ARXIV:2604.28178 · MEDICAL AI · SUBMITTED 01 MAY · 15:04 UTC · FRESHNESS STALE

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

Lincan Li · Zheng Chen · Yushun Dong · arXiv

Leveraging LLMs to refine graph structures for EEG seizure diagnosis, improving accuracy and interpretability by removing noisy connections.

Ship in 2-4 weeks›Score7.0Evidence unverified

Opportunity summary

Pain Leveraging LLMs to refine graph structures for EEG seizure diagnosis, improving accuracy and interpretability by removing noisy connections.

Evidence 0 refs | 3 sources | 50% coverage

Blocker Evidence unverified

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PROBLEM

Leveraging LLMs to refine graph structures for EEG seizure diagnosis, improving accuracy and interpretability by removing noisy connections. Existing graph construction methods, whether correlation-based or learning-based, often generate redundant or irrelevant edges due to…

METHOD

Full abstract

Electroencephalogram (EEG) signals are vital for automated seizure detection, but their inherent noise makes robust representation learning challenging. Existing graph construction methods, whether correlation-based or learning-based, often generate redundant or irrelevant edges due to the noisy nature of EEG data. This significantly impairs the quality of graph representation and limits downstream task performance. Motivated by the remarkable reasoning and contextual understanding capabilities of large language models (LLMs), we explore the idea of using LLMs as graph edge refiners. Specifically, we propose a two-stage framework: we first verify that LLM-based edge refinement can effectively identify and remove redundant connections, leading to significant improvements in seizure detection accuracy and more meaningful graph structures. Building on this insight, we further develop a robust solution where the initial graph is constructed using a Transformer-based edge predictor and multilayer perceptron, assigning probability scores to potential edges and applying a threshold to determine their existence. The LLM then acts as an edge set refiner, making informed decisions based on both textual and statistical features of node pairs to validate the remaining connections. Extensive experiments on TUSZ dataset demonstrate that our LLM-refined graph learning framework not only enhances task performance but also yields cleaner and more interpretable graph representations.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments on TUSZ dataset demonstrate that our LLM-refined graph learning framework not only enhances task performance but also yields cleaner and more interpretable…

WHY NOW

Medical AI moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.

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Opportunity summary

Score7.0

PainLeveraging LLMs to refine graph structures for EEG seizure diagnosis, improving accuracy and interpretability by removing noisy connections.

Evidence0 refs | 3 sources | 50% coverage

Blockerno shell-level blocker reported

Analysis summary

Leveraging LLMs to refine graph structures for EEG seizure diagnosis, improving accuracy and interpretability by removing noisy connections.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Competitive landscape

Leveraging LLMs to refine graph structures for EEG seizure diagnosis, improving accuracy and interpretability by removing noisy connections.

Segment

Medical AI

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

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Competitive landscape

Leveraging LLMs to refine graph structures for EEG seizure diagnosis, improving accuracy and interpretability by removing noisy connections.

Segment

Medical AI

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

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

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

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