000153585 001__ 153585
000153585 005__ 20251017144554.0
000153585 0247_ $$2doi$$a10.1007/s10633-019-09720-8
000153585 0248_ $$2sideral$$a116839
000153585 037__ $$aART-2020-116839
000153585 041__ $$aeng
000153585 100__ $$adel Castillo, M. O.
000153585 245__ $$aIdentification of clusters in multifocal electrophysiology recordings to maximize discriminant capacity (patients vs. control subjects)
000153585 260__ $$c2020
000153585 5060_ $$aAccess copy available to the general public$$fUnrestricted
000153585 5203_ $$aPurpose: To propose a new method of identifying clusters in multifocal electrophysiology (multifocal electroretinogram: mfERG; multifocal visual-evoked potential: mfVEP) that conserve the maximum capacity to discriminate between patients and control subjects. Methods: The theoretical framework proposed creates arbitrary N-size clusters of sectors. The capacity to discriminate between patients and control subjects is assessed by analysing the area under the receiver operator characteristic curve (AUC). As proof of concept, the method is validated using mfERG recordings taken from both eyes of control subjects (n = 6) and from patients with multiple sclerosis (n = 15). Results: Considering the amplitude of wave P1 as the analysis parameter, the maximum value of AUC = 0.7042 is obtained with N = 9 sectors. Taking into account the AUC of the amplitudes and latencies of waves N1 and P1, the maximum value of the AUC = 0.6917 with N = 8 clustered sectors. The greatest discriminant capacity is obtained by analysing the latency of wave P1: AUC = 0.8854 with a cluster of N = 12 sectors. Conclusion: This paper demonstrates the effectiveness of a method able to determine the arbitrary clustering of multifocal responses that possesses the greatest capacity to discriminate between control subjects and patients when applied to the visual field of mfERG or mfVEP recordings. The method may prove helpful in diagnosing any disease that is identifiable in patients’ mfERG or mfVEP recordings and is extensible to other clinical tests, such as optical coherence tomography.
000153585 536__ $$9info:eu-repo/grantAgreement/ES/ISCIII/PI17-01726$$9info:eu-repo/grantAgreement/ES/ISCIII/RETICS-RD16-0008-020$$9info:eu-repo/grantAgreement/ES/ISCIII/RETICS-RD16-0008-029$$9info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/DPI2017-88438-R
000153585 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000153585 590__ $$a2.379$$b2020
000153585 591__ $$aOPHTHALMOLOGY$$b37 / 62 = 0.597$$c2020$$dQ3$$eT2
000153585 592__ $$a0.784$$b2020
000153585 593__ $$aOphthalmology$$c2020$$dQ2
000153585 593__ $$aSensory Systems$$c2020$$dQ2
000153585 593__ $$aPhysiology (medical)$$c2020$$dQ2
000153585 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000153585 700__ $$aCordón, B.
000153585 700__ $$aSánchez Morla, E. M.
000153585 700__ $$aVilades, E.
000153585 700__ $$aRodrigo, M. J.
000153585 700__ $$aCavaliere, C.
000153585 700__ $$aBoquete, L.
000153585 700__ $$0(orcid)0000-0001-6258-2489$$aGarcia-Martin, E.$$uUniversidad de Zaragoza
000153585 7102_ $$11013$$2646$$aUniversidad de Zaragoza$$bDpto. Cirugía$$cÁrea Oftalmología
000153585 773__ $$g140, 1 (2020), 43-53$$pDoc. ophthalmol.$$tDOCUMENTA OPHTHALMOLOGICA$$x0012-4486
000153585 85641 $$uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85073966855&doi=10.1007%2fs10633-019-09720-8&partnerID=40&md5=ed97aebb4f7e311595cbe7776ba07a19$$zTexto completo de la revista
000153585 8564_ $$s2197483$$uhttps://zaguan.unizar.es/record/153585/files/texto_completo.pdf$$yVersión publicada
000153585 8564_ $$s1710474$$uhttps://zaguan.unizar.es/record/153585/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000153585 909CO $$ooai:zaguan.unizar.es:153585$$particulos$$pdriver
000153585 951__ $$a2025-10-17-14:12:35
000153585 980__ $$aARTICLE