{
  "annotation_id": "b14ca19c-f81d-4c2a-8fdf-e408dbd7a03f",
  "date_created": "2026-03-02T18:01:03.719000Z",
  "date_modified": "2026-03-02T18:01:03.719000Z",
  "file_hash": "65ee103f2a60ecb3a833094038cdc4579924fc71f431cd05b2dbaabe16f21025",
  "private": false,
  "record": {
    "abstract": "We present the multiscale entropy analysis of short term physiological time\nseries of simultaneously acquired samples of heart rate, blood pressure and\nlung volume, from healthy subjects and from subjects with Chronic Heart\nFailure. Evaluating the complexity of signals at the multiple time scales\ninherent in physiologic dynamics, we find that healthy subjects show more\ncomplex time series at large time scales; on the other hand, at fast time\nscales, which are more influenced by respiration, the pathologic dynamics of\nblood pressure is the most random. These results robustly separate healthy and\npathologic groups. We also propose a multiscale approach to evaluate\ninteractions between time series, by performing a multivariate autoregressive\nmodelling of the coarse grained time series: this analysis provides several new\nquantitative indicators which are statistically correlated with the pathology.",
    "arxiv_id": "physics/0510259",
    "authors": [
      "L. Angelini",
      "R. Maestri",
      "D. Marinazzo",
      "L. Nitti",
      "M. Pellicoro",
      "G. D. Pinna",
      "S. Stramaglia",
      "S. A. Tupputi"
    ],
    "categories": [
      "physics.med-ph",
      "cond-mat.dis-nn",
      "physics.data-an",
      "q-bio.QM"
    ],
    "title": "Multiscale analysis of heart rate, blood pressure and respiration time   series",
    "url": "https://arxiv.org/abs/physics/0510259"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "74035048-753e-45ea-8d1d-ec0552045452",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}