ArticlePhysical and engineering sciences in medicine2025
Bppv nystagmus signals diagnosis framework based on deep learning.
Article in Physical and engineering sciences in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The trial behind it
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Who cites it
5 citing papers in PubMed.
- Wavelet Scalogram-Based Nystagmus Classification for Automated Benign Paroxysmal Positional Vertigo Subtype Diagnosis.Journal of imaging informatics in medicine · 2026Article
- Diagnosis of Benign Paroxysmal Positional Vertigo: Review of Different Diagnostic Methods.Audiology research · 2026Review
- Comprehensive Review of Nystagmus and Vertigo Diagnostics: From Pathological Foundations to AI-Driven Telemedicine.Sensors (Basel, Switzerland) · 2026Review
- Interpretable polar-flow decomposition for torsional nystagmus lateralization in posterior-canal BPPV.Frontiers in neurology · 2026Article
- Artificial intelligence-driven clinical auxiliary diagnosis of benign paroxysmal positional vertigo.Frontiers in neurologyReview
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.