Automated nystagmus detection: Accuracy of slow-phase and quick-phase algorithms to determine the presence of nystagmus

Ariel A Winnick; Chih-Chung Chen; Tzu-Pu Chang; Yu-Hung Kuo; Ching-Fu Wang; Chin-Hsun Huang; Chun-Chen Yang

doi:10.1016/j.jns.2022.120392

Automated nystagmus detection: Accuracy of slow-phase and quick-phase algorithms to determine the presence of nystagmus

J Neurol Sci. 2022 Nov 15:442:120392. doi: 10.1016/j.jns.2022.120392. Epub 2022 Aug 28.

Authors

Ariel A Winnick¹, Chih-Chung Chen², Tzu-Pu Chang³, Yu-Hung Kuo⁴, Ching-Fu Wang⁵, Chin-Hsun Huang⁶, Chun-Chen Yang⁶

Affiliations

¹ Soroka University Hospital and Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel; Department of Neurology, University of South Alabama, Mobile, AL, USA.
² Dizziness and Balance Disorder Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan; Taipei Neuroscience Institute, Taipei Medical University, New Taipei City, Taiwan; Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
³ Department of Neurology/Neuro-medical Scientific Center, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan; Department of Neurology, School of Medicine, Tzu Chi University, Hualien, Taiwan. Electronic address: neurochang0617@gmail.com.
⁴ Department of Research, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan.
⁵ Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; Biomedical Engineering Research and Development Center, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; Neurobit Technologies Co., Ltd., Taipei, Taiwan.
⁶ Neurobit Technologies Co., Ltd., Taipei, Taiwan.

PMID: 36058057
DOI: 10.1016/j.jns.2022.120392

Abstract

Purpose: To verify the accuracy of automated nystagmus detection algorithms.

Method: Video-oculography (VOG) plots were analyzed from consecutive patients with dizziness presenting to a neurology clinic. Data were recorded for 30 s in upright position with fixation block. For automated nystagmus detection, slow-phase algorithm parameters included mean and median slow-phase velocity (SPV), and slow-phase duration ratio. Quick-phase algorithm parameters included saccadic difference and saccadic ratio. For verification, two independent blinded assessors reviewed VOG traces and videos and coded presence or absence of nystagmus. Assessor consensus was used as reference standard. Accuracy of slow-phase and quick-phase algorithm parameters were compared, and ROC analysis was performed.

Results: Among 524 analyzed VOG traces, 99 were verified as nystagmus present and 425 were verified as nystagmus absent. Prevalence of nystagmus in the sample population was 18.9%. In ROC analysis, areas under the curve of individual algorithm parameters were 0.791-0.896. With optimal thresholds for determining presence or absence of nystagmus, algorithm sensitivity (70.7-87.9%), specificity (71.8-84.0%), and negative predictive value (91.7-96.4%) were ideal, but positive predictive value (38.8-53.4%) was not ideal. Combining algorithm parameters using logistic regression models mildly improved detection accuracy.

Conclusion: Both slow-phase and fast-phase algorithms were accurate for detecting nystagmus. Due to low positive predictive value, the utility of independent automated nystagmus detection systems is limited in clinical settings with low prevalence of nystagmus. Combining parameters using logistic regression models appears to improve detection accuracy, indicating that machine learning may potentially optimize the accuracy of future automated nystagmus detection systems.

Keywords: Algorithm; Dizziness; Nystagmus; Vestibular; Video-oculography; vertigo.

MeSH terms

Algorithms
Humans
Nystagmus, Pathologic* / diagnosis