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Automated oscillometric blood pressure monitors are commonly used to measure blood pressure for many patients at home, office, and medical centers, and they have been actively studied recently. These devices usually provide a single blood pressure point and they are not able to indicate the uncertainty of the measured quantity. We propose a new technique using an ensemble-based recursive methodology to measure uncertainty for oscillometric blood pressure measurements. There are three stages we consider: the first stage is pre-learning to initialize good parameters using the bagging technique. In the second stage, we fine-tune the parameters using the ensemble-based recursive methodology that is used to accurately estimate blood pressure and then measure the uncertainty for the systolic blood pressure and diastolic blood pressure in the third stage.

Blood pressure (BP) is an important physiological marker of human health. It is commonly measured by a cuff-based monitor via either auscultatory or oscillometric methods. Recently, significant research has been conducted to mathematically estimate BP from pulse transit time (PTT) to enable cuffless and continuous BP measurement. In this research, a new time reference, RJ interval, which is the time delay between electrocardiogram (ECG) R peak and ballistocardiogram (BCG) J peak was evaluated to determine if it can be used as a surrogate of PTT in cuffless BP estimation. Biomedical signals from ten healthy subjects were acquired by BCG, ECG and PPG sensors and the continuous reference BP data were collected by a cuff-based Finometer PRO BP monitor. An exponential model was employed to estimate systolic blood pressure (SBP) using RJ interval and PTT. RJ intervals extracted from ECG and BCG were shown to be useful in evaluating trends of SBP and can be the surrogate of PTT in cuffless SBP estimation.

Accuracy in blood pressure (BP) estimation is essential for proper diagnosis and management of hypertension. Motion artifacts are considered external sources of inaccuracy and can be due to sudden arm motion, muscle tremor, shivering, and transport vehicle vibrations. In the proposed work, a new algorithmic stage is integrated in a non-invasive BP monitor. This stage suppresses the effect of the motion artifact and adjusts the pressure estimation before displaying it to users. The proposed stage is based on a 3-axis accelerometer signal, which helps in the accurate detection of the motion artifact. Both transient motion artifacts and artifact due to vibrations are suppressed using algorithms based on Empirical Mode Decomposition (EMD). Measurements with human subjects show that the proposed algorithms considerably improved the accuracy of the blood pressure estimates in comparison with the commonly-used conventional oscillometric algorithm that does not include an EMD-based stage for artifact suppression, and allowed the estimates to meet the requirements of the international ANSI/AAMI/ISO standard., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: Current oscillometric blood pressure measurement devices generally provide only single-point estimates for systolic and diastolic blood pressures and rarely provide confidence ranges for these estimates. A novel methodology to obtain confidence intervals (CIs) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) estimates from a single oscillometric blood pressure measurement is presented., Methods: The proposed methodology utilizes the multiple regression technique to fuse optimally a set of SBP and DBP estimates obtained through different algorithms. However, the set of SBP and DBP estimates is a small number to determine the CI of each individual subject. To address this issue, the weighted bootstrap approach based on the multiple regression technique was used to generate a pseudo sample set for the SBP and the DBP. In this paper, the multiple regression technique can estimate the best-fitting surface of an efficient function that relates the input sample set as an independent vector to the auscultatory nurse measurement as a dependent vector to estimate regression coefficients. Consequently, the coefficients are assigned to an eight-sample set obtained from the fusion of different algorithms as optimally weighted parameters. CIs are also estimated using the conventional methods on the set of fused SBP and DBP estimates for comparison purposes., Results: The proposed method was applied to an experimental dataset of 85 patients. The results indicated that the proposed approach provides better blood pressure estimates than the existing algorithms and, in addition, is able to provide CIs for a single measurement., Conclusions: The CIs derived from the proposed scheme are much smaller than those calculated by conventional methods except for the pseudo maximum amplitude-envelope algorithm for both the SBP and the DBP, probably because of the decrease in the standard deviation through the increase in the pseudo measurements using the weighted bootstrap method for each subject. The proposed methodology is likely the only one currently available that can provide CIs for single-sample blood pressure measurements., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Current noninvasive blood pressure (BP) measurement methods, such as the oscillometric method, estimate the systolic and diastolic blood pressure (SBP and DBP) at two random instants in time and do not take into account the natural variability in BP. The standard for automated BP devices sets a maximum allowable system error of ±5 mmHg, even though natural BP variability often exceeds these limits. This paper proposes a new approach using simultaneous recordings of the oscillometric and continuous arterial pulse waveforms to augment the conventional noninvasive measurement by providing (1) the mean SBP and DBP over the measurement interval and the associated confidence intervals of the mean, (2) the standard deviation of SBP and DBP over the measurement interval, which indicates the degree of fluctuation in BP and (3) an indicator as to whether or not the oscillometric reading is an outlier. Recordings with healthy subjects demonstrate the potential utility of this approach to characterize BP, to detect outlier measurements, and that it does not suffer from bias relative to the conventional oscillometric method.

A novel pulse morphology-based approach for estimation of blood pressure from non-invasive oscillometric blood pressure measurement is presented. Quantitative measures that describe the pulse morphology are utilized to obtain the estimates of mean arterial, systolic, and diastolic pressures. Preliminary results obtained from a small set of measurements are used to demonstrate the feasibility of the proposed approach. The estimates obtained through pulse morphology-based approach is compared with those obtained from a commercial blood pressure device.

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. [ABSTRACT FROM AUTHOR]

An important means for preventing and managing cardiovascular disease is the non-invasive estimation of blood pressure. There is particular interest in developing approaches that provide accurate cuffless and continuous estimation of this important vital sign. This paper proposes a method that uses dynamic changes of the pulse waveform over short time intervals and calibrates the system based on a mathematical model that relates reflective PTT (R-PTT) to blood pressure. An advantage of the method is that it only requires collecting the photoplethysmogram (PPG) using one optical sensor, in addition to initial non-invasive measurements of blood pressure that are used for calibration. This method was applied to data from 30 patients, resulting in a mean error (ME) of 0.59 mmHg, a standard deviation of error (SDE) of 7.07 mmHg, and a mean absolute error (MAE) of 4.92 mmHg for diastolic blood pressure (DBP) and an ME of 2.52 mmHg, an SDE of 12.15 mmHg, and an MAE of 8.89 mmHg for systolic blood pressure (SBP). These results demonstrate the possibility of using the PPG signal for the cuffless continuous estimation of blood pressure based on the analysis of calibrated changes in cardiovascular dynamics, possibly in conjunction with other methods that are currently being researched. [ABSTRACT FROM AUTHOR]

Noninvasive blood pressure (BP) measurement is an important tool for managing hypertension and cardiovascular disease. However, automated noninvasive BP measurement devices, which are usually based on the oscillometric method, do not always provide accurate estimation of BP. It has been found that change in arterial stiffness (AS) is an underlying mechanism of disagreement between an oscillometric BP monitor and a sphygmomanometer. This problem is addressed by incorporating parameters related to AS in the algorithm for BP measurement. Pulse transit time (PTT) is first used to estimate AS parameters, which are fixed into a model of the oscillometric envelope. This model can then be used to perform curve fitting to the measured signal using only four parameters: systolic BP, diastolic BP, mean BP, and lumen area at zero transmural pressure. The proposed technique is independent of the experimentally determined characteristic ratios that are commonly used in existing oscillometric methods. The accuracy of the proposed technique was evaluated by comparing with the same model without incorporation of AS, and with reference BP device measurements. The new method achieved standard deviation of error less than 8 mmHg and mean error less than 5 mmHg. The results show consistency with ANSI/AAMI SP-10 standard for noninvasive BP measurement techniques. [ABSTRACT FROM AUTHOR]

Accuracy of blood pressure (BP) measurement is a challenging issue in oscillometry. Most of the automated noninvasive BP monitors estimate BP from envelope of the measured oscillometric pulses. The peak and the trough of the oscillometric pulses are very sensitive to noise caused by breathing, heart-rate variability, motion artifacts, muscle contraction, and environmental noise. Therefore, accuracy of the estimated BP based on the oscillometric waveform envelopes is not reliable in some cases. Recently, employing a modeling approach to estimate BP, we obtained the accurate results for a set of healthy subjects. The method is based on the lumen area oscillations model and estimates BP by comparing the actual and corresponding simulated waveforms. The method’s accuracy worsened when we tested it on a broader range of healthy subjects, while a significant drop was observed when the method was used for patients with chronic cardiovascular diseases. The work presented in this paper represents an improved version of our previous approach. We tested the proposed method on both healthy subjects and patients with chronic cardiovascular diseases, and compared the results to two popular BP estimation algorithms: maximum amplitude algorithm and maximum/minimum slope algorithm. We observed up to 56.7% and 57.3% improvements in mean absolute error, 98.9% and 64.4% improvements in mean error, 50% and 59% improvements in standard deviation of errors, and up to 57.6% and 55.8% in measurement uncertainty for the estimated systolic and diastolic pressures, respectively. [ABSTRACT FROM AUTHOR]

A variety of oscillometric algorithms have been recently proposed in the literature for estimation of blood pressure (BP). However, these algorithms possess specific strengths and weaknesses that should be taken into account before selecting the most appropriate one. In this paper, we propose a fusion method to exploit the advantages of the oscillometric algorithms and circumvent their limitations. The proposed fusion method is based on the computation of the weighted arithmetic mean of the oscillometric algorithms estimates, and the weights are obtained using a Bayesian approach by minimizing the mean square error. The proposed approach is used to fuse four different oscillometric blood pressure estimation algorithms. The performance of the proposed method is evaluated on a pilot dataset of 150 oscillometric recordings from 10 subjects. It is found that the mean error and standard deviation of error are reduced relative to the individual estimation algorithms by up to 7 mmHg and 3 mmHg in estimation of systolic pressure, respectively, and by up to 2 mmHg and 3 mmHg in estimation of diastolic pressure, respectively. [ABSTRACT FROM AUTHOR]

Blood pressure (BP) readings in oscillometry are very sensitive to the posture of the body, arm, and body movements during the BP measurements, so measuring conditions are the first important factors for trusted BP readings. Next is the BP estimation algorithm, which is responsible to convert the cuff deflation curve (CDC) pressure signal to accurate BP readings. With proper measuring conditions and an accurate BP estimation algorithm one can expect trusted BP readings. Trustworthiness of the BP readings is still a challenging issue in automated oscillometric BP monitors, and patients need to see the doctor for trusted measurements. To this end, we have proposed a novel method called a Dynamic Threshold Algorithm (DTA) that evaluates trustworthiness of the BP readings immediately after the BP is estimated, such that the patient can decide whether to repeat the measurement or not. DTA employs the heart rate (HR) of the subject and determines a specific threshold (TR). TR is used to determine maximum and minimum limits for trustable pressures (SBP2, DBP2) of a given subject. The limits are called trusted boundaries (TB). Trusted boundaries are compared with the estimated systolic blood pressure (SBP) and diastolic blood pressure (DBP) to determine trustworthiness of the measured BP. BP readings are trusted if estimated SBP and DBP are inside the TB and untrusted or labeled an outlier if otherwise. In this research, DTA is applied on three different data- sets of healthy and sick subjects, outliers are determined and removed from the datasets, and remaining recordings are validated against references and compared with validated results of original datasets. According to observations, improvements were significant after outliers were removed from the datasets. [ABSTRACT FROM PUBLISHER]

An accurate noninvasive estimation of mean arterial pressure (MAP) is of great importance in the evaluation of circulatory function and prognosis of some cardiovascular diseases. This paper proposes a novel oscillometric MAP estimation method based on the dependence of pulse transit time (PTT) on cuff pressure (CP). The PTT computed as the time interval between the electrocardiogram (ECG) R-peaks and the maximum slope points on the oscillometric pulses is mathematically modeled by considering the cuff-arm-artery system and the blood flow dynamics. It is then analytically shown that MAP can be approximated as the CP at which the PTT is maximum. Based on our theoretical findings, a new method of MAP estimation from simultaneous ECG and oscillometric blood pressure measurements is proposed. Our proposed method is validated with a pilot study in which 150 recordings from 10 subjects are analyzed. The reference MAP is computed from the systolic and diastolic pressures measured by the Food and Drug Administration-approved Omron HEM-790IT monitor using three different formulas given in the literature. The performance of our proposed method is compared with the maximum amplitude and zero-crossing methods in terms of mean error (ME), mean absolute error, and standard deviation of error (SDE). It is found that our proposed method achieves improvements of more than 20% in SDE compared with the maximum amplitude method and more than 50% in ME compared with the zero-crossing method. [ABSTRACT FROM PUBLISHER]

Oscillometry is a popular technique for automatic estimation of blood pressure (BP). However, most of the oscillometric algorithms rely on empirical coefficients for systolic and diastolic pressure evaluation that may differ in various patient populations, rendering the technique unreliable. A promising complementary technique for automatic estimation of BP, based on the dependence of pulse transit time (PTT) on cuff pressure (CP) (PTT–CP mapping), has been proposed in the literature. However, a theoretical grounding for this technique and a nonparametric BP estimation approach are still missing. In this paper, we propose a novel coefficient-free BP estimation method based on PTT–CP dependence. PTT is mathematically modeled as a function of arterial lumen area under the cuff. It is then analytically shown that PTT–CP mappings computed from various points on the arterial pulses can be used to directly estimate systolic, diastolic, and mean arterial pressure without empirical coefficients. Analytical results are cross-validated with a pilot investigation on ten healthy subjects where 150 simultaneous electrocardiogram and oscillometric BP recordings are analyzed. The results are encouraging whereby the mean absolute errors of the proposed method in estimating systolic and diastolic pressures are 5.31 and 4.51 mmHg, respectively, relative to the Food and Drug Administration approved Omron monitor. Our work thus shows promise toward providing robust and objective BP estimation in a variety of patients and monitoring situations. [ABSTRACT FROM PUBLISHER]

Although estimation of average blood pressure is commonly done with oscillometric measurements, confidence intervals (CIs) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) are not usually estimated. This paper adopts bootstrap methodologies to build CI from a small sample set of measurements, which is a situation commonly encountered in practice. Three bootstrap methodologies, namely, nonparametric percentile bootstrap, standard bootstrap, and bias-corrected and accelerated bootstrap are investigated. A two-step methodology is proposed based on pseudomeasurements using bootstrap principles to first derive the pseudomaximum amplitudes and then the pseudoenvelopes (PEs). The SBP and DBP are estimated using the new relationships between mean cuff pressure and PE and then the CIs for such estimates are obtained. In order to reduce the amount of processing, a single-step methodology that directly derives PE using bootstrap principles is also presented. Application of the proposed methodology on an experimental data set of 85 patients with five sets of measurements for each patient has yielded a narrower CI than the currently available conventional methods such as Student's t-distribution method. [ABSTRACT FROM PUBLISHER]

In this paper, we present a novel feature-based neural network (NN) approach for estimation of blood pressure (BP) from wrist oscillometric measurements. Unlike previous methods that use the raw oscillometric waveform envelope (OMWE) as input to the NN, in this paper, we propose to use features extracted from the envelope. The OMWE is mathematically modeled as a sum of two Gaussian functions. The optimum parameters of this model are found by minimizing the least squares error between the model and the OMWE using the Levenberg–Marquardt algorithm and are used as features. Two separate feed-forward NNs (FFNNs) are then designed to estimate the systolic and diastolic BPs using these features. The FFNNs are trained using the resilient backpropagation learning algorithm and tested on a data set of BP measurements recorded from 85 subjects. The performance is then compared with that of the conventional maximum amplitude algorithm, adaptive neuro-fuzzy inference system, and already published NN-based methods. It is found that the proposed approach achieves lower values of mean absolute error and standard deviation of error in the estimation of BP. In addition, the proposed approach has the following advantages: lower complexity with respect to the design parameters, smaller training data set, and lower computational load. [ABSTRACT FROM PUBLISHER]

Breathing causes fluctuations in blood pressure and contributes to variations in blood pressure estimates. In order to reduce the variability in the blood pressure estimates induced by breathing, either the breathing signal should be removed from the oscillometric blood pressure signal, or its effects should be suppressed. This paper presents a hybrid method that combines homomorphic and adaptive signal processing techniques to extract the breathing signal from the oscillometric signal with or without a simultaneously recorded electrocardiogram (ECG). The quality of the extracted breathing signal and the depth of breathing are assessed using the reference breathing signals. The breathing signals extracted using the accompanying ECG signal were found to be superior in quality compared to the ones extracted from the oscillometric waveform. The blood pressure estimates were evaluated before and after the breathing suppression techniques were implemented. As a result of the breathing suppression, the fluctuation of the systolic and diastolic blood pressure estimates obtained from a database of 85 healthy subjects is reduced. [ABSTRACT FROM AUTHOR]

Oscillometry is the most common measurement method used in electronic blood pressure (BP) monitors. However, most of the existing oscillometric algorithms employ empirical ratios on the oscillometric waveform envelope (OMWE) to determine the systolic pressure (SP) and diastolic pressure (DP). As these algorithms do not consider the cardiovascular system parameters that may vary due to health conditions or age, the pressure estimates are not always reliable. In this paper, we develop a new mathematical model for the OMWE by incorporating an existing model of the cuff-arm-artery system. [ABSTRACT FROM PUBLISHER]

This paper introduces a novel approach to estimate the systolic and diastolic blood pressure ratios (SBPR and DBPR) based on the maximum amplitude algorithm (MAA) using a Gaussian mixture regression (GMR). The relevant features, which clearly discriminate the SBPR and DBPR according to the targeted groups, are selected in a feature vector. The selected feature vector is then represented by the Gaussian mixture model. The SBPR and DBPR are subsequently obtained with the help of the GMR and then mapped back to SBP and DBP values that are more accurate than those obtained with the conventional MAA method. [ABSTRACT FROM PUBLISHER]

Background: Current oscillometric blood pressure measurement devices generally provide only singlepoint estimates for systolic and diastolic blood pressures and rarely provide confidence ranges for these estimates. A novel methodology to obtain confidence intervals (CIs) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) estimates from a single oscillometric blood pressure measurement is presented. Methods: The proposed methodology utilizes the multiple regression technique to fuse optimally a set of SBP and DBP estimates obtained through different algorithms. However, the set of SBP and DBP estimates is a small number to determine the CI of each individual subject. To address this issue, the weighted bootstrap approach based on the multiple regression technique was used to generate a pseudo sample set for the SBP and the DBP. In this paper, the multiple regression technique can estimate the best-fitting surface of an efficient function that relates the input sample set as an independent vector to the auscultatory nurse measurement as a dependent vector to estimate regression coefficients. Consequently, the coefficients are assigned to an eight-sample set obtained from the fusion of different algorithms as optimally weighted parameters. CIs are also estimated using the conventional methods on the set of fused SBP and DBP estimates for comparison purposes. Results: The proposed method was applied to an experimental dataset of 85 patients. The results indicated that the proposed approach provides better blood pressure estimates than the existing algorithms and, in addition, is able to provide CIs for a single measurement. Conclusions: The CIs derived from the proposed scheme are much smaller than those calculated by conventional methods except for the pseudo maximum amplitude-envelope algorithm for both the SBP and the DBP, probably because of the decrease in the standard deviation through the increase in the pseudo measurements using the weighted bootstrap method for each subject. The proposed methodology is likely the only one currently available that can provide CIs for single-sample blood pressure measurements. [ABSTRACT FROM AUTHOR]