Software for analysing multifocal visual evoked potential signal latency progression. (1st April 2015)

• Record Type: Journal Article
• Authors: de Santiago, L.; Klistorner, A.; Ortiz, M.; Fernández-Rodríguez, A.J.; Rodríguez Ascariz, J.M.; Barea, R.; Miguel-Jiménez, J.M.; Boquete, L.
• Is Part Of: Computers in biology and medicine. Volume 59(2015)
• Page Start: 134
• Page End: 141
• Publication Date: 2015-04-01
• Subjects: mfVEP -- Latency analysis -- Cross-correlation -- Progression studies -- Quadrants -- Rings -- Hemispheres; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2015.02.004
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: This paper describes a new non-commercial software application (mfVEP 2 ) developed to process multifocal visual-evoked-potential (mfVEP) signals in latency (monocular and interocular) progression studies. Method: The software performs analysis by cross-correlating signals from the same patients. The criteria applied by the software include best channels, signal window, cross-correlation limits and signal-to-noise ratio (SNR). Software features include signal display comparing different tests and groups of sectors (quadrants, rings and hemispheres). Results: The software׳s performance and capabilities are demonstrated on the results obtained from a patient with acute optic neuritis who underwent 9 follow-up mfVEP tests. Numerical values and graphics are presented and discussed for this case. Conclusions: The authors present a software application used to study progression in mfVEP signals. It is also useful in research projects designed to improve mfVEP techniques. This software makes it easier for users to manage the signals and allows them to choose various ways of selecting signals and representing results. Highlights: New software application with which to process mfVEP signal latency. Several important parameters can be configured before starting analysis. Signal display comparing different tests. Detects which zone of the eye is most affected. Achieves high latency measurement accuracy.