Evaluation of nystagmus and direction in videonystagmography using wavelet transform and deep learning

Benign paroxysmal positional vertigo is a common vestibular disorder characterized by nystagmus, an involuntary eye movement indicative of vestibular dysfunction. Accurate diagnosis of nystagmus remains challenging due to its complex patterns and the dependency on examiner expertise. This study introduces a novel classification framework combining wavelet transform and deep learning to enhance the diagnostic accuracy of nystagmus in videonystagmography. Pupil trajectories were extracted from videonystagmography data and preprocessed to remove motion artifacts. Wavelet transform was applied to convert time-series signals into two-dimensional time-frequency representations. Convolutional neural network was utilized to classify nystagmus presence and direction, achieving an overall accuracy of 87% and sensitivity of 88% for directional classification. The integration of wavelet transform with convolutional neural networks effectively captured temporal and frequency features, demonstrating superior performance over conventional methods. This method holds potential to revolutionize nystagmus diagnosis, providing clinicians with a reliable and efficient tool for early detection of vestibular disorders.