dorsal/arxiv
View SchemaHeart Rate Variability: Measures and Models
| Authors | Malvin C. Teich, Steven B. Lowen, Bradley M. Jost, Karin Vibe-Rheymer, Conor Heneghan |
|---|---|
| Categories | |
| ArXiv ID | physics/0008016 |
| URL | https://arxiv.org/abs/physics/0008016 |
| Journal | Nonlinear Biomedical Signal Processing, Vol. II, Dynamic Analysis and Modeling; edited by M. Akay (IEEE Press, New York, 2001), ch. 6, pp. 159-213 |
Abstract
We focus on various measures of the fluctuations of the sequence of intervals between beats of the human heart, and how such fluctuations can be used to assess the presence or likelihood of cardiovascular disease. We examine sixteen such measures and their suitability for correctly classifying heartbeat records of various lengths as normal or revealing the presence of cardiac dysfunction, particularly congestive heart failure. Using receiver-operating-characteristic analysis we demonstrate that scale-dependent measures prove substantially superior to scale-independent ones. The wavelet-transform standard deviation at a scale near 32 heartbeat intervals, and its spectral counterpart near 1/32 cycles/interval, turn out to provide reliable results using heartbeat records just minutes long. We further establish for all subjects that the human heartbeat has an underlying stochastic origin rather than arising from a chaotic attractor. Finally, we develop a mathematical point process that emulates the human heartbeat time series for both normal subjects and heart-failure patients.
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"abstract": "We focus on various measures of the fluctuations of the sequence of intervals\nbetween beats of the human heart, and how such fluctuations can be used to\nassess the presence or likelihood of cardiovascular disease. We examine sixteen\nsuch measures and their suitability for correctly classifying heartbeat records\nof various lengths as normal or revealing the presence of cardiac dysfunction,\nparticularly congestive heart failure. Using receiver-operating-characteristic\nanalysis we demonstrate that scale-dependent measures prove substantially\nsuperior to scale-independent ones. The wavelet-transform standard deviation at\na scale near 32 heartbeat intervals, and its spectral counterpart near 1/32\ncycles/interval, turn out to provide reliable results using heartbeat records\njust minutes long. We further establish for all subjects that the human\nheartbeat has an underlying stochastic origin rather than arising from a\nchaotic attractor. Finally, we develop a mathematical point process that\nemulates the human heartbeat time series for both normal subjects and\nheart-failure patients.",
"arxiv_id": "physics/0008016",
"authors": [
"Malvin C. Teich",
"Steven B. Lowen",
"Bradley M. Jost",
"Karin Vibe-Rheymer",
"Conor Heneghan"
],
"categories": [
"physics.bio-ph",
"nlin.CD",
"physics.data-an",
"physics.med-ph",
"q-bio.QM",
"q-bio.TO"
],
"journal_ref": "Nonlinear Biomedical Signal Processing, Vol. II, Dynamic Analysis\n and Modeling; edited by M. Akay (IEEE Press, New York, 2001), ch. 6, pp.\n 159-213",
"title": "Heart Rate Variability: Measures and Models",
"url": "https://arxiv.org/abs/physics/0008016"
},
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