Accuracy Of Blood Pressure Estimation Research Articles

A review: Blood pressure monitoring based on PPG and circadian rhythm.

The demand for ambulatory blood pressure monitoring (ABPM) is increasing due to the global rise in cardiovascular disease patients. However, conventional ABPM methods are discontinuous and can disrupt daily activities and sleep patterns. Photoplethysmography (PPG) is gaining attention from researchers due to its simplicity, portability, affordability, and ease of signal acquisition. This paper critically examines the advancements achieved in the technology of PPG-guided noninvasive blood pressure (BP) monitoring and explores future opportunities. We have performed a literature search using the Web of Science and PubMed search engines, from January 2018 to October 2023, for PPG signal quality assessment (SQA), cuffless BP estimation using single PPG, and associations between circadian rhythm and BP. Based on this foundation, we first examine the impact of PPG signal quality on blood pressure estimation results while focusing on methods for assessing PPG signal quality. Subsequently, the methods documented for estimating cuff-free BP from PPG signals are summarized. Furthermore, the study examines how individual differences affect the accuracy of BP estimation, incorporating the factors that influence arterial blood pressure (ABP) and elucidating the impact of circadian rhythm on blood pressure. Finally, there will be a summary of the study's findings and suggestions for future research directions.

Improving the accuracy of blood pressure estimation using a photoplethysmogram due to additional training of an artificial neural network

The paper presents a neural network algorithm for cuffless blood pressure measurement based on computer analysis of the photoplethysmogram signal. An approach is proposed that allows you to significantly reduce the requirements for the size of the training sample required to build an effective machine learning model. The essence of the approach is the procedure for retraining a pre-trained neural network model using a limited amount of training data. At the same time, it is possible to achieve such values of the accuracy of estimating blood pressure values as in full-fledged training, but at the same time, the practical use of the method is significantly simplified.

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.

LSTM Multi-Stage Transfer Learning for Blood Pressure Estimation Using Photoplethysmography

Considerable research has been devoted to developing machine-learning models for continuous Blood Pressure (BP) estimation. A challenging problem that arises in this domain is the selection of optimal features with interpretable models for medical professionals. The aim of this study was to investigate evidence-based physiologically motivating features based on a solid physiological background of BP determinants. A powerful and compact set of features encompassing six physiologically oriented features was extracted in addition to another set of features consisting of six commonly used features for comparison purposes. In this study, we proposed a BP predictive model using Long Short-Term Memory (LSTM) networks with multi-stage transfer learning approach. The proposed model topology consists of three cascaded stages. First, a BP classification stage. Second, a Mean Arterial Pressure (MAP) regression stage to further approximate a quantity proportional to Vascular Resistance (VR) using the extracted Cardiac Output (CO) from the PPG signal. Third, the main BP estimation stage. The final stage (final BP prediction) is able to exploit embedded correlations between BP and the proposed features along with derived outputs carrying hemodynamic characteristics through the sub-sequence stages. We also constructed traditional single-stage Artificial Neural Network (ANN) and LSTM-based models to appraise the performance gain of our proposed model. The models were tested and evaluated on 40 subjects from the MIMIC II database. The LSTM-based multi-stage model attained a MAE ± SD of 2.03 ± 3.12 for SBP and 1.18 ± 1.70 mmHg for DBP. The proposed set of features resulted in drastic error reduction, of up to 86.21%, compared to models trained on the commonly used features. The superior performance of the proposed multi-stage model provides confirmatory evidence that the selected transferable features among the stages coupled with the high-performing multi-stage topology enhance blood pressure estimation accuracy using PPG signals. This indicates the compelling nature and sufficiency of the proposed efficient features set.

A Wearable and Flexible Photoplethysmogram Sensor Patch for Cuffless Blood Pressure Estimation With High Accuracy

Hypertension is a very significant public health problem and has become a potential risk for cardiovascular and renal diseases. Blood pressure (BP) including systolic BP (SBP) and diastolic BP (DBP) is a vital parameter that should be monitored regularly for the early detection and prevention of chronic diseases. The traditional cuff-based BP monitors can cause discomfort to the users due to inflation and deflation. Here, a wearable and flexible photoplethysmography (PPG) patch sensor based on the polydimethylsiloxane (PDMS) substrate and double-layer serpentine structure with a closed loop that can omit the transfer printing process is proposed for cuffless BP estimation. Three models are adopted to estimate the BP. The accuracy of SBP and DBP obtained are 3.68 ± 2.37 and 4.9 ± 3.78 mmHg for the first model and 3.61 ± 2.62 and 4.72 ± 3.46 mmHg for the second model, respectively. The third model introduces the Kalman filtering to optimize the solution of the coefficients of the second model to forecast BP. As a result, the accuracy of SBP and DBP as high as 2.94 ± 2.35 and 3.41 ± 2.94 mmHg, respectively, is realized. The results show that the introduction of the Kalman filter algorithm to forecast BP can not only reduce the number of calibrations, but also improve the accuracy of BP estimation.

Remote blood pressure monitoring with a wearable photoplethysmographic device in patients undergoing coronary angiography: the senbiosys substudy.

The purpose of this study is to evaluate the accuracy of the Senbiosys device in measuring blood pressure (BP) by photoplethysmography (PPG) in patients undergoing coronary angiography. This is a substudy within the Senbiosys trial, which is a prospective, single-arm, single-center study, evaluating the accuracy of BP estimation of the Senbiosys device compared to invasive BP. Patients referred for coronary angiography underwent invasive BP measurement and simultaneously wore the Senbiosys ring. SBP and DBP estimations measured by the Senbiosys device were compared with invasive measurements. A total of 25 patients were included. Overall, 708 epochs with adequate PPG signal belonging to 17 patients were analyzed. A total of 84% of the SBP estimates and 99% of the DBP estimates have an absolute error of less than 10 mmHg compared with the invasive measurements. Mean difference was 2.3 ± 7.0 mmHg and 0.5 ± 3.5 mmHg for SBP and DBP, respectively. The Senbiosys device is accurate enough to determine BP in a selected population undergoing coronary angiography.

Non-contact Blood Pressure Estimation Method Based on Blood Pressure Category Classification.

In recent years, non-contact Blood Pressure (BP) measurement has been attracting attention for measuring our health status in daily life. A Doppler radar can observe pulse waves caused by chest wall displacement due to heartbeat. BP can be estimated by constructing a BP estimation model using BP-related features obtained from the pulse wave. However, compared to when modeling for each subject, the BP esti-mation accuracy deteriorates significantly when modeling with multiple subjects including the testing subject. To deal with this limitation, BP category classification has been introduced into PhotoPlethysmoGraphy (PPG)-based BP estimation. In this paper, we develop a Doppler radar-based BP estimation method based on BP category classification. In the proposed method, the pulse waves extracted from a Doppler radar are classified into three categories, "Low BP", "Normal BP", and "High BP" by k-Nearest Neighbor (kNN) based on the features that correlate with BP. The SBP estimation model is trained for each BP category. After the BP category classification, SBP is then estimated by using the model corresponding to the classified BP category. The experimental results showed that the proposed method with BP category classification estimated SBP accurately, compared to without BP category classification.

Cuffless blood pressure monitoring from a wristband with calibration-free algorithms for sensing location based on bio-impedance sensor array and autoencoder

Continuous monitoring of blood pressure (BP) is essential for the prediction and the prevention of cardiovascular diseases. Cuffless BP methods based on non-invasive sensors integrated into wearable devices can translate blood pulsatile activity into continuous BP data. However, local blood pulsatile sensors from wearable devices suffer from inaccurate pulsatile activity measurement based on superficial capillaries, large form-factor devices and BP variation with sensor location which degrade the accuracy of BP estimation and the device wearability. This study presents a cuffless BP monitoring method based on a novel bio-impedance (Bio-Z) sensor array built in a flexible wristband with small-form factor that provides a robust blood pulsatile sensing and BP estimation without calibration methods for the sensing location. We use a convolutional neural network (CNN) autoencoder that reconstructs an accurate estimate of the arterial pulse signal independent of sensing location from a group of six Bio-Z sensors within the sensor array. We rely on an Adaptive Boosting regression model which maps the features of the estimated arterial pulse signal to systolic and diastolic BP readings. BP was accurately estimated with average error and correlation coefficient of 0.5 ± 5.0 mmHg and 0.80 for diastolic BP, and 0.2 ± 6.5 mmHg and 0.79 for systolic BP, respectively.

Neckband-Based Continuous Blood Pressure Monitoring Device With Offset-Tolerant ROIC

This study presents a wearable neckband device for continuous cuffless blood pressure (BP) monitoring, where reliable neck-based BP measurement is achieved by utilizing the electrocardiogram (ECG) and impedance plethysmogram (IPG). Optimal composition and position of sensing electrodes around the neck were decided with consideration for physiological characteristics of the human body, and the core parameter of IPG magnitude was experimentally selected to improve the BP estimation accuracy. For its small-featured noises-tolerant detection capability, a customized readout integrated circuit (ROIC) was developed, which includes two kinds of analog front-ends (AFEs) for biopotential and bio-impedance. Both AFEs are proposed to include an attenuator-assisted hybrid DC-servo loop (DSL) structure, achieving precise pulse transit time (PTT) calculation by lowering the high-pass filtering cutoff frequency and also stable signal acquisition by widening the offset-cancellation range. Real-time PTT monitoring and BP estimation are wirelessly conducted, and its continuous operation time with 350-mAh battery capacity is longer than 5 hours. Efficacy of the proposed neckband BP device was evaluated for 8 healthy subjects under the physical stress condition, where the mean correlation coefficient and the standard deviation were achieved as 0.8508 and 4.17mmHg respectively.

Real-time Signal-to-Noise Ratio Optimization of Bio-Impedance Signal for Cuffless Blood Pressure Monitoring.

Continuous and unobtrusive blood pressure (BP) monitoring provides significant advantages in predicting the onset of cardiovascular disease. Bio-impedance sensing is a prominent method for continuous BP monitoring in a wearable form factor that can effectively measure blood pulsations from the arteries and translate them into BP. However, assessing the quality of the bio-impedance signal captured from small electrodes placed on the skin is required to determine the accuracy of BP estimation. In wearable devices, frequent movements of the electrodes on the skin are expected which cause non-optimal contact quality between the electrodes and the skin. This can lead to high skin-electrode impedance which can cause saturation of the current injection module of the bio-impedance device. This phenomenon degrades the signal quality In this paper, we present an automatic gain control (AGC) circuit that controls the amplitude of the current injection into the body based on sensing the skin-electrode impedance to ensure injection of maximum current to maximize the signal-to-noise ratio (SNR) while avoiding saturation of the current injection module. In this work, the proposed AGC method shows higher quality of blood pulsation from bio-impedance signal measured from a human subject with 1.59 dB improvement in SNR which leads to a better estimation of blood pressure.Clinical Relevance- The proposed automatic gain control (AGC) circuit establishes a more accurate method of continuous blood pressure monitoring using bio-impedance.

Heart rate variability enhances the accuracy of non-invasive continuous blood pressure estimation under blood loss.

To propose a new method for real-time monitoring of blood pressure of blood loss (BPBL), this article combines pulse transit time (PTT) and heart rate variability (HRV) as input parameters to build a model for BPBL estimation. In this article, effective parameters such as PTT, R-R interval (RRI), and HRV were extracted and used to establish the blood pressure (BP) estimation. Three BP estimation models were created: the PTT model, the RRI model, and the HRV model, and they were divided into an experimental group and a control group. Finally, the effects of the different estimation models on the accuracy of BPBL were evaluated using the experimental results. The result showed that both the RRI model and the HRV model have a good improvement effect on the prediction accuracy of BPBL, and the HRV model has the highest prediction accuracy than the PTT model and the RRI model. The correlation coefficients between the actual systolic BP (SBP) and diastolic BP (DBP) and the estimated SBP and DBP of the HRV model were 0.9580 and 0.9749, respectively, and the root-mean-square error of the HRV model for both SBP and DBP were 7.59 and 6.56mmHg, respectively. The results suggest that the accuracy of the BPBL estimated by the HRV models is better than that of the PTT model, which means that HRV seems to be more effective in improving the accuracy of BP estimation compared with RRI. These results in this article provide a new idea for other researchers in the field of BPBL estimation research.

Blood pressure assessment with in-ear photoplethysmography

Objective. In this study, we aimed to estimate blood pressure (BP) from in-ear photoplethysmography (PPG). This novel implementation provided an unobtrusive and steady way of recording PPG, whereas previous PPG measurements were mostly performed at the wrist, finger, or earlobe. Methods. The time between forward and reflected PPG waves was very short at the ear site. To minimize errors introduced by feature extraction, a multi-Gaussian decomposition of in-ear PPG was performed. Both hand-crafted and whole-based features were extracted and the best combination of features was selected using a backward-search wrapper method and evaluated by the Akaike information criteria. Hemodynamic parameters such as compliance and inertance were estimated from a four-element Windkessel (WK4) model, which was used to pre-classify PPG signals and generate different BP estimation algorithms. Calibration was done by using previous measurements from the same class. To validate this novel approach, 53 subjects were recruited for a one-month follow-up study, and 17 subjects were recruited for a two-month follow-up study. Calibrated systolic BP estimation accuracy was significantly improved with inertance-based pre-classification, while diastolic BP showed less improvement. Results. With proper feature selection, pre-classification and calibration, we have achieved a mean absolute error of 5.35 mmHg for SBP estimation, compared to 6.16 mmHg if no pre-classification was carried out. The performance did not deteriorate in two months, showing a decent BP trend-tracking ability. Conclusion. The study demonstrated the feasibility of in-ear PPG to reliably measure BP, which represents an important technological advancement in terms of unobtrusiveness and steadiness.

Non-invasive cuff-less blood pressure machine learning algorithm using photoplethysmography and prior physiological data.

Conventional blood pressure (BP) measurement methods have a number of drawbacks such as being invasive, cuff-based or requiring manual operation. Many studies are focussed on emerging methods of noninvasive, cuff-less and continuous BP measurement, and using only photoplethysmography to estimate BP has become popular. Although it is well known that physiological characteristics of the subject are important in BP estimation, this has not been widely explored. This article presents a novel method which adopts photoplethysmography and prior knowledge of a subject's physiological features to estimate DBP and SBP. Features extracted from a fingertip photoplethysmography signal and prior knowledge of a subject's physiological characteristics, such as gender, age, height, weight and BMI is used to estimate BP using three different machine learning models: artificial neural networks, support vector machine and least absolute shrinkage and selection operator regression. The accuracy of BP estimation obtained when prior knowledge of the physiological characteristics are incorporated into the model is superior to those which do not take the physiological characteristics into consideration. In this study, the best performing algorithm is an artificial neural network which obtains a mean absolute error and SD of 4.74 ± 5.55 mm Hg for DBP and 9.18 ± 12.57 mm Hg for SBP compared to 6.61 ± 8.04 mm Hg for DBP and 11.12 ± 14.20 mm Hg for SBP without prior knowledge. The inclusion of prior knowledge of the physiological characteristics can improve the accuracy of BP estimation using machine learning methods, and the incorporation of more physiological characteristics enhances the accuracy of the BP estimation.

Simultaneous Measurements of Capacitive Electrocardiogram and Ballistocardiogram Using In-Bed Fabric Sheet Electrode for Blood Pressure Estimation.

We recently reported the possibility of the simultaneous measurement of capacitive electrocardiogram (cEGC) and capacitive ballistocardiogram (cBCG) using non-contact capacitive coupling systems in bed. However, the relations of the two measurements with blood pressure (BP) have not been elucidated and cBCG measurements were unstable. Therefore, in this study, we improved the electrode and circuit, and succeeded in stable simultaneous measurement of cECG and cBCG. Synchronous averaging was performed to confirm the similarity of the waveform to the reproducibility of cBCG. Valsalva test (VT) was performed on seven subjects, and four verification intervals were set after VT. Then, the estimated blood pressure value was calculated using the instantaneous heart rate as the time difference parameter. We examined the relationship between estimated systolic blood pressure (eSBP) and systolic blood pressure (SBP) and observed a high positive correlation. cBCG contains components that are strongly related to BP. In the future, the electrode will be improved to measure cBCG at two points simultaneously at regular intervals to avoid shifting the Pulse Beat Arrival Time (PBAT) time axis. Further, in order to enhance the accuracy of blood pressure estimation, a change in the respiratory phase is incorporated into the estimation of the blood pressure value.

Cuffless Single-Site Photoplethysmography for Blood Pressure Monitoring.

One in three adults worldwide has hypertension, which is associated with significant morbidity and mortality. Consequently, there is a global demand for continuous and non-invasive blood pressure (BP) measurements that are convenient, easy to use, and more accurate than the currently available methods for detecting hypertension. This could easily be achieved through the integration of single-site photoplethysmography (PPG) readings into wearable devices, although improved reliability and an understanding of BP estimation accuracy are essential. This review paper focuses on understanding the features of PPG associated with BP and examines the development of this technology over the 2010–2019 period in terms of validation, sample size, diversity of subjects, and datasets used. Challenges and opportunities to move single-site PPG forward are also discussed.

Robust blood pressure estimation from finger photoplethysmography using age-dependent linear models

Objective: This work aims to develop an efficient and robust age-dependent multiple linear regression (MLR) model to estimate blood pressure (BP) from a single-source photoplethysmography (PPG) and biometrics, which could be embedded in the microcontroller of pulse oximeters. Approach: Hemodynamic features were extracted from the PPG signal using its waveform, derivatives, and biometrics. Whole-based, feature-based, and fusion models were evaluated and compared for different age groups. Their performance was tested using 1086 subjects with a leave-one-subject-out cross-validation. The improvement by adding biometrics and the long-term calibration effect were investigated in detail. The relative importance of each feature was compared between different age groups and the implication was discussed. Main results: The fusion model achieved the best performance in subjects with well-defined PPG features, whereas the feature-based method was better suited for subjects with damped signals. Adding age significantly improved both systolic BP (SBP) and diastolic BP (DBP) estimation accuracy for older subjects (> 50 years old) with well-defined features, while it only improved diastolic BP accuracy for older subjects with damped signals. For younger subjects (≤ 50 years old), the contribution of age was very small. A simple subtraction of subject-specific calibration factors significantly reduced biometric-related errors, which also improved the linearity of BP estimation. The relative importance analysis of input features suggests that separate models are indeed necessary for different age groups with different signal qualities, especially for DBP estimation in older subjects. Significance: This study shows a reasonable BP estimation accuracy with age-dependent MLR models, which may help to equip current pulse oximeters with additional functionalities.

Optimal Design of a New Strain-Type Sensor for Cuff-Less Blood Pressure Measurement via Finite Element Modeling and Taguchi Method

A new cuff-less blood pressure (BP) sensor is designed and optimized in this paper. This BP sensor is in a shape of a circular disc of polymer enclosing an electrode. As the sensor is attached to the wrist artery, the dynamic bending of the sensor due to small artery pulsation induces variation in the electrode strain, and then, its resistance varies. With adequate amplification to sense resistance variation, the pulsation signals can be successfully obtained. With the measured pulsation representing well the blood volume pulsation (BVP), BP can be estimated next via the pulse wave velocity (PWV) theory. Effort is dedicated by this paper to optimize the sizes and geometry of the proposed sensor to maximize the electrode curvature change for largest measured signal-to-noise ratio (SNR) possible, which maximizes the accuracy of BP estimation. To this end, the optimization via the finite element modeling and the Taguchi method is carried out. In results, the optimal sizes in height/radius, fillet, hardness, and the electrode position of the polymer disc are successfully derived, which are in the height of 2 mm, the radius of 6mm, the distance between the planar electrode and the bottom surface of sensor being 1 mm, the hardness of the encapsulating polymer being 25 in shore A hardness, and the fillets at the bottom of the polymer disc being 1 mm in radii. BP is measured for 59 subjects. The resulted errors are 3.60 ± 5.40 mmHg (mean difference ± standard deviation) and 1.15 ± 1.63 mmHg for SBP and DBP, respectively. The correlations are 0.8787 and 0.9648, demonstrating well the favorable performance of the developed sensor.

Blood Pressure Estimation Using Photoplethysmography Only: Comparison between Different Machine Learning Approaches.

Introduction Blood pressure (BP) has been a potential risk factor for cardiovascular diseases. BP measurement is one of the most useful parameters for early diagnosis, prevention, and treatment of cardiovascular diseases. At present, BP measurement mainly relies on cuff-based techniques that cause inconvenience and discomfort to users. Although some of the present prototype cuffless BP measurement techniques are able to reach overall acceptable accuracies, they require an electrocardiogram (ECG) and a photoplethysmograph (PPG) that make them unsuitable for true wearable applications. Therefore, developing a single PPG-based cuffless BP estimation algorithm with enough accuracy would be clinically and practically useful. Methods The University of Queensland vital sign dataset (online database) was accessed to extract raw PPG signals and its corresponding reference BPs (systolic BP and diastolic BP). The online database consisted of PPG waveforms of 32 cases from whom 8133 (good quality) signal segments (5 s for each) were extracted, preprocessed, and normalised in both width and amplitude. Three most significant pulse features (pulse area, pulse rising time, and width 25%) with their corresponding reference BPs were used to train and test three machine learning algorithms (regression tree, multiple linear regression (MLR), and support vector machine (SVM)). A 10-fold cross-validation was applied to obtain overall BP estimation accuracy, separately for the three machine learning algorithms. Their estimation accuracies were further analysed separately for three clinical BP categories (normotensive, hypertensive, and hypotensive). Finally, they were compared with the ISO standard for noninvasive BP device validation (average difference no greater than 5 mmHg and SD no greater than 8 mmHg). Results In terms of overall estimation accuracy, the regression tree achieved the best overall accuracy for SBP (mean and SD of difference: −0.1 ± 6.5 mmHg) and DBP (mean and SD of difference: −0.6 ± 5.2 mmHg). MLR and SVM achieved the overall mean difference less than 5 mmHg for both SBP and DBP, but their SD of difference was >8 mmHg. Regarding the estimation accuracy in each BP categories, only the regression tree achieved acceptable ISO standard for SBP (−1.1 ± 5.7 mmHg) and DBP (−0.03 ± 5.6 mmHg) in the normotensive category. MLR and SVM did not achieve acceptable accuracies in any BP categories. Conclusion This study developed and compared three machine learning algorithms to estimate BPs using PPG only and revealed that the regression tree algorithm was the best approach with overall acceptable accuracy to ISO standard for BP device validation. Furthermore, this study demonstrated that the regression tree algorithm achieved acceptable measurement accuracy only in the normotensive category, suggesting that future algorithm development for BP estimation should be more specific for different BP categories.

Multi-Wavelength Photoplethysmography Enabling Continuous Blood Pressure Measurement With Compact Wearable Electronics.

To fight the "silent killer" hypertension, continuous blood pressure (BP) monitoring has been one of the most desired functions in wearable electronics. However, current BP measuring principles and protocols either involve a vessel occlusion process with a cuff or require multiple sensing nodes on the body, which makes it difficult to implement them in compact wearable electronics like smartwatches and wristbands with long-term wearability. In this work, we proposed a highly compact multi-wavelength photoplethysmography (MWPPG) module and a depth-resolved MWPPG approach for continuous monitoring of BP and systemic vascular resistance (SVR). By associating the wavelength-dependent light penetration depth in the skin with skin vasculatures, our method exploited the pulse transit time (PTT) on skin arterioles for tracking SVR (n = 20). Then, we developed an arteriolar PTT-based method for beat-to-beat BP measurement. The BP estimation accuracy of the proposed arteriolar PTT method was validated against Finometer (n = 20) and the arterial line (n = 4). The correlation between arteriolar PTT and SVR was theoretically deduced and experimentally validated on 20 human subjects performing various maneuvers. The proposed arteriolar PTT-based method outperformed the traditional arterial PTT-based method with better BP estimation accuracy and simpler measurement setup, i.e., with a single sensing node. The proposed depth-resolved MWPPG method can provide accurate measurements of SVR and BP, which are traditionally difficult to measure in a noninvasive or continuous fashion. This MWPPG work provides the wearable healthcare electronics of compact size with a low-cost and physiology-based solution for continuous measurement of BP and SVR.

Cuffless blood-pressure estimation method using a heart-rate variability-derived parameter

Objective: Cuffless blood-pressure (BP) estimation has attracted widespread interest owing to its potential for long-term, non-invasive BP monitoring. But it is still impractical in clinical settings, mainly owing to ongoing challenges with respect to accuracy in hypertensive patients. To better estimate the BP, the current study proposes a new cuffless estimation method that includes a sympathetic tone, which has been reported with a varied pattern in hypertensive patients. Approach: First, the heart-rate variability of all subjects is investigated, and a new parameter, the heart-rate power spectrum ratio (HPSR), is proposed to indicate BP dynamics under sympathetic regulation. Then, a new BP estimation model is constructed by integrating HPSP with the pulse transit time (PTT) and photoplethysmography intensity ratio. The estimation accuracy is further evaluated by making comparisons with the standard sphygmomanometer BP on 60 subjects (29 hypertensive and 31 normotensive). Main results: A significant increase in HPSR was observed in hypertensive patients compared to normotensive subjects. Of the 60 subjects, the estimation accuracy was 0.73 ± 10.04 mmHg for systolic BP (SBP) and 0.90 ± 7.10 mmHg for diastolic BP (DBP) in hypertensive patients, which is comparable to 0.54 ± 7.52 mmHg for SBP and 0.82 ± 6.20 mmHg for DBP in normotensive subjects. Furthermore, the proposed method overperformed the traditional PTT-based algorithm by reducing the 3 mmHg error in the standard deviation in hypertensive patients. Significance: The results of the current study demonstrate that the inclusion of factors associated with autonomic activities in the BP estimation model would improve the BP estimation accuracy, especially for hypertensive subjects.