Arterial blood pressure feature estimation using photoplethysmography

Armin Soltan Zadi, Raichel Alex, Rong Zhang, Donald E. Watenpaugh, Khosrow Behbehani

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

Continuous and noninvasive monitoring of blood pressure has numerous clinical and fitness applications. Current methods of continuous measurement of blood pressure are either invasive and/or require expensive equipment. Therefore, we investigated a new method for the continuous estimation of two main features of blood pressure waveform: systolic and diastolic pressures. The estimates were obtained from a photoplethysmography signal as input to the fifth order autoregressive moving average models. The performance of the method was evaluated using beat-to-beat full-wave blood pressure measurements from 15 young subjects, with no known cardiovascular disorder, in supine position as they breathed normally and also while they performed a breath-hold maneuver. The level of error in the estimates, as measured by the root mean square of the model residuals, was less than 5 mmHg during normal breathing and less than 8 mmHg during the breath-hold maneuver. The mean of model residuals both during normal breathing and breath-hold maneuvers was considered to be less than 3.2 mmHg. The dependency of the accuracy of the estimates on the subject data was assessed by comparing the modeling errors for the 15 subjects. Less than 1% of the models showed significant differences (p < 0.05) from the other models, which indicates a high level of consistency among the models.

Original languageEnglish (US)
Pages (from-to)104-111
Number of pages8
JournalComputers in Biology and Medicine
Volume102
DOIs
StatePublished - Nov 1 2018

Keywords

  • Autoregulation
  • Blood pressure
  • Estimation
  • Hemodynamics
  • Modeling
  • SaO2
  • Sleep disorders

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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