Features extraction for medical characterization of nystagmus

Slama, Amine Ben; Mouelhi, Aymen; Cherni, Mohamed Ali; Manoubi, Sondes; Mbarek, C.; Trabelsi, H.; Sayadi, Mounir

doi:10.1109/atsip.2016.7523094

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…There is much previous work on the automated detection of nystagmus events from eye movement data [28,23,32,9,5]. Existing techniques usually involve the detection of saccades, by way of information contained in the velocity signal.…”

Section: Introductionmentioning

confidence: 99%

Automatic nystagmus detection and quantification in long-term continuous eye-movement data

Newman

Phillips

Cox

et al. 2019

Computers in Biology and Medicine

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Introductionmentioning

confidence: 99%

Automatic nystagmus detection and quantification in long-term continuous eye-movement data

Newman

Phillips

Cox

et al. 2019

Computers in Biology and Medicine

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show abstract

“…Various VNG researches using VNG have been conducted [13][14][15][16][17]. In [13], researchers suggested a new method to solve the problem of estimating the eye position in VNG analysis brought about by deformable contour methods.…”

Section: Introductionmentioning

confidence: 99%

“…They suggested a method based on position, amplitude, and duration that could track saccade movement with high accuracy. In [14], a method of vestibular disease analysis for VNG applications was proposed, and new features were suggested based on Fisher's criteria for the diagnosis of nystagmus. In [15], researchers proposed a method for medical characteristic analysis with the displacement vectors of nystagmus using Gaussian mixture models (GMM).…”

Section: Introductionmentioning

confidence: 99%

A head-mounted goggle-type video-oculography system for vestibular function testing

Kong

Lee

et al. 2018

J Image Video Proc.

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Disorders of the vestibular system possibly decrease vision, causing abnormal nystagmus and dizziness. To diagnose abnormal nystagmus, various studies have been reported including the use of rotating chair tests and videonystagmography. However, these tests are unsuitable for home use due to their high costs. Thus, a low-cost video-oculography system is necessary to obtain clinical features at home. In this paper, we present a goggle-type low-cost video-oculography system using an infrared camera and Raspberry Pi board for tracking the pupils and evaluating a vestibular system. Horizontal eye movement is derived from video data obtained from an infrared camera and infrared light-emitting diodes, and the velocity of head rotation is obtained from a gyroscope sensor. Each pupil was extracted using a morphology operation and a contour detection method. Rotatory chair tests were conducted with our developed device. To evaluate our system, gain, symmetry, and phase were measured and compared System 2000.

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A low-cost video-oculography system for vestibular function testing

Park

Kong

Nam

2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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In order to remain in focus during head movements, vestibular-ocular reflex causes eyes to move in the opposite direction to head movement. Disorders of vestibular system decrease vision, causing abnormal nystagmus and dizziness. To diagnose abnormal nystagmus, various studies have been reported including the use of rotating chair tests and videonystagmography. However, these tests are unsuitable for home use due to their high costs. Thus, a low-cost video-oculography system is necessary to obtain clinical features at home. In this paper, we present a low-cost video-oculography system using an infrared camera and Raspberry Pi board for tracking the pupils and evaluating a vestibular system. Horizontal eye movement is derived from video data obtained from an infrared camera and infrared light-emitting diodes, and the velocity of head rotation is obtained from a gyroscope sensor. Each pupil was extracted using a morphology operation and a contour detection method. Rotatory chair tests were conducted with our developed device. To evaluate our system, gain, asymmetry, and phase were measured and compared with System 2000. The average IQR errors of gain, phase and asymmetry were 0.81, 2.74 and 17.35, respectively. We showed that our system is able to measure clinical features.

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Features extraction for medical characterization of nystagmus

Cited by 5 publications

References 11 publications

Automatic nystagmus detection and quantification in long-term continuous eye-movement data

Automatic nystagmus detection and quantification in long-term continuous eye-movement data

A head-mounted goggle-type video-oculography system for vestibular function testing

A low-cost video-oculography system for vestibular function testing

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