Figure - uploaded by Ole Marius Hoel Rindal
Content may be subject to copyright.
Source publication
Objective:
Pulse 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...
Contexts in source publication
Context 1
... characteristics of the test subjects are presented in Table 1. Group average change from baseline (defined as the average of the two last measurements during rest period 1) to maximum or minimum for all parameters Illustration of the test protocol with isometric exercise, dynamic exercise and rest periods. ...Context 2
... characteristics of the test subjects are presented in Table 1. Group average change from baseline (defined as the average of the two last measurements during rest period 1) to maximum or minimum for all parameters Illustration of the test protocol with isometric exercise, dynamic exercise and rest periods. ...Citations
... Real-time BP estimation using PAT measurement relies on the correlation between the two variables. Prior work has reported on this relationship with mixed results 5,18,32,66,67 . Due to the correlation between PAT measurements and HR 21, 27, 68 , we suspect that many studies using PAT for BP estimation may actually be overly reliant on the correlation between HR and BP. ...
Pulse arrival time (PAT) is known to be correlated with blood pressure.
Although PAT can be measured using electrocardiography (ECG), photoplethysmography (PPG), and other signals commonly available in clinical settings, recent literature has noted that devices recording these waveforms are often subject to many hardware-specific factors related to digital filtering, clock synchronization, temporal resolution, and latency.
These factors can introduce relative timing errors between the ECG and PPG signals, resulting in a situation where traditional approaches for PAT measurement will not work as intended.
In this work, we propose a methodology that accounts for these confounding factors and generates precise measurements of PAT using standard bedside monitoring equipment.
This technique involves using heart rate variability to match heartbeats across waveforms and experimentally profiling the timing systems of bedside medical devices to correct various timing-related artifacts.
To improve the precision of the resulting PAT measurements, we model temporal uncertainties stemming from the finite temporal resolution of the waveform samples.
We apply this approach to a dataset with roughly 1.6 million hours of continuous ECG and PPG data from over 10,000 unique patients at a pediatric intensive care unit (ICU).
After demonstrating that the observed timing artifacts are consistent across the entire dataset, we show that accounting for them results in more reasonable distributions of PAT measurements across age groups.
It is our hope that this work will spur discussion around the standardization of PAT measurement using routinely collected signals in a clinical environment.
... PAT is often used as a proxy of BP. In a few words, if the pressure increases, pulse waves propagate more rapidly, and consequently the PAT measured decreases [11]. Noticeable results have already been achieved by this feature [8]. ...
Objective
Cuff blood pressure monitoring is uncomfortable, limited for daily-routine and it provides sparse measurements in time. To enable continuous non-invasive monitoring, a new cuffless blood pressure (BP) meter based on photoplethysmography (PPG) is assessed in a first clinical trial. It aims at exploring the relationship between BP variations and optical features, including a wide range of intra-individual BP variations.
Design and method
The cohort of the clinical trial (NCT05393401, approved by Human Research Review Board (CPP SM I)) includes 11 healthy subjects (5F/6M, age [19-60] years). Each subject undertakes a sequence of physical / mental activities and relaxation. The subject is monitored with PPG and ECG sensors at a sampling rate of 1 kHz. Measurements are realized with PPG sensors on the wrist, directly above the radial artery that was located by ultrasound. All these signals are collected and synchronised by a custom-built multi-sensing platform that was designed in-house. The Pulse Arrival Times (PAT) are computed from ECG R-peaks and PPG signals for each heartbeat, and then averaged every 20 s. In parallel, the subject's BP is measured every 20 s with a medical-grade continuous meter.
Results
The collected dataset provides a wide range of variations of the PAT (0.23 +/- 0.19 s) and of the systolic BP (132 +/- 19 mmHg). Based on this dataset, a Gaussian process model is trained with cross-validation to predict the systolic BP from PAT values. The Root Mean Square Error (RMSE) is 18 mmHg. The Bland-Altman plot shows the distribution of residuals as a function of the mean of both measured and predicted BP. 95% of predicted values are within +/- 32 mmHg around the target.
Conclusions
Thanks to the measurement protocol based on both active periods and relaxation, it is possible to measure a wide range of PAT and BP values with healthy subjects. Such variations are necessary to build a reliable prediction model. Future work will improve this preliminary model by taking into account instant heartrate and individual physiological parameters.
... The study had three main aims. First, we aimed to compare BP predictions of the PAT-based BP model, derived from a general population cohort (25), with the measurements of a conventional cuff-based oscillometric BP device (ReferenceBP) during 24ABPM in subjects with and without hypertension. Second, we aimed to compare the user acceptability of the cuffless device with the ReferenceBP device. ...
... The same ReferenceBP device was used at both centers. A detailed description of the prototype cuffless device, developed by Aidee Health AS, has been given previously (25,27,28). In short, it is a wearable chest belt that measures electrophysiological and optical signals in form of an electrocardiogram (ECG) and a PPG and has an inertial measurement unit (IMU) consisting of a 3D accelerometer and a 3D gyroscope. ...
... Cuffless BP predictions were obtained by using PAT calculated from the ECG and PPG signals. The model to obtain BP values from PAT estimations was developed from a different dataset, considered a general population cohort, during isometric exercise induced BP changes (25). The model is a simple best-fit linear equation with an intercept term and a coefficient to determine either systolic BP (SBP) or diastolic BP (DBP) from measured PAT. ...
Objective
24-hour ambulatory blood pressure monitoring (24ABPM) is state of the art in out-of-office blood pressure (BP) monitoring. Due to discomfort and technical limitations related to cuff-based 24ABPM devices, methods for non-invasive and continuous estimation of BP without the need for a cuff have gained interest. The main aims of the present study were to compare accuracy of a pulse arrival time (PAT) based BP-model and user acceptability of a prototype cuffless multi-sensor device (cuffless device), developed by Aidee Health AS, with a conventional cuff-based oscillometric device (ReferenceBP) during 24ABPM.
Methods
Ninety-five normotensive and hypertensive adults underwent simultaneous 24ABPM with the cuffless device on the chest and a conventional cuff-based oscillometric device on the non-dominant arm. PAT was calculated using the electrocardiogram (ECG) and photoplethysmography (PPG) sensors incorporated in the chest-worn device. The cuffless device recorded continuously, while ReferenceBP measurements were taken every 20 minutes during daytime and every 30 minutes during nighttime. Two-minute PAT-based BP predictions corresponding to the ReferenceBP measurements were compared with ReferenceBP measurements using paired t-tests, bias, and limits of agreement.
Results
Mean (SD) of ReferenceBP compared to PAT-based daytime and nighttime systolic BP (SBP) were 129.7 (13.8) mmHg versus 133.6 (20.9) mmHg and 113.1 (16.5) mmHg versus 131.9 (23.4) mmHg. Ninety-five % limits of agreements were [-26.7, 34.6 mmHg] and [-20.9, 58.4 mmHg] for daytime and nighttime SBP respectively. The cuffless device was reported to be significantly more comfortable and less disturbing than the ReferenceBP device during 24ABPM.
Conclusions
In the present study, we demonstrated that a general PAT-based BP model had unsatisfactory agreement with ambulatory BP during 24ABPM, especially during nighttime. If sufficient accuracy can be achieved, cuffless BP devices have promising potential for clinical assessment of BP due to the opportunities provided by continuous BP measurements during real-life conditions and high user acceptability.
... A prototype cuffless BP sensor (cuffless BP device) was used in this study (7)(8)(9). It consists of a one-channel electrocardiogram (ECG) sensor, a photo-plethysmography (PPG) sensor and an inertial measurement unit (3D accelerometer and 3D gyroscope) integrated in a wearable chest belt. ...
... One or more of these factors likely affect generalizability of PAT as a cuffless surrogate measurement. Several studies have shown that varying between-individuals relationships between PAT and BP are a major limitation (9,18,19). The improved accuracy of the complex individualized models indicates that features extracted from ECG and PPG sensors can enable non-invasive cuffless BP monitoring, but these models are patient-specific (and potentially cannot be generalized for all subjects) and rely on machine learning without any a priori physiological knowledge. ...
Objective
Continuous non-invasive cuffless blood pressure (BP) monitoring may reduce adverse outcomes in hospitalized patients if accuracy is approved. We aimed to investigate accuracy of two different BP prediction models in critically ill intensive care unit (ICU) patients, using a prototype cuffless BP device based on electrocardiogram and photoplethysmography signals. We compared a pulse arrival time (PAT)-based BP model (generalized PAT-based model) derived from a general population cohort to more complex and individualized models (complex individualized models) utilizing other features of the BP sensor signals.
Methods
Patients admitted to an ICU with indication of invasive BP monitoring were included. The first half of each patient’s data was used to train a subject-specific machine learning model (complex individualized models). The second half was used to estimate BP and test accuracy of both the generalized PAT-based model and the complex individualized models. A total of 7,327 measurements of 15 s epochs were included in pairwise comparisons across 25 patients.
Results
The generalized PAT-based model achieved a mean absolute error (SD of errors) of 7.6 (7.2) mmHg, 3.3 (3.1) mmHg and 4.6 (4.4) mmHg for systolic BP, diastolic BP and mean arterial pressure (MAP) respectively. Corresponding results for the complex individualized model were 6.5 (6.7) mmHg, 3.1 (3.0) mmHg and 4.0 (4.0) mmHg. Percentage of absolute errors within 10 mmHg for the generalized model were 77.6, 96.2, and 89.6% for systolic BP, diastolic BP and MAP, respectively. Corresponding results for the individualized model were 83.8, 96.2, and 94.2%. Accuracy was significantly improved when comparing the complex individualized models to the generalized PAT-based model in systolic BP and MAP, but not diastolic BP.
Conclusion
A generalized PAT-based model, developed from a different population was not able to accurately track BP changes in critically ill ICU patients. Individually fitted models utilizing other cuffless BP sensor signals significantly improved accuracy, indicating that cuffless BP can be measured non-invasively, but the challenge toward generalizable models remains for future research to resolve.
... When investigating BPR during exercise, a continuous cuff-less approach would be superior compared to cuff-based measurements, to avoid discomfort and inaccuracy caused by movement. However, PAT is a single parameter, and its relationship to different parameters of BP is not yet fully understood (Heimark et al., 2021). Still, the correlation coefficient (on an individual basis) between PAT and SBP in a recent review by Welykholowa et al. (2020) was shown to be r = 0.84 (range 0.42-0.98) in studies utilizing the ECG + PPG modality. ...
... PAT was calculated for each cardiac cycle from the R-peak in the ECG signal to the foot in the PPG signal recorded from the skin vasculature at chest level (Heimark et al., 2021). The PAT values were filtered by applying a moving median filter with a window size of 30 cycles, only keeping PAT values within 20% of the median of the window. ...
... If less than 5 valid PAT values existed in the window, the PAT measurement was discarded. DBP was not considered in the present analysis due to a lack of relationship between the two variables (Heimark et al., 2021). ...
Introduction: There is a lack of data describing the blood pressure response (BPR) in well-trained individuals. In addition, continuous bio-signal measurements are increasingly investigated to overcome the limitations of intermittent cuff-based BP measurements during exercise testing. Thus, the present study aimed to assess the BPR in well-trained individuals during a cycle ergometer test with a particular focus on the systolic BP (SBP) and to investigate pulse arrival time (PAT) as a continuous surrogate for SBP during exercise testing.
Materials and Methods: Eighteen well-trained male cyclists were included (32.4 ± 9.4 years; maximal oxygen uptake 63 ± 10 ml/min/kg) and performed a stepwise lactate threshold test with 5-minute stages, followed by a continuous test to voluntary exhaustion with 1-min increments when cycling on an ergometer. BP was measured with a standard automated exercise BP cuff. PAT was measured continuously with a non-invasive physiological measurements device (IsenseU) and metabolic consumption was measured continuously during both tests.
Results: At lactate threshold (281 ± 56 W) and maximal intensity test (403 ± 61 W), SBP increased from resting values of 136 ± 9 mmHg to maximal values of 219 ± 21 mmHg and 231 ± 18 mmHg, respectively. Linear within-participant regression lines between PAT and SBP showed a mean r ² of 0.81 ± 17.
Conclusion: In the present study focusing on the BPR in well-trained individuals, we observed a more exaggerated systolic BPR than in comparable recent studies. Future research should follow up on these findings to clarify the clinical implications of the high BPR in well-trained individuals. In addition, PAT showed strong intra-individual associations, indicating potential use as a surrogate SBP measurement during exercise testing.
Introduction
Non-invasive cuffless blood pressure devices have shown promising results in accurately estimating blood pressure when comparing measurements at rest. However, none of commercially available or prototype cuffless devices have yet been validated according to the appropriate standards. The aim of the present study was to bridge this gap and evaluate the ability of a prototype cuffless device, developed by Aidee Health AS, to track changes in blood pressure compared to a non-invasive, continuous blood pressure monitor (Human NIBP or Nexfin) in a laboratory set up. The performance was evaluated according to the metrics and statistical methodology described in the ISO 81060-3:2022 standard. However, the present study is not a validation study and thus the study was not conducted according to the ISO 81060-3:2022 protocol, e.g., non-invasive reference and distribution of age not fulfilled.
Method
Data were sampled continuously, beat-to-beat, from both the cuffless and the reference device. The cuffless device was calibrated once using the reference BP measurement. Three different techniques (isometric exercise, mental stress, and cold pressor test) were used to induce blood pressure changes in 38 healthy adults.
Results
The mean difference (standard deviation) was 0.3 (8.7) mmHg for systolic blood pressure, 0.04 (6.6) mmHg for diastolic blood pressure, and 0.8 (7.9) mmHg for mean arterial pressure, meeting the Accuracy requirement of ISO 81060-3:2022 (≤6.0 (10.0) mmHg). The corresponding results for the Stability criteria were 1.9 (9.2) mmHg, 2.9 (8.1) mmHg and 2.5 (9.5) mmHg. The acceptance criteria for the Change requirement were achieved for the 85th percentile of ≤50% error for diastolic blood pressure and mean arterial pressure but were higher than the limit for systolic blood pressure (56% vs. ≤50%) and for all parameters for the 50th percentile (32%–39% vs. ≤25%).
Conclusions
The present study demonstrated that the cuffless device could track blood pressure changes in healthy adults across different activities and showed promising results in achieving the acceptance criteria from ISO 81060-3:2022.
Objective: Cardiac Index (CI) is a key physiologic parameter to ensure end organ perfusion in the pediatric intensive care unit (PICU). Determination of CI requires invasive cardiac measurements and is not routinely done at the PICU bedside. To date, there is no gold standard non-invasive means to determine CI. This study aims to use a novel non-invasive methodology, based on routine continuous physiologic data, called Pulse Arrival Time (PAT) as a surrogate for CI in patients with normal Ejection Fraction.
Approach: Electrocardiogram (ECG) and photoplethysmogram (PPG) signals were collected from beside monitors at a sampling frequency of 250 samples per second. Continuous PAT, derived from the ECG and PPG waveforms was averaged per patient. Pearson’s correlation coefficient was calculated between PAT and CI, PAT and heart rate (HR), and PAT and ejection fraction (EF).
Main Results: Twenty patients underwent right heart cardiac catheterization. The mean age of patients was 11.7±5.4 years old, ranging from 11 months old to 19 years old, the median age was 13.4 years old. HR in this cohort was 93.8±17.0 beats per minute. The average EF was 54.4±9.6%. The average CI was 3.51±0.72 L/min/m2, with ranging from 2.6 to 4.77 L/min/m2. The average PAT was 0.31±0.12 seconds. Pearson correlation analysis showed a positive correlation between PAT and CI (0.57, p < 0.01). Pearson correlation between HR and CI, and correlation between EF and CI was 0.22 (p = 0.35) and 0.03 (p = 0.23) respectively. The correlation between PAT, when indexed by HR (i.e. PAT × HR), and CI minimally improved to 0.58 (p < 0.01).
Significance: This pilot study demonstrates that PAT may serve as a valuable surrogate marker for CI at the bedside, as a non-invasive and continuous modality in the PICU. The use of PAT in clinical practice remains to be thoroughly investigated.
