British Hypertension Society Research Articles

Toward IoT-Based Medical Edge Devices: PPG-Based Blood Pressure Estimation Application

The Internet of Things (IoT) now provide a considerable advantage in terms of intelligent devices. For example, sensor data of medical equipment could be used to extract meaningful insights to enable a range of tasks to be performed intelligently at the device location, such as blood pressure (BP) prediction several minutes ahead. In this article, we propose a computing device called Medical Edge, designed to convert conventional medical equipment into IoT-enabled devices. We developed an interworking proxy that allows Medical Edge to be interfaced with medical devices, such as patient monitors (e.g., GE CARESCAPE B650), to smoothly collect physiological data and upload them to an IoT server platform. We used a oneM2M standard-based server platform to provide access to these data in a standardized manner. To demonstrate a promising application of our proposed Medical Edges, we performed a study on BP estimation based on photoplethysmography (PPG) signal only. We propose a hybrid neural network architecture and apply it with a publicly available data set called multiparameter intelligent monitoring in intensive care II (MIMIC II). The model consists of five 1-D convolutional neural networks (CNNs), three Bi-directional long short-term memory networks, and four fully connected layers. The mean absolute error (MAE) and standard deviation (STD) of the proposed model were, respectively, 0.95 and 1.44 millimetres of mercury (mmHg) for diastolic BP (DBP), and 1.38 and 2.13 mmHg for systolic BP (SBP). The experimental results are in full compliance with the international standards of the Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS).

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.].

Camera-Based Blood Pressure Estimation via Windkessel Model and Waveform Features

Blood pressure (BP) is generally regarded as the vital sign most strongly correlated with human health. However, for decades, BP measurement has involved a cuff, which causes discomfort and even carries a risk of infection, given the current prevalence of COVID-19. Some studies address these problems using remote photoplethysmography (rPPG), which has shown great success in heart rate detection. Nevertheless, these approaches are not robust, and few have been evaluated with a sufficiently large dataset. We propose an rPPG-based BP estimation algorithm that predicts BP by leveraging the Windkessel model and hand-crafted waveform characteristics. A waveform processing procedure is presented for the rPPG signals to obtain a robust waveform template and thus extract BP-related features. Redundant and unstable features are eliminated via Monte Carlo simulation and according to their relationship with latent parameters (LSs) in the Windkessel model. For a comprehensive evaluation, the Chiao Tung BP (CTBP) dataset was constructed. The experiment was conducted over a four-week period of time to evaluate the validity period of the personalization in our system. On all the data, the proposed method outperforms the benchmark algorithms and yields mean absolute errors (MAEs) of 6.48 and 5.06 mmHg for systolic BP (SBP) and diastolic BP (DBP), respectively. The performance achieves a “B” grade according to the validation protocol from the British Hypertension Society (BHS) for both SBP and DBP.

S-28-1: CARDIOVASCULAR OUTCOMES IN ADULTS WITH HYPERTENSION WITH EVENING VERSUS MORNING DOSING OF USUAL ANTIHYPERTENSIVES IN THE UK. THE TIME STUDY

Background: Studies have suggested that evening dosing with antihypertensive therapy might have better outcomes than morning dosing. The Treatment in Morning versus Evening study aimed to investigate whether evening dosing of usual antihypertensive medication improves major cardiovascular outcomes compared with morning dosing in patients with hypertension. Methods: The TIME study is a prospective, pragmatic, decentralised, parallel group study in the UK, that recruited adults with hypertension and taking at least one antihypertensive medication. Eligible participants were randomly assigned 1 to 1, without restriction, stratification, or minimisation, to take all of their usual antihypertensive medications in either the morning, 0600 to 1000 h, or in the evening, 2000 to 0000 h.. Participants were followed up for the composite primary endpoint of vascular death or hospitalisation for non fatal myocardial infarction or non fatal stroke. Endpoints were identified by participant report or record linkage to National Health Service datasets and adjudicated by a committee masked to treatment allocation. The primary endpoint was assessed as the time to first occurrence of an event in the intention to treat population i.e. all participants randomly assigned to a treatment group. Findings: Between Dec 17, 2011, and June 5, 2018, 24610 individuals were screened and 21104 were randomly assigned 10503 to evening and 10601 to morning dosing groups. Mean age at study entry was 65·1 years, SD 9·3, 12136 or 57·5 percent participants were men; 8968, 90·5 percent were White, and 2725 or 13·0 percent had a previous cardiovascular disease. By the end of study follow up on March 31, 2021, median follow up was 5·2 years, IQR 4·9, 5·7, 529 of 10503 participants assigned to evening treatment and 318 of 10601 assigned to morning treatment had withdrawn from all follow up. A primary endpoint event occurred in 362 participants assigned to evening treatment that is 0·69 events per 100 patient years and 390 assigned to morning treatment that is 0·72 events per 100 patient years, unadjusted hazard ratio 0·95, 95percent CI 0·83 to 1·10, p 0·53. No safety concerns were identified. An analysis of outcome by adherence to dosing time will be presented at the meeting. Interpretation Evening dosing of usual antihypertensive medication was not different from morning dosing in terms of major cardiovascular outcomes. Patients can be advised that they can take their regular antihypertensive medications at a convenient time that minimises any undesirable effects. Funding British Heart Foundation and the British and Irish Hypertension Society.

S-04-3: VALIDATION OF BLOOD PRESSURE DEVICES AS PER 2020 WHO TECHNICAL SPECIFICATIONS: A SCOPING REVIEW OF GLOBAL LITERATURE

Objective: To (1) identify and summarize the global research literature on validation of automated non-invasive blood pressure measurement devices (BPMD) with upper arm cuff, (2) develop a repository of validated BPMD in compliance 2020 WHO technical specifications (3) identify challenges and gaps in evidence base on validated BPMD and develop novel strategies to address them. Design and method: A scoping review was conducted according to the Joanna Briggs Institute methodology using ‘Population, Concept, Context’ concept. Empirical studies validating BPMD complying with the 2020 WHO technical specifications (i.e. semi-automated/automated non-invasive with upper arm cuff), published in English between 2000 and 2022 were eligible for inclusion in the review. We searched MEDLINE, Web of Science, Scopus, EMBASE, CINAHL, CENTRAL, ProQuest and daBL website. We snowballed references from website reports. Title-abstract, full-text screening, and data extraction was conducted in duplicate by four reviewers (PICOPortal, MS Excel). Descriptive data analysis was performed on MS Excel and Tableau. Results: Of 11,825 identified records, we included 269 studies that fulfilled the inclusion criteria. A total of 251 BPMD from 89 manufacturers were assessed for validation methods. Omron (29%), Microlife (10%) and A&D Company (8%) were the top three BPMD manufacturers that underwent validations. China (20%), UK (13%) and USA (12%) were the top three countries where these validations were undertaken. The three most frequently used validation protocols were (Figure1): European Society of Hypertension-International protocol (ESH-IP) 2002 (27%), ESH-IP 2010 (25%) and Modified British Hypertension Society 1993 (16%). Nearly 50%, 40% and 48% of the validated BPMD were intended for use in clinical- and home/self-measurement, and for general adults, respectively. Of the 55% studies that were funded, 72% received support (finances/ BPMD) from device manufacturers. The publications did not reveal details about the following: device manufacturer (13%), country of validation (26%), settings for intended BPMD use (29%), and the target population for BPMD use (22%). Conclusions: While most (89%) of the BPMD included in the review fulfilled the validation criteria, there are considerable gaps in terms of the settings and countries of validation. In designing validation studies, research methods should focus on validating BPMD for specific target population and conditions (e.g. pregnancy or children), and take range of arm circumferences into considerations. Finally, we call for improvement in quality of reporting the validation studies to improve transparency and comparability. This could be best achieved by initiating a dialogue with all stakeholders in blood pressure research and practice.

A Refined Blood Pressure Estimation Model Based on Single Channel Photoplethysmography.

This study proposed a refined BP prediction strategy that using single-channel photoplethysmography (PPG) signals to stratify populations by cardiovascular status before BP estimation. Combining demographic characteristics (age, gender) and pulse wave morphological features, the random forest was applied to screen two kinds of typical cardiovascular diseases (CVDs), with an accuracy of 92.2%. A deep learning model (BiLSTM-At) was proposed to estimate the long-term BP trend for different CVD groups. Transfer learning technique was used for personalized modeling to reduce computational complexity while improving performance. The method was validated on 255 patients with different CVDs. The mean absolute errors (MAEs) of systolic blood pressure (SBP) and diastolic blood pressure (DBP) estimation were 2.815 mmHg and 1.876 mmHg for normal subjects, 3.024 mmHg and 1.334 mmHg for AF subjects, and 4.444 mmHg and 2.549 mmHg for CA subjects. The results met the American Association for the Advancement of Medical Instrumentations (AAMI) and British Hypertension Society (BHS) Class A criteria. This indicated that our strategy has good performance and can realize long-term monitoring of BP through a small batch samples, with the potential to implement real-time monitoring in healthy devices.

Higher Order Derivative-Based Integrated Model for Cuff-Less Blood Pressure Estimation and Stratification Using PPG Signals

Precise blood pressure (BP) estimation is vital for diagnosing arterial hypertension and other cardiovascular ailments. The photoplethysmogram (PPG)-based cuff-less BP measurement is an alternative to the traditional cuff-based systems. The morphological, temporal, and frequency-domain-based features have been used for BP estimation in the PPG-based BP measurement systems. However, dealing with varying signal morphology and feature dependency on the fiducial points in PPG contours remains challenging and limits the performance of the existing BP estimation algorithms, especially in wearable devices. This work presents a novel approach that considers the nonlinear features of PPG signals evaluated using higher order derivatives. In particular, the PPG signal’s third and fourth derivative contours are used to extract features, such as fractal dimension, bubble entropy (BE), Lyapunov exponent, and moving slope. Machine learning algorithms such as random forest (RF), extreme gradient boosting (XGBoost), and support vector regression (SVR) models are used for BP estimation using the nonlinear features of PPG signals. The estimated BP values are further categorized based on five broad classes based on the BP stratification criteria such as hypotension, normal, prehypertension, stage-I, and stage-II hypertension, respectively. The performance of the suggested approach is evaluated using PPG signals from three publicly available databases (multiparameter intelligent monitoring in intensive care (MIMIC)-I, II, and III). The proposed estimation approach has outperformed the recent existing algorithms and achieved a minimum value of mean absolute error (MAE) ± standard deviation (STD) in (systolic BP (SBP) and diastolic BP (DBP) values) as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${0}.{74}\pm {2}.{42}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${0}.{35} \pm {1}.{06}$ </tex-math></inline-formula> , respectively, for the MIMIC-I database. The suggested approach has also achieved grade-A on the British Hypertension Society (BHS) standard.

PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms.

Cardiovascular diseases are one of the most severe causes of mortality, annually taking a heavy toll on lives worldwide. Continuous monitoring of blood pressure seems to be the most viable option, but this demands an invasive process, introducing several layers of complexities and reliability concerns due to non-invasive techniques not being accurate. This motivates us to develop a method to estimate the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using Photoplethysmogram (PPG) signals. We explore the advantage of deep learning, as it would free us from sticking to ideally shaped PPG signals only by making handcrafted feature computation irrelevant, which is a shortcoming of the existing approaches. Thus, we present PPG2ABP, a two-stage cascaded deep learning-based method that manages to estimate the continuous ABP waveform from the input PPG signal with a mean absolute error of 4.604 mmHg, preserving the shape, magnitude, and phase in unison. However, the more astounding success of PPG2ABP turns out to be that the computed values of Diastolic Blood Pressure (DBP), Mean Arterial Pressure (MAP), and Systolic Blood Pressure (SBP) from the estimated ABP waveform outperform the existing works under several metrics (mean absolute error of 3.449 ± 6.147 mmHg, 2.310 ± 4.437 mmHg, and 5.727 ± 9.162 mmHg, respectively), despite that PPG2ABP is not explicitly trained to do so. Notably, both for DBP and MAP, we achieve Grade A in the BHS (British Hypertension Society) Standard and satisfy the AAMI (Association for the Advancement of Medical Instrumentation) standard.

Non-invasive blood pressure estimation combining deep neural networks with pre-training and partial fine-tuning

Objective. Daily blood pressure (BP) monitoring is essential since BP levels can reflect the functions of heart pumping and vasoconstriction. Although various neural network-based BP estimate approaches have been proposed, they have certain practical shortcomings, such as low estimation accuracy and poor model generalization. Based on the strategy of pre-training and partial fine-tuning, this work proposes a non-invasive method for BP estimation using the photoplethysmography (PPG) signal. Approach. To learn the PPG-BP relationship, the deep convolutional bidirectional recurrent neural network (DC-Bi-RNN) was pre-trained with data from the public medical information mark for intensive care (MIMIC III) database. A tiny quantity of data from the target subject was used to fine-tune the specific layers of the pre-trained model to learn more individual-specific information to achieve highly accurate BP estimation. Main results. The mean absolute error and the Pearson correlation coefficient (r) of the proposed algorithm are 3.21 mmHg and 0.919 for systolic BP, and 1.80 mmHg and 0.898 for diastolic BP (DBP). The experimental results show that our method outperforms other methods and meets the requirements of the Association for the Advancement of Medical Instrumentation standard, and received an A grade according to the British Hypertension Society standard. Significance. The proposed method applies the strategy of pre-training and partial fine-tuning to BP estimation and verifies its effectiveness in improving the accuracy of non-invasive BP estimation.

An ultra-sensitive core-sheath fiber strain sensor based on double strain layered structure with cracks and modified MWCNTs/silicone rubber for wearable medical electronics

Flexible resistive sensors have attracted widespread attention in the field of wearable electronics. Inspired by the single-layer crack structure which has ultra-high sensitivity, a double strain layered crack structure is proposed to expand the maximum working strain (30%) of flexible sensor while maintaining the ultra-high sensitivity (GF = 2165 at 0–30% and 7381 at 25–30%). MWCNTs modified by 3-Aminopropyltriethoxysilane were filled into silicon rubber to form nanocomposites with fast response times (42 ms), high repeatability (>1000) and low hysteresis (7.08%). Furthermore, the respiratory rate, heart rate and blood pressure can be measured by the proposed sensor. The average errors of the respiratory rate and heart rate are less than 2% and 3% respectively. The mean error of systolic blood pressure, diastolic blood pressure and mean blood pressure is −0.0042 mmHg, 0.3730 mmHg, and 0.2472 mmHg respectively, meeting the standards of Association for the Advancement of Medical Instrumentation and A-level British Hypertension Society. This study shows that the proposed sensor has broad application prospects in high-precision wearable medical electronics.

Devising a method for predicting a blood pressure level based on electrocardiogram and photoplethysmogram signals

Determining the level of blood pressure (BP) in a non-invasive way and without a sphygmomanometer cuff is of great relevance when conducting continuous monitoring or screening studies. In this regard, a method for predicting BP parameters based on the signals of the photoplethysmogram (PPG) and electrocardiogram (ECG) signals has been developed. It is proposed to use, as informative features, the time of pulse wave propagation (PTT) and a set of calculated pulse parameters of PPG. PTT is defined as the time intervals between the R-wave of the ECG and the corresponding characteristic points on the PPG acquired optically from the finger. As parameters of the PPG pulse, the known characteristics of this signal described in the literature are used, as well as additional informative features selected during the study. In accordance with the above, the tools of machine learning theory were used to construct a classifier model and regression models. The approach described in this paper to determine BP makes it possible to use 10-second ECG signals in any of the 12 common leads and PPG signals from any optical type of sensor. The built model of the classifier detects three levels of BP: low, normal, and high, at the accuracy metric=0.8494. The regression models predict systolic, diastolic, and mean BP parameters in accordance with the requirements of the British Hypertension Society (BHS) standard by the magnitude of the absolute error. The proposed method for assessing the level of BP involves real-time measurements and can be used in the design of measuring equipment for screening studies, as well as in continuous monitoring tasks within the framework of BHS requirements.

Correction to: Abstracts from the 2022 Annual Scientific Meeting of the British and Irish Hypertension Society (BIHS)

Correction to: Abstracts from the 2022 Annual Scientific Meeting of the British and Irish Hypertension Society (BIHS)

Cuffless blood pressure estimation using chaotic features of photoplethysmograms and parallel convolutional neural network

Background and ObjectiveAs a nonlinear framework in dynamical system analysis, chaotic approaches are mainly applied to evolve the complexity of biological systems. Due to the chaotic nature of the cardiovascular systems, the nonlinear features can intuitively provide a reliable framework in blood pressure (BP) estimation. Cuffless BP estimation is usually carried out by establishing deep neural network models estimating the BP values through machine-learned features of photoplethysmogram (PPG) signals. MethodsIn this study, a novel parallel deep architecture is proposed to handle the machine-learned and chaotic features of PPG signals in estimating the actual BP values. The chaotic handcrafted features were the signal properties associated with the Poincare sections in the phase space and the recurrence plot-based measures called recurrence quantification analysis (RQA). Moreover, the measures quantifying the nonlinear properties of the temporal sequences such as correlation dimension, fractal dimension, Lyapunov exponent, and entropy-based quantities were also employed. The parallel architecture not only embedded the chaotic nature of PPG signals but also provided a facility to include the pseudo-periodic variations of PPGs by utilizing a concatenating layer. ResultsOur framework was examined on the public dataset, namely, Multi-Parameter Intelligent in Intensive Care II contained the recording of PPG, ECG and arterial blood pressure. The performance of the employed handcrafted features in distinguishing between the levels of BP values was investigated based on Spearman's statistics. In addition, our proposed scheme is evaluated in terms of Pearson's correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE). The best performance was achieved when the employed handcrafted features accompanied by PPG sequences were applied to the parallel deep network. In particular, the values of R, RMSE, and MAE were obtained 0.9529, 2.76 mmHg, and 1.73 mmHg for diastolic BP, and 0.9444, 6.18 mmHg, and 3.8 mmHg for systolic BP, respectively. Moreover, based on the requirements of the standards set by the British Hypertension Society (BHS), the proposed scheme achieved a grade of A. ConclusionsOur proposed scheme outperformed the state-of-the-art BP estimation methods. In addition, the results confirmed that the concatenation of the PPG-related machine-learned and nonlinear handcrafted features can be properly applied in continuous BP monitoring.

Systolic Blood Pressure Estimation from PPG Signal Using ANN

High blood pressure is one of the most important precursors for Cardiovascular Diseases (CVDs), the most common cause of death in 2020, as reported by the World Health Organization (WHO). Moreover, many patients affected by neurodegenerative diseases (e.g., Parkinson’s Disease) exhibit impaired autonomic control, with inversion of the normal circadian arterial pressure cycle, and consequent augmented cardiovascular and fall risk. For all these reasons, a continuous pressure monitoring of these patients could represent a significant prognostic factor, and help adjusting their therapy. However, the existing cuff-based methods cannot provide continuous blood pressure readings. Our work is inspired by the newest approaches based on the photoplethysmographic (PPG) signal only, which has been used to continuously estimate systolic blood pressure (SP), using artificial neural networks (ANN), in order to create more compact and wearable devices. Our first database was derived from the PhysioNet resource; we extracted PPG and arterial blood pressure (ABP) signals, collected at a sampling frequency of 125 Hz, in a hospital environment. It consists of 249,672 PPG periods and the relative SP values. The second database was collected at STMicroelectronics s.r.l., in Agrate Brianza, using the MORFEA3 wearable device and a digital cuff-based sphygmomanometer, as reference. The pre-processing phase, in order to remove noise and motion artifacts and to segment the signal into periods, was carried out on Matlab R2019b. The noise removal was one of the challenging parts of the study because of the inaccuracy of the PPG signal during everyday-life activity, and this is the reason why the MORFEA3 dataset was acquired in a controlled environment in a static position. Different solutions were implemented to choose the input features that best represent the period morphology. The first database was used to train the multilayer feed-forward neural network with a back-propagation model, whereas the second one was used to test it. The results obtained in this project are promising and match the Association for the Advancement of Medical Instruments (AAMI) and the British Hypertension Society (BHS) standards. They show a Mean Absolute Error of 3.85 mmHg with a Standard Deviation of 4.29 mmHg, under the AAMI standard, and reach the grade A under the BHS standard.

Abstracts from the 2022 Annual Scientific Meeting of the British and Irish Hypertension Society (BIHS)

Abstracts from the 2022 Annual Scientific Meeting of the British and Irish Hypertension Society (BIHS)

Recent Advances in Non-Invasive Blood Pressure Monitoring and Prediction Using a Machine Learning Approach.

Blood pressure (BP) monitoring can be performed either invasively via arterial catheterization or non-invasively through a cuff sphygmomanometer. However, for conscious individuals, traditional cuff-based BP monitoring devices are often uncomfortable, intermittent, and impractical for frequent measurements. Continuous and non-invasive BP (NIBP) monitoring is currently gaining attention in the human health monitoring area due to its promising potentials in assessing the health status of an individual, enabled by machine learning (ML), for various purposes such as early prediction of disease and intervention treatment. This review presents the development of a non-invasive BP measuring tool called sphygmomanometer in brief, summarizes state-of-the-art NIBP sensors, and identifies extended works on continuous NIBP monitoring using commercial devices. Moreover, the NIBP predictive techniques including pulse arrival time, pulse transit time, pulse wave velocity, and ML are elaborated on the basis of bio-signals acquisition from these sensors. Additionally, the different BP values (systolic BP, diastolic BP, mean arterial pressure) of the various ML models adopted in several reported studies are compared in terms of the international validation standards developed by the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) for clinically-approved BP monitors. Finally, several challenges and possible solutions for the implementation and realization of continuous NIBP technology are addressed.

Real-Time Cuffless Continuous Blood Pressure Estimation Using 1D Squeeze U-Net Model: A Progress toward mHealth.

Measuring continuous blood pressure (BP) in real time by using a mobile health (mHealth) application would open a new door in the advancement of the healthcare system. This study aimed to propose a real-time method and system for measuring BP without using a cuff from a digital artery. An energy-efficient real-time smartphone-application-friendly one-dimensional (1D) Squeeze U-net model is proposed to estimate systolic and diastolic BP values, using only raw photoplethysmogram (PPG) signal. The proposed real-time cuffless BP prediction method was assessed for accuracy, reliability, and potential usefulness in the hypertensive assessment of 100 individuals in two publicly available datasets: Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-I) and Medical Information Mart for Intensive Care (MIMIC-III) waveform database. The proposed model was used to build an android application to measure BP at home. This proposed deep-learning model performs best in terms of systolic BP, diastolic BP, and mean arterial pressure, with a mean absolute error of 4.42, 2.25, and 2.56 mmHg and standard deviation of 4.78, 2.98, and 3.21 mmHg, respectively. The results meet the grade A performance requirements of the British Hypertension Society and satisfy the AAMI error range. The result suggests that only using a short-time PPG signal is sufficient to obtain accurate BP measurements in real time. It is a novel approach for real-time cuffless BP estimation by implementing an mHealth application and can measure BP at home and assess hypertension.

DeepCNAP: A Deep Learning Approach for Continuous Noninvasive Arterial Blood Pressure Monitoring Using Photoplethysmography.

Arterial blood pressure (ABP) monitoring may permit the early diagnosis and management of cardiovascular disease (CVD); however, existing methods for measuring ABP outside the clinic use inconvenient cuff sphygmomanometry, or do not estimate continuous ABP waveforms. This study proposes a novel deep learning model DeepCNAP for estimating continuous BP waveform from a noninvasively measured photoplethysmography (PPG) signal in real-time. DeepCNAP was designed through the combination of deep convolutional networks and self-attention. The proposed method was constructed via 10-fold cross-validation based on the MIMIC database (the number of subjects = 942, recording time = 374.43 hours). The performance of DeepCNAP was evaluated from two perspectives: estimating ABP from PPG and classifying hemodynamically unstable events (i.e., hypertension, prehypertension, hypotension, and the normal state). The mean absolute errors of the BP estimates were 3.40 ± 4.36 mmHg for systolic BP, 1.75 ± 2.25 mmHg for diastolic BP, and 3.23 ± 2.21 mmHg for the BP waveform, indicating that DeepCNAP satisfies the standards of both the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). From the estimated BP, hypertension, prehypertension, hypotension, and the normal state were classified with 99.44, 97.58, 92.23, and 94.64% accuracy, respectively. DeepCNAP enables feasible real-time estimation of invasively measured ABP from noninvasive PPG. With its noninvasive nature, high accuracy, and clinical relevance, the proposed model could be valuable in general wards at hospitals and for wearable devices in daily life.

PPG-based blood pressure estimation can benefit from scalable multi-scale fusion neural networks and multi-task learning

The continuous measurement of blood pressure (BP) plays an important role in preventing cardiovascular diseases. However, common cuff-based devices with cumbersome operations are difficult to realize continuous BP monitoring. We propose a continuous BP estimation method based on the end-to-end multi-scale fusion and multi-task learning neural network (MSF-MTLNet). It can use the 8-second PPG segment and its derivatives as input and automatically learn their multi-scale features to improve BP accuracy. The proposed method implements feature extraction by adaptively fusing multiple convolution kernels with different sizes through the attention mechanism. Simultaneously, multi-task learning was introduced to discover task-relevant features. A total of 277,600 segments from 1825 patients in the University of California, Irvine database are used for experiments. They are divided into the independent training set, validation set, and test set according to the ratio of 8:1:1 in an inter-subject manner. The mean and standard deviation of estimation error for systolic blood pressure and diastolic blood pressure are 0.97 ± 8.87 mmHg and 0.55 ± 4.23 mmHg, respectively, which outperforms the current cutting-edge method. Our approach almost meets the Association for the Advancement of Medical Instrumentation standard. Considering the British Hypertension Society standards, it achieves an 'A' grade for DBP estimation and a 'B' grade for SBP estimation. Experimental results show that MSF-MTLNet can obtain promising precision in cuffless and continuous BP estimation.