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Vertical Nystagmus Recognition Based on Deep Learning

Vertical nystagmus is a common neuro-ophthalmic sign in vestibular medicine. Vertical nystagmus not only reflects the functional state of vertical semicircular canal but also reflects the effect of otoliths. Medical experts can take nystagmus symptoms as the key factor to determine the cause of dizz...

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Detalles Bibliográficos
Autores principales: Li, Haibo, Yang, Zhifan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920786/
https://www.ncbi.nlm.nih.gov/pubmed/36772631
http://dx.doi.org/10.3390/s23031592
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author Li, Haibo
Yang, Zhifan
author_facet Li, Haibo
Yang, Zhifan
author_sort Li, Haibo
collection PubMed
description Vertical nystagmus is a common neuro-ophthalmic sign in vestibular medicine. Vertical nystagmus not only reflects the functional state of vertical semicircular canal but also reflects the effect of otoliths. Medical experts can take nystagmus symptoms as the key factor to determine the cause of dizziness. Traditional observation (visual observation conducted by medical experts) may be biased subjectively. Visual examination also requires medical experts to have enough experience to make an accurate diagnosis. With the development of science and technology, the detection system for nystagmus can be realized by using artificial intelligence technology. In this paper, a vertical nystagmus recognition method is proposed based on deep learning. This method is mainly composed of a dilated convolution layer module, a depthwise separable convolution module, a convolution attention module, a Bilstm−GRU module, etc. The average recognition accuracy of the proposed method is 91%. Using the same training dataset and test set, the recognition accuracy of this method for vertical nystagmus was 2% higher than other methods.
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spelling pubmed-99207862023-02-12 Vertical Nystagmus Recognition Based on Deep Learning Li, Haibo Yang, Zhifan Sensors (Basel) Article Vertical nystagmus is a common neuro-ophthalmic sign in vestibular medicine. Vertical nystagmus not only reflects the functional state of vertical semicircular canal but also reflects the effect of otoliths. Medical experts can take nystagmus symptoms as the key factor to determine the cause of dizziness. Traditional observation (visual observation conducted by medical experts) may be biased subjectively. Visual examination also requires medical experts to have enough experience to make an accurate diagnosis. With the development of science and technology, the detection system for nystagmus can be realized by using artificial intelligence technology. In this paper, a vertical nystagmus recognition method is proposed based on deep learning. This method is mainly composed of a dilated convolution layer module, a depthwise separable convolution module, a convolution attention module, a Bilstm−GRU module, etc. The average recognition accuracy of the proposed method is 91%. Using the same training dataset and test set, the recognition accuracy of this method for vertical nystagmus was 2% higher than other methods. MDPI 2023-02-01 /pmc/articles/PMC9920786/ /pubmed/36772631 http://dx.doi.org/10.3390/s23031592 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Haibo
Yang, Zhifan
Vertical Nystagmus Recognition Based on Deep Learning
title Vertical Nystagmus Recognition Based on Deep Learning
title_full Vertical Nystagmus Recognition Based on Deep Learning
title_fullStr Vertical Nystagmus Recognition Based on Deep Learning
title_full_unstemmed Vertical Nystagmus Recognition Based on Deep Learning
title_short Vertical Nystagmus Recognition Based on Deep Learning
title_sort vertical nystagmus recognition based on deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920786/
https://www.ncbi.nlm.nih.gov/pubmed/36772631
http://dx.doi.org/10.3390/s23031592
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