Classification of audiograms by sequential testing using a dynamic Bayesian procedure

Özdamar, Özcan; Eilers, Rebecca E.; Miskiel, Edward; Widen, Judith E.

doi:10.1121/1.400114

Cited by 22 publications

(16 citation statements)

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“…The current method perhaps most resembles the technique of (Özdamar et al 1990), which also samples across a range of frequencies and intensities and informs estimates of one frequency using information from nearby frequencies. Rather than selecting between candidate audiogram patterns, however, the current method incorporates prior beliefs about psychometric functions into the covariance function, essentially expanding the number of candidate patterns to all possible patterns under the given covariance function and hyperparameters.…”

Section: Methodsmentioning

confidence: 99%

“…Techniques inspired by these methods have been applied in auditory threshold estimation on a per-frequency basis and have demonstrated threshold estimates consistent with traditional sampling techniques (Green 1992; Formby et al 1996; Leek et al 2000). Particularly noteworthy is a dynamic Bayesian technique that guides optimal sampling across a range of frequencies and intensities using interfrequency relationships derived from a database of candidate audiometric patterns (Özdamar et al 1990). …”

Section: Introductionmentioning

confidence: 99%

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Fast, Continuous Audiogram Estimation Using Machine Learning

et al. 2015

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast, Continuous Audiogram Estimation Using Machine Learning

et al. 2015

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“…Trials that did not contain a stimulus token were control trials. The threshold search procedure was automated using the Optimized Hearing Threshold Algorithm (OHTA) (Eilers et al 1990, 1991), as implemented in Intelligent Hearing Systems (IHS) Smart-IVRA system. The OHTA algorithm is based upon parameter estimation by sequential testing (PEST) rules (Taylor & Creelman, 1967).…”

Section: Experiments IImentioning

confidence: 99%

Dynamics of infant cortical auditory evoked potentials (CAEPs) for tone and speech tokens

Cone

Whitaker

2013

International Journal of Pediatric Otorhinolaryngology

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Objectives Cortical auditory evoked potentials (CAEPs) to tones and speech sounds were obtained in infants to: 1) further knowledge of auditory development above the level of the brainstem during the first year of life; 2) establish CAEP input-output functions for tonal and speech stimuli as a function of stimulus level and to 3) elaborate the data-base that establishes CAEP in infants tested while awake using clinically relevant stimuli, thus providing methodology that would have translation to pediatric audiological assessment. Hypotheses concerning CAEP development were that the latency and amplitude input-output functions would reflect immaturity in encoding stimulus level. In a second experiment, infants were tested with the same stimuli used to evoke the CAEPs. Thresholds for these stimuli were determined using observer-based psychophysical techniques. The hypothesis was that the behavioral thresholds would be correlated with CAEP input-output functions because of shared cortical response areas known to be active in sound detection. Design 36 infants, between the ages of 4-12 months (mean= 8 months, s.d.=1.8 months) and 9 young adults (mean age 21 years) with normal hearing were tested. First, CAEPs amplitude and latency input-output functions were obtained for 4 tone bursts and 7 speech tokens. The tone bursts stimuli were 50 ms tokens of pure tones at 0.5, 1.0, 2.0 and 4.0 kHz. The speech sound tokens, /a/, /i/, /o/, /u/, /m/, /s/, and /∫/, were created from natural speech samples and were also 50 ms in duration. CAEPs were obtained for tone burst and speech token stimuli at 10 dB level decrements in descending order from 70 dB SPL. All CAEP tests were completed while the infants were awake and engaged in quiet play. For the second experiment, observer-based psychophysical methods were used to establish perceptual threshold for the same speech sound and tone tokens. Results Infant CAEP component latencies were prolonged by 100-150 ms in comparison to adults. CAEP latency-intensity input output functions were steeper in infants compared to adults. CAEP amplitude growth functions with respect to stimulus SPL are adult-like at this age, particularly for the earliest component, P1-N1. Infant perceptual thresholds were elevated with respect to those found in adults. Furthermore, perceptual thresholds were higher, on average, than levels at which CAEPs could be obtained. When CAEP amplitudes were plotted with respect to perceptual threshold (dB SL), the infant CAEP amplitude growth slopes were steeper than in adults. Conclusions Although CAEP latencies indicate immaturity in neural transmission at the level of the cortex, amplitude growth with respect to stimulus SPL is adult-like at this age, particularly for the earliest component, P1-N1. The latency and amplitude input-output functions may provide additional information as to how infants perceive stimulus level. The reasons for the discrepancy between electrophysiologic and perceptual threshold may be due to immaturity in perceptual temporal resoluti...

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“…Our findings may prove beneficial for incorporation in newer screening algorithms such as the Classification of Audiograms by Sequential Testing (CAST) procedure (Ozdamar, Eilers, Miskiel, & Widen, 1990). Using VRA, CAST has been shown to provide valid, efficient, and reliable screening results in as few as 10-20 test (control, training, and stimulus) trials (Eilers, Ozdamar, & Steffens, 1993).…”

Section: Discussionmentioning

confidence: 88%

Effects of Reinforcer Duration on ʼthe Response Behavior of Preterm 2-Year-Olds in Visual Reinforcement Audiometry

Culpepper

Thompson²

1994

Ear and Hearing

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The purpose of this study was to investigate the effects of reinforcer duration (0.5, 1.5, and 4.0 sec) on response behavior to a 50-dB HL complex noise bandpass signal in visual reinforcement audiometry (VRA). Sixty preterm 2-year-olds (corrected age 24-30 mo) met the selection criteria and were used as subjects. Data indicated that decreasing the duration of a subject's exposure to the visual reinforcer resulted in more responses, with significantly slower habituation rates presented by subjects receiving brief (0.5 sec) versus long (4.0 sec) reinforcer duration. These results suggest that audiologists may increase the amount of audiometric information obtained from children in the upper age bracket of VRA by decreasing their exposure to the visual reinforcer.

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Classification of audiograms by sequential testing using a dynamic Bayesian procedure

Cited by 22 publications

References 0 publications

Fast, Continuous Audiogram Estimation Using Machine Learning

Fast, Continuous Audiogram Estimation Using Machine Learning

Dynamics of infant cortical auditory evoked potentials (CAEPs) for tone and speech tokens

Effects of Reinforcer Duration on ʼthe Response Behavior of Preterm 2-Year-Olds in Visual Reinforcement Audiometry

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