Mathematical Modeling of Arterial Blood Pressure Using Photo- Plethysmography Signal in Breath-hold Maneuver

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

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


recent research has shown that each apnea episode results in a significant rise of the beat-to-beat blood pressure followed by a drop to the pre-episode levels when patient resumes normal breathing. While the physiological implications of these repetitive and significant oscillations are still unknown, it is of interest to quantify them. Since current array of instruments deployed for polysomnography studies does not include beat-to-beat measurement of blood pressure, but includes oximetry which can supply pulsatile photoplethysmography (PPG) signal, in addition to percent oxygen saturation. Hence, we have investigated a new method for continuous estimation of systolic (SBP), diastolic (DBP), and mean (MBP) blood pressure waveforms from PPG. Peaks and troughs of PPG waveform are used as input to a 5th order autoregressive moving average model to construct estimates of SBP, DBP, and MBP waveforms. Since breath hold maneuvers are shown to faithfully simulate apnea episodes, we evaluated the performance of the proposed method in 7 subjects (4 F; $32 \pm 4$ yrs., BMI $24.57 \pm 3.87$ kg/m2) in supine position doing 5 breath holding maneuvers with 90s of normal breathing between them. The modeling error ranges were (all units are in mmHg $) 0.88 \pm 4.87$ to $- 2.19 \pm 5.73($ SBP); $0.29 \pm 2.39$ to $- 0.97 \pm 3.83($ DBP); and $- 0.42 \pm 2.64$ to $- 1.17 \pm 3.82($ MBP). The cross validation error ranges were $0.28 \pm 6.45$ to $- 1.74 \pm 6.55($ SBP); $0.09 \pm 3.37$ to $0.97 \pm 3.67($ DBP); and $0.33 \pm 4.34$ to $- 0.87 \pm 4.42($ MBP). The overall level of estimation error, as measured by the root mean squared of the model residuals, was less than 7 mmHg.

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics


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