{
  "abstract": "Background: Given the high prevalence of aortic heart disease and the importance of early diagnosis, there is a need to develop a simple screening method to identify significant aortic heart disease based on available biosignals, one of which is a single-channel electrocardiogram (ECG).\n\nAim: We aimed to develop a machine learning algorithm for analyzing single-channel electrocardiograms to identify patients with moderate to severe aortic stenosis and regurgitation, as well as significant aortic valve disease.\n\nMethods: Patients ( n = 2,503) underwent echocardiographic examination and single-channel I-lead ECG recording using a portable electrocardiograph. Moderate to severe aortic stenosis on echocardiography was present in 62 (2.4%) patients, and moderate to severe aortic regurgitation was present in 157 (6.3%). Single-channel ECG analysis was performed using advanced signal processing and machine learning techniques.\n\nResults: Both models were evaluated under an identical 10-fold group cross-validation framework. For detecting moderate or severe aortic stenosis, diagnostic accuracy was 0.650 (Se 57.9%, Sp 65.0%) for Balanced Random Forest and 0.662 (Se 46.4%, Sp 66.5%) for XGBoost. For moderate or severe aortic regurgitation, accuracy was 0.726 (Se 60.5%, Sp 73.3%) for Balanced Random Forest and 0.712 (Se 61.7%, Sp 71.9%) for XGBoost. For significant aortic valve disease, accuracy was 0.724 (Se 61.1%, Sp 73.4%) for Balanced Random Forest and 0.691 (Se 67.8%, Sp 69.2%) for XGBoost.\n\nConclusions: The results indicate good diagnostic accuracy and potential applicability for screening for significant aortic heart disease using single-channel ECG parameters analyzed with modern signal processing and machine learning techniques.\n\nClinical Trial Registration : https://clinicaltrial.gov/ , identifier NCT07099417.",
  "abstract_shown": true,
  "affiliations": [
    "Institute of Personalized Cardiology of Biomedical Science and Technology Park, Sechenov First Moscow State Medical University (Sechenov University)",
    "Department of Cardiology, Functional and Ultrasound Diagnostics of N.V. Sklifosovsky Institute for Clinical Medicine, Sechenov First Moscow State Medical University (Sechenov University)",
    "Russian Medical Academy of Continuous Professional Education",
    "Department of Cardiovascular Surgery and Invasive Cardiology, Sechenov First Moscow State Medical University (Sechenov University)"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Natalia Kuznetsova"
    },
    {
      "affs": [
        0
      ],
      "name": "Daniil Zelenov"
    },
    {
      "affs": [
        1
      ],
      "name": "Aleksandra Bogdanova"
    },
    {
      "affs": [
        1
      ],
      "name": "Ines Dhif"
    },
    {
      "affs": [
        2
      ],
      "name": "Ekaterina Shobonova"
    },
    {
      "affs": [
        1
      ],
      "name": "Anna Alekseeva"
    },
    {
      "affs": [
        1
      ],
      "name": "Dinara Mesitskaya"
    },
    {
      "affs": [
        1
      ],
      "name": "Abram Syrkin"
    },
    {
      "affs": [
        3
      ],
      "name": "Roman Komarov"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Philippe Kopylov"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Petr Chomakhidze"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/70xt0rh45ymrt.bib",
  "cited_by": 0,
  "doi": "10.3389/fcvm.2026.1969401",
  "fulltext_url": "https://europepmc.org/articles/PMC13687305",
  "id": "70xt0rh45ymrt",
  "issns": [
    "2297-055X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fcvm.2026.1969401/full",
  "language": "eng",
  "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",
      "2705|ECG Monitoring and Analysis"
    ]
  ],
  "openalex": "W7220931021",
  "pdf_url": "https://europepmc.org/articles/PMC13687305?pdf=render",
  "pmcid": "PMC13687305",
  "pmid": "42923322",
  "published": "2026-10-08",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/70xt0rh45ymrt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:10Z",
      "native_id": "10.3389/fcvm.2026.1969401",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fcvm.2026.1969401/full"
    },
    {
      "first_seen": "2026-10-30T12:20:33Z",
      "native_id": "MED:42923322",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42923322"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2705|ECG Monitoring and Analysis",
      "subfield": "cardiovascular"
    }
  ],
  "title": "A screening method for aortic valve disease based on spectral analysis of a single-channel electrocardiogram using machine learning algorithms",
  "title_text": "A screening method for aortic valve disease based on spectral analysis of a single-channel electrocardiogram using machine learning algorithms",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:33Z",
  "url": "https://research.draconitic.com/paper/70xt0rh45ymrt",
  "venue": "Frontiers in Cardiovascular Medicine",
  "versions": []
}