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Detection of Oculomotor Dysmetria From Mobile Phone Video of the Horizontal Saccades Task Using Signal Processing and Machine Learning Approaches.

Publication ,  Journal Article
Azami, H; Chang, Z; Arnold, SE; Sapiro, G; Gupta, AS
Published in: IEEE access : practical innovations, open solutions
January 2022

Eye movement assessments have the potential to help in diagnosis and tracking of neurological disorders. Cerebellar ataxias cause profound and characteristic abnormalities in smooth pursuit, saccades, and fixation. Oculomotor dysmetria (i.e., hypermetric and hypometric saccades) is a common finding in individuals with cerebellar ataxia. In this study, we evaluated a scalable approach for detecting and quantifying oculomotor dysmetria. Eye movement data were extracted from iPhone video recordings of the horizontal saccade task (a standard clinical task in ataxia) and combined with signal processing and machine learning approaches to quantify saccade abnormalities. Entropy-based measures of eye movements during saccades were significantly different in 72 individuals with ataxia with dysmetria compared with 80 ataxia and Parkinson's participants without dysmetria. A template matching-based analysis demonstrated that saccadic eye movements in patients without dysmetria were more similar to the ideal template of saccades. A support vector machine was then used to train and test the ability of multiple signal processing features in combination to distinguish individuals with and without oculomotor dysmetria. The model achieved 78% accuracy (sensitivity= 80% and specificity= 76%). These results show that the combination of signal processing and machine learning approaches applied to iPhone video of saccades, allow for extraction of information pertaining to oculomotor dysmetria in ataxia. Overall, this inexpensive and scalable approach for capturing important oculomotor information may be a useful component of a screening tool for ataxia and could allow frequent at-home assessments of oculomotor function in natural history studies and clinical trials.

Duke Scholars

Published In

IEEE access : practical innovations, open solutions

DOI

EISSN

2169-3536

ISSN

2169-3536

Publication Date

January 2022

Volume

10

Start / End Page

34022 / 34031

Related Subject Headings

  • 46 Information and computing sciences
  • 40 Engineering
  • 10 Technology
  • 09 Engineering
  • 08 Information and Computing Sciences
 

Citation

APA
Chicago
ICMJE
MLA
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Azami, H., Chang, Z., Arnold, S. E., Sapiro, G., & Gupta, A. S. (2022). Detection of Oculomotor Dysmetria From Mobile Phone Video of the Horizontal Saccades Task Using Signal Processing and Machine Learning Approaches. IEEE Access : Practical Innovations, Open Solutions, 10, 34022–34031. https://doi.org/10.1109/access.2022.3156964
Azami, Hamed, Zhuoqing Chang, Steven E. Arnold, Guillermo Sapiro, and Anoopum S. Gupta. “Detection of Oculomotor Dysmetria From Mobile Phone Video of the Horizontal Saccades Task Using Signal Processing and Machine Learning Approaches.IEEE Access : Practical Innovations, Open Solutions 10 (January 2022): 34022–31. https://doi.org/10.1109/access.2022.3156964.
Azami H, Chang Z, Arnold SE, Sapiro G, Gupta AS. Detection of Oculomotor Dysmetria From Mobile Phone Video of the Horizontal Saccades Task Using Signal Processing and Machine Learning Approaches. IEEE access : practical innovations, open solutions. 2022 Jan;10:34022–31.
Azami, Hamed, et al. “Detection of Oculomotor Dysmetria From Mobile Phone Video of the Horizontal Saccades Task Using Signal Processing and Machine Learning Approaches.IEEE Access : Practical Innovations, Open Solutions, vol. 10, Jan. 2022, pp. 34022–31. Epmc, doi:10.1109/access.2022.3156964.
Azami H, Chang Z, Arnold SE, Sapiro G, Gupta AS. Detection of Oculomotor Dysmetria From Mobile Phone Video of the Horizontal Saccades Task Using Signal Processing and Machine Learning Approaches. IEEE access : practical innovations, open solutions. 2022 Jan;10:34022–34031.

Published In

IEEE access : practical innovations, open solutions

DOI

EISSN

2169-3536

ISSN

2169-3536

Publication Date

January 2022

Volume

10

Start / End Page

34022 / 34031

Related Subject Headings

  • 46 Information and computing sciences
  • 40 Engineering
  • 10 Technology
  • 09 Engineering
  • 08 Information and Computing Sciences