Versek, Craig William; Banijamali, Mohammad Ali S; Bex, Peter J; Lashkari, Kameran; Kamarthi, Sagar V; Sridhar, Srinivas
Portable Objective Diagnostics using Visual Evoked Potentials for Age-related Macular Degeneration Journal Article
In: medRxiv, 2020.
@article{versek2020portable,
title = {Portable Objective Diagnostics using Visual Evoked Potentials for Age-related Macular Degeneration},
author = {Craig William Versek and Mohammad Ali S Banijamali and Peter J Bex and Kameran Lashkari and Sagar V Kamarthi and Srinivas Sridhar},
year = {2020},
date = {2020-01-01},
journal = {medRxiv},
publisher = {Cold Spring Harbor Laboratory Press},
abstract = {Delayed Dark Adapted vision Recovery (DAR) is a biomarker for Age-related Macular Degeneration (AMD); however, its measurement is burdensome for patients and examiners. We developed a portable, wireless, quick-setup system that employs a headset with a smartphone to deliver and analyze controlled dichoptic photobleach and pattern reversal stimuli, and with custom electroencephalography (EEG) electrodes, to measure objective Dark Adapted Visual Evoked Potentials (DAVEP) at multiple locations of the visual field in one comfortable 20-minute session, without requiring subject reporting. DAVEP responses post photobleach (up to 15 minutes), were measured concurrently in both eyes of 13 patients with AMD and 8 others not diagnosed with AMD. New unexpected features were observed in the DAVEP responses at high latencies to scotopic stimulus intensities. The amplitude recovery of the DAVEP response was significantly delayed in AMD patients compared with controls. We developed DAVEP1 scores, a simple metric for DAR, using it to successfully identify all 100% of AMD subjects and correctly classify 90% of subject eyes. Deficits in DAR in patients with AMD can be identified with this objective VEP based system using the DAVEP1 metric, a promising new objective biomarker for this disease that can be easily tested in a clinic.},
keywords = {Neurotechnology, Opthalmology},
pubstate = {published},
tppubtype = {article}
}
Delayed Dark Adapted vision Recovery (DAR) is a biomarker for Age-related Macular Degeneration (AMD); however, its measurement is burdensome for patients and examiners. We developed a portable, wireless, quick-setup system that employs a headset with a smartphone to deliver and analyze controlled dichoptic photobleach and pattern reversal stimuli, and with custom electroencephalography (EEG) electrodes, to measure objective Dark Adapted Visual Evoked Potentials (DAVEP) at multiple locations of the visual field in one comfortable 20-minute session, without requiring subject reporting. DAVEP responses post photobleach (up to 15 minutes), were measured concurrently in both eyes of 13 patients with AMD and 8 others not diagnosed with AMD. New unexpected features were observed in the DAVEP responses at high latencies to scotopic stimulus intensities. The amplitude recovery of the DAVEP response was significantly delayed in AMD patients compared with controls. We developed DAVEP1 scores, a simple metric for DAR, using it to successfully identify all 100% of AMD subjects and correctly classify 90% of subject eyes. Deficits in DAR in patients with AMD can be identified with this objective VEP based system using the DAVEP1 metric, a promising new objective biomarker for this disease that can be easily tested in a clinic.
Versek, Craig; Banijamali, S Mohammad Ali; Bex, Peter J; Lashkari, Kameran; Kamarthi, Sagar V; Sridhar, Srinivas
Portable Objective Diagnostics using Visual Evoked Potentials for Age-related Macular Degeneration Journal Article
In: medRxiv, pp. 2020–01, 2020.
@article{versek2020portableb,
title = {Portable Objective Diagnostics using Visual Evoked Potentials for Age-related Macular Degeneration},
author = {Craig Versek and S Mohammad Ali Banijamali and Peter J Bex and Kameran Lashkari and Sagar V Kamarthi and Srinivas Sridhar},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {medRxiv},
pages = {2020–01},
publisher = {Cold Spring Harbor Laboratory Press},
keywords = {Neurotechnology},
pubstate = {published},
tppubtype = {article}
}
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