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Open AccessArticle

Empirical Mode Decomposition-Based Filter Applied to Multifocal Electroretinograms in Multiple Sclerosis Diagnosis

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Biomedical Engineering Group, Department of Electronics, University of Alcala, 28801 Alcala de Henares, Spain
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School of Physics, University of Melbourne, Melbourne, VIC 3010, Australia
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Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain
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Aragon Institute for Health Research (IIS Aragon), Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), University of Zaragoza, 50009 Zaragoza, Spain
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RETICS-Oftared: Thematic Networks for Co-operative Research in Health for Ocular Diseases, 28040 Madrid, Spain
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Department of Psychiatry, Research Institute Hospital 12 de Octubre (i+12), 28041 Madrid, Spain
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Faculty of Medicine, Complutense University of Madrid, 28040 Madrid, Spain
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CIBERSAM: Biomedical Research Networking Centre in Mental Health, 28029 Madrid, Spain
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(1), 7; https://doi.org/10.3390/s20010007
Received: 16 September 2019 / Revised: 13 December 2019 / Accepted: 16 December 2019 / Published: 18 December 2019
As multiple sclerosis (MS) usually affects the visual pathway, visual electrophysiological tests can be used to diagnose it. The objective of this paper is to research methods for processing multifocal electroretinogram (mfERG) recordings to improve the capacity to diagnose MS. MfERG recordings from 15 early-stage MS patients without a history of optic neuritis and from 6 control subjects were examined. A normative database was built from the control subject signals. The mfERG recordings were filtered using empirical mode decomposition (EMD). The correlation with the signals in a normative database was used as the classification feature. Using EMD-based filtering and performance correlation, the mean area under the curve (AUC) value was 0.90. The greatest discriminant capacity was obtained in ring 4 and in the inferior nasal quadrant (AUC values of 0.96 and 0.94, respectively). Our results suggest that the combination of filtering mfERG recordings using EMD and calculating the correlation with a normative database would make mfERG waveform analysis applicable to assessment of multiple sclerosis in early-stage patients. View Full-Text
Keywords: multiple sclerosis; multifocal electroretinogram; empirical mode decomposition; electrophysiology; biomarker multiple sclerosis; multifocal electroretinogram; empirical mode decomposition; electrophysiology; biomarker
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de Santiago, L.; Ortiz del Castillo, M.; Garcia-Martin, E.; Rodrigo, M.J.; Sánchez Morla, E.M.; Cavaliere, C.; Cordón, B.; Miguel, J.M.; López, A.; Boquete, L. Empirical Mode Decomposition-Based Filter Applied to Multifocal Electroretinograms in Multiple Sclerosis Diagnosis. Sensors 2020, 20, 7.

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