Electrocardiogram-assisted blood pressure estimation

Contact pressure-guided wearable dual-channel bioimpedance device for continuous hemodynamic monitoring

Wearable devices for continuous monitoring of arterial pulse waves have the potential to improve the diagnosis, prognosis, and management of cardiovascular diseases. These pulse wave signals are often affected by the contact pressure between the wearable device and the skin, limiting the accuracy and reliability of hemodynamic parameter quantification. Here, we report a continuous hemodynamic monitoring device that enables the simultaneous recording of dual-channel bioimpedance and quantification of pulse wave velocity (PWV) used to calculate blood pressure (BP). Our investigations demonstrate the effect of contact pressure on bioimpedance and PWV. The pulsatile bioimpedance magnitude reached its maximum when the contact pressure approximated the mean arterial pressure of the subject. We employed PWV to continuously quantify BP while maintaining comfortable contact pressure for prolonged wear. The mean absolute error and standard deviation of the error compared to the reference value were determined to be 0.1 ± 3.3 mmHg for systolic BP, 1.3 ± 3.7 mmHg for diastolic BP, and -0.4 ± 3.0 mmHg for mean arterial pressure when measurements were conducted in the lying down position. This research demonstrates the potential of wearable dual-bioimpedance sensors with contact pressure guidance for reliable and continuous hemodynamic monitoring.

Hybrid Deep Morpho-Temporal Framework for Oscillometric Blood Pressure Measurement

Oscillometric blood pressure (BP) measurement devices are widely utilized as the primary automated BP measurement tools in non-specialist environments. However, their accuracy and reliability vary under different settings and for different age groups and health conditions. An essential constraint of current oscillometric BP measurement devices is their analysis algorithms' incapacity to capture the BP information encoded in the pattern of recorded oscillometric pulses to its fullest extent. In this article, we propose a new 2D oscillometric data representation that enables a full characterization of arterial system and empowers the application of deep learning to extract the most informative features correlated with BP. A hybrid convolutional-recurrent neural network was developed to capture the oscillometric pulses morphological information as well as their temporal evolution over the cuff deflation period from the 2D structure, and estimate BP. The performance of the proposed method was verified on three oscillometric databases collected from the wrist and upper arms of 245 individuals. It was found that it achieves a mean error and a standard deviation of error of as low as 0.08 mmHg and 2.4 mmHg in the estimation of systolic BP, and 0.04 mmHg and 2.2 mmHg in the estimation of diastolic BP, respectively. Our proposed method outperformed the state-of-the-art techniques and satisfied the current international standards for BP monitors by a wide margin. The proposed method shows promise toward robust and objective BP estimation in a variety of patients and monitoring situations.

Deep-learning-based blood pressure estimation using multi channel photoplethysmogram and finger pressure with attention mechanism

Recently, several studies have proposed methods for measuring cuffless blood pressure (BP) using finger photoplethysmogram (PPG) signals. This study presents a new BP estimation system that measures PPG signals under progressive finger pressure, making the system relatively robust to errors caused by finger position when using the cuffless oscillometric method. To reduce errors caused by finger position, we developed a sensor that can simultaneously measure multi-channel PPG and force signals in a wide field of view (FOV). We propose a deep-learning-based algorithm that can learn to focus on the optimal PPG channel from multi channel PPG using an attention mechanism. The errors (ME ± STD) of the proposed multi channel system were 0.43±9.35 mmHg and 0.21 ± 7.72 mmHg for SBP and DBP, respectively. Through extensive experiments, we found a significant performance difference depending on the location of the PPG measurement in the BP estimation system using finger pressure.

Learning and non-learning algorithms for cuffless blood pressure measurement: a review

The machine learning approach has gained a significant attention in the healthcare sector because of the prospect of developing new techniques for medical devices and handling the critical database of chronic diseases. The learning approach has potential to analyze complex medical data, disease diagnosis, and patient monitoring system, and to monitor e-health record. Non-invasive cuffless blood pressure (CLBP) measurement secured a significant position in the patient monitoring system. From a few recent decades, the importance of cuffless technology has been perceived towards continuous monitoring of blood pressure (BP) and supplementary efforts have been made towards its continuous monitoring. However, the optimal method that measures BP unambiguously and continuously has not yet emerged along with issues like calibration time, accuracy and long-term estimation of BP with miniaturizing hardware. The present study provides an insight into several learning algorithms along with their feature selection models. Various challenges and future improvements towards the current state of machine learning in healthcare industries are discussed in the present review. The bottom line of this study is to provide a comprehensive perspective of the machine learning approach of CLBP for the generation of highly precise predictive models for continuous BP measurement.

A novel method for continuous blood pressure estimation based on a single-channel photoplethysmogram signal

OBJECTIVE

Currently, continuous blood pressure (BP) measurements are mostly based on multi-sensor combinations and datasets with limited BP ranges. Besides, most BP-related features derive from the photoplethysmogram (PPG) signal. The mechanism of PPG signal formation is not considered. We aimed to design a noninvasive and continuous method for estimation of BP using a single PPG sensor, which takes the mechanism of PPG signal formation into account.

APPROACH

We prepared a dataset containing PPG signals for 294 patients from three public databases for constructing the BP estimation model. The features used in the model consisted of two types: novel features based on a multi-Gaussian model and existing features. The multi-Gaussian model fitted the different components (i.e. the main wave, the dicrotic wave and the tidal wave) of the PPG signal. Ensemble machine learning algorithms were applied to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP). When partitioning the dataset, there was an overlap between the training set and the testing set.

MAIN RESULTS

Datasets with a wide-range of SBP and DBP values (SBP ranging from 74 to 229 mmHg and DBP ranging from 26 to 141 mmHg) were used to evaluate our method. The mean and standard deviation of error for SBP and DBP estimations were -0.21 ± 5.21 mmHg and -0.19 ± 3.37 mmHg, respectively. The model performance fully met the Association for the Advancement of Medical Instrumentation standard and was grade 'A' on the British Hypertension Society standard.

SIGNIFICANCE

The multi-Gaussian model could be used to estimate BP, and our method was able to track a wide range of BP accurately. In addition our method is based on a single PPG sensor, making it very convenient.

External temperature sensor assisted a new low power photoplethysmography readout system for accurate measurement of the bio-signs

This study presents an external temperature sensor assisted a new low power, time-interleave, wide dynamic range, and low DC drift photoplethysmography (PPG) signal acquisition system to obtain the accurate measurement of various bio signs in real-time. The designed chip incorporates a 2-bit control programmable transimpedance amplifier (TIA), a high order filter, a 3:8 programmable gain amplifier (PGA) and 2 × 2 organic light-emitting diode (OLED) driver. Temperature sensor is used herein to compensate the adverse effect of low-skin-temperature on the PPG signal quality. The analog front-end circuit is implemented in the integrated chip with chip area of 2008 μm × 1377 μm and fabricated via TSMC T18 process. With the standard 1.8 V, the experimental result shows that the measured current sensing range is 20 nA-100 uA. The measured dynamic range of the designed readout circuit is 80 dB. The estimated signal to noise ratio is 60 dB@1 uA, and the measured input referred noise is 60.2 pA/Hz^½. The total power consumption of the designed chip is 31.32 µW (readout) + 1.62 mW (OLED driver@100% duty cycle). The non-invasive PPG sensor is applied to the wrist artery of the 40 healthy subjects for sensing the pulsation of the blood vessel. The experimental results show that for every 1 °C decrease in mean ambient temperature tends to 0.06 beats/min, 0.125 mmHg and 0.063 mmHg increase in hear rate (HR), systolic (SBP) and diastolic (DBP), respectively. Similarly, for every 1 °C increase in mean ambient temperature tends to 0.13 beats/min, 0.601 mmHg and 0.121 mmHg increase in HR, SBP and DBP, respectively. The measured accuracy and standard error for the HR estimation are 96%, and - 0.022 ± 2.589 beats/minute, respectively. The oxygen stauration (S_pO₂) measurement results shows that the mean absolute percentage error is less than 5%. The resultant errors for the SBP and DBP measurement are - 0.318 ± 5.19 mmHg and - 0.5 ± 1.91 mmHg, respectively.

Artificial Intelligence Based Blood Pressure Estimation From Auscultatory and Oscillometric Waveforms: A Methodological Review

Cardiovascular disease is the number one cause of death globally, with elevated blood pressure (BP) being the single largest risk factor. Hence, BP is an important physiological parameter used as an indicator of cardiovascular health. The use of automated non-invasive blood pressure (NIBP) measurement devices is growing, as measurements can be taken by patients at home. While the oscillometric technique is most common, some automated NIBP measurement methods have been developed based on the auscultatory technique. By utilizing (relatively) large BP data annotated by experts, models can be trained using machine learning and statistical concepts to develop novel NIBP estimation algorithms. Amongst artificial intelligence (AI) techniques, deep learning has received increasing attention in different fields due to its strength in data classification and feature extraction problems. This paper reviews AI-based BP estimation methods with a focus on recent advances in deep learning-based approaches within the field. Various architectures and methodologies proposed todate are discussed to clarify their strengths and weaknesses. Based on the literature reviewed, deep learning brings plausible benefits to the field of BP estimation. We also discuss some limitations which can hinder the widespread adoption of deep learning in the field and suggest frameworks to overcome these challenges.

Continuous blood pressure measurement from one-channel electrocardiogram signal using deep-learning techniques

Continuous blood pressure (BP) measurement is crucial for reliable and timely hypertension detection. State-of-the-art continuous BP measurement methods based on pulse transit time or multiple parameters require simultaneous electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Compared with PPG signals, ECG signals are easy to collect using wearable devices. This study examined a novel continuous BP estimation approach using one-channel ECG signals for unobtrusive BP monitoring. A BP model is developed based on the fusion of a residual network and long short-term memory to obtain the spatial-temporal information of ECG signals. The public multiparameter intelligent monitoring waveform database, which contains ECG, PPG, and invasive BP data of patients in intensive care units, is used to develop and verify the model. Experimental results demonstrated that the proposed approach exhibited an estimation error of 0.07 ± 7.77 mmHg for mean arterial pressure (MAP) and 0.01 ± 6.29 for diastolic BP (DBP), which comply with the Association for the Advancement of Medical Instrumentation standard. According to the British Hypertension Society standards, the results achieved grade A for MAP and DBP estimation and grade B for systolic BP (SBP) estimation. Furthermore, we verified the model with an independent dataset for arrhythmia patients. The experimental results exhibited an estimation error of -0.22 ± 5.82 mmHg, -0.57 ± 4.39 mmHg, and -0.75 ± 5.62 mmHg for SBP, MAP, and DBP measurements, respectively. These results indicate the feasibility of estimating BP by using a one-channel ECG signal, thus enabling continuous BP measurement for ubiquitous health care applications.

Non-thrust cervical manipulations reduce short-term pain and decrease systolic blood pressure during intervention in mechanical neck pain: a randomized clinical trial

Objectives: To evaluate the association of resting blood pressure with pain response and evaluate the cardiovascular effects of anterior-to-posterior [AP] versus lateral [LAT] techniques of cervical spine non-thrust manipulation [NTM].Methods: Forty-three (23 females) participants with non-chronic neck pain (mean age 29.00 ± SD 9.09 years) randomly received AP or LAT NTM to the cervical spine. Blood pressure and heart rate were measured before, during, and after the intervention. Disability and pain were measured pre- and post-intervention.Results: Resting systolic blood pressure (SBP) was significantly associated with average pain reduction two days later on univariate and multivariate analyses (coefficients -0.029 ± SD 0.013, p = 0.036; -0.026 ± 0.012, p = 0.032).No significant differences existed between AP and LAT NTM groups in disability, pain reduction, and cardiovascular variables. The decrease in 'worst neck pain' rating 2-days post-intervention was clinically significant within the AP (mean -2.43 ± SD 2.66) group. Mixed-effect model ANOVA revealed a significant change in SBP over time (estimate -1.94 ± SD 0.70, p = 0.007).Discussion: This spinal NTM study was the first to relate resting SBP with short-term pain reduction, demonstrating SBP-related hypoalgesia. In normotensive individuals with unilateral non-chronic neck pain, each 10 mmHg higher resting SBP was associated with a 0.29-unit decrease in average pain at follow-up when holding baseline pain constant.AP and LAT NTM equally reduced short-term pain and decreased SBP during-intervention, suggesting SBP-sympathoinhibition. These techniques have previously been shown to be sympatho-excitatory when delivered under different dosage parameters. SBP's mediating and moderating role should be investigated."Level of Evidence: 1b."

A New Wearable Device for Blood Pressure Estimation Using Photoplethysmogram

We present a novel smartwatch, CareUp®, for estimating the Blood Pressure (BP) in real time. It consists of two pulse oximeters: one placed on the back and one on the front of the device. Placing the index finger on the front oximeter starts the acquisition of two photoplethysmograms (PPG); the signals are then filtered and cross-correlated to obtain a Time Delay between them, called Pulse Transit Time (PTT). The Heart Rate (HR) (estimated from the finger PPG) and the PTT are then input in a linear model to give an estimation of the Systolic and Diastolic BP. The performance of the smartwatch in measuring BP have been validated in the Institut Coeur Paris Centre Turin (ICPC), using a sphygmomanometer, on 44 subjects. During the validation, the measures of the CareUp® were compared to those of two oscillometry-based devices already available on the market: Thuasne® and Magnien®. The results showed an accuracy comparable to the oscillometry-based devices and they almost agreed with the American Association for the Advancement of Medical Instrumentation standard for non-automated sphygmomanometers. The integration of the BP estimation algorithm in the smartwatch makes the CareUp® an easy-to-use, wearable device for monitoring the BP in real time.

A coefficient-free and continuous blood pressure estimation method based on the arterial lumen area model

Oscillometry is the most popular technique for automatic blood pressure (BP) estimation. This method relies on recording the cuff deflation curve (CDC) from a high suprasystolic BP (SSBP) to a low subdiastolic BP (SDBP) and is very sensitive to noise caused by breathing, motion artifacts, muscle contraction and the environment. We developed a unified BP estimation method involving two integrated sub-procedures based on the arterial lumen area (ALA) model and applied it for continuous BP measurement. Our proposed method is coefficient free and continuous. No empirical coefficients are applied, and the CDC varies within a low-pressure range of 40-80 mm Hg, which can be sustainably imposed on our wrists. The first sub-procedure estimates the mean arterial pressure (MAP) and arterial compliance parameter b under a complete CDC period. In the second sub-procedure, the systolic BP (SBP) and diastolic BP (DBP) are estimated based on the dynamic-updated oscillometric waveform. We applied this method on 300 continuously oscillometric traces recorded from 20 male and female healthy subjects aged 20-60 years. The validated results were compared with those from a double stethoscope check. We observed mean absolute errors of 4.77 and 4.24 mm Hg in estimating the SBP and DBP, respectively.

A Chair-Based Unconstrained/Nonintrusive Cuffless Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram

Hypertension is a well-known chronic disease that causes complications such as cardiovascular diseases or stroke, and thus needs to be continuously managed by using a simple system for measuring blood pressure. The existing method for measuring blood pressure uses a wrapping cuff, which makes measuring difficult for patients. To address this problem, cuffless blood pressure measurement methods that detect the peak pressure via signals measured using photoplethysmogram (PPG) and electrocardiogram (ECG) sensors and use it to calculate the pulse transit time (PTT) or pulse wave velocity (PWV) have been studied. However, a drawback of these methods is that a user must be able to recognize and establish contact with the sensor. Furthermore, the peak of the PPG or ECG cannot be detected if the signal quality drops, leading to a decrease in accuracy. In this study, a chair-type system that can monitor blood pressure using polyvinylidene fluoride (PVDF) films in a nonintrusive manner to users was developed. The proposed method also uses instantaneous phase difference (IPD) instead of PTT as the feature value for estimating blood pressure. Experiments were conducted using a blood pressure estimation model created via an artificial neural network (ANN), which showed that IPD could estimate more accurate readings of blood pressure compared to PTT, thus demonstrating the possibility of a nonintrusive blood pressure monitoring system.

Continuous Blood Pressure Monitoring as a Basis for Ambient Assisted Living (AAL) - Review of Methodologies and Devices

Blood pressure (BP) is a bio-physiological signal that can provide very useful information regarding human's general health. High or low blood pressure or its rapid fluctuations can be associated to various diseases or conditions. Nowadays, high blood pressure is considered to be an important health risk factor and major cause of various health problems worldwide. High blood pressure may precede serious heart diseases, stroke and kidney failure. Accurate blood pressure measurement and monitoring plays fundamental role in diagnosis, prevention and treatment of these diseases. Blood pressure is usually measured in the hospitals, as a part of a standard medical routine. However, there is an increasing demand for methodologies, systems as well as accurate and unobtrusive devices that will permit continuous blood pressure measurement and monitoring for a wide variety of patients, allowing them to perform their daily activities without any disturbance. Technological advancements in the last decade have created opportunities for using various devices as a part of ambient assisted living for improving quality of life for people in their natural environment. The main goal of this paper is to provide a comprehensive review of various methodologies for continuous cuff-less blood pressure measurement, as well as to evidence recently developed devices and systems for continuous blood pressure measurement that can be used in ambient assisted living applications.

NOVEL METHOD FOR ASSESSING ARTERIAL STIFFNESS BASED ON OSCILLOMETRIC BLOOD PRESSURE MEASUREMENT

Arterial stiffness is a major contributor to cardiovascular diseases. The aim of this study is to explore the physiological significance of the brachial mechanical parameters, which could be estimated from oscillometric blood pressure (BP) measurement, and investigate on the potential of these arterial parameters as an index of arterial stiffness. The mechanical characteristics of brachial artery were modeled based on the collapsible tube theory, which includes two important parameters to describe the compliance of brachial artery. After the model validation, the arterial parameters were estimated from the measured oscillometric envelope of 56 subjects by solving an inverse problem. The physiological significance of these parameters was explored by analyzing their association with pulse wave velocity (PWV) as well as with the BP. Arterial compliance parameters were successfully estimated from the envelope of the oscillometric pulse wave in the BP measurement. The parameters were found to be linearly associated with age, PWV, and BP. These results suggest that our method may provide a potential approach to assess arterial compliance based on oscillometric measurement of BP.

Monitoring the Relative Blood Pressure Using a Hydraulic Bed Sensor System

We propose a nonwearable hydraulic bed sensor system that is placed underneath the mattress to estimate the relative systolic blood pressure of a subject, which only differs from the actual blood pressure by a scaling and an offset factor. Two types of features are proposed to obtain the relative blood pressure, one based on the strength and the other on the morphology of the bed sensor ballistocardiogram pulses. The relative blood pressure is related to the actual by a scale and an offset factor that can be obtained through calibration. The proposed system is able to extract the relative blood pressure more accurately with a less sophisticated sensor system compared to those from the literature. We tested the system using a dataset collected from 48 subjects right after active exercises. Comparison with the ground truth obtained from the blood pressure cuff validates the promising performance of the proposed system, where the mean correlation between the estimate and the ground truth is near to 90% for the strength feature and 83% for the morphology feature.

Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques

BACKGROUND

Blood pressure (BP) measurements have been used widely in clinical and private environments. Recently, the use of ECG monitors has proliferated; however, they are not enabled with BP estimation. We have developed a method for BP estimation using only electrocardiogram (ECG) signals.

METHODS

Raw ECG data are filtered and segmented, and, following this, a complexity analysis is performed for feature extraction. Then, a machine-learning method is applied, combining a stacking-based classification module and a regression module for building systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP) predictive models. In addition, the method allows a probability distribution-based calibration to adapt the models to a particular user.

RESULTS

Using ECG recordings from 51 different subjects, 3129 30-s ECG segments are constructed, and seven features are extracted. Using a train-validation-test evaluation, the method achieves a mean absolute error (MAE) of 8.64 mmHg for SBP, 18.20 mmHg for DBP, and 13.52 mmHg for the MAP prediction. When models are calibrated, the MAE decreases to 7.72 mmHg for SBP, 9.45 mmHg for DBP and 8.13 mmHg for MAP.

CONCLUSION

The experimental results indicate that, when a probability distribution-based calibration is used, the proposed method can achieve results close to those of a certified medical device for BP estimation.

Systolic peak foot-to-apex time interval, a novel oscillometric technique for systolic blood pressure measurement

Background:

Noninvasive blood pressure (BP) measurement is essential for the study of human physiology but automatic oscillometric devices only estimate SBP and DBP using various, undisclosed algorithms, precluding standardization and interchangeability. We propose a novel approach by tracking, during pneumatic cuff deflation, the time interval from the foot to the apex of the systolic peak of the oscillometric signal, which reaches a maximum concomitant with the first Korotkoff sound.

Method:

In 145 study participants and patients (group 1), we measured the systolic brachial artery blood pressure by Korotkoff sound recording, conventional oscillometry, and our fully automated systolic peak foot-to-apex time interval (SFATI) technique. In 35 other patients (group 2), we compared SFATI with intra-arterial measurement.

Results:

In group 1, the concordance correlation coefficient was 0.989 and 0.984 between SFATI and Korotkoff sounds, 0.884 and 0.917 between oscillometry and Korotkoff sounds, and 0.882 and 0.919 between SFATI and oscillometry, respectively, on the left and right arm. In group 2, it was 0.72 between SFATI and intra-arterial measurement, 0.67 between oscillometry and intra-arterial measurement, and 0.92 between SFATI and Korotkoff sounds. In 40 study participants, the reproducibility study yielded a concordance coefficient of 0.95 for SFATI and 0.94 for Korotkoff sounds.

Conclusion:

SFATI BP measurement shows an excellent concordance with the auscultatory technique, offering a major improvement over current oscillometric techniques and allowing standardization.

An integrated blood pressure measurement system for suppression of motion artifacts

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.

The immediate cardiovascular response to joint mobilization of the neck - A randomized, placebo-controlled trial in pain-free adults

BACKGROUND

Some normotensive patients can have a spike in resting systolic blood pressure (SBP) in response to acute neck pain. Applying the typical dosage of mobilization may potentially result in a sympatho-excitatory response, further increasing resting SBP. Therefore, there is a need to explore other dosage regimens that could result in a decrease in SBP.

OBJECTIVES

To compare the blood pressure (BP) and heart rate (HR) response of pain-free, normotensive adults when receiving unilateral posterior-to-anterior mobilization (PA) applied to the neck versus its corresponding placebo (PA-P).

STUDY DESIGN

Double-Blind, Randomized Clinical Trial.

METHODS

44 (18 females) healthy, pain-free participants (mean age, 23.8 ± 3.04 years) were randomly allocated to 1 of 2 groups. Group 1 received a PA-P in which light touch was applied to the right 6th cervical vertebra. Group 2 received a PA to the same location. BP and HR were measured prior to, during, and after the application of PA or PA-P. A mixed-effect model of repeated measure analysis was used for statistical analysis.

RESULTS

During-intervention, the PA group had a significant reduction in SBP, while the placebo group had an increase in SBP. The change in SBP during-intervention was significantly different between the PA and the placebo group (p-value = 0.003). There were no significant between-group differences found for HR and diastolic BP (DBP). The overall group-by-time interaction was statistically significant for SBP (p-value = 0.01).

CONCLUSIONS

When compared to placebo, the dosage of applied PA resulted in a small, short-lived drop in SBP not exceeding the minimal detectable change. Trial registered at Germanctr.de (DRKS00005095).

Bayesian fusion algorithm for improved oscillometric blood pressure estimation

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.

Characteristic Ratio-Independent Arterial Stiffness-Based Blood Pressure Estimation

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.

Cuffless Blood Pressure Estimation Algorithms for Continuous Health-Care Monitoring

GOAL

Continuous blood pressure (BP) monitoring can provide invaluable information about individuals' health conditions. However, BP is conventionally measured using inconvenient cuff-based instruments, which prevents continuous BP monitoring. This paper presents an efficient algorithm, based on the pulse arrival time (PAT), for the continuous and cuffless estimation of the systolic BP, diastolic blood pressure (DBP), and mean arterial pressure (MAP) values.

METHODS

The proposed framework estimates the BP values through processing vital signals and extracting two types of features, which are based on either physiological parameters or whole-based representation of vital signals. Finally, the regression algorithms are employed for the BP estimation. Although the proposed algorithm works reliably without any need for calibration, an optional calibration procedure is also suggested, which can improve the system's accuracy even further.

RESULTS

The proposed method is evaluated on about a thousand subjects using the Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) standards. The method complies with the AAMI standard in the estimation of DBP and MAP values. Regarding the BHS protocol, the results achieve grade A for the estimation of DBP and grade B for the estimation of MAP.

CONCLUSION

We conclude that by using the PAT in combination with informative features from the vital signals, the BP can be accurately and reliably estimated in a noninvasive fashion.

SIGNIFICANCE

The results indicate that the proposed algorithm for the cuffless estimation of the BP can potentially enable mobile health-care gadgets to monitor the BP continuously.

An affordable cuff-less blood pressure estimation solution

This paper presents a cuff-less hypertension pre-screening device that non-invasively monitors the Blood Pressure (BP) and Heart Rate (HR) continuously. The proposed device simultaneously records two clinically significant and highly correlated biomedical signals, viz., Electrocardiogram (ECG) and Photoplethysmogram (PPG). The device provides a common data acquisition platform that can interface with PC/laptop, Smart phone/tablet and Raspberry-pi etc. The hardware stores and processes the recorded ECG and PPG in order to extract the real-time BP and HR using kernel regression approach. The BP and HR estimation error is measured in terms of normalized mean square error, Error Standard Deviation (ESD) and Mean Absolute Error (MAE), with respect to a clinically proven digital BP monitor (OMRON HBP1300). The computed error falls under the maximum standard allowable error mentioned by Association for the Advancement of Medical Instrumentation; MAE <; 5 mmHg and ESD <; 8mmHg. The results are validated using two-tailed dependent sample t-test also. The proposed device is a portable low-cost home and clinic bases solution for continuous health monitoring.

Estimated confidence interval from single blood pressure measurement based on algorithmic fusion

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.

A blood pressure monitoring method for stroke management

Blood pressure is one important risk factor for stroke prognosis. Therefore, continuous monitoring of blood pressure is crucial for preventing and predicting stroke. However, current blood pressure devices are mainly air-cuff based, which only can provide measurements intermittently. This study proposed a new blood pressure estimation method based on the pulse transit time to realize continuous monitoring. The proposed method integrated a linear model with a compensation algorithm. A calibration method was further developed to guarantee that the model was personalized for individuals. Variation and variability of pulse transit time were introduced to construct the compensation algorithm in the model. The proposed method was validated by the data collected from 30 healthy subjects, aged from 23 to 25 years old. By comparing the estimated value to the measurement from an oscillometry, the result showed that the mean error of the estimated blood pressure was −0.2 ± 2.4 mmHg and 0.5 ± 3.9 mmHg for systolic and diastolic blood pressure, respectively. In addition, the estimation performance of the proposed model is better than the linear model, especially for the diastolic blood pressure. The results indicate that the proposed method has promising potential to realize continuous blood pressure measurement.

Blood pressure and heart rate response to posteriorly directed pressure applied to the cervical spine in young, pain-free individuals: a randomized, repeated-measures, double-blind, placebo-controlled study

STUDY DESIGN

Randomized clinical trial. Objectives To compare the blood pressure (BP) and heart rate (HR) response of healthy volunteers to posteriorly directed (anterior-to-posterior [AP]) pressure applied to the cervical spine versus placebo.

BACKGROUND

Manual therapists employ cervical spine AP mobilizations for various cervical-shoulder pain conditions. However, there is a paucity of literature describing the procedure, cardiovascular response, and safety profile.

METHODS

Thirty-nine (25 female) healthy participants (mean ± SD age, 24.7 ± 1.9 years) were randomly assigned to 1 of 2 groups. Group 1 received a placebo, consisting of light touch applied to the right C6 costal process. Group 2 received AP pressure at the same location. Blood pressure and HR were measured prior to, during, and after the application of AP pressure. One-way analysis of variance and paired-difference statistics were used for data analysis.

RESULTS

There was no statistically significant difference between groups for mean systolic BP, mean diastolic BP, and mean HR (P >.05) for all time points. Within-group comparisons indicated statistically significant differences between baseline and post-AP pressure HR (-2.8 bpm; 95% confidence interval: -4.6, -1.1) and between baseline and post-AP pressure systolic BP (-2.4 mmHg; 95% confidence interval: -3.7, -1.0) in the AP group, and between baseline and postplacebo systolic BP (-2.6 mmHg; 95% confidence interval: -4.2, -1.0) in the placebo group. No participants reported any adverse reactions or side effects within 24 hours of testing.

CONCLUSION

AP pressure caused a statistically significant physiologic response that resulted in a minor drop in HR (without causing asystole or vasodepression) after the procedure, whereas this cardiovascular change did not occur for those in the placebo group. Within both groups, there was a small but statistically significant reduction in systolic BP following the procedure.

Utility of a nonlinear joint dynamical framework to model a pair of coupled cardiovascular signals

We have recently proposed a correlated model to provide a Gaussian mixture representation of the cardiovascular signals, with promising results in identifying rhythm disturbances. The approach provides a transformation of the data into a set of integrable Gaussians distributed over time. Looking into the model from a new joint modeling perspective, it is capable of assembling a filtered estimation, and can be used to derive temporal information of the waveforms. In this paper, we present a step-by-step derivation of the joint model putting correlation assumptions together to conclude a minimal joint description for a pair of ECG-ABP signals. We then probe novel applications of this model, including Kalman filter based denoising and fiducial point detection. In particular, we use the joint model for denoising and employ the denoised signals for pulse transit time (PTT) estimation. We analyzed more than 70 h of data from 76 patients from the MIMIC database to illustrate the accuracy of the algorithm. We have found that this method can be effectively used for robust joint ECG-ABP noise suppression, with mean signal-to-noise ratio (SNR) improvement up to 23.2 (12.0) dB and weighted diagnostic distortion measures as low as 2.1 (3.3)% for artificial (real) noises, respectively. In addition, we have estimated the error distributions for QT interval, systolic and diastolic blood pressure before and after filtering to demonstrate the maximal preservation of morphological features (ΔQT: mean ± std = 2.2 ± 6.1 ms; ΔSBP: mean ± std = 2.3 ± 1.9 mmHg; ΔDBP: mean ± std = 1.9 ± 1.4 mmHg). Finally, we have been able to present a systematic approach for robust PTT estimation (r = 0.98, p <; 0.001, mean ± std of error = -0.26 ± 2.93 ms). These findings may have important implications for reliable monitoring and estimation of clinically important features in clinical settings. In conclusion, the proposed framework opens the door to the possibility of deploying a hybrid system that integrates these algorithmic approaches for index estimation and filtering scenarios with high output SNRs and low distortion.

Compressive sensing of electrocardiogram signals by promoting sparsity on the second-order difference and by using dictionary learning

A new algorithm for the reconstruction of electrocardiogram (ECG) signals and a dictionary learning algorithm for the enhancement of its reconstruction performance for a class of signals are proposed. The signal reconstruction algorithm is based on minimizing the lp pseudo-norm of the second-order difference, called as the lp(2d) pseudo-norm, of the signal. The optimization involved is carried out using a sequential conjugate-gradient algorithm. The dictionary learning algorithm uses an iterative procedure wherein a signal reconstruction and a dictionary update steps are repeated until a convergence criterion is satisfied. The signal reconstruction step is implemented by using the proposed signal reconstruction algorithm and the dictionary update step is implemented by using the linear least-squares method. Extensive simulation results demonstrate that the proposed algorithm yields improved reconstruction performance for temporally correlated ECG signals relative to the state-of-the-art lp(1d)-regularized least-squares and Bayesian learning based algorithms. Also for a known class of signals, the reconstruction performance of the proposed algorithm can be improved by applying it in conjunction with a dictionary obtained using the proposed dictionary learning algorithm.

Coefficient-free blood pressure estimation based on pulse transit time-cuff pressure dependence

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.