Digital biomarkers from gaze tests for classification of central and peripheral lesions in acute vestibular syndrome

Abstract

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Acute vestibular syndrome (AVS) is characterised by a sudden vertigo, gait instability, nausea and nystagmus. Accurate and rapid triage of patients with AVS to differentiate central (potentially sinister) from peripheral (usually benign) root causes is a challenge faced across emergency medicine settings. While there exist bedside exams which can reliably differentiate serious cases, they are underused due to clinicians’ general unfamiliarity and low confidence interpreting results. Nystagmus is a fundamental part of AVS and can facilitate triaging, but identification of relevant characteristics requires expertise. This work presents two quantitative digital biomarkers from nystagmus analysis, which capture diagnostically-relevant information. The directionality biomarker evaluates changes in direction to differentiate spontaneous and gaze-evoked (direction-changing) nystagmus, while the intensity differential biomarker describes changes in intensity across eccentric gaze tests. In order to evaluate biomarkers, 24 sets of three gaze tests (left, right, and primary) are analysed. Both novel biomarkers were found to perform well, particularly directionality which was a perfect classifier. Generally, the biomarkers matched or eclipsed the performance of quantitative nystagmus features found in the literature. They also surpassed the performance of a support vector machine classifier trained on the same dataset, which achieved an accuracy of 75%. In conclusion, these biomarkers simplify the diagnostic process for non-specialist clinicians, bridging the gap between emergency care and specialist evaluation, ultimately benefiting patients with AVS.

Keywords

• acute vestibular syndrome
• gaze test
• biomarkers
• vertigo
• nystagmus
• machine learning