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Analysing nystagmus waveforms: a computational framework.

Richard V Abadi¹, Ozgur E Akman², Gemma E Arblaster^3,4

¹Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PT, UK. r.abadi@manchester.ac.uk.

Scientific Reports

|May 8, 2021

Summary

This summary is machine-generated.

We developed a computational method to analyze nystagmus waveforms, using permutation entropy to objectively measure oscillation complexity and aid diagnosis. This approach helps characterize eye movement disorders over time.

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Area of Science:

Ophthalmology
Computational Neuroscience
Biomedical Engineering

Background:

Nystagmus waveforms are complex and challenging to analyze.
The Heimann-Bielschowsky Phenomenon involves vertical nystagmus secondary to visual loss.
Current methods may not fully capture the dynamics of nystagmus oscillations.

Purpose of the Study:

To present a novel computational framework for analyzing nystagmus waveforms.
To characterize the state and dynamical changes of nystagmus over time.
To aid in clinical diagnosis and quantify therapeutic effects.

Main Methods:

Employed linear and nonlinear time series analyses.
Utilized two-dimensional binocular eye movement recordings from 5 adult subjects.
Applied phase space reconstruction and calculated permutation entropy.

Main Results:

Identified periodic, quasiperiodic, and nonperiodic oscillations in subjects.
Permutation entropy effectively distinguished between different oscillation types.
Permutation entropy provided an objective index of nystagmus complexity (0.15-0.21).

Conclusions:

The proposed computational framework comprehensively characterizes nystagmus.
Permutation entropy offers an objective measure for nystagmus complexity and differential diagnosis.
This methodology facilitates the investigation of nystagmus dynamics and potential therapeutic interventions.