Wavelet Scalogram-Based Nystagmus Classification for Automated Benign Paroxysmal Positional Vertigo Subtype Diagnosis

Benign paroxysmal positional vertigo (BPPV) is the most common cause of vestibular vertigo, yet accurate diagnosis remains challenging without specialist expertise. This study aimed to develop and evaluate an artificial intelligence (AI)-based diagnostic algorithm for BPPV using video-nystagmography (VNG) recordings. A total of 1352 VNG recordings were collected from patients with posterior semicircular canal (PSCC) BPPV (n = 234), lateral semicircular canal (LSCC) BPPV (canalolithiasis, n = 188; cupulolithiasis, n = 58), and healthy controls (n = 224) at a single tertiary referral hospital, yielding 1738 signal segments (1312 nystagmus segments annotated by two otolaryngologists and 426 normal segments). Eye trajectory signals were extracted using a deep learning-based VNG eye-tracking algorithm, transformed into two-dimensional (2D) scalogram representations via continuous wavelet transform (CWT), and classified using task-specific backbone architectures selected through systematic comparison under a patient-level data split. Ablation analysis showed that CWT-based 2D representations significantly outperformed raw one-dimensional (1D) time-series baselines. The final models achieved area under the receiver operating characteristic curve (AUROC) values of 0.974 for horizontal and 0.961 for torsional nystagmus classification. For BPPV subtype diagnosis, recording-level accuracy was 0.935 for the PSCC and 0.938 for the LSCC evaluation; F1-scores exceeded 0.84 for all PSCC classes and ranged from 0.59 to 0.98 for LSCC classes, with canalolithiasis variants outperforming cupulolithiasis variants. These findings suggest the feasibility of AI-assisted BPPV diagnosis from VNG recordings.