Portable multi-focal visual evoked potential diagnostics for multiple sclerosis/optic neuritis patients.

Autor: Banijamali SMA; Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA., Versek C; NeuroFieldz Inc, Newton, MA, USA., Babinski K; Department of Neurology, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA., Kamarthi S; Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA., Green-LaRoche D; Department of Neurology, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA., Sridhar S; NeuroFieldz Inc, Newton, MA, USA. s.sridhar@northeastern.edu.; Department of Physics, Department of Bioengineering and Department of Chemical Engineering, Northeastern University, Boston, MA, 02115, USA. s.sridhar@northeastern.edu.
Jazyk: angličtina
Zdroj: Documenta ophthalmologica. Advances in ophthalmology [Doc Ophthalmol] 2024 Aug; Vol. 149 (1), pp. 23-45. Date of Electronic Publication: 2024 Jul 03.
DOI: 10.1007/s10633-024-09980-z
Abstrakt: Purpose: Multiple sclerosis (MS) is a neuro-inflammatory disease affecting the central nervous system (CNS), where the immune system targets and damages the protective myelin sheath surrounding nerve fibers, inhibiting axonal signal transmission. Demyelinating optic neuritis (ON), a common MS symptom, involves optic nerve damage. We've developed NeuroVEP, a portable, wireless diagnostic system that delivers visual stimuli through a smartphone in a headset and measures evoked potentials at the visual cortex from the scalp using custom electroencephalography electrodes.
Methods: Subject vision is evaluated using a short 2.5-min full-field visual evoked potentials (ffVEP) test, followed by a 12.5-min multifocal VEP (mfVEP) test. The ffVEP evaluates the integrity of the visual pathway by analyzing the P100 component from each eye, while the mfVEP evaluates 36 individual regions of the visual field for abnormalities. Extensive signal processing, feature extraction methods, and machine learning algorithms were explored for analyzing the mfVEPs. Key metrics from patients' ffVEP results were statistically evaluated against data collected from a group of subjects with normal vision. Custom visual stimuli with simulated defects were used to validate the mfVEP results which yielded 91% accuracy of classification.
Results: 20 subjects, 10 controls and 10 with MS and/or ON were tested with the NeuroVEP device and a standard-of-care (SOC) VEP testing device which delivers only ffVEP stimuli. In 91% of the cases, the ffVEP results agreed between NeuroVEP and SOC device. Where available, the NeuroVEP mfVEP results were in good agreement with Humphrey Automated Perimetry visual field analysis. The lesion locations deduced from the mfVEP data were consistent with Magnetic Resonance Imaging and Optical Coherence Tomography findings.
Conclusion: This pilot study indicates that NeuroVEP has the potential to be a reliable, portable, and objective diagnostic device for electrophysiology and visual field analysis for neuro-visual disorders.
(© 2024. The Author(s).)
Databáze: MEDLINE
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