Ambulatory assessment of phonotraumatic vocal hyperfunction using glottal airflow measures estimated from neck-surface acceleration

Abstract

Phonotraumatic vocal hyperfunction (PVH) is associated with chronic misuse and/or abuse of voice that can result in lesions such as vocal fold nodules. The clinical aerodynamic assessment of vocal function has been recently shown to differentiate between patients with PVH and healthy controls to provide meaningful insight into pathophysiological mechanisms associated with these disorders. However, all current clinical assessment of PVH is incomplete because of its inability to objectively identify the type and extent of detrimental phonatory function that is associated with PVH during daily voice use. The current study sought to address this issue by incorporating, for the first time in a comprehensive ambulatory assessment, glottal airflow parameters estimated from a neck-mounted accelerometer and recorded to a smartphone-based voice monitor. We tested this approach on 48 patients with vocal fold nodules and 48 matched healthy-control subjects who each wore the voice monitor for a week. Seven glottal airflow features were estimated every 50 ms using an impedance-based inverse filtering scheme, and seven high-order summary statistics of each feature were computed every 5 minutes over voiced segments. Based on a univariate hypothesis testing, eight glottal airflow summary statistics were found to be statistically different between patient and healthy-control groups. L1-regularized logistic regression for a supervised classification task yielded a mean (standard deviation) area under the ROC curve of 0.82 (0.25) and an accuracy of 0.83 (0.14). These results outperform the state-of-the-art classification for the same classification task and provide a new avenue to improve the assessment and treatment of hyperfunctional voice disorders.

Fig 1. Example of VHM system.

Illustration of the smartphone-based ambulatory voice monitor that uses a neck-surface accelerometer attached to the skin halfway between the thyroid prominence and the suprasternal notch of a female subject.

Fig 2. Representation of the subglottal system.

(a) Accelerometer position and sub1 and sub2 system parts. (b) A mechano-acoustic analogy of the subglottal system including load impedance from skin. Reproduced with permission.

Fig 3. Example of ambulatory IBIF analysis.

(A) Estimated glottal airflow waveform and (B) its derivative, showing how time-domain measures were derived per glottal cycle. Measures were then averaged over all cycles to yield a single value per frame for each time-domain measure.

Fig 4. Spectrum of the frame in Fig 3(A).

Fig 5. Flowchart.

Feature extraction and classification process for 96 subjects.

Fig 6. Performance results across subject pairs with L1-logistic regression.

Area Under the ROC Curve (AUC), Accuracy, Sensitivity, Specificity. The red crosses indicates the average value for each performance metric.

Fig 7. Classification results from L1-logistic regression.

The threshold (blue line) at 0.57 classifies correctly 79 from 96 subjects (82.3%).

Fig 8. F-score distributions from Table 5.

From all 26 features (rightmost box plot) to only one feature (H1-H2 95th%, leftmost box plot).

Fig 9. Odds ratio association with phonotraumatic subjects.