{
  "abstract": "Autonomic regulation and the heart's electrical activity are both central to normal cardiac function, yet there is currently no direct way to capture the interaction between them from a standard ECG. This paper introduces the Autonomic-Ventricular Coherence Index (AVCI), a single-lead ECG method that measures the beat-to-beat spectral coherence between variability in RR intervals and variability in QRS area. AVCI was developed and evaluated on 74 recordings from three PhysioNet databases (MIT-BIH Normal Sinus Rhythm, n = 18; BIDMC Congestive Heart Failure, n = 15; and MIT-BIH Arrhythmia, n = 41), with a further six recordings from the MIT-BIH Long-Term ECG Database used for preliminary independent validation. Coherence was integrated over the high-frequency band (0.15-0.40 Hz). Because the source databases differ in sampling rate and recording length, AVCI was examined both under each database's native recording conditions and after acquisition standardization, harmonizing all recordings to a common 128 Hz and 30-minute duration, additionally upsampling to 360 Hz for 30 min under uniform acquisition, and applying a two-tier duration-stratified framework. The analysis code is available at https://github.com/JayaSreejai/AVCI.git. Under native conditions, AVCI demonstrated a high capacity to distinguish between congestive heart failure and normal sinus rhythm (AUC 0.844). Like SDNN, it performed moderately in harmonized acquisition (NSR vs. CHF AUC 0.647; NSR vs. all-disease AUC 0.619). Under native conditions, machine learning models trained on balanced datasets performed well (KNN accuracy 79.5%, ROC-AUC 85.7, SVM accuracy 79.0%, ROC-AUC 83.0), whereas under harmonized conditions, they produced average results. R-peak identification was consistent across QRS windows and accurate (F1 0.892). These results show that AVCI's diagnostic performance depends on acquisition parameters, particularly sampling rate and recording duration. AVCI is a practical and understandable measure of autonomic-ventricular coupling from a single ECG lead. Combined with machine learning, it offers a promising path toward more robust and explainable cardiovascular diagnostics. Standardized acquisition protocols are key to reliable clinical use.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computer Science Engineering and Information Systems, Vellore Institute of Technology Deemed to be University, Vellore, India."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jayasree R"
    },
    {
      "affs": [
        0
      ],
      "name": "Chemmalar Selvi G"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2qxmq811wzj91.bib",
  "cited_by": 0,
  "doi": "10.3389/fdgth.2026.1964571",
  "fulltext_url": "https://europepmc.org/articles/PMC13713656",
  "id": "2qxmq811wzj91",
  "issns": [
    "2673-253X"
  ],
  "landing_url": "https://europepmc.org/article/MED/42952703",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2705|Heart Rate Variability and Autonomic Control"
    ]
  ],
  "openalex": "W7221780497",
  "pdf_url": "https://europepmc.org/articles/PMC13713656?pdf=render",
  "pmcid": "PMC13713656",
  "pmid": "42952703",
  "published": "2026-10-14",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/2qxmq811wzj91.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:20:41Z",
      "native_id": "MED:42952703",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42952703"
    },
    {
      "first_seen": "2026-10-31T12:24:10Z",
      "native_id": "PMC13713656",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fdgth.2026.1964571"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2705|Heart Rate Variability and Autonomic Control",
      "subfield": "cardiovascular"
    }
  ],
  "title": "The autonomic-ventricular coherence index: a novel single-lead ECG method with acquisition-dependent discrimination.",
  "title_text": "The autonomic-ventricular coherence index: a novel single-lead ECG method with acquisition-dependent discrimination.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:21:17Z",
  "url": "https://research.draconitic.com/paper/2qxmq811wzj91",
  "venue": "Frontiers in digital health",
  "versions": []
}