Research in Vestibular Science

Original Articles

Development of An Algorithm for Slippage-Induced Motion Artifacts Reduction in Video-Nystagmography: Yerin Lee, Young Joon Seo, Sejung Yang; Res Vestib Sci. 2022;21(4):104-110. Published online December 15, 2022; DOI: https://doi.org/10.21790/rvs.2022.21.4.104

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Abstract PDF: Objectives
The slippage of the video-nystagmography devices causes motion artifacts in the trajectory of the pupil and thus results in distortion in the nystagmus waveform. In this study, the moving average was proposed to reduce slippage-induced motion artifacts from the real-world data obtained in the field.
Methods
The dataset consists of an infrared video of positional tests performed on eight patients with a lateral semicircular canal benign paroxysmal positional vertigo. The trajectories of the pupil were obtained from the video with binarization, morphological operation, and elliptical fitting algorithm. The acquired data was observed and the section where the slippage occurred was labeled by an otolaryngologist. The moving average with windows of various lengths was calculated and subtracted from the original signal and evaluated to find the most adequate parameter to reduce the motion artifact.
Results
The period of nystagmus in the given data was found to be ranged from 0.01 to 4 seconds. The slippages that appeared in the data can be categorized into fast and slow slippages. The length, distance, and speed of trajectories in the slippage ranges were also measured to find the characteristics of the motion artifact in video-nystagmography data. The shape of the nystagmus waveform was preserved, and the motion artifacts were reduced in both types of slippages when the length of the window in moving average was set to 1 second.
Conclusions
The algorithm developed in this study is expected to minimize errors caused by slippage when developing a diagnostic algorithm that can assist clinicians.

Auto-Pattern Recognition for Diagnosis in Benign Paroxysmal Positional Vertigo Using Principal Component Analysis: A Preliminary Study: O-Hyeon Gwon, Tae Hoon Kong, Jaehong Key, Sejung Yang, Young Joon Seo; Res Vestib Sci. 2022;21(1):6-18. Published online March 15, 2022; DOI: https://doi.org/10.21790/rvs.2022.21.1.6

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1 Crossref

Objectives
The aim of this study was to develop a filtering algorithm for raw nystagmus images and a diagnostic assistive algorithm using a principal component analysis (PCA) to distinguish the different types of benign paroxysmal positional vertigo (BPPV).
Methods
Fifteen video clips of clinical data with typical nystagmus patterns of BPPV (13 cases) and with normal nystamgmus (two cases) were preprocessed when applied the thresholding, morphology operation, residual noise filtering, and center point extraction stages. We analyzed multiple data clusters in a single frame via a PCA; in addition, we statistically analyzed the horizontal and vertical components of the main vector among the multiple data clusters in the canalolithiasis of the lateral semicircular canal (LSCC) and the posterior semicircular canal (PSCC).
Results
We obtained a clear imaginary pupil and data on the fast phases and slow phases after preprocessing the images. For a normal patient, a round shape of clustered dots was observed. Patients with LSCC showed an elongated horizontal shape, whereas patients with PSCC showed an oval shape at the (x, y) coordinates. The scalar values (mm) of the horizontal component of the main vector when performing a PCA between the LSCC- and PSCC-BPPV were substantially different (102.08±20.11 vs. 32.36±12.52 mm, respectively; p=0.0012). Additionally, the salar ratio of horizontal to vertical components in LSCC and PSCC exhibited a significant difference (16.11±10.74 mm vs. 2.61±1.07 mm, respectively; p=0.0023).
Conclusions
The data of a white simulated imaginary pupil without any background noise can be a separate monitoring option, which can aid clinicians in determining the types of BPPV exhibited. Therefore, this analysis algorithm will provide assistive information for diagnosis of BPPV to clinicians.

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Development of An Algorithm for Slippage-Induced Motion Artifacts Reduction in Video-Nystagmography
Yerin Lee, Young Joon Seo, Sejung Yang
Research in Vestibular Science.2022; 21(4): 104. CrossRef

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