Document Type
Article
Publication Date
9-27-2025
Abstract
Benign paroxysmal positional vertigo (BPPV), a common type of vertigo with complex etiologies, is traditionally diagnosed using video nystagmography (VNG). Current automated methods lack diagnostic precision owing to subjective interpretation of eye movement characteristics. To address these challenges, we introduce a large language model-augmented Gram-based attentive neural ordinary differential equation (LLM-GAODE), an innovative and data-driven framework integrating eye-tracking technology with a Gram-based attention mechanism and a neural ordinary differential equation network to improve BPPV classification. Furthermore, when the neural network exhibits low confidence in its predictions, an LLM can supplement the process with advanced reasoning in natural language. LLM-GAODE was evaluated using an extensive VNG dataset provided by a collaborative university hospital. Results suggest that LLM-GAODE significantly outperforms existing benchmarks in trajectory classification for BPPV diagnosis. The framework enhances BPPV diagnostic accuracy and achieves state-of-the-art performance in open-source trajectory classification benchmarks. The code is available at https://github.com/XiheQiu/LLM-GAODE.
Keywords
attention mechanism, large language models (LLMs), neural ordinary differential equation network (neural-ode), time series classification, video nystagmography (VNG) classification
Language
English
Publication Title
Knowledge Based Systems
Grant
23ZR1425400
Rights
© 2025 The Author(s). This is an Open Access work distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Qiu, X., Shi, S., Li, B., Tan, X., Gao, Y., & Li, S. (2025). LLM-GAODE: Large-language-model augmented neural ordinary differential equation network for video nystagmography classification. Knowledge-Based Systems, 114050. https://doi.org/10.1016/j.knosys.2025.114050
Manuscript Version
Final Publisher Version