Objective estimation of loudness growth in hearing-impaired listeners

Silva, Ikaro; Epstein, Michael

doi:10.1121/1.3666024

Cited by 5 publications

(5 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Two databases are used in this study, both located at the PhysioNet website: Evoked Auditory Responses in Hearing Impaired (EARH) [34] and Evoked Auditory Responses in Normals (EARNDB) [33]. They were provided under the same conditions, and consisted of Auditory Brainstem Response (ABR) and Otoacoustic Emission (OAE) signals, although in this work only the ABR ones are used.…”

Section: Databasementioning

confidence: 99%

Application of Signal Classifiers in Auditory Evoked Potentials for the Detection of Pathological Patients

Baldiviezo

Barberia

Bontempo

et al. 2023

TCAM

View full text Add to dashboard Cite

The auditory brainstem response (ABR) by evoked potentials is a widespread auditory pathway assessment technique. This is largely applied due to its cost-effectiveness, practicality and ease of use. In contrast, it requires a trained professional to carry out the analysis of the results. This motivates several research efforts to increase the independence of the diagnostician. To this end, the present work shows the ability of three signal classification tools to differentiate ABR studies of normal hearing subjects from those who may have some pathology. As a starting point, the PhysioNet short term auditory evoked potentials databases are used to calculate the features later applied to construct the dataset. The features used are diverse classes of permutation entropy, fractal dimension, the Lyapunov exponent and the zero crossing rate. To ensure more accurate results, a Montecarlo simulation of one thousand trials is employed to train the classifiers and test the results. The goodness of the classification is performed upon the basis of the computation of quality parameters, such as the area under the receiver operating characteristic curve (ROC), the F1 score coefficient and the accuracy. In all the cases the methodology proposed gives high performance results that encourage the line of research of the present work.

show abstract

Section: Databasementioning

confidence: 99%

Application of Signal Classifiers in Auditory Evoked Potentials for the Detection of Pathological Patients

Baldiviezo

Barberia

Bontempo

et al. 2023

TCAM

View full text Add to dashboard Cite

show abstract

“…The data from PhysioNet were validated and used in submissions in peer reviewed journals. 6,7 There are two separate databases in PhysioNet, a Normal Database and Hearing-impaired Database. 6,7 As shown in Figure 1, the data used in this study consists of eight normal and seven hearing-impaired adult subjects.…”

Section: Datamentioning

confidence: 99%

“…6,7 There are two separate databases in PhysioNet, a Normal Database and Hearing-impaired Database. 6,7 As shown in Figure 1, the data used in this study consists of eight normal and seven hearing-impaired adult subjects. There are a total of eight hearing-impaired adult subjects in the PhysioNet database but one was excluded because it used a different stimulus frequency.…”

Section: Datamentioning

confidence: 99%

Hearing Screening through Frequency Analysis of Auditory Brainstem Response Using PhysioNet Data

Lee Ramos,

Bontogon,

Collanto

et al. 2023

Acta Med Philipp

View full text Add to dashboard Cite

Objectives. Responding to the reality of neonate patients with delayed childhood development due to late diagnosis of and intervention on hearing impairment, this study aims to determine the features based on time-frequency domain of auditory brainstem response (ABR) signals and to test the protocol on ABR signals from PhysioNet.Methods. This is done by pre-processing, performing time-frequency analysis, and characterizing hearing impairment using the dominant features of the ABR. In this study, normal (N) and hearing impaired (HI) ABR adult human signals were acquired from Physionet.org, a publicly available database. Considering its high signal-to-noise ratio, numerous filters and transformations were applied to extract the ABR. Consequently, the features acquired — dominant frequency and bigrams, were used as data classifiers.Results. Initial results using only N classifiers, that is features from the Normal dataset, and bandpass Chebyshev filter with a lower cut-off frequency of 60 Hz show that the tests yielded low to middle sensitivity. Further tests were done to improve the sensitivity that incorporated the HI classifiers, used data filtered with a low cut-off frequency of 300 Hz, and data divided per stimulus intensity level.Conclusion. Conclusions made are 1) data with both N and HI classifiers have higher sensitivity than those using only N classifiers, 2) data with a Chebyshev cut-off frequency of 300 Hz have a higher sensitivity than those with 60 Hz, and 3) data divided per intensity level have a higher sensitivity than data analyzed as a whole, and that features with stimulus intensity in middle ranges have a better distinction between HI and N patients.

show abstract

“…Analysis focused on Loudness Categories 2 through 6 (following Shi et al (2007), for example), avoiding expected effects of faster loudness growth near threshold and excluding the discomfort level for which we had a reduced data set in a region that is less informative about loudness growth (Hellman and Meiselman, 1993;Silva and Epstein, 2012). Loudness Category was treated as an interval scale, with loudness responses plotted against median signal level in dB hearing loss.…”

Section: Patterns Of Aided Loudness For Participants With S/phlmentioning

confidence: 99%

Patterns of Aided Loudness Growth in Experienced Adult Listeners with Early-Onset Severe–Profound Hearing Loss

Gottermeier

Filippo

2018

J Am Acad Audiol

View full text Add to dashboard Cite

The variety of aided loudness growth patterns identified reflects the diversity known to characterize individuals with early-onset S/PHL. Loudness rating at the validation stage of HA fit with these listeners is likely to reveal nonnormal loudness, signaling need for further HA adjustment. High satisfaction, however, despite nonnormal loudness growth, suggests that listeners with poor auditory speech recognition may benefit more from aided loudness that supports pattern perception (via the time-intensity waveform of speech), different from most current-day prescription fits.

show abstract

Objective estimation of loudness growth in hearing-impaired listeners

Cited by 5 publications

References 33 publications

Application of Signal Classifiers in Auditory Evoked Potentials for the Detection of Pathological Patients

Application of Signal Classifiers in Auditory Evoked Potentials for the Detection of Pathological Patients

Hearing Screening through Frequency Analysis of Auditory Brainstem Response Using PhysioNet Data

Patterns of Aided Loudness Growth in Experienced Adult Listeners with Early-Onset Severe–Profound Hearing Loss

Contact Info

Product

Resources

About