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Detection of normal and slow saccades using implicit piecewise polynomial approximation

The quantitative analysis of saccades in eye movement data unveils information associated with intention, cognition, and health status. Abnormally slow saccades are indicative of neurological disorders and often imply a specific pathological disturbance. However, conventional saccade detection algor...

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Autores principales: Dai, Weiwei, Selesnick, Ivan, Rizzo, John-Ross, Rucker, Janet, Hudson, Todd
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8212426/
https://www.ncbi.nlm.nih.gov/pubmed/34125160
http://dx.doi.org/10.1167/jov.21.6.8
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author Dai, Weiwei
Selesnick, Ivan
Rizzo, John-Ross
Rucker, Janet
Hudson, Todd
author_facet Dai, Weiwei
Selesnick, Ivan
Rizzo, John-Ross
Rucker, Janet
Hudson, Todd
author_sort Dai, Weiwei
collection PubMed
description The quantitative analysis of saccades in eye movement data unveils information associated with intention, cognition, and health status. Abnormally slow saccades are indicative of neurological disorders and often imply a specific pathological disturbance. However, conventional saccade detection algorithms are not designed to detect slow saccades, and are correspondingly unreliable when saccades are unusually slow. In this article, we propose an algorithm that is effective for the detection of both normal and slow saccades. The proposed algorithm is partly based on modeling saccadic waveforms as piecewise-quadratic signals. The algorithm first decreases noise in acquired eye-tracking data using optimization to minimize a prescribed objective function, then uses velocity thresholding to detect saccades. Using both simulated saccades and real saccades generated by healthy subjects and patients, we evaluate the performance of the proposed algorithm and 10 other detection algorithms. We show the proposed algorithm is more accurate in detecting both normal and slow saccades than other algorithms.
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spelling pubmed-82124262021-06-22 Detection of normal and slow saccades using implicit piecewise polynomial approximation Dai, Weiwei Selesnick, Ivan Rizzo, John-Ross Rucker, Janet Hudson, Todd J Vis Methods The quantitative analysis of saccades in eye movement data unveils information associated with intention, cognition, and health status. Abnormally slow saccades are indicative of neurological disorders and often imply a specific pathological disturbance. However, conventional saccade detection algorithms are not designed to detect slow saccades, and are correspondingly unreliable when saccades are unusually slow. In this article, we propose an algorithm that is effective for the detection of both normal and slow saccades. The proposed algorithm is partly based on modeling saccadic waveforms as piecewise-quadratic signals. The algorithm first decreases noise in acquired eye-tracking data using optimization to minimize a prescribed objective function, then uses velocity thresholding to detect saccades. Using both simulated saccades and real saccades generated by healthy subjects and patients, we evaluate the performance of the proposed algorithm and 10 other detection algorithms. We show the proposed algorithm is more accurate in detecting both normal and slow saccades than other algorithms. The Association for Research in Vision and Ophthalmology 2021-06-14 /pmc/articles/PMC8212426/ /pubmed/34125160 http://dx.doi.org/10.1167/jov.21.6.8 Text en Copyright 2021, The Authors https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License.
spellingShingle Methods
Dai, Weiwei
Selesnick, Ivan
Rizzo, John-Ross
Rucker, Janet
Hudson, Todd
Detection of normal and slow saccades using implicit piecewise polynomial approximation
title Detection of normal and slow saccades using implicit piecewise polynomial approximation
title_full Detection of normal and slow saccades using implicit piecewise polynomial approximation
title_fullStr Detection of normal and slow saccades using implicit piecewise polynomial approximation
title_full_unstemmed Detection of normal and slow saccades using implicit piecewise polynomial approximation
title_short Detection of normal and slow saccades using implicit piecewise polynomial approximation
title_sort detection of normal and slow saccades using implicit piecewise polynomial approximation
topic Methods
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8212426/
https://www.ncbi.nlm.nih.gov/pubmed/34125160
http://dx.doi.org/10.1167/jov.21.6.8
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