Fast, Continuous Audiogram Estimation Using Machine Learning

Abstract

Objectives Pure-tone audiometry has been a staple of hearing assessments for decades. Many different procedures have been proposed for measuring thresholds with pure tones by systematically manipulating intensity one frequency at a time until a discrete threshold function is determined. The authors have developed a novel nonparametric approach for estimating a continuous threshold audiogram using Bayesian estimation and machine learning classification. The objective of this study is to assess the accuracy and reliability of this new method relative to a commonly used threshold measurement technique. Design The authors performed air conduction pure-tone audiometry on 21 participants between the ages of 18 and 90 years with varying degrees of hearing ability. Two repetitions of automated machine learning audiogram estimation and 1 repetition of conventional modified Hughson-Westlake ascending-descending audiogram estimation were acquired by an audiologist. The estimated hearing thresholds of these two techniques were compared at standard audiogram frequencies (i.e., 0.25, 0.5, 1, 2, 4, 8 kHz). Results The two threshold estimate methods delivered very similar estimates at standard audiogram frequencies. Specifically, the mean absolute difference between estimates was 4.16 ± 3.76 dB HL. The mean absolute difference between repeated measurements of the new machine learning procedure was 4.51 ± 4.45 dB HL. These values compare favorably to those of other threshold audiogram estimation procedures. Furthermore, the machine learning method generated threshold estimates from significantly fewer samples than the modified Hughson-Westlake procedure while returning a continuous threshold estimate as a function of frequency. Conclusions The new machine learning audiogram estimation technique produces continuous threshold audiogram estimates accurately, reliably, and efficiently, making it a strong candidate for widespread application in clinical and research audiometry.

Conjoint psychometric field estimation for bilateral audiometry

“…Stimuli are delivered to each ear individually, but inference is drawn for both ears simultaneously. This procedure extends the rovingfrequency procedure of machine-learning audiometry, already shown to deliver equivalent thresholds to the fixed-frequency Hughson-Westlake procedure (Song et al, 2015b), into a roving-ear procedure. The stimuli themselves are still pure tones and the instructions to participants are unchanged: Respond every time you hear a tone as soon as you hear it.…”

“…As a feasibility check, unconstrained conjoint hearing threshold estimation was performed bilaterally on a normal-hearing human listener, and the results were compared to those from disjoint estimation. The procedures for disjoint estimation were exactly as described previously (Song et al, 2015b). The procedures for conjoint estimation matched the sound delivery and response characteristics of the disjoint procedure, but tone selection and hearing threshold estimation were performed according to the principles developed for the conjoint simulations above.…”

“…Zhang, 2013; Lewi, Butera, & Paninski, 2009;Paninski, Pillow, & Lewi, 2007;Pillow & Park, 2016), including the use of GP methods (Park, Horwitz, & Pillow, 2011;Pillow & Park, 2016;Rad & Paninski, 2010;Song et al, 2015a). One of the key advantages GPs provide is the ability to titrate prior beliefs finely under the same estimation architecture, leading to procedures that can be both flexible and efficient (Song et al, 2017;Song et al, 2018;Song et al, 2015b). Applying such tools to behavioral assessment provides similar benefits.…”

“…This framework naturally scales to multiple input dimensions, provides great flexibility for estimating a wide variety of functions, and sets the stage for other novel theoretical advances. One such advance involves actively learning which stimuli would be most valuable to deliver in order to rapidly converge onto an accurate estimate (Song et al, 2018;Song et al, 2015b). This procedure, referred to as active testing, yields estimation efficiencies at least as high as those of adaptive parametric testing.…”

“…Estimating full psychometric field audiograms at the same frequency resolution using serial logistic regression would require up to 20 h and is rarely performed for obvious reasons (Allen & Wightman, 1994). An appropriately designed active learning procedure can estimate a full audiogram in less time than is required to estimate a threshold audiogram using conventional methods (Song et al, 2018;Song et al, 2015b).…”