Empirical mode decomposition processing to improve multifocal-visual-evoked-potential signal analysis in multiple sclerosis
Autores
Santiago Rodrigo, Luis de; Sánchez Morla, Eva María; Blanco Velasco, Roman; Miguel Jiménez, Juan Manuel; Amo Usanos, Carlos; [et al.]Identificadores
Enlace permanente (URI): http://hdl.handle.net/10017/32999DOI: 10.1371/journal.pone.0194964
ISSN: 1932-6203
Fecha de publicación
2018-04-20Filiación
Universidad de Alcalá. Departamento de Electrónica; Universidad de Alcalá. Departamento de Cirugía, Ciencias Médicas y SocialesPatrocinadores
Agencia Estatal de Investigación
Universidad de Alcalá
Cita bibliográfica
De Santiago L, Sánchez-Morla E, Blanco
R, Miguel JM, Amo C, Ortiz del Castillo M, et al.
(2018) Empirical mode decomposition processing
to improve multifocal-visual-evoked-potential
signal analysis in multiple sclerosis. PLoS ONE 13, (4): e0194964
Proyectos
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/
info:eu-repo/grantAgreement/UAH//GC2016-004
Tipo de documento
info:eu-repo/semantics/article
Versión
info:eu-repo/semantics/publishedVersion
Versión del editor
https://doi.org/10.1371/journal.pone.0194964Derechos
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Derechos de acceso
info:eu-repo/semantics/openAccess
Resumen
Objective To study the performance of multifocal-visual-evoked-potential (mfVEP) signals filtered using empirical mode decomposition (EMD) in discriminating, based on amplitude, between control and multiple sclerosis (MS) patient groups, and to reduce variability in interocular latency in control subjects. Methods MfVEP signals were obtained from controls, clinically definitive MS and MS-risk progression patients (radiologically isolated syndrome (RIS) and clinically isolated syndrome (CIS)). The conventional method of processing mfVEPs consists of using a 1&#-35 Hz bandpass frequency filter (XDFT). The EMD algorithm was used to decompose the XDFTUXXIINVENTITY_nbsp;signals into several intrinsic mode functions (IMFs). This signal processing was assessed by computing the amplitudes and latencies of the XDFT and IMF signals (XEMD). The amplitudes from the full visual field and from ring 5 (9.8&#-15° eccentricity) were studied. The discrimination index was calculated between controls and patients. Interocular latency values were computed from the XDFTUXXIINVENTITY_nbsp;and XEMDsignals in a control database to study variability. Results Using the amplitude of the mfVEP signals filtered with EMD (XEMD) obtains higher discrimination index values than the conventional method when control, MS-risk progression (RIS and CIS) and MS subjects are studied. The lowest variability in interocular latency computations from the control patient database was obtained by comparing the XEMD signals with the XDFT signals. Even better results (amplitude discrimination and latency variability) were obtained in ring 5 (9.8UXXIINVENTITY_-15° eccentricity of the visual field). Conclusions Filtering mfVEP signals using the EMD algorithm will result in better identification of subjects at risk of developing MS and better accuracy in latency studies. This could be applied to assess visual cortex activity in MS diagnosis and evolution studies
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