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Modeling distortion product otoacoustic emission input/output functions using segmented regression.
Goldman-B; Sheppard-L; Kujawa-SG; Seixas-NS
J Acoust Soc Am 2006 Nov; 120(5)(Part 1):2764-2776
Distortion product otoacoustic emissions (DPOAEs) are low-level acoustic signals, the detection of which involves extraction from a background of noise. Boege and Janssen [J. Acoust. Soc. Am. 111, 1810-1818 (2002)] described a method for modeling the presence and growth of these responses. While improving growth function parameter estimation, this technique excludes a significant fraction of the data (especially low-level responses), and relies on ad hoc model fit acceptance criteria. The statistical difficulties associated with these limitations are described, and a weighted segmented linear regression model that avoids them is proposed. A simple test is presented for the presence of DPOAE growth. This technique is compared to that of Boege and Janssen in a dataset of 9 556 input/output (I/O) functions collected over 4 years on 866 ears from 379 construction apprentices and 63 age-matched controls. Comparisons are made on the entire dataset and within audiometric hearing loss categories. Segmented regression avoids the statistical pitfalls of the previous method, allows estimation of the threshold and slope of auditory response on a far greater number of I/O functions, and improves estimation of these parameters in this dataset. The potential for this method to yield more sensitive metrics of hearing function and compromise is discussed.
Models; Acoustics; Acoustic-signals; Noise; Noise-induced-hearing-loss; Hearing-loss; Audiometry; Auditory-system; Auditory-feedback
Issue of Publication
The Journal of the Acoustical Society of America
University of Washington
Page last reviewed: September 2, 2020
Content source: National Institute for Occupational Safety and Health Education and Information Division