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Research Paper

A Video-Oculography Based Telemedicine System for Automated Nystagmus Identification

[+] Author and Article Information
Wayne Swart

e-mail: wswart@sun.ac.za

Cornie Scheffer

Professor
e-mail: cscheffer@sun.ac.za

Kristiaan Schreve

Associate Professor
e-mail: kschreve@sun.ac.za
Biomedical Engineering Research Group (BERG),
Department of Mechanical and Mechatronic Engineering,
University of Stellenbosch,
Private Bag X1,
Matieland Stellenbosch,
7602South Africa

Manuscript received October 8, 2012; final manuscript received March 26, 2013; published online July 3, 2013. Assoc. Editor: Carl A. Nelson.

J. Med. Devices 7(3), 031002 (Jul 03, 2013) (7 pages) Paper No: MED-12-1124; doi: 10.1115/1.4024647 History: Received October 08, 2012; Revised March 26, 2013

This paper presents the research and results for an automated jerk-type nystagmus identification system that makes use of an efficient, low cost video-oculography (VOG) device, designed for telemedicine applications. The pupil position is estimated by a hybrid tracking algorithm from the captured VOG images. It is also shown that wavelet analysis with an appropriate mother wavelet, coupled with well-defined geometric constraints can provide a reliable and robust nystagmus identification algorithm. Some original research regarding robust analysis for signals with mixed content is also presented.

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References

Figures

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Fig. 3

Pupil extracted via histogram processing

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Fig. 2

Real world VOG image

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Fig. 1

VOG concept schematic

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Fig. 4

Graphical definition of closing

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Fig. 5

Closed image: The estimated pupil reconstruction

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Fig. 6

Pupil edge image achieved with Sobel edge detection

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Fig. 7

Pupil tracking result

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Fig. 8

Pupil tracking with no closing: (a) edge image and (b) pupil boundary estimate

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Fig. 9

Mallat filter bank

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Fig. 10

Daubechies two wavelet: (a) wavelet function and (b) scaling function

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Fig. 11

First level detail coefficients of nystagmus wavelet decomposition

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Fig. 12

Second level detail coefficients of nystagmus wavelet decomposition showing proposed threshold values

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Fig. 13

Extrema location and linear reconstruction of signal

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Fig. 14

Nystagmus identification in signal containing random eye motion components

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