Background: Vestibular Hypofunction (VH) and hearing loss can affect quality of life and lead to disability, especially in the elderly. Studies investigating presbycusis and vestibular function in the aging population have been conducted separately, but few have examined the combination of both conditions in older patients, with inconsistent results that may be due to small sample sizes or heterogeneity in the methods used to assess vestibular function. We aimed to characterize the occurrence of VH in patients with presbycusis using the video head impulse test (vHIT), which is a specific and reliable assessment tool for VH. Methods: A prospective, cross-sectional study of 200 individuals was conducted at Hamad Medical Corporation-Qatar in Doha, Qatar. Adults aged 50 years and older with bilateral age-related hearing loss were screened for eligibility and those who were eligible completed the vHIT. Patient history, demographics, audiological and clinical data were collected. VH was defined as vestibulo-ocular reflex (VOR) gain of <0.79 and presence of corrective saccades. Results: In our study population, (n = 200 mean age 64.1 ± 8.4 [Range = 37], 31.5% females), VH was identified in 12 out of 200 patients (6.0%, 95% CI: 3.3–10.1) with bilateral age-related hearing loss (ARHL), equivalent to approximately 1 in 17 patients. VH was detected in the left ear in 11 patients (5.5%, 95% CI: 2.9–9.9), the right ear in five patients (2.5%, 95% CI: 0.8–5.8), and bilaterally in four patients (2.0%, 95% CI: 0.6–5.4). Conclusions: In our study population, vestibular hypofunction was observed in 6% of patients with bilateral symmetrical age-related sensorineural hearing loss, suggesting that vestibular screening may be useful in this population at risk.

Tinnitus is the perception of sounds like ringing or buzzing in the ears without any external source, varying in intensity and potentially becoming chronic. This study aims to enhance the understanding and treatment of tinnitus by analyzing a dataset related to tinnitus therapy, focusing on electroencephalography (EEG) signals from patients undergoing treatment. The objectives of the study include applying various preprocessing techniques to ensure data quality, such as noise elimination and standardization of sampling rates, and extracting essential features from EEG signals, including power spectral density and statistical measures. The novelty of this research lies in its innovative approach to representing different channels of EEG signals as new graph network representations without losing any information. This transformation allows for the use of Graph Neural Networks (GNNs), specifically Graph Convolutional Networks (GCNs) combined with Long Short-Term Memory (LSTM) networks, to model intricate relationships and temporal dependencies within the EEG data. This method enables a comprehensive analysis of the complex interactions between EEG channels. The study reports an impressive accuracy rate of 99.41%, demonstrating the potential of this novel approach. By integrating graph representation and deep learning, this research introduces a new methodology for analyzing tinnitus therapy data, aiming to contribute to more effective treatment strategies for tinnitus sufferers.