British Hypertension Society Protocol Research Articles

Continuous blood pressure monitoring based on transformer encoders and stacked attention gated recurrent units

Continuous blood pressure monitoring (CBPM) is critical to support the accurate prevention and reliable treatment of cardiovascular diseases. To achieve efficient multi-information interaction and further improve the monitoring performance, this research proposes an intelligent model based on transformer encoders and stacked attention gated recurrent units (TE-SAGRU) for CBPM. Long-term multi-source feature sequences with rich information are initially extracted from photoplethysmography (PPG) and electrocardiography (ECG) signals. The paralleled transformer encoders are constructed for different source feature sequences to obtain high-level feature representations and preserve respective long-term independence. The multiple stacked attention gated recurrent units are cross-connected for multi-interactive feature fusion and promoting complementarity effects of multi-source features on CBPM. Comprehensive comparison experiments are carried out to validate the effectiveness of the TE-SAGRU model, using the dataset with 1000 subjects derived from the MIMIC-III database. The continuous monitoring errors of the TE-SAGRU model for systolic blood pressure (SBP) and diastolic blood pressure (DBP) are 3.91 ± 5.65 mmHg and 2.29 ± 3.01 mmHg. The monitored results pass the requirement of the Association for the Advancement of Medical Instrumentation (AAMI) standard and achieve Grade A of the British Hypertension Society (BHS) protocol.

Schrödinger spectrum based continuous cuff-less blood pressure estimation using clinically relevant features from PPG signal and its second derivative

The presented study estimates cuff-less blood pressure (BP) from photoplethysmography (PPG) signals using multiple machine-learning (ML) models and the semi-classical signal analysis (SCSA) technique. The study proposes a novel signal reconstruction algorithm, which optimizes the semi-classical constant of the SCSA approach and eliminates the trade-off between complexity and accuracy during signal reconstruction. It predicts BP values using regression algorithms by processing reconstructed PPG signals’ spectral features, extracting clinically relevant PPG and its second derivative’s (SDPPG) morphological features. The developed method was assessed using a virtual in-silico dataset with more than 4000 subjects and the Multi-Parameter Intelligent Monitoring in Intensive Care Units (MIMIC-II) dataset. Results showed that the method attained a mean absolute error (MAE) of 5.37 and 2.96 mmHg for systolic and diastolic BP, respectively, using the CatBoost algorithm. This approach met the Association for the Advancement of Medical Instrumentation’s standard and achieved Grade A for all BP categories in the British Hypertension Society protocol. The proposed framework performs well even when applied to a combined clinically relevant database originating from MIMIC-III and the Queensland dataset. The proposed method’s performance is further evaluated in a non-clinical setting with noisy and deformed PPG signals to validate the efficacy of the SCSA method. The noise stress tests further showed that the algorithm maintained its key feature detection, signal reconstruction capability, and estimation accuracy up to a 10 dB SNR ratio. The proposed cuff-less BP estimation technique has the potential to perform well in mobile healthcare devices due to its straightforward implementation approach.

Continuous blood pressure monitoring using photoplethysmography and electrocardiogram signals by random forest feature selection and GWO-GBRT prediction model

Blood pressure (BP) is an important indicator of a person's cardiovascular status, but accurate, continuous BP monitoring remains a challenge. To improve the monitoring performance of continuous BP using electrocardiogram (ECG) and photoplethysmography (PPG) signals, a continuous BP monitoring method based on random forest feature selection (RFFS) and a gray wolf optimization-gradient boosting regression tree (GWO-GBRT) prediction model is developed in this study. First, features in time, frequency, and time–frequency domains are extracted from PPG and ECG signals to supply comprehensive characteristics for BP monitoring. Then, the RFFS method is employed to select sensitive features highly correlated with BP from the candidate feature sets, which reduced redundant information and improved monitoring efficiency. Next, a hybrid prediction model of the gray wolf optimization (GWO) technique and gradient boosting regression tree (GBRT) algorithm is established to learn the dependency relationship between BP and sensitive features. And a new fitness function of GWO is designed to balance the monitoring accuracy and consistency. Finally, ablation and comparative experiments demonstrate the effectiveness and advancement of the proposed method, using the ECG and PPG signals of 150 people downloaded from the MIMIC-III database. The mean absolute error (MAE) and standard deviation (STD) of the proposed method in predicting systolic blood pressure were 2.91 mmHg and 3.97 mmHg, and those of diastolic blood pressure were 1.71 mmHg and 2.35 mmHg. Its monitoring accuracy has surpassed the Association for Advancement of Medical Instrumentation (AAMI) standard and reached the “A” level standard of the British Hypertension Society (BHS) protocol.

Noninvasive Cuffless Blood Pressure Estimation With Dendritic Neural Regression.

Blood pressure (BP) is one of the most important indicators of health. BP that is too high or too low causes varying degrees of diseases, such as renal impairment, cerebrovascular incidents, and cardiovascular diseases. Since traditional cuff-based BP measurement techniques have the drawbacks of patient discomfort and the impossibility of continuous BP monitoring, noninvasive cuffless continuous BP measurement has become a popular topic. The common noninvasive approach uses machine-learning (ML) algorithms to estimate BP by using the features extracted from simultaneous photoplethysmogram (PPG) and electrocardiogram (ECG) signals, such as the pulse transit time and pulse wave velocity. This study investigates the BP estimation performance of the novel dendritic neural regression (DNR) method proposed by us. Unlike conventional neural networks, DNR utilizes the multiplication operator as the excitation function in each dendritic branch, inspired by biological neuron phenomena, and can effectively capture nonlinear relationships between distinct input features. In addition, AMSGrad is used as the optimization algorithm to further enhance the dendritic neural model's performance. The experimental results show that by being fed a combination of the raw features extracted from the ECG and PPG signals and the components of the BP mathematical models, DNR can increase the accuracy of systolic BP, diastolic BP, and mean arterial pressure estimation significantly, which are superior to the state-of-the-art ML techniques. According to the British Hypertension Society protocol, DNR achieves a grade of A for the long-term BP estimation. Considering its architectural simplicity and powerful performance, the proposed method can be regarded as a reliable tool for estimating long-term continuous BP in a noninvasive cuffless way.

Ability and accuracy of patient-performed blood pressure monitoring among pregnant women in urban Ghana

Patient-performed blood pressure monitoring in pregnancy is rarely performed in low- and middle-income country settings, including Ghana. The clinical efficacy of home blood pressure monitoring relies on a pregnant patient being able to independently execute the correct steps to position and use a blood pressure monitor and to achieve accurate blood pressure measurements. This study aimed to (1) assess whether pregnant women can correctly use an automatic blood pressure monitor to check their blood pressure before and after a brief training and (2) determine whether blood pressure values measured by pregnant women using an automatic monitor are similar to values measured by a healthcare provider using a standard clinic monitor. This was a cross-sectional study conducted at the Korle Bu Teaching Hospital, a tertiary hospital in Accra, Ghana. Participants were adult pregnant women presenting for their first prenatal care visit. Data collection was performed by 2 Ghanaian physicians. Information on demographics, obstetrical history, and past medical history was collected. A brief training was provided on the correct use of the blood pressure monitor, including a verbal script, annotated photographs, and a hands-on demonstration. Pre- and posttraining assessments using a 9-item checklist of correct preparation, position, and use of an automatic blood pressure monitor were performed. Following a modified British Hypertension Society protocol, a series of 4 blood pressure measurements were taken, alternating between provider performed using a clinic monitor and patient performed using an automatic monitor intended for individual use and validated in pregnancy. Among 176 participants, the mean age was 31.5 years (±5.6), and 130 (73.9%) were multiparous. Regarding socioeconomic characteristics, 128 (72.7%) were married, 171 (97.2%) had public insurance, and 87 (49.7%) had completed ≤9 years of formal education. Regarding clinical blood pressure issues, 19 (10.9%) had a history of a hypertensive disorder in a previous pregnancy, and 6 (3.4%) had chronic hypertension. Before receiving any training, 21 participants (12.1%) performed all 9 steps correctly to prepare, position, and use the automatic blood pressure monitor. Comparing pretraining vs posttraining ability, statistically significant increases were seen in the correct performance of each step and the mean number of steps performed correctly (6.1±1.8 vs 9.0±0.2, respectively; P<.001) and proportion performing all 9 steps correctly (12.1% vs 96.6%, respectively; P<.001). The mean difference between doctor-performed and patient-performed blood pressure measurements was 5.6±4.8 mm Hg for systolic blood pressure values and 3.4±3.08 mm Hg for diastolic blood pressure values, with most differences within 5 mm Hg for both systolic blood pressure values (102/176 [58.0%]) and diastolic blood pressure values (141/176 [80.1%]). After a brief training, pregnant women in Ghana demonstrated that they are able to use an automatic blood pressure monitor to check their blood pressure correctly and accurately.

Validation of Blood Pressure Devices as per 2020 World Health Organization Technical Specifications: A Scoping Review of Global Literature.

To identify and summarize the global research literature on validation of automated noninvasive blood pressure measurement devices (BPMDs) with upper arm cuff, develop a repository of validated BPMDs in compliance with the 2020 World Health Organization technical specifications, and identify challenges and gaps in evidence base on validated BPMDs. A scoping review was conducted. Primary research validating BPMDs complying with the 2020 World Health Organization technical specifications (ie, semiautomated/automated noninvasive devices with upper arm cuff), published in English between January 2000 and December 2021, was included. We searched MEDLINE, Web of Science, Scopus, EMBASE, CINAHL, CENTRAL, ProQuest and the dabl website. We included 269 studies validating 251 BPMDs across 89 manufacturers. Omron (29%), Microlife (10%), and A&D Company (8%) were the top 3 manufacturers. The 3 most frequently used validation protocols were the European Society of Hypertension-international protocol 2002 (27%), European Society of Hypertension-international protocol 2010 (25%), and modified British Hypertension Society protocol 1993 (16%), respectively. Nearly 45% of the validated BPMDs were intended for use in clinical settings, 38% were for home or self-measurement use, and 48% were for general adults. Most studies reported that BPMDs passed the validation criteria. There was inadequate reporting across studies, especially pertaining to validation settings. Most BPMDs fulfilled the validation criteria. However, there are considerable gaps in BPMD research in terms of geographical representation, including specific target populations and diseases/conditions, and a range of arm circumferences. Additionally, a potential strategy is required to accelerate the adoption of the Association for the Advancement of Medical Instrumentation (AAMI)/European Society of Hypertension/International Organization for Standardization Universal Standard (International Organization for Standardization 81060-2:2018) for BPMD validation.

Validation of the BPLab® 24-Hour Blood Pressure Monitoring System According to the European Standard BS EN 1060-4:2004 and British Hypertension Society Protocol [Retraction

[This retracts the article DOI: 10.2147/MDER.S25800.].

A computationally efficient CNN-LSTM neural network for estimation of blood pressure from features of electrocardiogram and photoplethysmogram waveforms

Continuous blood pressure (BP) monitoring would significantly improve diagnosis and treatment of hypertension. Current at-home monitoring relies on uncomfortable and unreliable cuff-based devices, which are incapable of continuous measurement. In this work, we present a new hybrid neural network (NN) that combines convolutional layers with long short-term memory (LSTM) layers to classify systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean arterial pressure (MAP), using 12 straightforward features extracted from electrocardiogram (ECG) and photoplethysmogram (PPG) waveforms. Our proposed network achieves mean absolute errors (MAEs) of 4.53 mmHg, 3.37 mmHg and 3.36 mmHg for SBP, DBP and MAP respectively. Additionally, our scheme passes the criteria outlined by the Association for the Advancement of Medical Instrumentation (AAMI) and achieves an A grade in accordance with the British Hypertension Society (BHS) protocol. These results provide a deep learning approach to BP estimation that could be implemented in low-power wearable devices.

Cuff-Less Blood Pressure Estimation From Photoplethysmography via Visibility Graph and Transfer Learning.

This paper presents a new solution that enables the use of transfer learning for cuff-less blood pressure (BP) monitoring via short duration of photoplethysmogram (PPG). The proposed method estimates BP with low computational budget by 1) creating images from segments of PPG via visibility graph (VG), hence, preserving the temporal information of the PPG waveform, 2) using pre-trained deep convolutional neural network (CNN) to extract feature vectors from VG images, and 3) solving for the weights and bias between the feature vectors and the reference BPs with ridge regression. Using the University of California Irvine (UCI) database consisting of 348 records, the proposed method achieves a best error performance of 0.00±8.46 mmHg for systolic blood pressure (SBP), and -0.04±5.36 mmHg for diastolic blood pressure (DBP), respectively, in terms of the mean error (ME) and the standard deviation (SD) of error, ranking grade B for SBP and grade A for DBP under the British Hypertension Society (BHS) protocol. Our novel data-driven method offers a computationally-efficient end-to-end solution for rapid and user-friendly cuff-less PPG-based BP estimation.

Attention-based residual improved U-Net model for continuous blood pressure monitoring by using photoplethysmography signal

Blood pressure (BP) is an important clinical indicator for cardiovascular health assessment, and accurate monitoring of continuous BP is still a challenging task. In this paper, an attention-based residual improved U-Net (ARIU) model is proposed to improve the accuracy of continuous BP monitoring by using the photoplethysmography (PPG) signal. This model consists of an improved U-Net employed to learn the high dimensional features from PPG signal, an attention module embedded in the skip connections to reduce redundancy of learning features, and a residual module replaced common convolution to prevent degradation problems and enhance generalization performance. The raw PPG signals and arterial BP download from the MIMIC-III database, the first and second derivatives of PPG signal are utilized as additional inputs to increase the multiform of input information, and a data input way of parallel-based fusion are adopted to improve the effectiveness of information mining. After data preprocessing, the dataset used in this study contains 150,000 samples, belonging to 100 subjects. The reliability of the proposed model is verified by the ablation experiments, and the advancement of the model is demonstrated by the comparison experiments with other state-of-art methods. The mean absolute error (MAE) and standard deviation (STD) of systolic blood pressure (SBP) predicted by the proposed model are 4.75 mmHg and 6.72 mmHg respectively, and that of diastolic blood pressure is 2.81 mmHg and 4.59 mmHg. The results meet the requirements of the Advancement of Medical Instrumentation (AAMI) and reach the “Grade A” of the British Hypertension Society (BHS) protocol.

Comparison of the BPLab® Sphygmomanometer for Ambulatory Blood Pressure Monitoring with Mercury Sphygmomanometry in Pregnant Women: Validation Study According to the British Hypertension Society Protocol [Retraction

[This retracts the article DOI: 10.2147/VHRM.S82381.].

Motion Artifact Resilient Cuff-Less Blood Pressure Monitoring Using a Fusion of Multi-Dimensional Seismocardiograms.

Blood pressure (BP) monitoring is critical to raise awareness of hypertension and hypotension, yet the commonly used techniques require the person staying still along with a cuff around the arm. Some cuff-less approaches have been researched, but all hinder the person from moving around. To address the challenge, we propose using a fusion of accelerometers to achieve motion artifact resilient blood pressure monitoring. Such technique is accomplished with the motion artifact removal process and feature extraction from multi-dimensional seismocardiograms. The efficacy of our BP monitoring models is validated in 19 young healthy adults. Both the diastolic and systolic BP monitoring models fulfill the AAMI standard and British Hypertension Society protocol. For sitting still BP monitoring, the mean and standard deviation of diastolic and systolic difference errors (DE) are 0.09 ± 4.10 and -0.25 ± 5.45 mmHg; moreover, the mean absolute difference errors (MADE) are 3.62 and 4.73 mmHg. In walking motions, the DE are 1.15 ±4.47 mmHg for diastolic BP and -0.38 ± 6.67 for systolic BP; furthermore, the MADE are 3.36 and 5.07 mmHg, respectively. The motion artifact resilient cuff-less BP monitoring reveals the potential of portable BP monitoring in healthcare environments.

Cuff-Less Blood Pressure Estimation via Small Convolutional Neural Networks.

Deep learning-based cuff-less blood pressure (BP) estimation methods have recently gained increased attention as they can provide accurate BP estimation with only one physiological signal as input. In this paper, we present a simple and effective method for cuff-less BP estimation by training a small-scale convolutional neural network (CNN), modified from LeNet-5, with images created from short segments of the photoplethysmogram (PPG) signal via visibility graph (VG). Results show that the trained modified LeNet-5 model achieves an error performance of 0.184±7.457 mmHg for the systolic BP (SBP), and 0.343±4.065 mmHg for the diastolic BP (DBP) in terms of the mean error (ME) and the standard deviation (SD) of error between the estimated and reference BP. Both the SBP and the DBP accuracy rank grade A under the British Hypertension Society (BHS) protocol, demonstrating that our proposed method is an accurate way for cuff-less BP estimation.

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.

Comparing Intra-Arterial, Auscultatory, and Oscillometric Measurement Methods for Arterial Blood Pressure.

AIMThis study aimed to compare the measurement results of arterial blood pressure obtained through intra-arterial, auscultatory, and oscillometric methods.METHODThis prospective and descriptive study was conducted with 180 patients hospitalized in the intensive care units of cardiovascular surgery and anesthesia. Arterial blood pressures of the patients in the study were measured with 3 methods, and the mean arterial pressure values obtained by each method were analyzed to find out whether they were different or consistent.RESULTSThe average systolic blood pressure value using the intra-arterial method was found to be 125.47 ± 21.39 mm Hg, and the average of diastolic blood pressure measurement obtained using the oscillometric method was the highest (73.91 ± 10.62 mm Hg). The highest correlation was seen between the arterial BP measurements of the intra-arterial and auscultatory methods (systolic [0.96] and diastolic [0.90]). According to the British and Irish Hypertension Society protocol, a very good agreement between the diastolic blood pressure values and a good agreement between the systolic blood pressure values were obtained.CONCLUSIONThe measurement results obtained through the auscultatory method more consistent with the results obtained through the intra-arterial method compared with those obtained using the oscillometric method.

A hybrid neural network for continuous and non-invasive estimation of blood pressure from raw electrocardiogram and photoplethysmogram waveforms

Background and objectives: Continuous and non-invasive blood pressure monitoring would revolutionize healthcare. Currently, blood pressure (BP) can only be accurately monitored using obtrusive cuff-based devices or invasive intra-arterial monitoring. In this work, we propose a novel hybrid neural network for the accurate estimation of blood pressure (BP) using only non-invasive electrocardiogram (ECG) and photoplethysmogram (PPG) waveforms as inputs.Methods: This work proposes a hybrid neural network combines the feature detection abilities of temporal convolutional layers with the strong performance on sequential data offered by long short-term memory layers. Raw electrocardiogram and photoplethysmogram waveforms are concatenated and used as network inputs. The network was developed using the TensorFlow framework. Our scheme is analysed and compared to the literature in terms of well known standards set by the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI).Results: Our scheme achieves extremely low mean absolute errors (MAEs) of 4.41 mmHg for SBP, 2.91 mmHg for DBP, and 2.77 mmHg for MAP. A strong level of agreement between our scheme and the gold-standard intra-arterial monitoring is shown through Bland Altman and regression plots. Additionally, the standard for BP devices established by AAMI is met by our scheme. We also achieve a grade of ‘A’ based on the criteria outlined by the BHS protocol for BP devices.Conclusions: Our CNN-LSTM network outperforms current state-of-the-art schemes for non-invasive BP measurement from PPG and ECG waveforms. These results provide an effective machine learning approach that could readily be implemented into non-invasive wearable devices for use in continuous clinical and at-home monitoring.

Validation of the HL868ED upper-arm blood pressure monitor for clinical use and self-measurement according to the British Hypertension Society Protocol: Erratum

Validation of the HL868ED upper-arm blood pressure monitor for clinical use and self-measurement according to the British Hypertension Society Protocol: Erratum

Central Blood Pressure Estimation From Distal PPG Measurement Using Semiclassical Signal Analysis Features

Background and objective: Blood pressure (BP) is one of the crucial indicators that contains valuable medical information about cardiovascular activities. Developing photoplethysmography (PPG)-based cuffless BP estimation algorithms with enough robustness and accuracy is clinically useful in practice, due to its simplicity and noninvasiveness. In this paper, we have developed and tested two frameworks for arterial blood pressure (ABP) estimation at the central arteries using photoplethysmography and electrocardiogram. Methods: Supervised learning, as adapted by most studies regarding this topic, is introduced by comparing three machine learning algorithms. Features are extracted using semi-classical signal analysis (SCSA) tools. To further increase the accuracy of estimation, another BP estimation algorithm is presented. A single feed-forward neural network (FFNN) is utilized for BP regression with PPG features, which are extracted by SCSA and later used by FFNN as the network input. Both BP estimation algorithms perform robustly against MIMIC II database to guarantee statistical reliability. Results: We evaluated the performance against the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS) standards, and we have compared the standard deviation (STD) of estimation error with current state of the arts. With the AAMI standard, the first method yields comparable performance against existing literature in the estimation of BP values. Regarding the BHS protocol, the second method achieves grade A in the estimation of BP values. Conclusion: We conclude that by using the PPG signal in combination with informative features from the Schrödinger's spectrum, the BP can be non-invasively estimated in a reliable and accurate way. Furthermore, the proposed frameworks could potentially enable applications of cuffless estimation of the BP and development of mobile healthcare device.

Validation of the Rossmax AC1000f upper-arm blood pressure monitor in adults according to the British Hypertension Society Protocol.

To evaluate the accuracy of the Rossmax AC1000f oscillometric upper-arm blood pressure monitor in adults according to the British Hypertension Society Protocol. Eighty-five subjects (male 42 and female 43) with a mean age of 48.1 years were recruited to fulfill the blood pressure distribution criteria of the British Hypertension Society Protocol in general population using the same arm sequential blood pressure measurement method. For each subject, we sequentially measured blood pressure using a mercury sphygmomanometer (two observers) and the AC1000f device (one supervisor). Data analysis was carried out according to the British Hypertension Society Protocol. A total of 255 comparison pairs were obtained for analysis. The device achieved grade A. The percentage of blood pressure differences within 5, 10 and 15 mmHg was 66.7, 92.2 and 98.0%, respectively, for SBP, and 64.7, 90.2 and 98.4%, respectively, for DBP. The average (±SD) of the device-observer differences was -0.5 ± 6.0 mmHg for SBP and -1.4 ± 5.9 mmHg for DBP, respectively. The automated upper-arm blood pressure monitor Rossmax AC1000f has passed the requirements of the British Hypertension Society Protocol, and hence can be recommended for blood pressure measurement in adults.

Validation of the HL868ED upper-arm blood pressure monitor for clinical use and self-measurement according to the British Hypertension Society Protocol.

The present study aimed to evaluate the accuracy of the Health & Life automated oscillometric upper-arm blood pressure monitor HL868ED for blood pressure monitoring according to the British Hypertension Society Protocol. We recruited individuals until there were 85 eligible participants and their blood pressure could meet the blood pressure distribution requirements specified by the British Hypertension Society Protocol. For each individual, we sequentially measured systolic and diastolic blood pressures using a mercury sphygmomanometer (two observers) and the HL868ED device (one supervisor). Data analysis was carried out according to the British Hypertension Society Protocol. The device achieved grade A. The percentage of blood pressure differences within 5, 10 and 15 mmHg was 68%, 91% and 98%, respectively, for systolic blood pressure, and 79, 96 and 99%, respectively, for diastolic blood pressure. The average (±SD) of the device-observer differences was -1.8 ± 6.0 mmHg for systolic and 0.1 ± 4.7 mmHg for diastolic blood pressures, respectively. The Health & Life automated upper-arm blood pressure monitor HL868ED has passed the requirements of the British Hypertension Society Protocol, and hence can be recommended for blood pressure measurement in adults.