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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Structural Joints: Synovial Joints01:16

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Horizontal nystagmus identification with joint SAM segmentation and time series classification.

Chen Lin1,2,3, Hanyue Yang1,2, Haiyan Wu4

  • 1Institute of Information Science, Beijing Jiaotong University, Beijing, China.

European Archives of Oto-Rhino-Laryngology : Official Journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : Affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
|February 9, 2026
PubMed
Summary

This study introduces a deep learning model for detecting horizontal nystagmus using SAM segmentation and time series classification. The method achieves 81% precision, improving diagnostic efficiency for vestibular disorders.

Keywords:
Deep learningHorizontal nystagmus identificationMotion trajectoryPupil localization

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Area of Science:

  • Ophthalmology
  • Neurology
  • Computer Science

Background:

  • Nystagmus is an involuntary eye movement indicating vestibular pathway asymmetry.
  • Deep learning methods are increasingly used for analyzing eye movement videos to detect nystagmus.
  • Current methods aim to enhance diagnostic efficiency for oculomotor disorders.

Purpose of the Study:

  • To propose a novel deep learning model for horizontal nystagmus detection.
  • To integrate SAM segmentation with time series classification for improved accuracy.
  • To enhance the diagnostic efficiency of nystagmus detection using video analysis.

Main Methods:

  • A convolutional neural network was used to filter invalid video frames.
  • Segment Anything Model (SAM) extracted pupil motion trajectories for nystagmus analysis.
  • Spatial attention and a multi-scale 1D convolutional classifier determined horizontal nystagmus.

Main Results:

  • Pupil localization accuracy reached 79.53% on a clinical dataset.
  • Nystagmus detection precision achieved 81%, outperforming existing methods.
  • The model demonstrated significantly better performance in nystagmus detection.

Conclusions:

  • The developed approach offers an efficient and accurate method for horizontal nystagmus detection.
  • This provides a clinically applicable solution for early screening of vestibular disorders.
  • The findings support timely diagnosis and management of vestibular conditions.