{
  "annotation_id": "645b4b68-de5d-4497-bc23-5f80b333dfcd",
  "date_created": "2026-03-02T18:00:56.226000Z",
  "date_modified": "2026-03-02T18:00:56.226000Z",
  "file_hash": "e5bf9c9444cb3163ea5cc2a43a0d008551deb27c8a92a9cbe5ebd847dc7f5d51",
  "private": false,
  "record": {
    "abstract": "We present a Bayesian dynamical inference method for characterizing\ncardiorespiratory (CR) dynamics in humans by inverse modelling from blood\npressure time-series data. This new method is applicable to a broad range of\nstochastic dynamical models, and can be implemented without severe\ncomputational demands. A simple nonlinear dynamical model is found that\ndescribes a measured blood pressure time-series in the primary frequency band\nof the CR dynamics. The accuracy of the method is investigated using surrogate\ndata with parameters close to the parameters inferred in the experiment. The\nconnection of the inferred model to a well-known beat-to-beat model of the\nbaroreflex is discussed.",
    "arxiv_id": "physics/0503053",
    "authors": [
      "D. G. Luchinsky",
      "V. N. Smelyanskiy",
      "M. M. Millonas",
      "A. Stefanovska",
      "P. V. E. McClintock"
    ],
    "categories": [
      "physics.data-an",
      "physics.med-ph"
    ],
    "doi": "10.1103/PhysRevE.72.021905",
    "title": "Nonlinear Statistical Modelling and Model Discovery for   Cardiorespiratory Data",
    "url": "https://arxiv.org/abs/physics/0503053"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "96273c0d-3704-4408-8e28-d02eae1a755e",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}