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Novel mathematical algorithm for pupillometric data analysis.

Matthew C Canver¹, Adam C Canver, Karen E Revere

¹Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States; F.M. Kirby Center for Molecular Ophthalmology, Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA, United States.

Computer Methods and Programs in Biomedicine

|October 17, 2013

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A new mathematical algorithm automates pupillometry analysis for retinal and optic nerve function. This method accurately extracts key pupillary light reflex parameters, reducing errors and speeding up data derivation.

Area of Science:

Ophthalmology
Neuroscience
Biomedical Engineering

Background:

Pupillometry assesses retinal and optic nerve function via pupillary light reflex.
Current analysis methods can be time-consuming and prone to human error.

Purpose of the Study:

To develop and validate a mathematical algorithm for automated pupillometry data analysis.
To expedite the derivation of physiologically relevant pupillary parameters.

Main Methods:

Applied a low-pass finite impulse response (FIR) filter to raw pupillometry data.
Utilized thresholding to remove artifacts from blinking, tracking loss, or head movement.
Extracted twelve key parameters including baseline diameter, response time, and velocities.

Keywords:

Algorithm Pupillometry Retinal function

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Main Results:

The algorithm successfully processed dynamic pupillary diameter recordings.
No significant differences were found between algorithm-derived and manually calculated parameters (p ≥ 0.05).
Twelve critical pupillary light reflex parameters were accurately extracted.

Conclusions:

The developed algorithm automates and expedites pupillometry analysis, minimizing human error.
This tool provides reliable metrics for studying retinal disease progression.
It will aid in evaluating visual recovery in pre-clinical and clinical settings for optic nerve and retinal disorders.