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Related Experiment Videos

Inter-patient multi-label ECG classification via low-rank adaptation fine-tuned large language models with dynamic

Yuhao Cheng1, Lirong Wang2

  • 1Wuxi University, Wuxi, People's Republic of China.

Physiological Measurement
|July 2, 2026
PubMed
Summary

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This study introduces a novel framework for electrocardiogram analysis, improving cardiac diagnosis by adapting Large Language Models to patient variability and disease correlations. The approach enhances accuracy, especially in complex cases with limited data.

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional electrocardiogram (ECG) analysis faces challenges with patient variability and multi-label disease correlations.
  • Existing Large Language Model (LLM) approaches often limit adaptability by freezing internal parameters, hindering personalized signal pattern analysis.

Purpose of the Study:

  • To develop an advanced framework for ECG analysis that overcomes inter-patient variability and models complex multi-label disease correlations.
  • To enhance the diagnostic accuracy of ECG interpretation using adaptable LLMs and dynamic graph convolutional networks.

Main Methods:

  • Integration of LLMs fine-tuned with Low Rank Adaptation (LoRA) for adaptive signal feature encoding.
  • Utilizing a multi-label dynamic graph convolutional network to capture intricate correlations between various cardiac diseases.
Keywords:
dynamic graph convolutional networkinter-patientlarge language modelslow-rank adaptationmulti-label electrocardiogram classification

Related Experiment Videos

  • Employing refined semantic embeddings for accurate classification, reducing the need for extensive patient-specific labeled data.

    • Main Results:

    • Achieved F1-scores of 0.781 (5-class) and 0.370 (20-class) on the PTB-XL dataset, surpassing state-of-the-art methods by 12.4% and 12.9%.
    • Demonstrated robust cross-dataset generalization on CPSC2018 with an F1-score of 0.606, an 18.8% relative improvement.
    • Successfully addressed inter-patient variability and disease correlations in ECG analysis.

    Conclusions:

    • The proposed framework offers a scalable and accurate solution for cardiac diagnosis, particularly in clinical settings with limited annotated data.
    • This novel approach enhances diagnostic capabilities by effectively modeling complex ECG signal patterns and disease relationships.