{
  "annotation_id": "d062cd35-a276-44cb-8600-43024f759448",
  "date_created": "2026-03-02T18:01:21.754000Z",
  "date_modified": "2026-03-02T18:01:21.754000Z",
  "file_hash": "5515b836bffc8548d51569c013df7cae40f11c892525d78bfaa4865c7023c421",
  "private": false,
  "record": {
    "abstract": "Receiver-operating-characteristic (ROC) analysis was used to assess the\nsuitability of various heart rate variability (HRV) measures for correctly\nclassifying electrocardiogram records of varying lengths as normal or revealing\nthe presence of heart failure. Scale-dependent HRV measures were found to be\nsubstantially superior to scale-independent measures (scaling exponents) for\ndiscriminating the two classes of data over a broad range of record lengths.\nThe wavelet-coefficient standard deviation at a scale near 32 heartbeat\nintervals, and its spectral counterpart near 1/32 cycles per interval, provide\nreliable results using record lengths just minutes long. A jittered\nintegrate-and-fire model built around a fractal Gaussian-noise kernel provides\na realistic, though not perfect, simulation of heartbeat sequences.",
    "arxiv_id": "physics/9901007",
    "authors": [
      "Stefan Thurner",
      "Markus C. Feurstein",
      "Steven B. Lowen",
      "Malvin C. Teich"
    ],
    "categories": [
      "physics.med-ph",
      "q-bio"
    ],
    "doi": "10.1103/PhysRevLett.81.5688",
    "journal_ref": "Phys. Rev. Lett. 81, 5688-5691 (1998)",
    "title": "Receiver-Operating-Characteristic Analysis Reveals Superiority of   Scale-Dependent Wavelet and Spectral Measures for Assessing Cardiac   Dysfunction",
    "url": "https://arxiv.org/abs/physics/9901007"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "a351b780-3951-48e4-a6a4-efd96d0b56b9",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}