Blood Pressure Estimation Research Articles

Hybrid modeling on reconstitution of continuous arterial blood pressure using finger photoplethysmography

The continuous estimation of arterial blood pressure (ABP) waveforms directly from single-channel photoplethysmography (PPG) signals will predictably change the way to monitor blood pressure in the future. This article proposed a new hybrid mathematical model for continuous blood pressure monitoring by investigating the relationship between the finger PPG signal and the radial ABP signal based on a public database. Considering potential damping factors and wave propagation/reflection in blood circulation, we combined the electrical network model with the tube-load model. The optimal range of model parameters was obtained through the system identification method to realize the individualized continuous blood pressure measurements. Compared with the invasive measurement, the hybrid model performed superior blood pressure estimation with high consistency. The estimated ABP waveforms correlated highly with the reference waveforms with an average correlation coefficient of 0.96. The mean absolute error/standard deviation of the estimated systolic blood pressure (SBP), mean arterial blood pressure (MAP), and diastolic blood pressure (DBP) were 3.0/4.4, 2.1/3.0 and 2.1/3.2 mmHg, respectively. The results met the requirements of the Association for the Advancement of Medical Instrumentation (AAMI). The hybrid model is expected to be embedded in small wearable devices to directly estimate the continuous blood pressure waveforms at the radial artery site through the PPG signals, pioneering the synchronous non-sensing monitoring on blood pressure and blood flow.

A deep learning method for continuous noninvasive blood pressure monitoring using photoplethysmography

Objective. The aim of this study is to investigate continuous blood pressure waveform estimation from a plethysmography (PPG) signal, thus providing more human cardiovascular status information than traditional cuff-based methods. Approach. The proposed method utilizes the feature extraction ability of a convolution neural network to estimate blood pressure (BP) from PPG signals without the need for waveform analysis and signal feature extraction. Main results. The network achieved mean absolute errors and standard deviations of 2.55 ± 3.92 mmHg for systolic BP (SBP), 1.66 ± 2.76 mmHg for diastolic BP (DBP), and 2.52 ± 3.02 mmHg for overall pressure waveform. The results meet the best levels of the protocols of the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). Significance. The proposed method shows promise for noninvasive continuous BP monitoring in hospital wards and daily life, which can assist in clinical diagnosis, disease treatment, and rehabilitation.

Survey and Evaluation of Hypertension Machine Learning Research.

Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision-making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. Methods and Results The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension-related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real-time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Conclusions Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption.

A non-contact continuous blood pressure measurement method based on video stream

Hypertension is the primary disease that endangers human health. A convenient and accurate blood pressure measurement method can help to prevent the hypertension. This paper proposed a continuous blood pressure measurement method based on facial video signal. Firstly, color distortion filtering and independent component analysis were used to extract the video pulse wave of the region of interest in the facial video signal, and the multi-dimensional feature extraction of the pulse wave was preformed based on the time-frequency domain and physiological principles; Secondly, an integrated feature selection method was designed to extract the universal optimal feature subset; After that, we compared the single person blood pressure measurement models established by Elman neural network based on particle swarm optimization, support vector machine (SVM) and deep belief network; Finally, we used SVM algorithm to build a general blood pressure prediction model, which was compared and evaluated with the real blood pressure value. The experimental results showed that the blood pressure measurement results based on facial video were in good agreement with the standard blood pressure values. Comparing the estimated blood pressure from the video with standard blood pressure value, the mean absolute error (MAE) of systolic blood pressure was 4.9 mm Hg with a standard deviation (STD) of 5.9 mm Hg, and the MAE of diastolic blood pressure was 4.6 mm Hg with a STD of 5.0 mm Hg, which met the AAMI standards. The non-contact blood pressure measurement method based on video stream proposed in this paper can be used for blood pressure measurement.

A PPG-Based Calibration-Free Cuffless Blood Pressure Estimation Method Using Cardiovascular Dynamics.

Traditional cuff-based sphygmomanometers for measuring blood pressure can be uncomfortable and particularly unsuitable to use during sleep. A proposed alternative method uses dynamic changes in the pulse waveform over short intervals and replaces calibration with information from photoplethysmogram (PPG) morphology to provide a calibration-free approach using a single sensor. Results from 30 patients show a high correlation of 73.64% for systolic blood pressure (SBP) and 77.72% for diastolic blood pressure (DBP) between blood pressure estimated with the PPG morphology features and the calibration method. This suggests that the PPG morphology features could replace the calibration stage for a calibration-free method with similar accuracy. Applying the proposed methodology on 200 patients and testing on 25 new patients resulted in a mean error (ME) of -0.31 mmHg, a standard deviation of error (SDE) of 4.89 mmHg, a mean absolute error (MAE) of 3.32 mmHg for DBP and an ME of -4.02 mmHg, an SDE of 10.40 mmHg, and an MAE of 7.41 mmHg for SBP. These results support the potential for using a PPG signal for calibration-free cuffless blood pressure estimation and improving accuracy by adding information from cardiovascular dynamics to different methods in the cuffless blood pressure monitoring field.

Circulating Interlukin-32 and Altered Blood Pressure Control in Individuals with Metabolic Dysfunction.

Fatty liver disease is most frequently related to metabolic dysfunction (MAFLD) and associated comorbidities, heightening the risk of cardiovascular disease, and is associated with higher hepatic production of IL32, a cytokine linked with lipotoxicity and endothelial activation. The aim of this study was to examine the relationship between circulating IL32 concentration and blood pressure control in individuals with metabolic dysfunction at high risk of MAFLD. IL32 plasma levels were measured by ELISA in 948 individuals with metabolic dysfunction enrolled in the Liver-Bible-2021 cohort. Higher circulating IL32 levels were independently associated with systolic blood pressure (estimate +0.008 log10 per 1 mmHg increase, 95% c.i. 0.002-0.015; p = 0.016), and inversely correlated with antihypertensive medications (estimate -0.189, 95% c.i. -0.291--0.088, p = 0.0002). Through multivariable analysis, IL32 levels predicted both systolic blood pressure (estimate 0.746, 95% c.i 0.173-1.318; p = 0.010) and impaired blood pressure control (OR 1.22, 95% c.i. 1.09-1.38; p = 0.0009) independently of demographic and metabolic confounders and of treatment. This study reveals that circulating IL32 levels are associated with impaired blood pressure control in individuals at risk of cardiovascular disease.

Accuracy of a smartphone application for blood pressure estimation in Bangladesh, South Africa, and Tanzania

Undetected and unmonitored hypertension carries substantial mortality and morbidity, especially during pregnancy. We assessed the accuracy of OptiBPTM, a smartphone application for estimating blood pressure (BP), across diverse settings. The study was conducted in community settings: Gaibandha, Bangladesh and Ifakara, Tanzania for general populations, and Kalafong Provincial Tertiary Hospital, South Africa for pregnant populations. Based on guidance from the International Organization for Standardization (ISO) 81,060–2:2018 for non-invasive BP devices and global consensus statement, we compared BP measurements taken by two independent trained nurses on a standard auscultatory cuff to the BP measurements taken by a research version of OptiBPTM called CamBP. For ISO criterion 1, the mean error was 0.5 ± 5.8 mm Hg for the systolic blood pressure (SBP) and 0.1 ± 3.9 mmHg for the diastolic blood pressure (DBP) in South Africa; 0.8 ± 7.0 mmHg for the SBP and −0.4 ± 4.0 mmHg for the DBP in Tanzania; 3.3 ± 7.4 mmHg for the SBP and −0.4 ± 4.3 mmHg for the DBP in Bangladesh. For ISO criterion 2, the average standard deviation of the mean error per subject was 4.9 mmHg for the SBP and 3.4 mmHg for the DBP in South Africa; 6.3 mmHg for the SBP and 3.6 mmHg for the DBP in Tanzania; 6.4 mmHg for the SBP and 3.8 mmHg for the DBP in Bangladesh. OptiBPTM demonstrated accuracy against ISO standards in study populations, including pregnant populations, except in Bangladesh for SBP (criterion 2). Further research is needed to improve performance across different populations and integration within health systems.

Causal inference based cuffless blood pressure estimation: A pilot study

Enabled by wearable sensing, e.g., photoplethysmography (PPG) and electrocardiography (ECG), and machine learning techniques, study on cuffless blood pressure (BP) measurement with data-driven methods has become popular in recent years. However, causality has been overlooked in most of current studies. In this study, we aim to examine the feasibility of causal inference for cuffless BP estimation. We first attempt to detect wearable features that are causally related, rather than correlated, to BP changes by identifying causal graphs of interested variables with fast causal inference (FCI) algorithm. With identified causal features, we then employ time-lagged link to integrate the mechanism of causal inference into the BP estimated model. The proposed method was validated on 62 subjects with their continuous ECG, PPG and BP signals being collected. We found new causal features that can better track BP changes than pulse transit time (PTT). Further, the developed causal-based estimation model achieved an estimation error of mean absolute difference (MAD) being 5.10 mmHg and 2.85 mmHg for SBP and DBP, respectively, which outperformed traditional model without consideration of causality. To the best of our knowledge, this work is the first to study the causal inference for cuffless BP estimation, which can shed light on the mechanism, method and application of cuffless BP measurement.

Investigation of Optimal Light Source Wavelength for Cuffless Blood Pressure Estimation Using a Single Photoplethysmography Sensor.

Routine blood pressure measurement is important for the early detection of various diseases. Recently, cuffless blood pressure estimation methods that do not require cuff pressurization have attracted attention. In this study, we investigated the effect of the light source wavelength on the accuracy of blood pressure estimation using only two physiological indices that can be calculated with photoplethysmography alone, namely, heart rate and modified normalized pulse volume. Using a newly developed photoplethysmography sensor that can simultaneously measure photoplethysmograms at four wavelengths, we evaluated its estimation accuracy for systolic blood pressure, diastolic blood pressure, and mean arterial pressure against a standard cuff sphygmomanometer. Mental stress tasks were used to alter the blood pressure of 14 participants, and multiple linear regression analysis showed the best light sources to be near-infrared for systolic blood pressure and blue for both diastolic blood pressure and mean arterial pressure. The importance of the light source wavelength for the photoplethysmogram in cuffless blood pressure estimation was clarified.

MonEco: a Novel Health Monitoring Ecosystem to Predict Respiratory and Cardiovascular Disorders

Objectives: the present manuscript introduces a health monitoring ecosystem called MonEco, to monitor and predict respiratory and cardiovascular disorders. Material and methods: the system comprehends a tablet application called eCardio and two smart devices named CareUp and UpNEA. eCardio is an application available for iOS devices that predicts cardiovascular risk based on user' data and habits. CareUp is a smartwatch for blood pressure estimation and fitness tracking. UpNEA is a smart glove for sleep monitoring, detecting sleep disruptive breathing events. Results: MonEco smart devices embed novel algorithms and top-notch home health care monitoring technologies. The user can access data collected via a web application hosted by a remote server (AeneA), allowing clinicians to follow up on a patient's health. Conclusion: MonEco wants to inspire and disclose the architecture of a connected health monitoring ecosystem.

A Multi-Parameter Fusion Method for Cuffless Continuous Blood Pressure Estimation Based on Electrocardiogram and Photoplethysmogram

Blood pressure (BP) is an essential physiological indicator to identify and determine health status. Compared with the isolated BP measurement conducted by traditional cuff approaches, cuffless BP monitoring can reflect the dynamic changes in BP values and is more helpful to evaluate the effectiveness of BP control. In this paper, we designed a wearable device for continuous physiological signal acquisition. Based on the collected electrocardiogram (ECG) and photoplethysmogram (PPG), we proposed a multi-parameter fusion method for noninvasive BP estimation. An amount of 25 features were extracted from processed waveforms and Gaussian copula mutual information (MI) was introduced to reduce feature redundancy. After feature selection, random forest (RF) was trained to realize systolic BP (SBP) and diastolic BP (DBP) estimation. Moreover, we used the records in public MIMIC-III as the training set and private data as the testing set to avoid data leakage. The mean absolute error (MAE) and standard deviation (STD) for SBP and DBP were reduced from 9.12 ± 9.83 mmHg and 8.31 ± 9.23 mmHg to 7.93 ± 9.12 mmHg and 7.63 ± 8.61 mmHg by feature selection. After calibration, the MAE was further reduced to 5.21 mmHg and 4.15 mmHg. The result showed that MI has great potential in feature selection during BP prediction and the proposed multi-parameter fusion method can be used for long-term BP monitoring.

Broadening the Utilization of Flexible and Wearable Pressure Sensors for the Monitoring of Health and Physiological Activities

Recently, the high need for artificial intelligence, human motion monitoring, and wearable devices that can be utilized to track human health conditions, particularly for patients experiencing any sickness, has drawn serious interest from researchers. The flexible pressure sensors are a promising possibility for these human–machine interface applications because of their quick reaction, huge sensitivity, and ultra-low detection limit. These flexible sensing devices provide real-time statistics about the conditions of the human body, like blood pressure and heartbeat estimation, to recognize cardiovascular-related diseases. We have developed a versatile and wearable capacitive sensing device by changing the polydimethylsiloxane (PDMS) dielectric layer's structure using a scrubber layer which introduced the porosity in the dielectric layer. The developed sensor showed excellent response under static pressure and dynamic pressure applications. Due to large sensitivity, high working stability, and speedy reaction (120 ms), the versatility of the pressure sensor has been demonstrated in various human motion monitoring applications like wrist bending, palm grip, elbow bending, knee twisting, and vocal-cord vibration detection. In addition, the developed sensor was also used in human–machine interface applications to perceive heartbeat and wrist-pulse motion under typical and after-exercise conditions.

Continuous cuffless blood pressure monitoring with a wearable ring bioimpedance device

Smart rings provide unique opportunities for continuous physiological measurement. They are easy to wear, provide little burden in comparison to other smart wearables, are suitable for nocturnal settings, and can be sized to provide ideal contact between the sensors and the skin at all times. Continuous measuring of blood pressure (BP) provides essential diagnostic and prognostic value for cardiovascular health management. However, conventional ambulatory BP measurement devices operate using an inflating cuff that is bulky, intrusive, and impractical for frequent or continuous measurements. We introduce ring-shaped bioimpedance sensors leveraging the deep tissue sensing ability of bioimpedance while introducing no sensitivity to skin tones, unlike optical modalities. We integrate unique human finger finite element model with exhaustive experimental data from participants and derive optimum design parameters for electrode placement and sizes that yields highest sensitivity to arterial volumetric changes, with no discrimination against varying skin tones. BP is constructed using machine learning algorithms. The ring sensors are used to estimate arterial BP showing peak correlations of 0.81, and low error (systolic BP: 0.11 ± 5.27 mmHg, diastolic BP: 0.11 ± 3.87 mmHg) for >2000 data points and wide BP ranges (systolic: 89–213 mmHg and diastolic: 42–122 mmHg), highlighting the significant potential use of bioimpedance ring for accurate and continuous estimation of BP.

Estimation of blood pressure In adult population in Baiji

Data was collected from 60 cases in age groups from 40-60 years ( 30 female and 30 male ).The cases included in this study was collected randomly in Baiji hospital from June to October 2006 .The data was obtained by using a questionnaire . It was found that the most common cases use Beta blockers as antihypertensive drugs , 12 cases use B-blocker plus Angiotensin converting enzymeinhibitors and 5 cases use B-blocker with diuretics.There are 8 cases use Angiotensin converting enzyme inhibitor only and 7 cases use vasodilator drugs .Regarding the measurement of bloodpressure and heart rate , in 36 cases complaining from increase blood pressure above the normal level there is increase in heart rate in 30 cases ( heart rate more than 100 beat / minute ) , andin24cases of normal blood pressure there is increase in heart rate in 5 cases only .The aim of this study is to investigate the prevalence of hypertension in age groups from 40-60 years in Baiji from June toOctober with some investigations

Simultaneous photoplethysmography and blood flow measurements towards the estimation of blood pressure using speckle contrast optical spectroscopy.

Non-invasive continuous blood pressure monitoring remains elusive. There has been extensive research using the photoplethysmographic (PPG) waveform for blood pressure estimation, but improvements in accuracy are still needed before clinical use. Here we explored the use of an emerging technique, speckle contrast optical spectroscopy (SCOS), for blood pressure estimation. SCOS provides measurements of both blood volume changes (PPG) and blood flow index (BFi) changes during the cardiac cycle, and thus provides a richer set of parameters compared to traditional PPG. SCOS measurements were taken on the finger and wrists of 13 subjects. We investigated the correlations between features extracted from both the PPG and BFi waveforms with blood pressure. Features from the BFi waveforms were more significantly correlated with blood pressure than PPG features ( R = - 0.55, p = 1.1 × 10-4 for the top BFi feature versus R = - 0.53, p = 8.4 × 10-4 for the top PPG feature). Importantly, we also found that features combining BFi and PPG data were highly correlated with changes in blood pressure ( R = - 0.59, p = 1.7 × 10-4 ). These results suggest that the incorporation of BFi measurements should be further explored as a means to improve blood pressure estimation using non-invasive optical techniques.

Blood pressure estimation and its recalibration assessment using wrist cuff blood pressure monitor.

The rapid evolution of wearable technology in healthcare sectors has created the opportunity for people to measure their blood pressure (BP) using a smartwatch at any time during their daily activities. Several commercially-available wearable devices have recently been equipped with a BP monitoring feature. However, concerns about recalibration remain. Pulse transit time (PTT)-based estimation is required for initial calibration, followed by periodic recalibration. Recalibration using arm-cuff BP monitors is not practical during everyday activities. In this study, we investigated recalibration using PTT-based BP monitoring aided by a deep neural network (DNN) and validated the performance achieved with more practical wrist-cuff BP monitors. The PTT-based prediction produced a mean absolute error (MAE) of 4.746 ± 1.529 mmHg for systolic blood pressure (SBP) and 3.448 ± 0.608 mmHg for diastolic blood pressure (DBP) when tested with an arm-cuff monitor employing recalibration. Recalibration clearly improved the performance of both DNN and conventional linear regression approaches. We established that the periodic recalibration performed by a wrist-worn BP monitor could be as accurate as that obtained with an arm-worn monitor, confirming the suitability of wrist-worn devices for everyday use. This is the first study to establish the potential of wrist-cuff BP monitors as a means to calibrate BP monitoring devices that can reliably substitute for arm-cuff BP monitors. With the use of wrist-cuff BP monitoring devices, continuous BP estimation, as well as frequent calibrations to ensure accurate BP monitoring, are now feasible.

A benchmark for machine-learning based non-invasive blood pressure estimation using photoplethysmogram

Blood Pressure (BP) is an important cardiovascular health indicator. BP is usually monitored non-invasively with a cuff-based device, which can be bulky and inconvenient. Thus, continuous and portable BP monitoring devices, such as those based on a photoplethysmography (PPG) waveform, are desirable. In particular, Machine Learning (ML) based BP estimation approaches have gained considerable attention as they have the potential to estimate intermittent or continuous BP with only a single PPG measurement. Over the last few years, many ML-based BP estimation approaches have been proposed with no agreement on their modeling methodology. To ease the model comparison, we designed a benchmark with four open datasets with shared preprocessing, the right validation strategy avoiding information shift and leak, and standard evaluation metrics. We also adapted Mean Absolute Scaled Error (MASE) to improve the interpretability of model evaluation, especially across different BP datasets. The proposed benchmark comes with open datasets and codes. We showcase its effectiveness by comparing 11 ML-based approaches of three different categories.

A Machine Learning Approach to the Non-Invasive Estimation of Continuous Blood Pressure Using Photoplethysmography

Blood pressure is an important vital sign that sometimes requires continuous measurement. The current methods include cuff measurements (manual auscultation and oscillometric techniques) for non-continuous measurement and invasive arterial cannulation for continuous measurement. The use of photoplethysmography as a cuffless, non-invasive, and continuous blood pressure measurement system is investigated through the use of four neural networks. These predict the systolic blood pressure, diastolic blood pressure, mean arterial blood pressure, and waveform shape. The models are trained on 890 h of data from 1669 patients in the MIMIC-III database. Feature-trained artificial neural networks predict the systolic blood pressure to 5.26 ± 6.53 mmHg (mean error ± standard deviation), the diastolic blood pressure to 2.96 ± 3.31 mmHg, and the mean arterial pressure to 3.27 ± 3.55 mmHg. These are used to shift and scale the predicted waveform, allowing the waveform prediction neural network to optimise for the wave shape rather than the amplitude. The waveform prediction has 86.4% correlation with the actual arterial blood pressure waveform. All results meet international clinical blood pressure measurement standards and could potentially change how blood pressure is measured in both clinical and research settings. However, more data from healthy individuals and analysis of the models’ biases based on clinical features is required.

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).

Remote Blood Pressure Estimation via the Spatiotemporal Mapping of Facial Videos

Blood pressure (BP) monitoring is vital in daily healthcare, especially for cardiovascular diseases. However, BP values are mainly acquired through a contact-sensing method, which is inconvenient and unfriendly for BP monitoring. This paper proposes an efficient end-to-end network for estimating BP values from a facial video to achieve remote BP estimation in daily life. The network first derives a spatiotemporal map of a facial video. Then, it regresses the BP ranges with a designed blood pressure classifier and simultaneously calculates the specific value with a blood pressure calculator in each BP range based on the spatiotemporal map. In addition, an innovative oversampling training strategy was developed to handle the problem of unbalanced data distribution. Finally, we trained the proposed blood pressure estimation network on a private dataset, MPM-BP, and tested it on a popular public dataset, MMSE-HR. As a result, the proposed network achieved a mean absolute error (MAE) and root mean square error (RMSE) of 12.35 mmHg and 16.55 mmHg on systolic BP estimations, and those for diastolic BP were 9.54 mmHg and 12.22 mmHg, which were better than the values obtained in recent works. It can be concluded that the proposed method has excellent potential for camera-based BP monitoring in the indoor scenarios in the real world.