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. 2024 May 26;24(11):3417.
doi: 10.3390/s24113417.

Deep Learning-Based Nystagmus Detection for BPPV Diagnosis

Sae Byeol Mun  1 , Young Jae Kim  2 , Ju Hyoung Lee  3 , Gyu Cheol Han  3 , Sung Ho Cho  4 , Seok Jin  5 , Kwang Gi Kim  1   6
Affiliations

Deep Learning-Based Nystagmus Detection for BPPV Diagnosis

Sae Byeol Mun et al. Sensors (Basel). .

Abstract

In this study, we propose a deep learning-based nystagmus detection algorithm using video oculography (VOG) data to diagnose benign paroxysmal positional vertigo (BPPV). Various deep learning architectures were utilized to develop and evaluate nystagmus detection models. Among the four deep learning architectures used in this study, the CNN1D model proposed as a nystagmus detection model demonstrated the best performance, exhibiting a sensitivity of 94.06 ± 0.78%, specificity of 86.39 ± 1.31%, precision of 91.34 ± 0.84%, accuracy of 91.02 ± 0.66%, and an F1-score of 92.68 ± 0.55%. These results indicate the high accuracy and generalizability of the proposed nystagmus diagnosis algorithm. In conclusion, this study validates the practicality of deep learning in diagnosing BPPV and offers avenues for numerous potential applications of deep learning in the medical diagnostic sector. The findings of this research underscore its importance in enhancing diagnostic accuracy and efficiency in healthcare.

Keywords: benign paroxysmal positional vertigo; convolutional neural network; horizontal nystagmus; nystagmus detection; pupil tracking.

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Conflict of interest statement

Author Sung Ho Cho was employed by the company AMJ Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Pupil detection results using a least-squares fitting algorithm. The red circle represents the edge of the pupil, while the blue dot indicates the center of the pupil.
Figure 2
Figure 2
Pupil tracking and eye movement quantification results.
Figure 3
Figure 3
Correcting for missing values (NA) in horizontal pupil movement. (A) Serrated eye movements in patients with nystagmus, (B) eye movement data with missing values, (C) data with linear interpolation, (D) data calibrated to the last detected position.
Figure 4
Figure 4
The deep learning model architecture for nystagmus detection. (A) The details of the first proposed CNN1D model, (B) the details of the second proposed CNN-LSTM1D model.
Figure 5
Figure 5
Comparison of ROC curves among the four nystagmus detection models.
See this image and copyright information in PMC

References

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