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Objective: To compare the difference in pre- to postoperative speech performance of patients qualifying for a cochlear implant (CI) in quiet, +10 dB signal-to-noise ratio (SNR), and +5 dB SNR. Study Design: Retrospective. Setting: Tertiary referral center. Patients: Fifty-eight post-lingually deafened, unilateral CI recipients from three Groups were included: 1) those who met CI candidacy criteria with AzBio sentences in quiet, 2) in noise at +10 dB SNR but not in quiet, 3) and in noise at +5 dB SNR but not in quiet or +10 dB SNR. Intervention: Unilateral CI. Main Outcome Measures: Pre- and 1 year postoperative speech recognition scores. Results: Best-aided AzBio speech recognition of individuals in Group 1 improved significantly for all test conditions and improved significantly for Groups 2 and 3 in the +10 and +5 dB SNR test conditions postoperatively. When tested with their CI alone however, while AzBio speech recognition of individuals in Group 1 and Group 2 improved significantly in the quiet and +10 dB SNR conditions, speech recognition was not significantly changed postoperatively under any testing condition for individuals in Group 3. Conclusions: While individuals qualifying for a CI only in the +5 dB SNR condition may derive significant benefit from implantation in best aided conditions, speech understanding outcomes can be more variable thus warranting additional counseling before implantation and case-by-case consideration of listening needs and goals.

In children with hypertrophic cardiomyopathy, the spatial peaks QRS-T angle is associated with ventricular arrhythmia burden with high negative predictive value and odds ratio.

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Purpose The overall goal of the current study was to determine whether noise type plays a role in perceptual quality ratings. We compared quality ratings using various noise types and signal-to-noise ratio (SNR) ranges using hearing aid simulations to consider the effects of hearing aid processing features. Method Ten older adults with bilateral mild to moderately severe sensorineural hearing loss rated the sound quality of sentences processed through a hearing aid simulation and presented in the presence of five different noise types (six-talker babble, three-talker conversation, street traffic, kitchen, and fast-food restaurant) at four SNRs (3, 8, 12, and 20 dB). Results Everyday noise types differentially affected sound quality ratings even when presented at the same SNR: Kitchen and three-talker noises were rated significantly higher than restaurant, traffic, and multitalker babble, which were not different from each other. The effects of noise type were most pronounced at poorer SNRs. Conclusions The findings of this study showed that noise types differentially affected sound quality ratings. The differences we observed were consistent with the acoustic characteristics of the noise types. Noise types having lower envelope fluctuations yielded lower quality ratings than noise types characterized by sporadic high-intensity events at the same SNR.

A goal in fitting hearing aids is to find settings that improve listener judgments of the amplified speech quality. Objective metrics that predict speech quality could assist in this fitting procedure and, thus, become useful tools in the clinic. The Hearing-Aid Speech Quality Index version 2 (HASQI v2) is effective in predicting speech quality ratings for speech processed through a hearing aid. However, HASQI v2 requires a signal-to-noise ratio (SNR) of approximately 40 dB to provide reliable measurements of low levels of nonlinear distortion, and this favorable an SNR is not always found in a clinical examination room or medical office where hearing-aid measurements are performed. Two modifications to HASQI v2 are considered in this presentation to improve the measurement accuracy in noisy situations. The first is applying dynamic-range compression to the test signal to enhance low-intensity speech sounds that could be masked by background noise, and the second is using a multilayer neural network to match noisy measurements to the corresponding measurements made in a quiet laboratory. The benefits of these modifications to HASQI are evaluated using a hearing-aid simulation, and verification is provided using commercial hearing-aid measurements made in an audiology clinic.

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Computational auditory metrics are used to characterize hearing-aid fittings in the laboratory and the clinic. The purpose of this study is to compare the speech intelligibility index (SII) as a clinical metric to the laboratory based SII and the hearing-aid speech perception index (HASPI) and hearing-aid speech quality index (HASQI) laboratory metrics. These comparisons are drawn from a comprehensive dataset of hearing-aid fittings for 120 hearing-aid recipients from a hospital-based audiology clinic. Hearing-aid devices are drawn from multiple manufacturers and multiple technology levels, and a total of nine hearing-aid devices are included. Acoustic recordings are made with the hearing-aids positioned on the KEMAR manikin and include multiple hearing-aid settings for each patient (manufacturer’s recommended fit, first fit by the audiologist, and the final fitting selected by the patient). Metric values are computed for each of the settings, and these metric comparisons highlight the advantages and challenges of each metric in characterizing hearing-aid signal processing. In addition, the results provide insight regarding hearing-aid fittings from the manufacturer’s fitting versus clinician/patient driven fittings. [Work supported by NIH R01 DC012289, NIH NRSA T32DC012280, and GN Hearing OCG6790B.]