Synergistic integration of Multi-View Brain Networks and advanced machine learning techniques for auditory disorders diagnostics.

ABSTRACT: In the field of audiology, achieving accurate discrimination of auditory impairments remains a formidable challenge. Conditions such as deafness and tinnitus exert a substantial impact on patients' overall quality of life, emphasizing the urgent need for precise and efficient classification methods. This study introduces an innovative approach, utilizing Multi-View Brain Network data acquired from three distinct cohorts: 51 deaf patients, 54 with tinnitus, and 42 normal controls. Electroencephalogram (EEG) recording data were meticulously collected, focusing on 70 electrodes attached to an end-to-end key with 10 regions of interest (ROI). This data is synergistically integrated with machine learning algorithms. To tackle the inherently high-dimensional nature of brain connectivity data, pr

SUBMITTER: Ahmed MAO 

PROVIDER: S-EPMC10788326 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature