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The Classification of Valid and Invalid Beats of Three-Dimensional Nystagmus Eye Movement Signals Using Machine Learning Methods
Joint Authors
Hirvonen, Timo P.
Joutsijoki, Henry
Aalto, Heikki
Juhola, Martti
Source
Advances in Artificial Neural Systems
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-12-10
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Information Technology and Computer Science
Abstract EN
Nystagmus recordings frequently include eye blinks, noise, or other corrupted segments that, with the exception of noise, cannot be dampened by filtering.
We measured the spontaneous nystagmus of 107 otoneurological patients to form a training set for machine learning-based classifiers to assess and separate valid nystagmus beats from artefacts.
Video-oculography was used to record three-dimensional nystagmus signals.
Firstly, a procedure was implemented to accept or reject nystagmus beats according to the limits for nystagmus variables.
Secondly, an expert perused all nystagmus beats manually.
Thirdly, both the machine and the manual results were united to form the third variation of the training set for the machine learning-based classification.
This improved accuracy results in classification; high accuracy values of up to 89% were obtained.
American Psychological Association (APA)
Juhola, Martti& Aalto, Heikki& Joutsijoki, Henry& Hirvonen, Timo P.. 2013. The Classification of Valid and Invalid Beats of Three-Dimensional Nystagmus Eye Movement Signals Using Machine Learning Methods. Advances in Artificial Neural Systems،Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-512544
Modern Language Association (MLA)
Juhola, Martti…[et al.]. The Classification of Valid and Invalid Beats of Three-Dimensional Nystagmus Eye Movement Signals Using Machine Learning Methods. Advances in Artificial Neural Systems No. 2013 (2013), pp.1-11.
https://search.emarefa.net/detail/BIM-512544
American Medical Association (AMA)
Juhola, Martti& Aalto, Heikki& Joutsijoki, Henry& Hirvonen, Timo P.. The Classification of Valid and Invalid Beats of Three-Dimensional Nystagmus Eye Movement Signals Using Machine Learning Methods. Advances in Artificial Neural Systems. 2013. Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-512544
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-512544