Automated classification of benign paroxysmal positional vertigo from video-nystagmography using a delay-aware neural network.
Overview
- School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia
- Central Clinical School, University of Sydney, Sydney, Australia
- Institute of Clinical Neurosciences, Royal Prince Alfred Hospital, Sydney, Australia
- School of Engineering, Design and Built Environment, Western Sydney University, Sydney, Australia
- Centre for Artificial Intelligence and Optimization, Torrens University, Sydney, Australia
Abstract
Nystagmus is a key indicator of vestibular disorders, including benign paroxysmal positional vertigo (BPPV). Accurate diagnosis of BPPV is essential, as it is treatable with specific bedside maneuvers that lead to rapid symptom resolution, thereby improving patient outcomes and reducing unnecessary treatments. In clinical practice, identification of positional nystagmus relies on eliciting and interpreting eye movements during provocative maneuvers, with or without video nystagmography (VNG). This process can be subjective and difficult to standardize when signals are subtle, noisy, or temporally variable. We present DSF-BPPVNet, a delay-aware neural architecture for BPPV classification from VNG traces. The model combines temporal convolution, delayed-state feedback, and residual refinement to support classification from temporally structured eye-movement signals. The model was evaluated on 3,111 VNG traces from 705 patients using 5-fold cross-validation and compared with established deep-learning baselines. In the patient-independent setting, DSF-BPPVNet achieved the strongest overall performance among the evaluated models, with an F1-score of 0.819 ± 0.020. Explainability analyses were also performed to characterize model attribution patterns and temporal weighting behavior.
Reproduced under the paper's license (CC BY), from the paper cited above.
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Version 1, 28 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 9 keywords, 4 MeSH terms, 28 references.
Cite
This paper
Chaturvedi, K., Yang, N., Hannigan, I., Reid, N., Bradshaw, A., Thiyagarajan, K., Jan, T., Braytee, A., Welgampola, M. S., & Prasad, M. (2026). Automated classification of benign paroxysmal positional vertigo from video-nystagmography using a delay-aware neural network. Scientific reports, 16(1), 22586. https://
BibTeX
@article{chaturvedi2026a
author = {Chaturvedi, Kunal and Yang, Nicholas and Hannigan, Imelda and Reid, Nicole and Bradshaw, Andrew and Thiyagarajan, Karthick and Jan, Tony and Braytee, Ali and Welgampola, Miriam S and Prasad, Mukesh},
title = {{Automated classification of benign paroxysmal positional vertigo from video-nystagmography using a delay-aware neural network}},
journal = {Scientific reports},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {22586},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/
url = {https://
pmid = {42151355},
pmcid = {PMC13381937}
}
RIS
TY - JOUR
AU - Chaturvedi, Kunal
AU - Yang, Nicholas
AU - Hannigan, Imelda
AU - Reid, Nicole
AU - Bradshaw, Andrew
AU - Thiyagarajan, Karthick
AU - Jan, Tony
AU - Braytee, Ali
AU - Welgampola, Miriam S
AU - Prasad, Mukesh
TI - Automated classification of benign paroxysmal positional vertigo from video-nystagmography using a delay-aware neural network
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/
VL - 16
IS - 1
SP - 22586
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/
UR - https://
LA - en
ER -
CSL-JSON
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