age and occupation. We derived features from weeklong neck-surface acceleration recordings by using distributions of sound pressure level and fundamental frequency over 5-min windows of the acceleration signal and normalized these features so that intersubject comparisons were meaningful. We then used supervised machine learning to show that the two groups exhibit distinct vocal behaviors that can be detected using the acceleration signal. We were able to correctly classify 22 of the 24 subjects, suggesting that in the future measures of the acceleration signal could be used to detect patients with the types of aberrant vocal behaviors that are associated with hyperfunctional voice disorders.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/TBME.2013.2297372
