Cargando…
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-equalizin...
Autores principales: | Chang, Young-Soo, Park, Heesung, Hong, Sung Hwa, Chung, Won-Ho, Cho, Yang-Sun, Moon, Il Joon |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6546232/ https://www.ncbi.nlm.nih.gov/pubmed/31158267 http://dx.doi.org/10.1371/journal.pone.0217790 |
Ejemplares similares
-
Objective Test of Cochlear Dead Region: Electrophysiologic Approach using Acoustic Change Complex
por: Kang, Soojin, et al.
Publicado: (2018) -
Cochlear Implantation for Profound Hearing Loss After Multimodal Treatment for Neuroblastoma in Children
por: Ryu, Nam-Gyu, et al.
Publicado: (2015) -
Predicting the Cochlear Dead Regions Using a Machine Learning-Based Approach with Oversampling Techniques
por: Chang, Young-Soo, et al.
Publicado: (2021) -
Spectrotemporal Modulation Detection and Speech Perception by Cochlear Implant Users
por: Won, Jong Ho, et al.
Publicado: (2015) -
Relationship between spectrotemporal modulation detection and music perception in normal-hearing, hearing-impaired, and cochlear implant listeners
por: Choi, Ji Eun, et al.
Publicado: (2018)