Benign Paroxysmal Positional Vertigo Disorders Classification Using Eye Tracking Data

Nystagmus is a neurological condition characterized by involuntary and rhythmic eye movements. These abnormal eye movements can be indicative of various underlying neurological and vestibular disorders, impacting visual stability and affecting an individual’s perception of their surroundings. Benign Paroxysmal Positional Vertigo (BPPV) is a special case of nystagmus where brief episodes of dizziness are triggered by specific head movements. However, the accurate diagnosis of BPPV still heavily relies on the precise interpretation of nystagmus induced by positional tests, which often require specialized expertise. In this paper, we developed an AI framework that detects and tracks the movement of pupil central and dilation overtime. These time-series data are then classified into different types of nystagmus. In contrast to classical image processing approaches, we employ convolutional neural networks as a baseline and use the averaging of numerous models without incurring extra inference or memory costs. The results from experiments demonstrate that when given the patient’s eye video data, our framework is capable of classifying the specific BPPV disorder out of six possible types with an average accuracy of 91% on the publically available challenging and unbalanced dataset.