BPPV Nystagmus Signals Diagnosis Framework Based on Deep Learning

Abstract Benign Paroxysmal Positional Vertigo (BPPV) is a prevalent vestibular disorder encountered in clinical settings. Diagnosis of this condition primarily relies on the observation of nystagmus, which involves monitoring the eye movements of patients. However, existing medical equipment for collecting and analyzing nystagmus data has notable limitations and deficiencies. To address this challenge, a comprehensive BPPV nystagmus data collection and intelligent analysis framework has been developed. Our framework leverages a neural network model, Egeunet, in conjunction with mathematical statistical techniques like Fast Fourier Transform (FFT), enabling precise segmentation of eye structures and accurate analysis of eye movement data. Furthermore, an eye movement analysis method has been introduced, designed to enhance clinical decision-making, resulting in more intuitive and clear analysis outcomes. Benefiting from the high sensitivity of our eye movement capture and its robustness in the face of environmental conditions and noise, our BPPV nystagmus data collection and intelligent analysis framework has demonstrated outstanding performance in BPPV detection.