{
  "abstract": "Twelve-lead ECG signals are of great value in cardiovascular disease screening and assisted diagnosis, but their multi-label recognition process is simultaneously affected by cross-lead structural dependencies, disease label co-occurrence relationships, and the dispersed distribution of pathological evidence in long-sequence signals. To address these issues, this study proposes a deep learning model for twelve-lead ECG multi-label diagnosis. The model adopts a Transformer-Mamba hybrid structure as the temporal encoding backbone to extract local waveform variations and long-range rhythm features from ECG signals. On this basis, Disease-aware Dual Relational Graph Modeling is designed to establish structured relational representations from both lead relationships and disease label relationships. Furthermore, Pathology-aware Temporal Evidence Aggregation is introduced to adaptively aggregate key pathological evidence from long-sequence ECG segments, thereby improving the discriminative ability for multiple disease labels. This study compares the proposed method with various mainstream deep learning models on the public dataset PTB-XL dataset and the clinical dataset. The results show that the proposed method achieves an Acc of 84.77 % , an F1 of 70.34 % , a Macro-AUC of 91.14 % , and a Macro-AUPRC of 78.10 % on PTB-XL dataset; and an Acc of 89.25 % , an F1 of 76.08 % , a Macro-AUC of 91.48 % , and a Macro-AUPRC of 80.14 % on Clinical dataset. Overall, the proposed method outperforms the compared methods, validating its effectiveness in the twelve-lead ECG multi-label diagnosis task.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Vascular Surgery, The Affiliated Hospital, Southwest Medical University",
    "Department of Intervention Medicine, The Affiliated Hospital, Southwest Medical University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Hao Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Yi-feng Huang"
    },
    {
      "affs": [
        1
      ],
      "name": "Lu-pin Yang"
    },
    {
      "affs": [
        0
      ],
      "name": "Yong Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Hu-qiang He"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2wx2qds0yxjqn.bib",
  "cited_by": 0,
  "doi": "10.3389/fcvm.2026.1904647",
  "fulltext_url": null,
  "id": "2wx2qds0yxjqn",
  "issns": [
    "2297-055X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fcvm.2026.1904647/full",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/2wx2qds0yxjqn.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T12:21:26Z",
      "native_id": "10.3389/fcvm.2026.1904647",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fcvm.2026.1904647/full"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:2297-055X",
      "subfield": "cardiovascular"
    }
  ],
  "title": "Medical-oriented multi-label diagnosis of twelve-lead ECG signals via disease-aware relational modeling and pathology-aware temporal evidence aggregation",
  "title_text": "Medical-oriented multi-label diagnosis of twelve-lead ECG signals via disease-aware relational modeling and pathology-aware temporal evidence aggregation",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T00:21:24Z",
  "url": "https://research.draconitic.com/paper/2wx2qds0yxjqn",
  "venue": "Frontiers in Cardiovascular Medicine",
  "versions": []
}