RT info:eu-repo/semantics/article
T1 Empirical mode decomposition processing to improve multifocal-visual-evoked-potential signal analysis in multiple sclerosis
A1 Santiago Rodrigo, Luis de
A1 Sánchez Morla, Eva María
A1 Blanco Velasco, Roman
A1 Miguel Jiménez, Juan Manuel
A1 Amo Usanos, Carlos
A1 Ortiz del Castillo, Miguel
A1 López Dorado, Almudena
A1 Boquete Vázquez, Luciano
K1 Electrónica
K1 Electronics
K1 Medicina
K1 Medicine
AB 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
SN 1932-6203
YR 2018
FD 2018-04-20
LK http://hdl.handle.net/10017/32999
UL http://hdl.handle.net/10017/32999
LA eng
NO Agencia Estatal de Investigación
DS MINDS@UW
RD 26-abr-2024