A computer-aided diagnosis of multiple sclerosis based on mfVEP recordings
AuthorsSantiago Rodrigo, Luis de; Sánchez Morla, Eva María; Ortiz del Castillo, Miguel; López Guillén, María Elena; Amo Usanos, Carlos; [et al.]
IdentifiersPermanent link (URI): http://hdl.handle.net/10017/37429
Public Library of Science
AffiliationUniversidad de Alcalá. Departamento de Electrónica; Universidad de Alcalá. Departamento de Cirugía, Ciencias Médicas y Sociales
Agencia Estatal de Investigación
Instituto de Salud Carlos III
Santiago L., Sánchez Morla E.M., Ortiz M., López E., Amo Usanos C., Alonso-Rodríguez M.C., et al. 2019, "A computer-aided diagnosis of multiple sclerosis based on mfVEP recordings", PLoS ONE vol. 14, no. 4, e0214662.
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-88438-R/ES/INVESTIGACION DE LA TECNICA DE POTENCIALES EVOCADOS VISUALES MULTIFOCALES. APLICACION EN ESTUDIOS DE EVOLUCION DE ESCLEROSIS MULTIPLE Y EVALUACION DE MEDICAMENTOS/
RETICS RD16/0008/0020 (Instituto de Salud Carlos III)
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Introduction: The aim of this study is to develop a computer-aided diagnosis system to identify subjects at differing stages of development of multiple sclerosis (MS) using multifocal visual-evoked potentials (mfVEPs). Using an automatic classifier, diagnosis is performed first on the eyes and then on the subjects. Patients: MfVEP signals were obtained from patients with Radiologically Isolated Syndrome (RIS) (n = 30 eyes), patients with Clinically Isolated Syndrome (CIS) (n = 62 eyes), patients with definite MS (n = 56 eyes) and 22 control subjects (n = 44 eyes). The CIS and MS groups were divided into two subgroups: those with eyes affected by optic neuritis (ON) and those without (non-ON). Methods: For individual eye diagnosis, a feature vector was formed with information about the intensity, latency and singular values of the mfVEP signals. A flat multiclass classifier (FMC) and a hierarchical classifier (HC) were tested and both were implemented using the k-Nearest Neighbour (k-NN) algorithm. The output of the best eye classifier was used to classify the subjects. In the event of divergence, the eye with the best mfVEP recording was selected. Results: In the eye classifier, the HC performed better than the FMC (accuracy = 0.74 and extended Matthew Correlation Coefficient (MCC) = 0.68). In the subject classification, accuracy = 0.95 and MCC = 0.93, confirming that it may be a promising tool for MS diagnosis. Chirped-pulse φOTDR provides distributed strain measurement via a time-delay estimation process. We propose a lower bound for performance, after reducing sampling error and compensating phase-noise. We attempt to reach the limit, attaining unprecedented pε/√Hz sensitivities. Conclusion: In addition to amplitude (axonal loss) and latency (demyelination), it has shown that the singular values of the mfVEP signals provide discriminatory information that may be used to identify subjects with differing degrees of the disease.