Continuous Blood Pressure Measurement Research Articles

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.

Development of a Continuous Blood Pressure Measurement and Cardiovascular Multi-Indicator Platform for Asian Populations by Using a Back Propagation Neural Network and Dual Photoplethysmography Sensor Signal Acquisition Technology

This study proposed a measurement platform for continuous blood pressure estimation based on dual photoplethysmography (PPG) sensors and a back propagation neural network (BPNN) that can be used for continuous and rapid measurement of blood pressure and analysis of cardiovascular-related indicators. The proposed platform measured the signal changes in PPG and converted them into physiological indicators, such as pulse transit time (PTT), pulse wave velocity (PWV), perfusion index (PI), heart rate (HR), and pulse wave analysis (PWA); these indicators were then fed into the BPNN to calculate blood pressure. The hardware of the experiment comprised 2 PPG components (i.e., Raspberry Pi 3 Model B and analog-to-digital converter [MCP3008]), which were connected using a serial peripheral interface. The BPNN algorithm converted the stable dual PPG signals acquired from the strictly standardized experimental process into various physiological indicators as input parameters and finally obtained the systolic blood pressure (SBP) and diastolic blood pressure (DBP). To increase the robustness of the BPNN model, this study input data of 100 Asian participants into the training database, including those with and without cardiovascular disease, each with a proportion of approximately 50%. The experimental results revealed that the mean and standard deviation of SBP were2.23±2.24 mmHg, with a mean squared error of 3.15 mmHg. The mean and standard deviation of DBP was3.5±3.53 mmHg, with a mean squared error of 4.96 mmHg. The proposed real-time blood pressure measurement system exhibited a mean accuracy of 98.22% and 95.58% for SBP and DBP, respectively.

ClearSight™ finger cuff versus invasive arterial pressure measurement in patients with body mass index above 45 kg/m2

BackgroundMeasuring blood pressure in patients with obesity is challenging. The ClearSight™ finger cuff (FC) uses the vascular unloading technique to provide continuous non-invasive blood pressure measurements. We aimed to test the agreement of the FC with invasive radial arterial monitoring (INV) in patients with obesity.MethodsParticipants had a body mass index (BMI) ≥45 kg/m2 and underwent laparoscopic bariatric surgery. FC and INV measurements were obtained simultaneously every 5 min on each patient, following induction of anesthesia. Agreement over time was assessed using modified Bland-Altman plots and error grid analysis permitted clinical interpretation of the results. Four-quadrant plots allowed assessment of concordance in blood pressure changes.ResultsThe 30 participants had a median (IQR) BMI of 50.2 kg/m2 (IQR 48.3–55.3). The observed bias (SD, 95% limits of agreement) for systolic blood pressure (SBP) was 14.3 mmHg (14.1, -13.4 – 42.0), 5.2 mmHg (10.9, -16.0 – 26.5) for mean arterial pressure (MAP) and 2.6 mmHg (10.8, -18.6 – 23.8) for diastolic blood pressure (DBP). Error grid analysis showed that the proportion of readings in risk zones A-E were 90.8, 6.5, 2.7, 0 and 0% for SBP and 91.4, 4.3, 4.3, 0 and 0% for MAP, respectively. Discordance occurred in ≤8% of pairs for consecutive change in SBP, MAP and DBP.ConclusionsThe vascular unloading technique was not adequately in agreement with radial arterial monitoring. Evaluation in a larger sample is required before recommending this technique for intraoperative monitoring of patients with BMI ≥45 kg/m2.

With More than 2000 Times Increased Plasma Concentration, Interleukin‐17A Does Not Increase Arterial Blood Pressure in Mice

Background T-lymphocytes are important for angiotensin II (ANGII)-hypertension in mice. T-helper 17 cells produce the pro-inflammatory cytokine interleukin-17 (IL-17). Plasma IL-17A is increased in both hypertensive animals and humans. IL-17A KO mice are protected against ANGII-induced hypertension. Anti-IL-17 treatment of hypertensive mice decreases blood pressure. There is a lack of data directly documenting a hypertensive effect of IL-17A. The objective of this study was to investigate the direct effect of IL-17A on blood pressure in mice. It was hypothesised that continuous i.v. infusion of IL-17A in mice will increase blood pressure and amplify ANGII-induced hypertension. Methods A model of continuous IL-17A i.v. infusion and blood pressure measurement was used by implantation of indwelling catheters in the femoral vein and artery. Femoral artery catheter was used for continuous blood pressure recording every 5 min and to collect undisturbed arterial blood in live non-stressed mice. After a 5-day recovery time upon operation, baseline blood pressure was measured for 3 days. Then, mice received IL-17A at 0.1, 1.0 and 10 ug/day for 2, 2 and 4 days respectively. In another experiment, mice were given ANGII (60ng/kg/min) and IL-17A (1 ug/day) or saline for 9 days. In an acute bolus experiment, mice received 10, 20, 40 and 80 ug IL-17A in one bolus i.v. infusion with 10 min. interval. Continuous blood pressure measurements were obtained and EDTA blood was collected before and after infusion from all mice. Plasma IL-17A levels was assessed before and after infusion using ELISA. Results IL-17A dosis-step-up experiments with concentrations 0.1, 1.0 and 10 ug/day did not cause any blood pressure increase, instead blood pressure decreased significantly from 106 (102-112) mmHg at baseline to 104 (99-110) mmHg, 102 (98-108) mmHg and 99 (95-103) mmHg during IL-17A infusion of dose 1, 2 and 3 respectively. Plasma IL-17A levels of these mice after experiment increased significantly up to 2500 times compared to baseline levels from 0.6 (0.5-1.1) pg/ml to 2523 (0.5-8252) pg/ml. Also, IL-17A did not accentuate ANGII-induced hypertension. Instead, blood pressure was reduced in mice receiving ANGII with IL-17A compared to ANGII and saline treated mice from 136 (130-139) mmHg to 124 (121-126) mmHg. Plasma IL-17A was significantly higher in IL-17A and ANGII treated mice compared to saline and ANGII treated mice. ANGII did not elevate plasma IL-17A levels compared to baseline levels. In the dose-step-up experiment heart rate was significantly reduced from 661 (637-585) BPM at baseline to 631 (604-652) BPM at the highest dose given. Conclusion In summary, despite up to 2500 times increase in circulating IL-17A from 2 different vendors and prolonged infusion for up to 9 days, no increase in blood pressure was observed during IL-17 infusion alone, and IL-17A did not accentuate ANGII effects neither. Instead a blood pressure reducing effect was observed. These data suggest that in complex models with inflammation, IL-17A is likely to act redundantly in concert with other mediators to increase blood pressure, but IL-17A dose not seem to have any direct blood pressure increasing effects in mice despite prolonged exposure.

Direct Mineralocorticoid Receptor (MR) Activation, Independent of Increases in Blood Pressure, Drive Sex Differences in Tregs in DOCA‐salt rats

Our lab has previously shown that greater T regulatory cells (Tregs) in females attenuate deoxycorticosterone acetate salt (DOCA)-induced increases in blood pressure (BP). Tregs do not contribute to BP control in male rats with DOCA hypertension. DOCA is a mineralocorticoid model of hypertension, and mineralocorticoid receptor activation alone can significantly increase fibrosis, oxidative stress, and inflammation, independent of an effect on BP. The goal of the current study was to test the hypothesis that direct mineralocorticoid effects, independent of increases in BP, drives sex differences in Tregs in DOCA-salt rats. At 10 wks of age, male and female Sprague-Dawley rats were anesthetized, a right uni-nephrectomy was performed, and a telemeter was implanted for continuous BP measurement. After one week of recovery, all rats received a subcutaneous 21-day slow-release DOCA pellet (200 mg) with 0.9% saline water to drink. To separate the effects of MR activation and increases in BP on DOCA-salt-induced alterations in renal Tregs, rats were randomized to the following groups: 1) BP lowering drugs hydrochlorothiazide (HCTZ; 55 mg/kg/day) and reserpine (RES; 4.5 mg/kg/day; N=9-12/group) or vehicle control (DOCA-salt only) or 2) MR blocker spironolactone (15 mg/kg/day; N=6/group) or vehicle control (DOCA-salt only). After 21 days, the remaining kidney was isolated for flow cytometric analysis of Tregs (CD3+CD4+FoxP3+). Treatment with HCTZ/RES prevented DOCA-induced increases in BP in both males (179 ± 3 mmHg vs 110 ± 5 mmHg; Ptreatment<0.0001) and females (157 ± 4 mmHg vs 101 ± 5 mmHg; Ptreatment<0.0001). Preventing DOCA-induced increases in BP did not alter sex differences in renal Tregs (Table) as females maintained more Tregs than males (Psex<0.0001; Ptreatment=0.11; Pinteraction=0.38). In contrast, treatment with spironolactone did not significantly alter BP in either male (180 ± 2 vs 178 ± 2 mmHg; Ptreatment=0.73) or female DOCA rats (170 ± 6 vs 162 ± 4 mmHg; Ptreatment=0.10). However, blocking the direct effects of MR activation with spironolactone abolished sex differences in renal Tregs by increasing Tregs (Table) in males (Psex=0.07; Ptreatment<0.0001; Pinteraction=0.04). Our data suggests that direct mineralocorticoid effects, independent of BP, drives sex differences in Tregs in DOCA-salt rats. Future studies will be designed to determine the specific mechanisms by which MR activation regulates Tregs in a sex-specific manner.

24-hour ambulatory blood pressure monitoring and hypertension related risk among HIV-positive and HIV-negative individuals: cross sectional study findings from rural Uganda.

Hypertension is diagnosed and treated based on blood pressure (BP) readings obtained in the clinic setting. Positive HIV status is associated with a higher prevalence of abnormal diurnal blood pressure patterns, diagnosed with ambulatory BP monitoring rather than the conventional method of BP measurement. Little is known about ambulatory BP profiles in people living with HIV (PLHIV) in low-income countries, especially within sub-Saharan Africa. In this study, we compared 24-hour ambulatory blood pressure profiles of 140 HIV-positive individuals vs. profiles in 166 HIV negative individuals living in rural Uganda. HIV was well-controlled, with all HIV seropositive participants reporting use of anti-retroviral therapy, and approximately 123 (88%) having undetectable viral load. Most participants reported ART use duration of less than 10 years. Compared to HIV negative participants, HIV positive participants had lower median 24-hour systolic blood pressure (110.4 mmHg (IQR: 105.7, 118.7) vs 117.7 mmHg (IQR: 110.8, 129.8), p<0.001), and 24-hour diastolic blood pressure (69.2 mmHg (IQR: 65.0, 74.9) vs. 71.9 mmHg (IQR: 67.2, 78.1), p=0.004). Adjusted results showed greater percentage systolic nocturnal dipping among PLHIV compared to HIV negative individuals (difference=2.70 (IQR: 0.94, 4.47), p <0.05). Results of the adjusted Poisson regression suggested lower prevalence of 24-hour and night hypertension among HIV positives compared to HIV negative, but were not statistically significant. Our data suggest that continuous 24-hour blood pressure measurements are lower in PLHIV on ART compared to HIV negative individuals.

Cuff-less continuous blood pressure measurement based on multiple types of information fusion

Non-invasive and cuff-less real-time continuous blood pressure monitoring plays an important role in both intensive care unit (ICU) and the management of chronic diseases such as hypertension and cardiovascular disease. Although pulse wave transit time (PTT) and pulse waveform parameters (PWPs) can be extracted from photoplethysmography (PPG) and electrocardiographic (ECG) to estimate blood pressure, the value of blood pressure is also constrained by personal characteristics parameters (PCPs). For example, the blood pressure may vary with height, weight and age. In this paper, we present a cuff-less continuous blood pressure measurement method based on multiple types of information fusion. First, we recruited 186 volunteers and measured their personal data (PPG signal, ECG signal, height, age, weight, gender). Second, extract the PTT and PWPs from the PPG signal and ECG signal. Then, according to the extracted parameters, the blood pressure models of control group and experimental group were established respectively (the experimental group uses the multiple types of information fusion method to model, the control group only considers PTT and PWPs). Experiments show that, comparing with the control group, the experimental group systolic blood pressure (SBP) correlation was increased from 0.9355 to 0.9948, root mean square error (RMSE) was reduced from 5.2 mmHg to 1.5 mmHg, while the diastolic blood pressure (DBP) correlation was improved from 0.9331 to 0.9931, RMSE was reduced from 2.9 mmHg to 0.9 mmHg. Therefore, the modeling method proposed in this paper can get more accurate blood pressure values. Further promotion, to achieve the measurement with the "modeling" method, all "restriction" elements must be taken into account in order to obtain a more accurate and stable model.

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.

Cerebral perfusion pressure and autoregulation in eclampsia—a case control study

Dynamic cerebral autoregulation and cerebral perfusion pressure are altered in pregnancies complicated by preeclampsia compared with normotensive pregnancies, but the connections of dynamic cerebral autoregulation, cerebral perfusion pressure, and cerebral complications in preeclampsia remain unclear. This study aimed to assess dynamic cerebral autoregulation and cerebral perfusion pressure after delivery in women with eclampsia, in women with preeclampsia both with and without severe features, and in normotensive women. This was a prospective case control study at a large referral hospital in Cape Town, South Africa. The recruitment of participants was done at diagnosis (cases) or at admission for delivery (controls). Transcranial Doppler examinations with continuous noninvasive blood pressure measurements and end-tidal CO2 monitoring were conducted for cases and controls after delivery. Cerebral perfusion pressure and dynamic cerebral autoregulation index were calculated, and values were compared among groups. We included 16 women with eclampsia, 18 women with preeclampsia with severe features, 32 women with preeclampsia without severe features, and 21 normotensive women with uncomplicated pregnancies. Dynamic cerebral autoregulation was depressed in pregnant women with eclampsia; (autoregulation index, 3.9; interquartile range, 3.1-5.2) compared with all other groups (those with preeclampsia with severe features, autoregulation index, 5.6 [interquartile range, 4.4-6.8]; those with preeclampsia without severe features, autoregulation index, 6.8 [interquartile range, 5.1-7.4]; and normotensive controls, autoregulation index, 7.1 [interquartile range, 6.1-7.9]). Pregnant women with eclampsia had increased cerebral perfusion pressure (109.5 mm Hg; interquartile range, 91.2-130.9) compared with those with preeclampsia without severe features and those with normal blood pressure (84 mm Hg [interquartile range, 73.0-122.0] and 80.0 mm Hg [interquartile range, 67.5-92.0], respectively); furthermore, there was no difference in cerebral perfusion pressure between pregnant women with eclampsia and pregnant women with preeclampsia with severe features (109.5 mm Hg [interquartile range, 91.2-130.9] vs 96.5 mm Hg [interquartile range, 75.8-110.5]). Cerebral perfusion pressure and dynamic cerebral autoregulation are altered in eclampsia and may be important in the pathophysiological pathway and constitute a therapeutic target in the prevention of cerebral complications in preeclampsia.

A novel art of continuous noninvasive blood pressure measurement

Wearable sensors to continuously measure blood pressure and derived cardiovascular variables have the potential to revolutionize patient monitoring. Current wearable methods analyzing time components (e.g., pulse transit time) still lack clinical accuracy, whereas existing technologies for direct blood pressure measurement are too bulky. Here we present an innovative art of continuous noninvasive hemodynamic monitoring (CNAP2GO). It directly measures blood pressure by using a volume control technique and could be used for small wearable sensors integrated in a finger-ring. As a software prototype, CNAP2GO showed excellent blood pressure measurement performance in comparison with invasive reference measurements in 46 patients having surgery. The resulting pulsatile blood pressure signal carries information to derive cardiac output and other hemodynamic variables. We show that CNAP2GO can self-calibrate and be miniaturized for wearable approaches. CNAP2GO potentially constitutes the breakthrough for wearable sensors for blood pressure and flow monitoring in both ambulatory and in-hospital clinical settings.

Agreement between continuous noninvasive finger cuff-derived and invasive arterial blood pressure measurements: Effect of data sampling and data processing.

The effect of different methods for data sampling and data processing on the results of comparative statistical analyses in method comparison studies of continuous arterial blood pressure (AP) monitoring systems remains unknown. We sought to investigate the effect of different methods for data sampling and data processing on the results of statistical analyses in method comparison studies of continuous AP monitoring systems. Prospective observational study. University Medical Center Hamburg-Eppendorf, Hamburg, Germany, from April to October 2019. 49 patients scheduled for neurosurgery with AP measurement using a radial artery catheter. We assessed the agreement between continuous noninvasive finger cuff-derived (CNAP Monitor 500; CNSystems Medizintechnik, Graz, Austria) and invasive AP measurements in a prospective method comparison study in patients having neurosurgery using all beat-to-beat AP measurements (Methodall), 10-s averages (Methodavg), one 30-min period of 10-s averages (Method30), Method30 with additional offset subtraction (Method30off), and 10 30-s periods without (Methodiso) or with (Methodiso-zero) application of the zero zone. The agreement was analysed using Bland-Altman and error grid analysis. For mean AP, the mean of the differences (95% limits of agreement) was 9.0 (-12.9 to 30.9) mmHg for Methodall, 9.2 (-12.5 to 30.9) mmHg for Methodavg, 6.5 (-9.3 to 22.2) mmHg for Method30, 0.5 (-9.5 to 10.5) mmHg for Method30off, 4.9 (-6.0 to 15.7) mmHg for Methodiso, and 3.4 (-5.9 to 12.7) mmHg for Methodiso-zero. Similar trends were found for systolic and diastolic AP. Results of error grid analysis were also influenced by using different methods for data sampling and data processing. Data sampling and data processing substantially impact the results of comparative statistics in method comparison studies of continuous AP monitoring systems. Depending on the method used for data sampling and data processing, the performance of an AP test method may be considered clinically acceptable or unacceptable.

Surrogate based continuous noninvasive blood pressure measurement.

Arterial blood pressure is one of the most often measured vital parameters in clinical practice. State-of-the-art noninvasive ABP measurement technologies have noticeable limitations and are mainly based on uncomfortable techniques of complete or partial arterial occlusion by cuffs. Most commonplace devices provide only intermittent measurements, and continuous systems are bulky and difficult to apply correctly for nonprofessionals. Continuous cuffless ABP measurements are still an unmet clinical need and a topic of ongoing research, with only few commercially available devices. This paper discusses surrogate-based noninvasive blood pressure measurement techniques. It covers measurement methods of continuously and noninvasively inferring BP from surrogate signals without applying external pressures, except for reference or initialization purposes. The BP is estimated by processing signal features, so called surrogates, which are modulated by variations of BP. Discussed techniques include well-known approaches such as pulse transit time and pulse arrival time techniques, pulse wave analysis or combinations thereof. Despite a long research history, these methods have not found widespread use in clinical and ambulatory practice, in part due to technical limitations and the lack of a standardized regulatory framework. This work summarizes findings from an invited workshop of experts in the fields covering clinical expertise, engineering aspects, commercialization and standardization issues. The goal is to provide an application driven outlook, starting with clinical needs, and extending to technical actuality. It provides an outline of recommended research directions and includes a detailed overview of clinical use case scenarios for these technologies, opportunities, and limitations.

A device employing a neural network for blood pressure estimation from the oscillatory pressure pulse wave and PPG signal

Purpose Wrist-cuff oscillometric blood pressure monitors are very popular in the portable medical device market. However, its accuracy has always been controversial. In addition to the oscillatory pressure pulse wave, the finger photoplethysmography (PPG) can provide information on blood pressure changes. A blood pressure measurement system integrating the information of pressure pulse wave and the finger PPG may improve measurement accuracy. Additionally, a neural network can synthesize the information of different types of signals and approximate the complex nonlinear relationship between inputs and outputs. The purpose of this study is to verify the hypothesis that a wrist-cuff device using a neural network for blood pressure estimation from both the oscillatory pressure pulse wave and PPG signal may improve the accuracy. Design/methodology/approach A PPG sensor was integrated into a wrist blood pressure monitor, so the finger PPG and the oscillatory pressure wave could be detected at the same time during the measurement. After the peak detection, curves were fitted to the data of pressure pulse amplitude and PPG pulse amplitude versus time. A genetic algorithm-back propagation neural network was constructed. Parameters of the curves were inputted into the neural network, the outputs of which were the measurement values of blood pressure. Blood pressure measurements of 145 subjects were obtained using a mercury sphygmomanometer, the developed device with the neural network algorithm and an Omron HEM-6111 blood pressure monitor for comparison. Findings For the systolic blood pressure (SBP), the difference between the proposed device and the mercury sphygmomanometer is 0.0062 ± 2.55 mmHg (mean ± SD) and the difference between the Omron device and the mercury sphygmomanometer is 1.13 ± 9.48 mmHg. The difference in diastolic blood pressure between the mercury sphygmomanometer and the proposed device was 0.28 ± 2.99 mmHg. The difference in diastolic blood pressure between the mercury sphygmomanometer and Omron HEM-6111 was −3.37 ± 7.53 mmHg. Originality/value Although the difference in the SBP error between the proposed device and Omron HEM-6111 was not remarkable, there was a significant difference between the proposed device and Omron HEM-6111 in the diastolic blood pressure error. The developed device showed an improved performance. This study was an attempt to enhance the accuracy of wrist-cuff oscillometric blood pressure monitors by using the finger PPG and the neural network. The hardware framework constructed in this study can improve the conventional wrist oscillometric sphygmomanometer and may be used for continuous measurement of blood pressure.

This Month in Anesthesiology

This Month in Anesthesiology

Real-time embedded control for continuous measurement of blood pressure: methodologic innovations

Continuous blood-pressure (BP) waveform can be measured by the volume-clamp method, where a closed-loop control over a fast pneumatic actuator reproduces BP in a finger cuff. Frequent interruptions, however, are necessary to update arterial-wall compliance to a maximum. We implemented an embedded control in hard real time that measures BP without those interruptions. A microcontroller executes the control system at 2 ms while a computer asynchronously handles the most demanding tasks. The system's design included the following methodological innovations: 1) the arterial-compliance changes were estimated with high-frequency vibrations superimposed on the cuff pressure; 2) this information was used to update the control setpoint to maximum compliance, thus avoiding measurement interruptions; 3) the closed-loop stability was guaranteed with a recurrent redesign of the control; 4) the arterial-wall dynamics were identified for each subject in the control's design, thus improving the method's performance.

Real-time embedded control for continuous measurement of blood pressure: methodologic innovations

Real-time embedded control for continuous measurement of blood pressure: methodologic innovations

Modeling of On-Chip Biosensor for the in Vivo Diagnosis of Hypertension in Wireless Body Area Networks

High Blood Pressure (HBP) over a long period of time leads to a medical condition known as Hypertension (HTN or HT), in which the arterial blood pressure is persistently inflated. In this research, a blood pressure biosensor has been implemented that monitors the different stages of hypertension by measuring the varying blood pressure values. Further, a system design has been proposed for the theranostics (combination of diagnosis and therapy) of hypertension. The proposed sensor takes blood pressure as the input, and gives the corresponding electrical signal variations at the output, so as to detect the exact stage of hypertension. The working principle of this BP sensor is based on the electrostatic transduction mechanism. The proposed system aims to design a low-cost, easily available, implantable lab-on-chip (LoC) platform for the continuous measurement of blood pressure by keeping track of the patient history, so as to prevent chronic hypertensive disorders. Further, such an implantable LoC theranostic platform is among the best possible solutions for the e-healthcare systems, owing to its potential in a wireless body area network (WBAN) using Internet of Things (IoT) and other similar technologies.

Examining chronic inflammatory markers on blood pressure measures in the presence of vitamin D insufficiency among indigenous cree adults: results from the cross-sectional Multi-Community Environment-and-Health Study in Eeyou Istchee, Quebec, Canada

ObjectiveHigh blood pressure (BP) is a risk factor for cardiovascular disease. Examining the role of inflammatory mediators on BP is important since vitamin D (VD) is a modifiable risk factor,...

Noninvasive Accelerometric Approach for Cuffless Continuous Blood Pressure Measurement

Pulse transit time (PTT) has been widely used for cuffless blood pressure (BP) measurement. However, it requires more than one cardiovascular signal involving more than one sensing device. In this article, we propose a method for cuffless continuous blood pressure measurement with the help of left ventricular ejection time (LVET). A microelectromechanical system (MEMS)-based accelerometric sensor acquires a seismocardiogram (SCG) signal at the chest surface, and then, the LVET information is extracted. Both systolic and diastolic BPs are estimated by calibrating the system with the original arterial BPs of the subjects. The performance evaluation is done using different statistical quantitative measures for the proposed method. The performance is also compared with two earlier approaches, where PTT intervals are measured from electrocardiogram (ECG)–photoplethysmogram (PPG) and SCG–PPG pairs. The performance results clearly show that the proposed method is comparable with the state-of-the-art methods. Also, the estimated BP is compared with the original one, measured through a reference system. It gives the mean errors of the systolic and diastolic BPs within the range of −0.197 ± 3.332 and −1.299 ± 2.578 mmHg, respectively. The BPs estimation errors satisfy the requirements of the IEEE standard 5 ± 8 mmHg deviation, and thus, our method may be used for ubiquitous continuous BP monitoring.

A Benchmark Study of Machine Learning for Analysis of Signal Feature Extraction Techniques for Blood Pressure Estimation Using Photoplethysmography (PPG)

Cardiovascular related diseases are the most significant health concern around the globe. The most crucial health indicator is blood pressure because it gives essential information about the health of a patient’s heart. Cardiovascular diseases can be detected early and prevented if blood pressure is monitored continuously and regularly. Blood pressure cuffs, which are widely used to control blood flow in the arm or wrist when measuring blood pressure, are not practical for continuous blood pressure measurement. However, biosignals can be used for blood pressure estimation; but it is still critical and challenging. In this paper, we conducted a comprehensive analysis of feature extraction techniques for blood pressure estimation by using PPG signals. The feature extraction techniques were further divided into three subgroups to analyse the significance of each group. Group A includes time-based features; group B presents statistical feature extraction, and group C presents frequency domain-based features. The analysis employed several machine learning algorithms and compared their performance from many perspectives. The experimental results from two publicly available datasets demonstrated that the set of features belonging to group A were more reliable than other techniques for blood pressure estimation. We found that deep learning models achieved better performance than all traditional machine learning methods. We also found that the GRU model and Bi-LSTM achieved the best performance for time-domain features for blood pressure estimation. We believe the findings of this benchmark study will help researchers choose the most appropriate method for feature extraction and machine learning algorithms.