{
  "abstract": "Deep learning models have demonstrated strong performance in electrocardiogram (ECG)-based cardiovascular disease detection, but face the challenge of systematic variation across individual patients. Patient-specific information, a cornerstone of precision medicine, may help models account for this variability, yet its integration into ECG analysis remains underexplored. We present a transformer architecture with cross-attention-based metadata conditioning for atrial fibrillation (AF) detection from single-lead ECGs, trained on a large classbalanced dataset including 77,261 patients. A systematic ablation study evaluated four metadata configurations including age, sex, ethnicity and AF-associated risk factors. All configurations achieved F1 scores between 96.7% and 96.8% on the held-out test set and between 92.7% and 93.4% on an independent multi-source dataset, confirming competitive performance and strong cross-dataset generalisation. Post-hoc explainability analysis revealed that age is the dominant demographic feature, while sex and ethnicity showed negligible contribution. QRS complexes and fibrillatory T-Q intervals were found to be the most highlighted segments, aligning with established clinical markers of AF. These findings provide a quantitative basis for evaluating when demographic conditioning is clinically justified, and establish an interpretable baseline for future patient-specific adaptive models.",
  "abstract_shown": true,
  "affiliations": [
    "Institute of Biomedical Engineering, TU Dresden, Fetscherstrase 29,Dresden, Germany"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Zannini Matteo"
    },
    {
      "name": "Malberg Hagen"
    },
    {
      "name": "Schmidt Martin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0hzjky96ygnax.bib",
  "cited_by": 0,
  "doi": "10.1515/cdbme-2026-0266",
  "fulltext_url": "https://doi.org/10.1515/cdbme-2026-0266",
  "id": "0hzjky96ygnax",
  "issns": [
    "2364-5504"
  ],
  "landing_url": "https://doi.org/10.1515/cdbme-2026-0266",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "2705|ECG Monitoring and Analysis"
    ]
  ],
  "openalex": "W7213229178",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "De Gruyter",
  "ris": "https://research.draconitic.com/paper/0hzjky96ygnax.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:47:19Z",
      "native_id": "5a6a71cec40c43fbba7187969086d79f",
      "source": "doaj",
      "url": "https://doi.org/10.1515/cdbme-2026-0266"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2705|ECG Monitoring and Analysis",
      "subfield": "cardiovascular"
    }
  ],
  "title": "An Explainable, Demographic-Aware ECG Transformer for Atrial Fibrillation Detection",
  "title_text": "An Explainable, Demographic-Aware ECG Transformer for Atrial Fibrillation Detection",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:47:19Z",
  "url": "https://research.draconitic.com/paper/0hzjky96ygnax",
  "venue": "Current Directions in Biomedical Engineering",
  "versions": []
}