Gaze shift dynamic visual acuity: A functional test of gaze stability that distinguishes unilateral vestibular hypofunction.

BACKGROUND: Embedded within most rapid head rotations are gaze shifts, which is an initial eye rotation to a target of interest, followed by a head rotation towards the same target. Gaze shifts are used to acquire an image that initially is outside of the participant's current field of vision. Currently, there are no tools available that evaluate the functional relevance of a gaze shift. OBJECTIVE: The purpose of our study was to measure dynamic visual acuity (DVA) while performing a gaze shift. METHODS: Seventy-one healthy participants (42.79±16.89 years) and 34 participants with unilateral vestibular hypofunction (UVH) (54.59±20.14 years) were tested while wearing an inertial measurement unit (IMU) sensor on the head and walking on a treadmill surrounded by three monitors. We measured visual acuity during three subcomponent tests: standing (static visual acuity), while performing an active head rotation gaze shift, and an active head rotation gaze shift while walking (gsDVAw). RESULTS: While doing gsDVAw, patients with Left UVH (n = 21) had scores worse (p = 0.023) for leftward (0.0446±0.0943 LogMAR) head rotation compared with the healthy controls (-0.0075±0.0410 LogMAR). Similarly, patients with right UVH (N = 13) had worse (p = 0.025) gsDVAw for rightward head motion (0.0307±0.0481 LogMAR) compared with healthy controls (-0.0047±0.0433 LogMAR). As a whole, gsDVAw scores were worse in UVH compared to the healthy controls when we included the ipsilesional head rotation on both sides gsDVAw (0.0061±0.0421 LogMAR healthy vs. 0.03926±0.0822 LogMAR UVH, p = 0.003). Controlling for age had no effect, the gsDVAw scores of the patients were always worse (p < 0.01). CONCLUSION: The gaze shift DVA test can distinguish gaze stability in patients with UVH from healthy controls. This test may be a useful measure of compensation for patients undergoing various therapies for their vestibular hypofunction.

Deep Learning-Based Noise Reduction Approach to Improve Speech Intelligibility for Cochlear Implant Recipients.

OBJECTIVE: We investigate the clinical effectiveness of a novel deep learning-based noise reduction (NR) approach under noisy conditions with challenging noise types at low signal to noise ratio (SNR) levels for Mandarin-speaking cochlear implant (CI) recipients. DESIGN: The deep learning-based NR approach used in this study consists of two modules: noise classifier (NC) and deep denoising autoencoder (DDAE), thus termed (NC + DDAE). In a series of comprehensive experiments, we conduct qualitative and quantitative analyses on the NC module and the overall NC + DDAE approach. Moreover, we evaluate the speech recognition performance of the NC + DDAE NR and classical single-microphone NR approaches for Mandarin-speaking CI recipients under different noisy conditions. The testing set contains Mandarin sentences corrupted by two types of maskers, two-talker babble noise, and a construction jackhammer noise, at 0 and 5 dB SNR levels. Two conventional NR techniques and the proposed deep learning-based approach are used to process the noisy utterances. We qualitatively compare the NR approaches by the amplitude envelope and spectrogram plots of the processed utterances. Quantitative objective measures include (1) normalized covariance measure to test the intelligibility of the utterances processed by each of the NR approaches; and (2) speech recognition tests conducted by nine Mandarin-speaking CI recipients. These nine CI recipients use their own clinical speech processors during testing. RESULTS: The experimental results of objective evaluation and listening test indicate that under challenging listening conditions, the proposed NC + DDAE NR approach yields higher intelligibility scores than the two compared classical NR techniques, under both matched and mismatched training-testing conditions. CONCLUSIONS: When compared to the two well-known conventional NR techniques under challenging listening condition, the proposed NC + DDAE NR approach has superior noise suppression capabilities and gives less distortion for the key speech envelope information, thus, improving speech recognition more effectively for Mandarin CI recipients. The results suggest that the proposed deep learning-based NR approach can potentially be integrated into existing CI signal processors to overcome the degradation of speech perception caused by noise.

Healthy-side dominance of cortical neuromagnetic responses in sudden hearing loss.

Previous brain imaging and mapping studies have reported findings indicating functional reorganization in the central auditory pathways of patients with profound unilateral hearing loss. This study reports for the first time to our knowledge, using a whole-head neuromagnetometer with monaural stimulation of both intact and affected ears, a pattern of healthy-side dominance for cortical neuromagnetic responses in adult patients in the early stage of idiopathic sudden sensorineural hearing loss, and a pattern of contralateral dominance is verified in controls.