Hearing aids and cochlear damage: the case against fitting the pure tone audiogram

Predicting cochlear dead regions in patients with hearing loss through a machine learning-based approach: A preliminary study

We propose a machine learning (ML)-based model for predicting cochlear dead regions (DRs) in patients with hearing loss of various etiologies. Five hundred and fifty-five ears from 380 patients (3,770 test samples) diagnosed with sensorineural hearing loss (SNHL) were analyzed. A threshold-equalizing noise (TEN) test was applied to detect the presence of DRs. Data were collected on sex, age, side of the affected ear, hearing loss etiology, word recognition scores (WRS), and pure-tone thresholds at each frequency. According to the cause of hearing loss as diagnosed by the physician, we categorized the patients into six groups: 1) SNHL with unknown etiology; 2) sudden sensorineural hearing loss (SSNHL); 3) vestibular schwannoma (VS); 4) Meniere's disease (MD); 5) noise-induced hearing loss (NIHL); or 6) presbycusis or age-related hearing loss (ARHL). To develop a predictive model, we performed recursive partitioning and regression for classification, logistic regression, and random forest. The overall prevalence of one or more DRs in test ears was 20.36% (113 ears). Among the 3,770 test samples, the overall frequency-specific prevalence of DR was 6.7%. WRS, pure-tone thresholds at each frequency, disease type (VS or MD), and frequency information were useful for predicting DRs. Sex and age were not associated with detecting DRs. Based on these results, we suggest possible predictive factors for determining the presence of DRs. To improve the predictive power of the model, a more flexible model or more clinical features, such as the duration of hearing loss or risk factors for developing DRs, may be needed.

Cochlear neuropathy and the coding of supra-threshold sound

Many listeners with hearing thresholds within the clinically normal range nonetheless complain of difficulty hearing in everyday settings and understanding speech in noise. Converging evidence from human and animal studies points to one potential source of such difficulties: differences in the fidelity with which supra-threshold sound is encoded in the early portions of the auditory pathway. Measures of auditory subcortical steady-state responses (SSSRs) in humans and animals support the idea that the temporal precision of the early auditory representation can be poor even when hearing thresholds are normal. In humans with normal hearing thresholds (NHTs), paradigms that require listeners to make use of the detailed spectro-temporal structure of supra-threshold sound, such as selective attention and discrimination of frequency modulation (FM), reveal individual differences that correlate with subcortical temporal coding precision. Animal studies show that noise exposure and aging can cause a loss of a large percentage of auditory nerve fibers (ANFs) without any significant change in measured audiograms. Here, we argue that cochlear neuropathy may reduce encoding precision of supra-threshold sound, and that this manifests both behaviorally and in SSSRs in humans. Furthermore, recent studies suggest that noise-induced neuropathy may be selective for higher-threshold, lower-spontaneous-rate nerve fibers. Based on our hypothesis, we suggest some approaches that may yield particularly sensitive, objective measures of supra-threshold coding deficits that arise due to neuropathy. Finally, we comment on the potential clinical significance of these ideas and identify areas for future investigation.

Classifying human audiometric phenotypes of age-related hearing loss from animal models

Age-related hearing loss (presbyacusis) has a complex etiology. Results from animal models detailing the effects of specific cochlear injuries on audiometric profiles may be used to understand the mechanisms underlying hearing loss in older humans and predict cochlear pathologies associated with certain audiometric configurations ("audiometric phenotypes"). Patterns of hearing loss associated with cochlear pathology in animal models were used to define schematic boundaries of human audiograms. Pathologies included evidence for metabolic, sensory, and a mixed metabolic + sensory phenotype; an older normal phenotype without threshold elevation was also defined. Audiograms from a large sample of older adults were then searched by a human expert for "exemplars" (best examples) of these phenotypes, without knowledge of the human subject demographic information. Mean thresholds and slopes of higher frequency thresholds of the audiograms assigned to the four phenotypes were consistent with the predefined schematic boundaries and differed significantly from each other. Significant differences in age, gender, and noise exposure history provided external validity for the four phenotypes. Three supervised machine learning classifiers were then used to assess reliability of the exemplar training set to estimate the probability that newly obtained audiograms exhibited one of the four phenotypes. These procedures classified the exemplars with a high degree of accuracy; classifications of the remaining cases were consistent with the exemplars with respect to average thresholds and demographic information. These results suggest that animal models of age-related hearing loss can be used to predict human cochlear pathology by classifying audiograms into phenotypic classifications that reflect probable etiologies for hearing loss in older humans.

Relationship between masking release in fluctuating maskers and speech reception thresholds in stationary noise

In contrast to normal-hearing (NH) listeners, hearing-impaired (HI) listeners often show strongly reduced masking release (MR) in fluctuating interferers, which has commonly been associated with spectral and temporal processing deficits. However, it has recently been proposed that the reduced MR could result from an increased speech recognition threshold (SRT) in stationary noise [Bernstein and Grant, J. Acoust. Soc. Am. 125, 3358-3372 (2009)]. This was tested by presenting noise-band vocoded as well as low-pass and high-pass filtered stimuli to NH listeners, thereby increasing their stationary-noise SRTs to those of the HI listeners. If the primary determinant of MR is the SRT in stationary noise then the amount of the MR should be independent of the type of processing used to obtain the stationary-noise SRT. However, the relation between the amount of MR and the stationary-noise SRT depended on the type of processing. For a fluctuating interferer, none of the processing conditions reduced the MR of the NH listeners to that of the HI listeners. In contrast, for an interfering talker, the results for vocoded stimuli were similar to those of the HI listeners. Overall, these results suggest that the observed MR is only partially related to the stationary-noise SRT.

Re-examining the relationship between audiometric profile and tinnitus pitch

OBJECTIVE

We explored the relationship between audiogram shape and tinnitus pitch to answer questions arising from neurophysiological models of tinnitus: 'Is the dominant tinnitus pitch associated with the edge of hearing loss?' and 'Is such a relationship more robust in people with narrow tinnitus bandwidth or steep sloping hearing loss?'

DESIGN

A broken-stick fitting objectively quantified slope, degree and edge of hearing loss up to 16 kHz. Tinnitus pitch was characterized up to 12 kHz. We used correlation and multiple regression analyses for examining relationships with many potentially predictive audiometric variables.

STUDY SAMPLE

67 people with chronic bilateral tinnitus (43 men and 24 women, aged from 22 to 81 years).

RESULTS

In this ample of 67 subjects correlation failed to reveal any relationship between the tinnitus pitch and the edge frequency. The tinnitus pitch generally fell within the area of hearing loss. The pitch of the tinnitus in a subset of subjects with a narrow tinnitus bandwidth (n = 23) was associated with the audiometric edge.

CONCLUSIONS

Our findings concerning subjects with narrow tinnitus bandwidth suggest that this can be used as an a priori inclusion criterion. A large group of such subjects should be tested to confirm these results.