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You searched for subject:(Hearing Threshold Prediction). Showing records 1 – 3 of 3 total matches.

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The Ohio State University

1. Kelly, John Kip. Estimation of Behavioral Thresholds in Normal Hearing Listeners Using Auditory Steady State Responses.

Degree: PhD, Speech and Hearing Science, 2009, The Ohio State University

The ability to obtain frequency specific information regarding a patient’s hearing sensitivity in an objective manner allows the evaluation of patient populations who cannot be tested through traditional behavioral methods. One method for obtaining this information is the auditory steady state response (ASSR). ASSR permits the testing of multiple carrier frequencies simultaneously and both ears simultaneously, unlike the auditory brainstem response (ABR). ASSR replaces subjective examiner interpretation of the response with statistical analyses not subject to the variability of human observers. Unlike ABR which has been in use for decades and utilizes relatively consistent stimuli and test protocols, the ASSR has only been in widespread clinical use for the past 6-8 years and consequently does not have the same level of standardization as ABR. ASSR can be elicited by a variety of stimulus types including but not limited to: (1) Sinusoidal amplitude modulated (SAM) tones, (2) Frequency modulated (FM) tones, (3) Mixed modulation (MM) tones, and (4) Toneburst (TB) trains. The ASSR response is found in the frequency domain at the frequency of modulation and is frequently differentiated from unrelated neural activity using an F-statistic to determine if the amplitude of the line spectra at the modulation frequency is statistically different from the surround physiologic noise. The current study sought to evaluate several common stimuli used in ASSR testing to determine if a more recently introduced stimulus (TB) emerges as a more appropriate stimulus for generating the response. Response detection and collection parameters were standardized so that any differences seen could be attributed to the stimulus. Both behavioral and ASSR thresholds were measured using SAM, MM, and TB stimuli in ten young adults with normal hearing (= 15 dB HL from 250-8000 Hz). Comparisons were then made between stimulus types to determine which stimuli could best predict a behavioral response for a pure tone matching the carrier frequency. The results of the current study indicate that the MM and TB stimuli provide lower ASSR thresholds than do SAM stimuli and that a regression model provides the most accurate estimates of behavioral threshold. The thresholds for an individually presented TB were consistently lower than for a TB at the same frequency that was presented in the multiple simultaneous paradigm (four simultaneous carrier frequencies presented to the ear). However the threshold predictions based on the two measurements were similar so little accuracy in prediction is lost by using multiple simultaneously presented tonebursts. The current study shows that while ASSR can provide reasonable estimates of hearing sensitivity when the mean data are examined for any given individual the accuracy of prediction can vary greatly. Advisors/Committee Members: Feth, Lawrence (Committee Chair).

Subjects/Keywords: Audiology; Auditory Steady State Response; ASSR; Hearing Threshold Prediction

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kelly, J. K. (2009). Estimation of Behavioral Thresholds in Normal Hearing Listeners Using Auditory Steady State Responses. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1237559225

Chicago Manual of Style (16th Edition):

Kelly, John Kip. “Estimation of Behavioral Thresholds in Normal Hearing Listeners Using Auditory Steady State Responses.” 2009. Doctoral Dissertation, The Ohio State University. Accessed March 23, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1237559225.

MLA Handbook (7th Edition):

Kelly, John Kip. “Estimation of Behavioral Thresholds in Normal Hearing Listeners Using Auditory Steady State Responses.” 2009. Web. 23 Mar 2019.

Vancouver:

Kelly JK. Estimation of Behavioral Thresholds in Normal Hearing Listeners Using Auditory Steady State Responses. [Internet] [Doctoral dissertation]. The Ohio State University; 2009. [cited 2019 Mar 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1237559225.

Council of Science Editors:

Kelly JK. Estimation of Behavioral Thresholds in Normal Hearing Listeners Using Auditory Steady State Responses. [Doctoral Dissertation]. The Ohio State University; 2009. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1237559225


University of Pretoria

2. De Waal, Rouviere. The use of artificial neural networks to predict pure tone thresholds in normal and hearing- impaired ears with distortion product otoacoustic emissions.

Degree: Speech-Language Pathology and Audiology, 2009, University of Pretoria

In the evaluation of special populations, such as neonates, infants and malingerers, audiologist often have to rely heavily on objective measurements to assess hearing ability. Current objective audiological procedures such as tympanometry, the acoustic reflex, auditory brainstem response and transient evoked otoacoustic emissions, however, have certain limitations, contributing to the need of an objective, non¬invasive, rapid, economic test of hearing that evaluate hearing ability in a wide range of frequencies. The purpose of this study was to investigate distortion product otoacoustic emissions (DPOAEs) as an objective test of hearing. The main aim was to attempt to predict hearing ability at 500 Hz, 1000 Hz, 2000 Hz and 4000 Hz with DPOAEs and artificial neural networks (ANNs) in normal and hearing-impaired ears. Other studies that attempted to predict hearing ability with DPOAEs and conventional statistical methods were only able to distinguish between normal and impaired hearing. Back propagation neural networks were trained with the pattern of all present and absent DPOAE responses of 11 DPOAE frequencies of eight DP Grams and pure tone thresholds at 500 Hz, 1000 Hz, 2000 Hz and 4000 Hz. The neural network used the learned correlation between these two data sets to predict hearing ability at 500 Hz, 1000 Hz, 2000 Hz and 4000 Hz. Hearing ability was not predicted as a decibel value, but into one of several categories spanning 10-15dB. Results indicated that prediction accuracy of normal hearing was 92% at 500 Hz, 87% at 1000 Hz, 84% at 2000 Hz and 91% at 4000 Hz. The prediction of hearing-impaired categories was less satisfactory, due to insufficient data for the ANNs to train on. The variables age and gender were included in some of the neural network runs to determine their effect on the distortion product. Gender had only a minor positive effect on prediction accuracy, but age affected prediction accuracy considerably in a positive way. The effect of the amount of data that the neural network had to train on was also investigated. A prediction versus ear count correlation strongly suggested that the inaccurate predictions of hearing-impaired categories is not a result of an inability of DPOAEs to predict pure tone thresholds in hearing impaired ears, but a result of insufficient data for the neural network to train on. This research concluded that DPOAEs and ANNs can be used to accurately predict hearing ability within 10dB in normal and hearing-impaired ears from 500 Hz to 4000 Hz for hearing losses of up to 65dB HL. Advisors/Committee Members: Prof J J Kruger (advisor), Mrs M E Soer (advisor).

Subjects/Keywords: Prediction of hearing threshold; Artificial neural networks; Age and gender; Distortion product otoacoustic emisslons; Objective hearing assessment; Otoacoustic errusslons; UCTD

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

De Waal, R. (2009). The use of artificial neural networks to predict pure tone thresholds in normal and hearing- impaired ears with distortion product otoacoustic emissions. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/26810

Chicago Manual of Style (16th Edition):

De Waal, Rouviere. “The use of artificial neural networks to predict pure tone thresholds in normal and hearing- impaired ears with distortion product otoacoustic emissions.” 2009. Masters Thesis, University of Pretoria. Accessed March 23, 2019. http://hdl.handle.net/2263/26810.

MLA Handbook (7th Edition):

De Waal, Rouviere. “The use of artificial neural networks to predict pure tone thresholds in normal and hearing- impaired ears with distortion product otoacoustic emissions.” 2009. Web. 23 Mar 2019.

Vancouver:

De Waal R. The use of artificial neural networks to predict pure tone thresholds in normal and hearing- impaired ears with distortion product otoacoustic emissions. [Internet] [Masters thesis]. University of Pretoria; 2009. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/2263/26810.

Council of Science Editors:

De Waal R. The use of artificial neural networks to predict pure tone thresholds in normal and hearing- impaired ears with distortion product otoacoustic emissions. [Masters Thesis]. University of Pretoria; 2009. Available from: http://hdl.handle.net/2263/26810


University of Pretoria

3. [No author]. The use of artificial neural networks to predict pure tone thresholds in normal and hearing- impaired ears with distortion product otoacoustic emissions .

Degree: 2009, University of Pretoria

In the evaluation of special populations, such as neonates, infants and malingerers, audiologist often have to rely heavily on objective measurements to assess hearing ability. Current objective audiological procedures such as tympanometry, the acoustic reflex, auditory brainstem response and transient evoked otoacoustic emissions, however, have certain limitations, contributing to the need of an objective, non¬invasive, rapid, economic test of hearing that evaluate hearing ability in a wide range of frequencies. The purpose of this study was to investigate distortion product otoacoustic emissions (DPOAEs) as an objective test of hearing. The main aim was to attempt to predict hearing ability at 500 Hz, 1000 Hz, 2000 Hz and 4000 Hz with DPOAEs and artificial neural networks (ANNs) in normal and hearing-impaired ears. Other studies that attempted to predict hearing ability with DPOAEs and conventional statistical methods were only able to distinguish between normal and impaired hearing. Back propagation neural networks were trained with the pattern of all present and absent DPOAE responses of 11 DPOAE frequencies of eight DP Grams and pure tone thresholds at 500 Hz, 1000 Hz, 2000 Hz and 4000 Hz. The neural network used the learned correlation between these two data sets to predict hearing ability at 500 Hz, 1000 Hz, 2000 Hz and 4000 Hz. Hearing ability was not predicted as a decibel value, but into one of several categories spanning 10-15dB. Results indicated that prediction accuracy of normal hearing was 92% at 500 Hz, 87% at 1000 Hz, 84% at 2000 Hz and 91% at 4000 Hz. The prediction of hearing-impaired categories was less satisfactory, due to insufficient data for the ANNs to train on. The variables age and gender were included in some of the neural network runs to determine their effect on the distortion product. Gender had only a minor positive effect on prediction accuracy, but age affected prediction accuracy considerably in a positive way. The effect of the amount of data that the neural network had to train on was also investigated. A prediction versus ear count correlation strongly suggested that the inaccurate predictions of hearing-impaired categories is not a result of an inability of DPOAEs to predict pure tone thresholds in hearing impaired ears, but a result of insufficient data for the neural network to train on. This research concluded that DPOAEs and ANNs can be used to accurately predict hearing ability within 10dB in normal and hearing-impaired ears from 500 Hz to 4000 Hz for hearing losses of up to 65dB HL. Advisors/Committee Members: Prof J J Kruger (advisor), Mrs M E Soer (advisor).

Subjects/Keywords: Prediction of hearing threshold; Artificial neural networks; Age and gender; Distortion product otoacoustic emisslons; Objective hearing assessment; Otoacoustic errusslons; UCTD

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

author], [. (2009). The use of artificial neural networks to predict pure tone thresholds in normal and hearing- impaired ears with distortion product otoacoustic emissions . (Masters Thesis). University of Pretoria. Retrieved from http://upetd.up.ac.za/thesis/available/etd-07292009-125000/

Chicago Manual of Style (16th Edition):

author], [No. “The use of artificial neural networks to predict pure tone thresholds in normal and hearing- impaired ears with distortion product otoacoustic emissions .” 2009. Masters Thesis, University of Pretoria. Accessed March 23, 2019. http://upetd.up.ac.za/thesis/available/etd-07292009-125000/.

MLA Handbook (7th Edition):

author], [No. “The use of artificial neural networks to predict pure tone thresholds in normal and hearing- impaired ears with distortion product otoacoustic emissions .” 2009. Web. 23 Mar 2019.

Vancouver:

author] [. The use of artificial neural networks to predict pure tone thresholds in normal and hearing- impaired ears with distortion product otoacoustic emissions . [Internet] [Masters thesis]. University of Pretoria; 2009. [cited 2019 Mar 23]. Available from: http://upetd.up.ac.za/thesis/available/etd-07292009-125000/.

Council of Science Editors:

author] [. The use of artificial neural networks to predict pure tone thresholds in normal and hearing- impaired ears with distortion product otoacoustic emissions . [Masters Thesis]. University of Pretoria; 2009. Available from: http://upetd.up.ac.za/thesis/available/etd-07292009-125000/

.