title: [How to analyse a vestibular evoked myogenic potential? Applying a non-linear [corrected] method].
abstract: BACKGROUND
  This article presents the investigation of the clinical utility of artificial neural networks as an alternative in interpreting vestibular evoked myogenic potential (VEMP).
  
  
  METHODS
  We developed an application that incorporates a neural network. This software reproduces and analyses the vestibular potential under study. The software also gives an estimated error for this process.
  
  
  RESULTS
  The correlation obtained in this study between the neural network and an experienced human observer was always above 95%.
  
  
  CONCLUSIONS
  The neural network created offers useful, reliable interpretations of vestibular evoked myogenic potential, in agreement with current clinical parameters.
authors.affiliation: Departamento de Otorrinolaringología, Facultad de Medicina, Universidad de Navarra, Pamplona, Spain. bendermh@hotmail.com
authors.forename: Jorge
authors.initials: J
authors.lastname: Rey-Martínez
authors.forename: Julio
authors.initials: J
authors.lastname: Rama-López
authors.forename: Nicolás
authors.initials: N
authors.lastname: Pérez-Fernández
authors.forename: Rafael
authors.initials: R
authors.lastname: Barona-De Guzmán
mesh_terms: D006801:Humans; D008575:Meniere Disease / Q000503:physiopathology; D009046:Motor Neurons / Q000502:physiology; D009334:Neck Muscles / Q000502:physiology; D016571:Neural Networks, Computer; D009464:Neuroma, Acoustic / Q000503:physiopathology; D017711:Nonlinear Dynamics; D011930:Reaction Time; D012018:Reflex / Q000502:physiology; D012021:Reflex, Abnormal; D014717:Vertigo / Q000503:physiopathology; D058585:Vestibular Evoked Myogenic Potentials / Q000502:physiology; D020338:Vestibular Neuronitis / Q000503:physiopathology; D014722:Vestibule, Labyrinth / Q000502:physiology