Cuffless Blood Pressure Measurement Research Articles

A Wireless Noninvasive Blood Pressure Measurement System Using MAX30102 and Random Forest Regressor for Photoplethysmography Signals

Hypertension, often termed “the silent killer”, is associated with cardiovascular risk and requires regular blood pressure (BP) monitoring. However, existing methods are cumbersome and require medical expertise, which is worsened by the need for physical contact, particularly during situations such as the coronavirus pandemic that started in 2019 (COVID-19). This study aimed to develop a cuffless, continuous, and accurate BP measurement system using a photoplethysmography (PPG) sensor and a microcontroller via PPG signals. The system utilizes a MAX30102 sensor and ESP-WROOM-32 microcontroller to capture PPG signals that undergo noise reduction during preprocessing. Peak detection and feature extraction algorithms were introduced, and their output data were used to train a machine learning model for BP prediction. Tuning the model resulted in identifying the best-performing model when using a dataset from six subjects with a total of 114 records, thereby achieving a coefficient of determination of 0.37/0.46 and a mean absolute error value of 4.38/4.49 using the random forest algorithm. Integrating this model into a web-based graphical user interface enables its implementation. One probable limitation arises from the small sample size (six participants) of healthy young individuals under seated conditions, thereby potentially hindering the proposed model’s ability to learn and generalize patterns effectively. Increasing the number of participants with diverse ages and medical histories can enhance the accuracy of the proposed model. Nevertheless, this innovative device successfully addresses the need for convenient, remote BP monitoring, particularly during situations like the COVID-19 pandemic, thus making it a promising tool for cardiovascular health management.

Non-invasive pulse arrival time as a surrogate for oscillometric systolic blood pressure changes during non-pharmacological intervention

Background. Non-invasive continuous blood pressure (BP) monitoring is of longstanding interest in various cardiovascular scenarios. In this context, pulse arrival time (PAT), i.e., a surrogate parameter for systolic BP (change), became very popular recently, especially in the context of cuffless BP measurement and dedicated lifestyle interventions. Nevertheless, there is also understandable doubt on its reliability in uncontrolled and mobile settings. Objective. The aim of this work is therefore the investigation whether PAT follows oscillometric systolic BP readings during moderate interventions by physical or mental activity using a medical grade handheld device for non-invasive PAT assessment. Approach. A study was conducted featuring an experimental group performing a physical and a mental task, and a control group. Oscillometric BP and PAT were assessed at baseline and after each intervention. Interventions were selected randomly but then performed sequentially in a counterbalanced order. Multivariate analyses of variance were used to test within-subject and between-subject effects for the dependent variables, followed by univariate analyses for post-hoc testing. Furthermore, correlation analysis was performed to assess the association of intervention effects between BP and PAT. Main results. The study included 51 subjects (31 females). Multivariate analysis of variances showed that effects in BP, heart rate, PAT and pulse wave parameters were consistent and significantly different between experimental and control groups. After physical activity, heart rate and systolic BP increased significantly whereas PAT decreased significantly. Mental activity leads to a decrease in systolic BP at stable heart rate. Pulse wave parameters follow accordingly by an increase of PAT and mainly unchanged pulse wave analysis features due to constant heart rate. Finally, also the control group behaviour was accurately registered by the PAT method compared to oscillometric cuff. Correlation analyses revealed significant negative associations between changes of systolic BP and changes of PAT from baseline to the physical task (−0.33 [−0.63, 0.01], p < 0.048), and from physical to mental task (−0.51 [−0.77, −0.14], p = 0.001), but not for baseline to mental task (−0.12 [−0,43,0,20], p = 0.50) in the experimental group. Significance. PAT and the used digital, handheld device proved to register changes in BP and heart rate reliably compared to oscillometric measurements during intervention. Therefore, it might add benefit to future mobile health solutions to support BP management by tracking relative, not absolute, BP changes during non-pharmacological interventions.

Using Data Augmentation to Improve the Accuracy of Blood Pressure Measurement Based on Photoplethysmography

Convenient and accurate blood pressure (BP) measurement is of great importance in both clinical and daily life. Although deep learning (DL) can achieve cuff-less BP measurement based on Photoplethysmography (PPG), the performance of DL is affected by few-shot data. Data augmentation becomes an effective way to enhance the size of the training data. In this paper, we use cropping, flipping, DTW barycentric averaging (DBA), generative adversarial network (GAN) and variational auto-encoder (VAE) for the data augmentation of PPG. Furthermore, a PE–CNN–GRU model is designed for cuff-less BP measurement applying position encoding (PE), convolutional neural networks (CNNs) and gated recurrent unit (GRU) networks. Experiment results based on real-life datasets show that VAE is the most suitable method for PPG data augmentation, which can reduce the mean absolute error (MAE) of PE–CNN–GRU when measuring systolic blood pressure (SBP) and diastolic blood pressure (DBP) by 18.80% and 19.84%. After the data augmentation of PPG, PE–CNN–GRU achieves accurate and cuff-less BP measurement, thus providing convenient support for preventing cardiovascular diseases.

A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithms

Background and objectiveHypertension is a potentially dangerous health condition that can be detected by measuring blood pressure (BP). Blood pressure monitoring and measurement are essential for preventing and treating cardiovascular diseases. Cuff-based devices, on the other hand, are uncomfortable and prevent continuous BP measurement. MethodsIn this study, a new non-invasive and cuff-less method for estimating Systolic Blood Pressure (SBP), Mean Arterial Pressure (MAP), and Diastolic Blood Pressure (DBP) has been proposed using characteristic features of photoplethysmogram (PPG) signals and nonlinear regression algorithms. PPG signals were collected from 219 participants, which were then subjected to preprocessing and feature extraction steps. Analyzing PPG and its derivative signals, a total of 46 time, frequency, and time-frequency domain features were extracted. In addition, the age and gender of each subject were also included as features. Further, correlation-based feature selection (CFS) and Relief F feature selection (ReliefF) techniques were used to select the relevant features and reduce the possibility of over-fitting the models. Finally, support vector regression (SVR), K-nearest neighbour regression (KNR), decision tree regression (DTR), and random forest regression (RFR) were established to develop the BP estimation model. Regression models were trained and evaluated on all features as well as selected features. The best regression models for SBP, MAP, and DBP estimations were selected separately. ResultsThe SVR model, along with the ReliefF-based feature selection algorithm, outperforms other algorithms in estimating the SBP, MAP, and DBP with the mean absolute error of 2.49, 1.62 and 1.43 mmHg, respectively. The proposed method meets the Advancement of Medical Instrumentation standard for BP estimations. Based on the British Hypertension Society standard, the results also fall within Grade A for SBP, MAP, and DBP. ConclusionThe findings show that the method can be used to estimate blood pressure non-invasively, without using a cuff or calibration, and only by utilizing the PPG signal characteristic features.

A multi-channel photoplethysmography array with contact-force regulation for tonoarteriographic imaging

Aim: Photoplethysmography (PPG) has gained widespread popularity as a non-invasive method for potential cuff-less blood pressure (BP) measurement in smart devices. However, the accuracy of PPG-based devices is often hindered by motion artifacts, site variability, and inconsistent contact force (CF). This study aims to investigate the influence of CF variations on PPG signals. Methods: To address these challenges, we present a novel approach involving a multi-channel PPG array integrated with CF regulation in the form of a wearable wristband. This platform enables the visualization of regional PPG/BP distribution while simultaneously monitoring CF. Moreover, our research explored the relationship between PPG waveform characteristics and CF during wrist extension. Results: The results of this study reveal that the PPG amplitude (PPGA) and the b/a ratios, computed from the second derivative peaks of the PPG AC pulse wave, exhibit inconsistency in reaction to CF variations. Notably, a shape correlation coefficient of 0.65069 between normalized PPG and flipped CF sheds light on how changes in posture affect PPG measurements. Conclusions: The proposed platform shows promise in mitigating the effects of CF and spatial positioning on PPG, thereby improving measurement precision and offering a novel approach to image tonoarteriographic (TAG) activities for continuous hypertension management.

Ability of a 24-h ambulatory cuffless blood pressure monitoring device to track blood pressure changes in clinical practice.

There is an increasing number of cuffless blood pressure (BP) measurement (BPM) devices. Despite promising results when comparing single measurements, the ability of these devices to track changes in BP levels over 24 h related to an initial calibration BP (CalibBP) is unknown. Our aim was to analyse this ability in a cuffless device using pulse transit time. We prospectively enrolled 166 participants for simultaneously performed cuffless (Somnotouch-NIBP) and cuff-based (Spacelabs 90217A/IEM Mobil-O-graph) 24 h BPM. As CalibBP for the cuffless device, first cuff-based BP was used. As surrogate for changes in BP levels after the CalibBP, we used the difference between the CalibBP and mean 24 h, awake and asleep BP measured by the two devices. In addition, we analysed the relationship between the difference of the CalibBP and the cuff-based BPM versus the difference between the cuff-based and the cuffless BPM devices. Mean(SD) difference between the CalibBP and mean 24hBP by the cuff-based or cuffless BP device were 7.4 (13.2) versus 1.8 (8.3) mmHg for systolic ( P < 0.0001) and 6.6 (6.8) versus 1.6 (5.8) mmHg for diastolic ( P < 0.0001). A near linear relationship was seen among the difference between the CalibBP and the cuff-based BPM values and the difference between the cuff-based and cuffless BPM device. Our data indicate a lower ability of the cuffless BPM device to track changes of BP levels after CalibBP. In addition, cuffless device accuracy was associated with the changes in BP levels after the initial CalibBP - the larger the BP level change, the larger the difference between the devices. https://www.clinicaltrials.gov ; Unique identifier: NCT03054688; NCT03975582.

HGCTNet: Handcrafted Feature-Guided CNN and Transformer Network for Wearable Cuffless Blood Pressure Measurement.

Biosignals collected by wearable devices, such as electrocardiogram and photoplethysmogram, exhibit redundancy and global temporal dependencies, posing a challenge in extracting discriminative features for blood pressure (BP) estimation. To address this challenge, we propose HGCTNet, a handcrafted feature-guided CNN and transformer network for cuffless BP measurement based on wearable devices. By leveraging convolutional operations and self-attention mechanisms, we design a CNN-Transformer hybrid architecture to learn features from biosignals that capture both local information and global temporal dependencies. Then, we introduce a handcrafted feature-guided attention module that utilizes handcrafted features extracted from biosignals as query vectors to eliminate redundant information within the learned features. Finally, we design a feature fusion module that integrates the learned features, handcrafted features, and demographics to enhance model performance. We validate our approach using two large wearable BP datasets: the CAS-BP dataset and the Aurora-BP dataset. Experimental results demonstrate that HGCTNet achieves an estimation error of 0.9 ± 6.5 mmHg for diastolic BP (DBP) and 0.7 ± 8.3 mmHg for systolic BP (SBP) on the CAS-BP dataset. On the Aurora-BP dataset, the corresponding errors are -0.4 ± 7.0 mmHg for DBP and -0.4 ± 8.6 mmHg for SBP. Compared to the current state-of-the-art approaches, HGCTNet reduces the mean absolute error of SBP estimation by 10.68% on the CAS-BP dataset and 9.84% on the Aurora-BP dataset. These results highlight the potential of HGCTNet in improving the performance of wearable cuffless BP measurements. The dataset and source code are available at https://github.com/zdzdliu/HGCTNet.

Feasibility and Effectiveness of a Ring-Type Blood Pressure Measurement Device Compared With 24-Hour Ambulatory Blood Pressure Monitoring Device.

This study aimed to evaluate the applicability and precision of a ring-type cuffless blood pressure (BP) measurement device, CART-I Plus, compared to conventional 24-hour ambulatory BP monitoring (ABPM). Forty patients were recruited, and 33 participants were included in the final analysis. Each participant wore both CART-I Plus and ABPM devices on the same arm for approximately 24 hours. BP estimation from CART-I Plus, derived from photoplethysmography (PPG) signals, were compared with the corresponding ABPM measurements. The CART-I Plus recorded systolic blood pressure (SBP)/diastolic blood pressure (DBP) values of 131.4±14.1/81.1±12.0, 132.7±13.9/81.9±11.9, and 128.7±14.6/79.3±12.2 mmHg for 24-hour, daytime, and nighttime periods respectively, compared to ABPM values of 129.7±11.7/84.4±11.2, 131.9±11.6/86.3±11.1, and 124.5±13.6/80.0±12.2 mmHg. Mean differences in SBP/DBP between the two devices were 1.74±6.69/-3.24±6.51 mmHg, 0.75±7.44/-4.41±7.42 mmHg, and 4.15±6.15/-0.67±5.23 mmHg for 24-hour, daytime, and nighttime periods respectively. Strong correlations were also observed between the devices, with r=0.725 and r=0.750 for transitions in SBP and DBP from daytime to nighttime, respectively (both p<0.001). The CART-I Plus device, with its unique ring-type design, shows promising accuracy in BP estimation and offers a potential avenue for continuous BP monitoring in clinical practice. ClinicalTrials.gov Identifier: NCT06084065.

First-in-Human Study for Evaluating the Accuracy of Smart Ring Based Cuffless Blood Pressure Measurement.

Recently, a ring-type cuffless blood pressure (BP) measuring device has been developed. This study was a prospective, single arm, first-in-human pivotal trial to evaluate accuracy of BP measurement by the new device. The ring-type smart wearable monitoring device measures photoplethysmography signals from the proximal phalanx and transmits the data wirelessly to a connected smartphone. For the BP comparison, a cuff was worn on the arm to check the reference BP by auscultatory method, while the test device was worn on the finger of the opposite arm to measure BP simultaneously. Measurements were repeated for up to three sets each on the left and right arms. The primary outcome measure was mean difference and standard deviation of BP differences between the test device and the reference readings. We obtained 526 sets of systolic BP (SBP) and 513 sets of diastolic BP (DBP) from 89 subjects, with ranges of 80 to 175 mmHg and 43 to 122 mmHg for SBP and DBP, respectively. In sample-wise comparison, the mean difference between the test device and the reference was 0.16 ± 5.90 mmHg (95% limits of agreement [LOA], -11.41, 11.72) in SBP and -0.07 ± 4.68 (95% LOA, -9.26, 9.10) in DBP. The test device showed a strong correlation with the reference for SBP (r = 0.94, P < 0.001) and DBP (r = 0.95, P < 0.001). There were consistent results in subject-wise comparison. The new ring-type BP measuring device showed a good correlation for SBP and DBP with minimal bias compared with an auscultatory method.

Performance of wearable watch-type home blood pressure measurement devices in a real-world clinical sample.

Independent testing of home blood pressure (BP) measurement (HBPM) devices is often lacking, particularly among older and multi-morbid patients. We studied the Bpro G2 (using tonometry), Omron HeartGuide (using occlusive oscillometric technology), and Heartisans (using photoplethysmography) wrist watch HBPM devicesagainst a gold standard brachial sphygmomanometer. To test device performance, we used the ISO81060-2 protocol (though this protocol cannot formally validate cuffless devices). We also used linear mixed models to compare adjusted longitudinal BP measurements between devices. Finally, as a surrogate for usability, we recorded instances of device failure where no BP measurement was returned. We enrolled 128 participants (median [Q1-Q3] age 53 [40-65] years, 51% male, 46% on antihypertensive drugs), of whom 100 were suitable for the primaryanalysis. All three devices had mean BP values within 5mmHg of sphygmomanometry. However, due to insufficient reliability (e.g., wider than accepted standard deviations of mean BP), none of the three devices passed all criteria required by the ISO81060-2 protocol. In adjusted longitudinal analyses, the Omron device also systematically underestimated systolic and diastolic BP (-8.46mmHg; 95% CI 6.07, 10.86; p < 0.001; and -2.53mmHg; 95% CI -4.03, -1.03; p = 0.001; respectively). Nevertheless, compared to the Omron device, BPro and Heartisans devices had increased odds of failure (BPro: odds ratio [OR] 5.24; p < 0.0001; Heartisans: OR 5.61; p < 0.001). While we could not formally validate the cuffless devices, our results show that wearable technologies will require improvements to offer reliable BP assessment. This study also highlights the need for validation protocols specifically designed for cuffless BP measurement technologies.

Practical Application of a New Cuffless Blood Pressure Measurement Method.

This study involved 167 patients aged 31 to 88 years (mean 64.2 ± 7.8 years) with normal blood pressure, high blood pressure, and compensated high blood pressure. During each session, three routine blood pressure measurements with intervals of 30 s were taken using a sphygmomanometer with an appropriate cuff size, and the mean value was selected for comparison. The measurements were carried out by two observers trained at the same time with a reference sphygmomanometer using a Y-shaped connector. In the minute following the last cuff-based measurements, an electrocardiogram (ECG) with an I-lead and a photoplethysmocardiogram were recorded simultaneously for 3 min with the CardioQVARK device. We compared the systolic and diastolic BP obtained from a cuff-based mercury sphygmomanometer and smartphone-case-based BP device: the CardioQVARK monitor. A statistical analysis plan was developed using the IEEE Standard for Wearable Cuffless Blood Pressure Devices. Bland-Altman plots were used to estimate the precision of cuffless measurements. The mean difference between the values defined by CardioQVARK and the cuff-based sphygmomanometer for systolic blood pressure (SBP) was 0.31 ± 3.61, while that for diastolic blood pressure (DBP) was 0.44 ± 3.76. The mean absolute difference (MAD) for SBP was 3.44 ± 2.5 mm Hg, and that for DBP was 3.21 ± 2.82 mm Hg. In the subgroups, the smallest error (less than 3 mm Hg) was observed in the prehypertension group, with a slightly larger error (up to 4 mm Hg) found among patients with a normal blood pressure and stage 1 hypertension. The largest error was found in the stage 2 hypertension group (4-5.5 mm Hg). The largest error was 4.2 mm Hg in the high blood pressure group. We, therefore, did not record an error in excess of 7 mmHg, the upper boundary considered acceptable in the IEEE recommendations. We also did not reach a mean error of 5 mmHg, the upper boundary considered acceptable according to the very recent ESH recommendations. At the same time, in all groups of patients, the systolic blood pressure was determined with an error of less than 5 mm Hg in more than 80% of patients. While this study shows that the CardioQVARK device meets the standards of IEEE, the Bland-Altman analysis indicates that the cuffless measurement of diastolic blood pressure has significant bias. The difference was very small and unlikely to be of clinical relevance for the individual patient, but it may well have epidemiological relevance on a population level. Therefore, the CardioQVARK device, while being worthwhile for monitoring patients over time, may not be suitable for screening purposes. Cuffless blood pressure measurement devices are emerging as a convenient and tolerable alternative to cuff-based devices. However, there are several limitations to cuffless blood pressure measurement devices that should be considered. For instance, this study showed a high proportion of measurements with a measurement error of <5 mmHg, while detecting a small, although statistically significant, bias in the measurement of diastolic blood pressure. This suggests that this device may not be suitable for screening purposes. However, its value for monitoring BP over time is confirmed. Furthermore, and most importantly, the easy measurement method and the device portability (integrated in a smartphone) may increase the self-awareness of hypertensive patients and, potentially, lead to an improved adherence to their treatment. The cuffless blood pressure technology developed in this study was tested in accordance with the IEEE protocol and showed great precision in patient groups with different blood pressure ranges. This approach, therefore, has the potential to be applied in clinical practice.

Photoplethysmography-based cuffless blood pressure estimation: an image encoding and fusion approach

Objective. Photoplethysmography (PPG) is a promising wearable technology that detects volumetric changes in microcirculation using a light source and a sensor on the skin’s surface. PPG has been shown to be useful for non-invasive blood pressure (BP) measurement. Deep learning-based BP measurements are now gaining popularity. However, almost all methods focus on 1D PPG. We aimed to design an end-to-end approach for estimating BP using image encodings from a 2D perspective. Approach. In this paper, we present a BP estimation approach based on an image encoding and fusion (BP-IEF) technique. We convert the PPG into five image encodings and use them as input. The proposed BP-IEF consists of two parts: an encoder and a decoder. In addition, three kinds of well-known neural networks are taken as the fundamental architecture of the encoder. The decoder is a hybrid architecture that consists of convolutional and fully connected layers, which are used to fuse features from the encoder. Main results. The performance of the proposed BP-IEF is evaluated on the UCI database in both non-mixed and mixed manners. On the non-mixed dataset, the root mean square error and mean absolute error for systolic BP (SBP) are 13.031 mmHg and 9.187 mmHg respectively, while for diastolic BP (DBP) they are 5.049 mmHg and 3.810 mmHg. On the mixed dataset, the corresponding values for SBP are 4.623 mmHg and 3.058 mmHg, while for DBP the values are 2.350 mmHg and 1.608 mmHg. In addition, both SBP and DBP estimation on the mixed dataset achieved grade A compared to the British Hypertension Society standard. The DBP estimation on the non-mixed dataset also achieved grade A. Significance. The results indicate that the proposed approach has the potential to improve on the current mobile healthcare for cuffless BP measurement.

The Investigation of Bio-impedance Analysis at a Wrist Phantom with Two Pulsatile Arteries.

Bio-impedance analysis (BIA) has been widely investigated for hemodynamic monitoring. However, previous works rarely modelled two synchronously pulsatile arteries (representing the radial and ulnar arteries) in the wrist/forearm model. This work aims to clarify and quantify the influences of two pulsatile arteries on BIA. First, two blood-filled arteries were structured in a 3D wrist segment using the finite element method (FEM). Afterwards, an easy-to-produce two-arteries artificial wrist was fabricated with two components: gelatine-based surrounding tissue phantom and saline blood phantom. A syringe driver was utilised to constrict the arteries, and the impedance signals were measured using a Multi-frequency Impedance Analyser (MFIA). Both simulation and experimental results demonstrated the non-negligible influences of the ulnar artery on the overall BIA, inducing unwanted resistance changes to the acquired signals from the radial artery. The phantom experiments revealed the summation of the individual resistance changes caused by a single pulsatile artery was approximately equal to the measured resistance change caused by two synchronously pulsatile arteries, confirming the measured impedance signal at the wrist contains the pulsatile information from both arteries. This work is the first simulation and phantom investigation into two synchronously pulsatile arteries under BIA in the distal forearm, providing a better insight and understanding in the morphology of measured impedance signals. Future research can accordingly select either a small spacing 4-spot electrode configuration for a single artery sensing or a band electrode configuration for overall pulsatile arteries sensing. A more accurate estimation of blood volume change and pulse wave analysis (PWA) could help to develop cuffless blood pressure measurement (BPM).

Cuffless Non-invasive Blood Pressure Measurement Using CNN-LSTM Model: A Correlation Study

Cardiovascular disease is a major concern for people all around the world and still remains as the main cause of death worldwide. Blood pressure has been identified as the most important risk factor. Having the ability to acquire continuous monitoring on this biological parameter plays a significant role in reducing the risk of getting cardiac disease. Many studies conducted utilize two biosignals and features manually extracted from signals as input to the model. However, these methods increase the computational complexity in the pre-processing stage as it involves signal synchronization, and the model performance is highly dependent on the selection of features. The main objective of this study is to build a hybrid convolutional neural network combined with Long-Short Term Memory (CNN-LSTM) model to estimate blood pressure from PPG signals, which eliminates the need for manual feature extraction. Correlation study is performed to evaluate the performance of the model, and it gives a direct visualization of the model’s performance in percentage. This research compared the correlation performance between MIMIC-II dataset, UKM dataset, and PPG-BP dataset using the CNN-LSTM model to estimate blood pressure from PPG signals. The results show that the UKM dataset performs the best, having the highest overall correlation at 0.53 for systolic blood pressure, and 0.29 for diastolic blood pressure. The model trained with this dataset is suitable to estimate systolic blood pressure ranging from 141 to 150mmHg, and diastolic blood pressure ranging 81 to 90 mmHg. In conclusion, among the three datasets, UKM dataset is the most suitable dataset to be used as the input of the CNN-LSTM model to perform cuffless blood pressure measurement with PPG signals.

Accuracy of cuff-less, continuous, and non-invasive blood pressure measurement in 24-h ABPM in children aged 5–17

PurposeAmbulatory, cuff-less blood pressure (BP) measurement devices are a promising trend to alleviate the strains of conventional, cuff-based BP determination. Cuff-less devices circumvent discomfort and nocturnal arousal reactions which can be triggered by cuff inflation from conventional, cuff-based ambulatory blood pressure measurement devices. Mitigating these discomforts is especially desirable when performing measurement in children. In this study we want to assess the clinical validity of a cuff-less BP measurement device for 24-h measurements in children and adolescents.Materials and methodsWe compared the simultaneously retrieved BP data of the cuff-less SOMNOtouch NIBP and the cuff-based Mobil-O-Graph in 24-h use in 90 children in the range from 5 to 17 years old.ResultsA total of 1218 valid measurement pairs showed a mean deviation of 0.99 mmHg (limits of agreement: 21.44/-19.46) for systolic and 3.03 mmHg (limits of agreement: 24.37/−18.31) for diastolic BP values. Patient-specific difference of means was within 15 mmHg in 97.7% (systolic BP) and 93.2% (diastolic BP) patients. 25.6% of nocturnal cuff inflations led to determinable, BP-relevant arousal reactions.ConclusionsThe SOMNOtouch NIBP demonstrated little measurement deviation of mean BP compared to the cuff-based technique over a broad spectrum of 24-h, ambulatory BP measurements in children and adolescents. Cuff-less blood pressure measurement relieves the issue of nocturnal arousal reactions which are shown to be frequently induced by cuff-based measurements. Driven by these promising results, we encourage ongoing efforts to create enough evidence on cuff-less BP measurement to promote it into broad clinical application.

Cuffless Blood Pressure Measurement Using Smartwatches: A Large-Scale Validation Study.

This study aimed to evaluate the performance of cuffless blood pressure (BP) measurement techniques in a large and diverse cohort of participants. We enrolled 3077 participants (aged 18-75, 65.16% women, 35.91% hypertensive participants) and conducted followed-up for approximately 1 month. Electrocardiogram, pulse pressure wave, and multiwavelength photoplethysmogram signals were simultaneously recorded using smartwatches; dual-observer auscultation systolic BP (SBP) and diastolic BP (DBP) reference measurements were also obtained. Pulse transit time, traditional machine learning (TML), and deep learning (DL) models were evaluated with calibration and calibration-free strategy. TML models were developed using ridge regression, support vector machine, adaptive boosting, and random forest; while DL models using convolutional and recurrent neural networks. The best-performing calibration-based model yielded estimation errors of 1.33 ± 6.43 mmHg for DBP and 2.31 ± 9.57 mmHg for SBP in the overall population, with reduced SBP estimation errors in normotensive (1.97 ± 7.85 mmHg) and young (0.24 ± 6.61 mmHg) subpopulations. The best-performing calibration-free model had estimation errors of -0.29 ± 8.78 mmHg for DBP and -0.71 ± 13.04 mmHg for SBP. We conclude that smartwatches are effective for measuring DBP for all participants and SBP for normotensive and younger participants with calibration; performance degrades significantly for heterogeneous populations including older and hypertensive participants. The availability of cuffless BP measurement without calibration is limited in routine settings. Our study provides a large-scale benchmark for emerging investigations on cuffless BP measurement, highlighting the need to explore additional signals or principles to enhance the accuracy in large-scale heterogeneous populations.

A new system for cuffless blood pressure measurement

High blood pressure (HBP) increases the risk of cardiovascular, brain, and renal diseases which can lead to severe illness and death. Due to the variability of BP values at different times during the day, continuous BP measurement would allow more effective early diagnosis of elevated blood pressure, and aid in the management of treatment. Various techniques have been proposed for cuffless BP measurement, often relying on the estimation of pulse transit time (PTT) to infer BP. However, challenges such as computational complexity and limited availability of BP monitoring sensors hinder the reliable and widespread implementation of continuous BP measurement. The proposed wristwatch-based system uses speech sounds together with photoplethysmogram (PPG) and electrocardiogram (ECG) signals, and uses a mathematical model which estimates the systolic BP (SBP) and diastolic BP (DBP) from these inputs.We have obtained Pearson correlation coefficient (PCC)s of 0.6094 for systolic BP and 0.6001 for diastolic BP in these experiments. The mean average errors (MAE) obtained are 8.06 mmHg for systolic BP and 7.48 mmHg for diastolic BP, respectively. This study provides meaningful results based on experimental outputs, demonstrating the feasibility of using speech signals in conjunction with PPG and ECG signals for BP estimation. The simplicity of our mathematical model enables efficient and practical implementation in real-time BP monitoring. As a pilot study, our work establishes a foundation for further research, emphasizing the need for enhancing accuracy and reliability, expanding the dataset, and exploring additional physiological parameters to advance continuous BP estimation.Our findings advance the development of cuffless BP monitoring, and address the computational complexity and real-time processing requirements. Future studies should build upon our work to refine and optimize the proposed approach, ultimately leading to improved continuous BP estimation in diverse populations.

Filtering-induced changes of pulse transmit time across different ages: a neglected concern in photoplethysmography-based cuffless blood pressure measurement.

Background: Pulse transit time (PTT) is a key parameter in cuffless blood pressure measurement based on photoplethysmography (PPG) signals. In wearable PPG sensors, raw PPG signals are filtered, which can change the timing of PPG waveform feature points, leading to inaccurate PTT estimation. There is a lack of comprehensive investigation of filtering-induced PTT changes in subjects with different ages. Objective: This study aimed to quantitatively investigate the effects of aging and PTT definition on the infinite impulse response (IIR) filtering-induced PTT changes. Methods: One hundred healthy subjects in five different ranges of age (i.e., 20-29, 30-39, 40-49, 50-59, and over 60years old, 20 subjects in each) were recruited. Electrocardiogram (ECG) and PPG signals were recorded simultaneously for 120s. PTT was calculated from the R wave of ECG and PPG waveform features. Eight PTT definitions were developed from different PPG waveform feature points. The raw PPG signals were preprocessed then further low-pass filtered. The difference between PTTs derived from preprocessed and filtered PPG signals, and the relative difference, were calculated and compared among five age groups and eight PTT definitions using the analysis of variance (ANOVA) or Scheirer-Ray-Hare test with post hoc analysis. Linear regression analysis was used to investigate the relationship between age and filtering-induced PTT changes. Results: Filtering-induced PTT difference and the relative difference were significantly influenced by age and PTT definition (p < 0.001 for both). Aging effect on filtering-induced PTT changes was consecutive with a monotonous trend under all PTT definitions. The age groups with maximum and minimum filtering-induced PTT changes depended on the definition. In all subjects, the PTT defined by maximum peak of PPG had the minimum filtering-induced PTT changes (mean: 16.16ms and 5.65% for PTT difference and relative difference). The changes of PTT defined by maximum first PPG derivative had the strongest linear relationship with age (R-squared: 0.47 and 0.46 for PTT difference relative difference). Conclusion: The filtering-induced PTT changes are significantly influenced by age and PTT definition. These factors deserve further consideration to improve the accuracy of PPG-based cuffless blood pressure measurement using wearable sensors.

Continual Learning for Cuffless Blood Pressure Measurement using PPG and ECG Signals.

Although numerous studies have been conducted on cuffless blood pressure (BP) estimation using machine learning methods, most of the data-driven models are static, with model parameters fixed after training is complete. However, BP is dynamic and the performance would degrade for a static model when the to-be predicted BP distribution deviates from the training BP distribution. In this paper, we propose a continual learning (CL) framework in which deep learning models are developed to learn dynamically and continuously for arterial BP (ABP) estimation with photoplethysmography (PPG) and electrocardiogram (ECG) waveforms. The effectiveness of the CL model is validated on UCI Repository and MIMIC-III database with a total of 132 individual samples, and compared with conventional training method. It was found that the CL model improved the ABP estimation accuracy in terms of mean absolute error (MAE) by 17.47% on average compared with conventional training model. Furthermore, the improvement increased with the variability of ABP. These results demonstrate that CL model has potential to estimate dynamic ABP, which has been challenging with conventional training.

A Hemodynamic Pulse Wave Simulator Designed for Calibration of Local Pulse Wave Velocities Measurement for Cuffless Techniques.

Devices for cuffless blood pressure (BP) measurement have become increasingly widespread in recent years. Non-invasive continuous BP monitor (BPM) devices can diagnose potential hypertensive patients at an early stage; however, these cuffless BPMs require more reliable pulse wave simulation equipment and verification methods. Therefore, we propose a device to simulate human pulse wave signals that can test the accuracy of cuffless BPM devices using pulse wave velocity (PWV). We design and develop a simulator capable of simulating human pulse waves comprising an electromechanical system to simulate the circulatory system and an arm model-embedded arterial phantom. These parts form a pulse wave simulator with hemodynamic characteristics. We use a cuffless device for measuring local PWV as the device under test to measure the PWV of the pulse wave simulator. We then use a hemodynamic model to fit the cuffless BPM and pulse wave simulator results; this model can rapidly calibrate the cuffless BPM's hemodynamic measurement performance. We first used multiple linear regression (MLR) to generate a cuffless BPM calibration model and then investigated differences between the measured PWV with and without MLR model calibration. The mean absolute error of the studied cuffless BPM without the MLR model is 0.77 m/s, which improves to 0.06 m/s when using the model for calibration. The measurement error of the cuffless BPM at BPs of 100-180 mmHg is 1.7-5.99 mmHg before calibration, which decreases to 0.14-0.48 mmHg after calibration. This study proposes a design of a pulse wave simulator based on hemodynamic characteristics and provides a standard performance verification method for cuffless BPMs that requires only MLR modeling on the cuffless BPM and pulse wave simulator. The pulse wave simulator proposed in this study can be used to quantitively assess the performance of cuffless BPMs. The proposed pulse wave simulator is suitable for mass production for the verification of cuffless BPMs. As cuffless BPMs become increasingly widespread, this study can provide performance testing standards for cuffless devices.