LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis

Problem
This preprint addresses the challenge of robust representation learning in EEG signals for automated seizure detection, which is hindered by the noise inherent in EEG data. Existing graph construction methods, whether correlation-based or learning-based, often produce redundant or irrelevant edges, compromising the quality of graph representations and limiting the performance of downstream tasks. The authors propose leveraging large language models (LLMs) to refine graph structures, filling a gap in the literature regarding the integration of LLMs in the context of EEG data representation.

Method
The authors introduce a two-stage framework for graph edge refinement. Initially, a Transformer-based edge predictor combined with a multilayer perceptron constructs the initial graph by assigning probability scores to potential edges. A threshold is applied to determine edge existence. In the second stage, an LLM is employed as an edge set refiner, utilizing both textual and statistical features of node pairs to validate and refine the remaining connections. This approach aims to eliminate redundant edges and enhance the interpretability of the graph structure. The training compute details are not disclosed, but the methodology emphasizes the integration of LLMs in the graph refinement process.

Results
The proposed LLM-refined graph learning framework was evaluated on the TUSZ dataset, demonstrating significant improvements in seizure detection accuracy compared to baseline methods. The authors report a notable increase in performance metrics, although specific numbers and comparisons to named baselines are not provided in the abstract. The results indicate that the refined graph structures lead to cleaner representations, which are crucial for effective seizure diagnosis.

Limitations
The authors acknowledge that the reliance on LLMs may introduce computational overhead and require substantial resources for training and inference. Additionally, the framework’s performance is contingent on the quality of the initial graph construction, which may still be influenced by the noise in EEG data. The study does not address the generalizability of the approach across different datasets or seizure types, nor does it explore the potential biases introduced by the LLM in the edge refinement process.

Why it matters
This work has significant implications for the field of automated seizure detection and EEG analysis. By demonstrating the effectiveness of LLMs in refining graph structures, the authors open avenues for further research into the application of advanced language models in other domains of biomedical signal processing. The enhanced interpretability of graph representations could facilitate better clinical decision-making and improve the overall reliability of seizure detection systems. This approach may also inspire similar methodologies in other areas where noisy data and complex relationships are prevalent.

Authors: Lincan Li, Zheng Chen, Yushun Dong
Source: arXiv:2604.28178
URL: https://arxiv.org/abs/2604.28178v1