Pulse Arrival Time Research Articles

Photoplethysmography Fast Upstroke Time Intervals Can Be Useful Features for Cuff-Less Measurement of Blood Pressure Changes in Humans.

Photoplethysmography (PPG) waveform analysis is being increasingly investigated for continuous, non-invasive, and cuff-less blood pressure (BP) measurement. However, the efficacy of this approach and the useful features and models remain largely unclear. The objectives were to develop easy-to-understand models relating PPG waveform features to BP changes (after a cuff calibration) and to determine their value in BP measurement accuracy. The study data comprised finger, toe, and ear PPG waveforms, an ECG waveform, and reference manual cuff BP measurements from 32 human subjects (25% hypertensive) before and after slow breathing, mental arithmetic, cold pressor, and nitroglycerin administration. Stepwise linear regression was employed to create parsimonious models for predicting the intervention-induced BP changes from popular PPG waveform features, pulse arrival time (PAT, time delay between ECG R-wave and PPG foot), and subject demographics. Leave-one-subject-out cross validation was applied to compare the BP change prediction root-mean-squared-errors (RMSEs) of the resulting models to reference models in which PPG waveform features were excluded. Finger b-time (PPG foot to minimum second derivative time interval) and ear "STT" (PPG amplitude divided by maximum derivative), when combined with PAT, reduced the systolic BP change prediction RMSE of reference models by 6-7% (p 0.022). Ear STT together with pulse width reduced the diastolic BP change prediction RMSE of the reference model by 13% (p = 0.003). The two PPG fast upstroke time intervals can offer some added value in cuff-less BP trending. This study offers important information towards achieving non-invasive and passive BP monitoring without a cuff.

Applied machine learning for blood pressure estimation using a small, real-world electrocardiogram and photoplethysmogram dataset.

Applying machine learning techniques to electrocardiography and photoplethysmography signals and their multivariate-derived waveforms is an ongoing effort to estimate non-occlusive blood pressure. Unfortunately, real ambulatory electrocardiography and photoplethysmography waveforms are inevitably affected by motion and noise artifacts, so established machine learning architectures perform poorly when trained on data of the Multiparameter Intelligent Monitoring in Intensive Care II type, a publicly available ICU database. Our study addresses this problem by applying four well-established machine learning methods, i.e., random forest regression, support vector regression, Adaboost regression and artificial neural networks, to a small, self-sampled electrocardiography-photoplethysmography dataset (n = 54) to improve the robustness of machine learning to real-world BP estimates. We evaluated the performance using a selection of optimal feature morphologies of waveforms by using pulse arrival time, morphological and frequency photoplethysmography parameters and heart rate variability as characterization data. On the basis of the root mean square error and mean absolute error, our study showed that support vector regression gave the best performance for blood pressure estimation from noisy data, achieving an mean absolute error of 6.97 mmHg, which meets the level C criteria set by the British Hypertension Society. We demonstrate that ambulatory electrocardiography- photoplethysmography signals acquired by mobile discrete devices can be used to estimate blood pressure.

Bp-Net: Cuff-Less, Calibration-Free, and Non-Invasive Blood Pressure Estimation Via a Generic Deep Convolutional Architecture

Objective: The paper focuses on development of robust and accurate processing solutions for continuous and cuff-less blood pressure (BP) monitoring. In this regard, a robust deep learning-based framework is proposed for computation of low latency, continuous, and calibration-free upper and lower bounds on the systolic and diastolic BP. Method: Referred to as the BP-Net, the proposed framework is a novel convolutional architecture that provides longer effective memory while achieving superior performance due to incorporation of casual dialated convolutions and residual connections. To utilize the real potential of deep learning in extraction of intrinsic features (deep features) and enhance the long-term robustness, the BP-Net uses raw Electrocardiograph (ECG) and Photoplethysmograph (PPG) signals without extraction of any form of hand-crafted features as it is common in existing solutions. Results: By capitalizing on the fact that datasets used in recent literature are not unified and properly defined, a benchmark dataset is constructed from the MIMIC-I and MIMIC-III databases obtained from PhysioNet. The proposed BP-Net is evaluated based on this benchmark dataset demonstrating promising performance and shows superior generalizable capacity. Conclusion: The proposed BP-Net architecture is more accurate than canonical recurrent networks and enhances the long-term robustness of the BP estimation task. Significance: The proposed BP-Net architecture addresses key drawbacks of existing BP estimation solutions, i.e., relying heavily on extraction of hand-crafted features, such as pulse arrival time (PAT), and; Lack of robustness. Finally, the constructed BP-Net dataset provides a unified base for evaluation and comparison of deep learning-based BP estimation algorithms.

The Oscillating Pulse Arrival Time as a Physiological Reason for the Difference between ECG- and Photoplethysmogram-Derived Heart Rate Variability Parameters

The Oscillating Pulse Arrival Time as a Physiological Reason for the Difference between ECG- and Photoplethysmogram-Derived Heart Rate Variability Parameters

Electrical Impedance Plethysmography Versus Tonometry To Measure the Pulse Wave Velocity in Peripheral Arteries in Young Healthy Volunteers: a Pilot Study.

The leading cause of health loss and deaths worldwide are cardiovascular diseases. A predictor of cardiovascular diseases and events is the arterial stiffness. The pulse wave velocity (PWV) can be used to estimate arterial stiffness non-invasively. The tonometer is considered as the gold standard for measuring PWV. This approach requires manual probe fixation above the artery and depends on the skills of the operator. Electrical impedance plethysmography (IPG) is an interesting alternative using skin surface sensing electrodes, that is miniaturizable, cost-effective and allows measurement of deeper arteries. The aim of this pilot study was to explore if IPG can be a suitable technique to measure pulse wave velocity in legs as an alternative for the tonometer technique. The PWV was estimated by differences in the ECG-gated pulse arrival times (PAT) at the a. femoralis, a. popliteal, a. tibialis dorsalis and a. dorsalis pedis in nine healthy young adults using IPG and the SphygmoCor tonometer as a reference. The estimated PWV results from bioimpedance and the tonometer were fairly in agreement, and the beat-to-beat variability in PAT was similar. This pilot study indicates that the use of IPG may be a good alternative for estimating PWV in the legs.

Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: a review from VascAgeNet.

The photoplethysmogram (PPG) signal is widely measured by clinical and consumer devices, and it is emerging as a potential tool for assessing vascular age. The shape and timing of the PPG pulse wave are both influenced by normal vascular aging, changes in arterial stiffness and blood pressure, and atherosclerosis. This review summarizes research into assessing vascular age from the PPG. Three categories of approaches are described: 1) those which use a single PPG signal (based on pulse wave analysis), 2) those which use multiple PPG signals (such as pulse transit time measurement), and 3) those which use PPG and other signals (such as pulse arrival time measurement). Evidence is then presented on the performance, repeatability and reproducibility, and clinical utility of PPG-derived parameters of vascular age. Finally, the review outlines key directions for future research to realize the full potential of photoplethysmography for assessing vascular age.

Reliable detection of pulse arrival time for partial discharge location in HV cable based on improved Allen-Bear algorithm

In multi-sensor data based partial discharge (PD) source location in high voltage (HV) cable, poor reliability of detection of pulse arrival time against noises, waveform distortion, amplitude attenuation is the major barriers to practical application. In this paper, an improved Allen-Bear PD pulse detection algorithm, which combines the advantage of the characteristic functions of the two time-window energy ratio based detection algorithms, is proposed for robust PD location. The most critical contribution of proposed method is the extension of frequency domain component compared to the characteristic functions of the original algorithm. Simulation and physical experiment results demonstrate that the proposed method not only ensures the detection accuracy of the PD pulse arrival time, but also improves the reliability under harsh practical scenarios. It performs significantly better than the existing detection algorithm in terms of anti-noise, anti-waveform distortion, time window length variation, etc. It has significant value for practical applications.

Blood pressure altering method affects correlation with pulse arrival time.

ObjectivePulse arrival time (PAT) is a potential main feature in cuff-less blood pressure (BP) monitoring. However, the precise relationship between BP parameters and PAT under varying conditions lacks a complete understanding. We hypothesize that simple test protocols fail to demonstrate the complex relationship between PAT and both SBP and DBP. Therefore, this study aimed to investigate the correlation between PAT and BP during two exercise modalities with differing BP responses using an unobtrusive wearable device.MethodsSeventy-five subjects, of which 43.7% had a prior diagnosis of hypertension, participated in an isometric and dynamic exercise test also including seated periods of rest prior to, in between and after. PAT was measured using a prototype wearable chest belt with a one-channel electrocardiogram and a photo-plethysmography sensor. Reference BP was measured auscultatory.ResultsMean individual correlation between PAT and SBP was −0.82 ± 0.14 in the full protocol, −0.79 ± 0.27 during isometric exercise and −0.77 ± 0.19 during dynamic exercise. Corresponding correlation between PAT and DBP was 0.25 ± 0.35, −0.74 ± 0.23 and 0.39 ± 0.41.ConclusionThe results confirm PAT as a potential main feature to track changes in SBP. The relationship between DBP and PAT varied between exercise modalities, with the sign of the correlation changing from negative to positive between type of exercise modality. Thus, we hypothesize that simple test protocols fail to demonstrate the complex relationship between PAT and BP with emphasis on DBP.

Removal and replacement of interference in tied-array radio pulsar observations using the spectral kurtosis estimator

ABSTRACT We describe how to implement the spectral kurtosis method of interference removal (zapping) on a digitized signal of averaged power values. Spectral kurtosis is a hypothesis test, analogous to the t-test, with a null hypothesis that the amplitudes from which power is formed belong to a ‘good’ distribution – typically Gaussian with zero mean – where power values are zapped if the hypothesis is rejected at a specified confidence level. We derive signal-to-noise ratios (SNRs) as a function of amount of zapping for folded radio pulsar observations consisting of a sum of signals from multiple telescopes in independent radio-frequency interference environments, comparing four methods to compensate for lost data with coherent (tied-array) and incoherent summation. For coherently summed amplitudes, scaling amplitudes from non-zapped telescopes achieves a higher SNR than replacing zapped amplitudes with artificial noise. For incoherently summed power values, the highest SNR is given by scaling power from non-zapped telescopes to maintain a constant mean. We use spectral kurtosis to clean a tied-array radio pulsar observation by the Large European Array for Pulsars: the signal from one telescope is zapped with time and frequency resolutions of $6.25\, \mathrm{ms}$ and $0.16\, \mathrm{MHz}$, removing interference, along with 0.27 per cent of ‘good’ data, giving an uncertainty of $0.25\, \mathrm{\mu \mathrm{ s}}$ in pulse time of arrival (TOA) for PSR J1022+1001. We use a single-telescope observation to demonstrate recovery of the pulse profile shape, with 0.6 per cent of data zapped and a reduction from 1.22 to $0.70\, \mathrm{\mu \mathrm{ s}}$ in TOA uncertainty.

Pulse arrival time as a surrogate of blood pressure

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}). 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.

Blood pressure-independent neurogenic effect on conductance and resistance vessels: a consideration for cuffless blood pressure measurement?

Pulse transit time (PTT) and pulse arrival time (PAT) are promising measures for cuffless arterial blood pressure (BP) estimation given the intrinsic arterial stiffness-BP relationship. However, arterial stiffness (and PTT) is altered by autonomically-driven smooth muscle tension changes, potentially independent of BP. This would limit PTT or PAT as accurate BP correlates, more so in resistance vessels than conductance arteries. To quantify if there is a measurable neurogenic effect on PAT measured using photoplethysmography (PPG) (path includes resistance vessels) and radial artery tonometry (path includes only conductance vessels) during physiologically induced BP changes. PATs were measured continuously in participants (n=15, 35±15 years, 9 male) using an electrocardiogram and, simultaneously, a Finometer® PRO finger sensor, a finger PPG sensor and radial artery tonometer during seated rest, cold pressor test, cycling and isometric handgrip (IHG) exercise. ΔBP/ΔPAT was calculated for each sensor and each condition. All interventions significantly increased BP. A significant difference was observed in ΔBP/ΔPAT between cycling and both the cold pressor test and IHG exercise (p<0.05). ΔBP/ΔPAT did not differ whether measured via PPG or tonometry. Under the conditions tested, autonomic function does not have a BP-independent effect on PAT where the path includes resistance vessels (PPG signal), likely due to the speed of the wave and the short path length of resistance vessels. Autonomic function therefore does not limit the ability for use of PPG as a signal for potentially estimating BP without a cuff.

Introduction of Boosting Algorithms in Continuous Non-Invasive Cuff-less Blood Pressure Estimation using Pulse Arrival Time.

Blood Pressure (BP) is a critical biomarker for cardiorespiratory health. Conventional non-invasive BP measurement devices are mostly built on the principle of auscultation, oscillometry, or tonometry. The strong correlation between the Pulse Arrival Time (PAT) and BP has enabled unconstrained cuff-less BP monitoring. In this paper, we exploited that relationship for estimating Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Mean Arterial pressure (MAP) values. The proposed model involves extraction of PAT values by denoising the signals using advanced filtering techniques and finally employing machine learning algorithms to estimate cuff-less BP. The results are validated against Advancement of Medical Instrumentation (AAMI) standards and British Hypertension Society (BHS) protocols. The proposed method meets the AAMI standards in the context of estimating DBP and MAP values. The model's accuracy achieved Grade A for both MAP and DBP values using the CatBoost algorithm, whereas it achieved grade A for MAP and Grade B for DBP using the XGBoost algorithm based on the BHS standards.

An investigation of the individualized, two-point calibration method for cuffless blood pressure estimation using pulse arrival time: an historical perspective using the Casio BP-100 digital watch.

The use of wearable cuffless blood pressure (BP) devices is becoming commercially prevalent with little published validation information. Most devices rely, at least in part, on the relationship between pulse arrival time (PAT) and BP, a theoretical fundamental relationship that was first commercially exploited in 1993 with the release of the Casio BP-100 digital watch. This study explored the PAT method of BP estimation in a commercial device where it first began, the Casio BP-100 (Model No. 900) digital watch, which employs an individualized, two-point calibration method. Device accuracy was determined by comparison to a conventional cuff-based BP device measurements. Twenty participants (11 female, 9 male) had BP measured using both devices at rest, during a 5-minute isometric hand-grip exercise and at 1-minute post-exercise. Due to bidirectional scatter of BP estimation by the BP-100 device, there was no significant difference between the reference device and the BP-100. The devices showed poor correlation for both systolic BP (SBP) (R=0.36, p=0.13) and diastolic BP (DBP) (R=0.044, p=0.37). However, on average the watch was able to provide correct directional changes in SBP but not DBP with exercise. Despite being an industry first, the Casio BP-100 watch employed a method that gives a great chance of accuracy: a two point, individualized calibration method - more detailed than calibration methods in more modern devices. The watch, on average across a cohort, provided some information on BP directional change but was uncorrelated with cuff-based BP measurement. If the utility of beat-by-beat BP estimation is to be utilized, limitations of this method need to be addressed.

Novel Cuffless Blood Pressure Estimation Method Using a Bayesian Hierarchical Model.

Continuous blood pressure (BP) monitoring is important for the prevention and early diagnosis of cardiovascular diseases. Cuffless BP estimation using pulse arrival time (PAT) via a mathematical model which enables continuous BP measurement has recently become a popular research topic. In this study, simultaneous biomedical signals from ten healthy subjects were acquired by electrocardiogram (ECG) and photoplethysmogram (PPG) sensors and the continuous reference BP data were collected by a cuff-based Finometer PRO BP monitor. A hierarchical model was applied to estimate the parameters of a nonlinear model which in turn is used to estimate systolic blood pressure (SBP) using PAT with few calibration measurements. The mean absolute difference (MAD) between the estimated SBP and reference SBP is 4.35±1.43 mmHg using the proposed hierarchical model with three calibration measurements and is 4.36±1.17 mmHg with a single calibration measurement.

PulseLab: An Integrated and Expandable Toolbox for Pulse Wave Velocity-based Blood Pressure Estimation.

In this paper, we introduce PulseLab, a comprehensive MATLAB toolbox that enables estimating the blood pressure (BP) from electrocardiogram (ECG) and photoplethysmogram (PPG) signals using pulse wave velocity (PWV)-based models. This universal framework consists of 6 sequential modules, covering end-to-end procedures that are needed for estimating BP from raw PPG/ECG data. These modules are "dataset formation", "signal pre-processing", "segmentation", "characteristic-points detection", "pulse transit time (PTT)/ pulse arrival time (PAT) calculation", and "model validation". The toolbox is expandable and its application programming interface (API) is built such that newly-derived PWV-BP models can be easily included. The toolbox also includes a user-friendly graphical user interface (GUI) offering visualization for step-by-step processing of physiological signals, position of characteristic points, PAT/PTT values, and the BP regression results. To the best of our knowledge, PulseLab is the first comprehensive toolbox that enables users to optimize their model by considering several factors along the process for obtaining the most accurate model for cuff-less BP estimation.

Personalization of pulse arrival time based blood pressure surrogates through single spot check measurements.

We investigate the effect of selective single parameter personalization on the performance of multi-parameter models for pulse arrival time (PAT) based blood pressure (BP) surrogates. Our data set stems from 15 surgery patients, and we selected from each patient 5 segments of 30 min length each. We evaluate the root mean squared BP tracking error of the two models with and without single parameter personalization. We further compare the BP tracking performance to a surrogate-free sample-and-hold approach, e.g., as afforded by conventional non-invasive blood pressure (NIBP) oscillometry. Parameter personalization is key to realizing a tracking performance benefit of PAT-based BP surrogates. The highest tracking error reduction of about 3.7 mmHg with respect to a sample-and-hold approach was reached with a personalized model which is linear in the pulse wave velocity domain. It achieves an estimation error of 7.8 mmHg with respect to a continuously measured invasive reference.Clinical Relevance-We give a performance analysis of PAT-based BP surrogates which are personalized to a patient with a single NIBP spot measurement. We show for surgery patients that patient-specific personalization enables continuous beat-to-beat BP monitoring over 30 min intervals with a average root mean squared error of less than 8 mmHg.

Transcutaneous Cervical Vagus Nerve Stimulation Inhibits the Reciprocal of the Pulse Transit Time's Responses to Traumatic Stress in Posttraumatic Stress Disorder.

Research has shown that transcutaneous cervical vagus nerve stimulation (tcVNS) yields downstream changes in peripheral physiology in individuals afflicted with posttraumatic stress disorder (PTSD). While the cardiovascular effects of tcVNS have been studied broadly in prior work, the specific effects of tcVNS on the reciprocal of the pulse transit time (1/PTT) remain unknown. By quantifying detectable effects, tcVNS can be further evaluated as a counterbalance to sympathetic hyperactivity during distress - specifically, we hypothesized that tcVNS would inhibit 1/PTT responses to traumatic stress. To investigate this, the electrocardiogram (ECG), photoplethysmogram (PPG), and seismocardiogram (SCG), were simultaneously measured from 24 human subjects suffering from PTSD. Implementing state-of-the-art signal quality assessment algorithms, relative changes in the pulse arrival time (PAT) and the pre-ejection period (PEP) were estimated solely from signal segments of sufficient quality. Thereby computing relative changes in 1/PTT, we find that tcVNS results in reduced 1/PTT responses to traumatic stress and the first minute of stimulation, compared to a sham control (corrected p < 0.05). This suggests that tcVNS induces inhibitory effects on blood pressure (BP) and/or vasoconstriction, given the established relationship between 1/PTT and these parameters.Clinical Relevance- Relative changes in 1/PTT are induced by varying vasomotor tone and/or BP - it has therefore piqued considerable interest as a potential surrogate of continuous BP. Studying its responses to tcVNS thus furthers understanding of tcVNS-induced cardiovascular modulation. The positive effects detailed herein suggest a potential role for tcVNS in the long-term management of PTSD.

Causality of cortical and cardiovascular activity during cyclic alternating pattern in non-rapid eye movement sleep.

The dynamic interplay between central and autonomic nervous system activities plays a pivotal role in orchestrating sleep. Macrostructural changes such as sleep-stage transitions or phasic, brief cortical events elicit fluctuations in neural outflow to the cardiovascular system, but the causal relationships between cortical and cardiovascular activities underpinning the microstructure of sleep are largely unknown. Here, we investigate cortical-cardiovascular interactions during the cyclic alternating pattern (CAP) of non-rapid eye movement sleep in a diverse set of overnight polysomnograms. We determine the Granger causality in both 507 CAP and 507 matched non-CAP sequences to assess the causal relationships between electroencephalography (EEG) frequency bands and respiratory and cardiovascular variables (heart period, respiratory period, pulse arrival time and pulse wave amplitude) during CAP. We observe a significantly stronger influence of delta activity on vascular variables during CAP sequences where slow, low-amplitude EEG activation phases (A1) dominate than during non-CAP sequences. We also show that rapid, high-amplitude EEG activation phases (A3) provoke a more pronounced change in autonomic activity than A1 and A2 phases. Our analysis provides the first evidence on the causal interplay between cortical and cardiovascular activities during CAP. Granger causality analysis may also be useful for probing the level of decoupling in sleep disorders. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

Pulse Arrival Time - A Sensitive Vital Parameter for the Detection of Mental Stress

Abstract Mental stress triggers positive inotropic and chronotropic effects as well as peripheral vasoconstriction. This alters the pulse arrival time (PAT), the duration between electrical excitation of the ventricles and arrival of the pulse wave in the periphery. We conducted a study to examine PAT during five rest blocks and under mental stress utilizing the Mannheim Multicomponent Stress Test. Electrocardiograms as well as finger and earlobe photoplethysmograms were recorded. PAT was calculated for over 135,000 heartbeats from 42 healthy volunteers as the time duration between the R peak in the electrocardiogram and the following pulse onset in the respective photoplethysmogram. To identify the effect of mental stress, block-wise PAT means were statistically analyzed with repeated measures ANOVA. The analyses showed significant differences between the block means for both PAT measures (p &lt; 0.001). Post-hoc tests revealed significantly reduced PAT during the stress block compared to all rest blocks for both PAT measures (p &lt; 0.001). We found no significant differences between the rest blocks. Our results support that PAT is a sensitive vital parameter for the detection of mental stress in healthy volunteers. This holds true for both measurement positions, the finger and the earlobe.

A Comparative Study on Instantaneous and Mean Pulse Arrival Time for Cuffless Blood Pressure Estimation

Pulse arrival time (PAT) is the delay time between the peak of the R-wave Electrocardiogram (ECG) signal and the peak of Photoplethysmogram (PPG) signals. This method is widely exploited for continuous cuffless blood pressure measurement. In the literature, the PAT was determined based on the mean at a certain number or certain period of heartbeats, but none of them deployed a single pulse wave for PAT calculation. Therefore, in this paper, a relationship between mean PAT (15 pulses ± Standard Deviation (SD)) and instantaneous PAT (a pulse) with blood pressure (BP) was investigated on thirteen healthy male volunteers (aged between 17 to 42 years) through a pedal exercise. The PAT is grouped into three (3) categories which depend on the spatial position of the PPG signal measured; finger (PATf), wrist (PATw), and underfoot (PATt). The ECG and the PPG signals were synchronized using a Nexus-10 MK II data acquisition device and Matlab software (R 2014b) for subsequent analysis. An oscillometric cuff-based blood pressure instrument (Ostar, P2) was used as a BP reference during the experiment. Statistical analysis showed no significant difference in the |r| value between mean (15 pulses ± SD) and instantaneous PAT-BP; hence both methods are applicable for BP estimation using the PAT-BP calibration technique.