Pulse arrival time as a surrogate of blood pressure

Abstract

Various models have been proposed for the estimation of blood pressure (BP) from pulse transit time (PTT). PTT is defined as the time delay of the pressure wave, produced by left ventricular contraction, measured between a proximal and a distal site along the arterial tree. Most researchers, when they measure the time difference between the peak of the R-wave in the electrocardiogram signal (corresponding to left ventricular depolarisation) and a fiducial point in the photoplethysmogram waveform (as measured by a pulse oximeter attached to the fingertip), describe this erroneously as the PTT. In fact, this is the pulse arrival time (PAT), which includes not only PTT, but also the time delay between the electrical depolarisation of the heart’s left ventricle and the opening of the aortic valve, known as pre-ejection period (PEP). PEP has been suggested to present a significant limitation to BP estimation using PAT. This work investigates the impact of PEP on PAT, leading to a discussion on the best models for BP estimation using PAT or PTT. We conducted a clinical study involving 30 healthy volunteers (53.3% female, 30.9 ± 9.35 years old, with a body mass index of 22.7 ± 3.2 kg/m$$^{2}$$ 2 ). Each session lasted on average 27.9 ± 0.6 min and BP was varied by an infusion of phenylephrine (a medication that causes venous and arterial vasoconstriction). We introduced new processing steps for the analysis of PAT and PEP signals. Various population-based models (Poon, Gesche and Fung) and a posteriori models (inverse linear, inverse squared and logarithm) for estimation of BP from PTT or PAT were evaluated. Across the cohort, PEP was found to increase by 5.5 ms ± 4.5 ms from its baseline value. Variations in PTT were significantly larger in amplitude, − 16.8 ms ± 7.5 ms. We suggest, therefore, that for infusions of phenylephrine, the contribution of PEP on PAT can be neglected. All population-based models produced large BP estimation errors, suggesting that they are insufficient for modelling the complex pathways relating changes in PTT or PAT to changes in BP. Although PAT is inversely correlated with systolic blood pressure (SBP), the gradient of this relationship varies significantly from individual to individual, from − 2946 to − 470.64 mmHg/s in our dataset. For the a posteriori inverse squared model, the root mean squared errors (RMSE) for systolic and diastolic blood pressure (DBP) estimation from PAT were 5.49 mmHg and 3.82 mmHg, respectively. The RMSEs for SBP and DBP estimation by PTT were 4.51 mmHg and 3.53 mmHg, respectively. These models take into account individual calibration curves required for accurate blood pressure estimation. The best performing population-based model (Poon) reported error values around double that of the a posteriori inverse squared model, and so the use of population-based models is not justified.

Analysis of Cardiac Pulse Arrival Time Series at Rest and during Physiological Stress

“…Moreover, analyzing PAT allows to distinguish the Pre-Ejection Period (PEP), i.e. the time interval between the depolarization of ventricle and the opening of the aortic valve, and the Pulse Transit Time (PTT), that is the time interval that the pressure wave takes to travel from the aortic valve to the peripheral arteries [7].…”

“…Among them, Heart Rate Variability (HRV) is the most used parameter for the assessment of the complex dynamics underlying the cardiovascular control [5], [6]. Other measures, such as the Pulse Arrival Time (PAT), can provide information not only on the regulatory activity of the autonomic nervous system (ANS), but also on variations of blood pressure (BP) [7] and arterial stiffness [8]. The PAT is the time delay between the electrical depolarization of the heart left ventricle and the following time of arrival of the pressure wave at the body periphery [7].…”

Features from the photoplethysmogram and the electrocardiogram for estimating changes in blood pressure

“…Much like PAT 1 , features extracted from the PPG waveform are thought to be subject-specific requiring individual calibration for accurate mapping to BP values 7 . Mukkamala et al 7 splits calibration strategies into three groups: individual, hybrid and population.…”

“…Thirty healthy volunteers with no history of cardiovascular disease, were recruited for the clinical study. The study protocol has been outlined previously 1,27 . The study took place at the Cardiovascular Clinical Research Facility within the John Radcliffe Hospital, Oxford, UK.…”

“…On average, we achieved an increase of 20 mmHg in SBP, with a maximum increase of 40 mmHg in a subset of participants. 1 28.0 (0.1) Age (years) 1 28 (9.0) Height (cm) 1 170.0 (18.0) Weight (kg) 1 69.5 (23.0) Body Mass Index (kg/m 2 ) 1 22.5 (5.2) Reference maximum ∆ BP per participant 1 ∆ SBP (mmHg) 20.0 (8.0) ∆ MAP (mmHg) 17.0 (10.0) ∆ DBP (mmHg) 15.5 (9.0) Reference ∆ BP values 2 ∆ SBP (mmHg) 6.5 (10.4) ∆ MAP (mmHg) 5.3 (8.8) ∆ DBP (mmHg) 4.7 (8.2) 1 Participant-wise median (interquartile range (IQR)), 2 mean (standard deviation) across dataset.…”