Modeling distortion product otoacoustic emission input/output functions using segmented regression

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Goldman, Bryan [1 ]
Sheppard, Lianne [2 ,3 ]
Kujawa, Sharon G. [4 ]
Seixas, Noah S. [3 ]
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[1] Fred Hutchinson Cancer Research Center, Seattle, WA 98109, United States
[2] Department of Biostatistics, University of Washington, Seattle, WA 98195, United States
[3] Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, United States
[4] Department of Otology and Laryngology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA 02114, United States
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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. © 2006 Acoustical Society of America;
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页码:2764 / 2776
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