Journal
PHYSIOLOGICAL MEASUREMENT
Volume 32, Issue 11, Pages 1821-1832Publisher
IOP PUBLISHING LTD
DOI: 10.1088/0967-3334/32/11/S08
Keywords
heart rate variability; sepsis; sample entropy
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Funding
- American Heart Association, National Institutes of Health, Swortzel Foundation
- Direct For Mathematical & Physical Scien
- Division Of Physics [757752] Funding Source: National Science Foundation
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We have applied principles of statistical signal processing and nonlinear dynamics to analyze heart rate time series from premature newborn infants in order to assist in the early diagnosis of sepsis, a common and potentially deadly bacterial infection of the bloodstream. We began with the observation of reduced variability and transient decelerations in heart rate interval time series for hours up to days prior to clinical signs of illness. We find that measurements of standard deviation, sample asymmetry and sample entropy are highly related to imminent clinical illness. We developed multivariable statistical predictive models, and an interface to display the real-time results to clinicians. Using this approach, we have observed numerous cases in which incipient neonatal sepsis was diagnosed and treated without any clinical illness at all. This review focuses on the mathematical and statistical time series approaches used to detect these abnormal heart rate characteristics and present predictive monitoring information to the clinician.
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