Blood Pressure Monitoring System Research Articles

Validation of the Meditech ABPM-06 24-hour blood pressure monitoring system in a pediatric population according to International Organization for Standardization Protocol 81060-2:2018.

Ambulatory blood pressure monitoring (ABPM) devices play a crucial role in diagnosing hypertension, not only in adults but also in pediatric patients. ABPM-06, the latest oscillometric device from Meditech Ltd. (Budapest, Hungary), is the focus of this study. The objective was to validate the ABPM-06 device using the International Organization for Standardization (ISO) 81060-2 : 2018 standard. A total of 86 healthy patients, consisting of 34 males and 52 females, aged between 3 and 17 years, were enrolled in this study. During the ambulatory phase, a total of 50 patients were enrolled, with 35 patients falling within the 3- to 12-year-old age range and 15 patients aged between 12 and 17 years. Additionally, for the dynamic test, 36 patients were selected, comprising of 10 individuals aged 3-12 years and 26 patients aged 12-17 years. These patients were recruited from the outpatient clinics of the Department of Pediatrics at Albert Szent-Györgyi University in Szeged, Hungary. The validation process involved utilizing the same-arm sequence protocol, both in resting positions and during stress testing. The ABPM-06 performed well in both clinical and ambulatory validations. In terms of validation criterion 1, the mean ± SD of the differences between the test device and reference blood pressure readings was -1.3 ± 3.5  mmHg for systolic and -0.1  ± 2.3 mmHg for diastolic, in children under the age of 12 years. For those over the age of 12 years, the mean ± SD of the differences was -2.8 ± 4.6 mmHg for systolic and -0.5 ± 2.7 mmHg for diastolic. Regarding the ambulatory validation, for children under 12 years old, the mean ± SD of the differences was -1.3  ± 3.5  mmHg for systolic and -0.1  ± 2.3  mmHg for diastolic. In the age group above 12 years, the mean ± SD of the differences was -2.8 ± 4.6 mmHg for systolic and -0.5 ± 2.7 mmHg for diastolic. Both tests successfully met the established criteria regarding the mean and SD values of the differences between the device readings and the observed SBP and DBP measurements. The ABPM-06 oscillometric device fully adheres to the ISO 81060-2 : 2018 standard requirements for ABPM determination in the pediatric population (ages 3-17 years). Consequently, this ABPM device proves to be suitable for effectively managing hypertension in children and adolescents.

Video-based beat-by-beat blood pressure monitoring via transfer deep-learning

Currently, learning physiological vital signs such as blood pressure (BP), hemoglobin levels, and oxygen saturation, from Photoplethysmography (PPG) signal, is receiving more attention. Despite successive progress that has been made so far, continuously revealing new aspects characterizes that field as a rich research topic. It includes a diverse number of critical points represented in signal denoising, data cleaning, employed features, feature format, feature selection, feature domain, model structure, problem formulation (regression or classification), and model combinations. It is worth noting that extensive research efforts are devoted to utilizing different variants of machine learning and deep learning models while transfer learning is not fully explored yet. So, in this paper, we are introducing a per-beat rPPG-to-BP mapping scheme based on transfer learning. An interesting representation of a 1-D PPG signal as a 2-D image is proposed for enabling powerful off-the-shelf image-based models through transfer learning. It resolves limitations about training data size due to strict data cleaning. Also, it enhances model generalization by exploiting underlying excellent feature extraction. Moreover, non-uniform data distribution (data skewness) is partially resolved by introducing logarithmic transformation. Furthermore, double cleaning is applied for training contact PPG data and testing rPPG beats as well. The quality of the segmented beats is tested by checking some of the related quality metrics. Hence, the prediction reliability is enhanced by excluding deformed beats. Varying rPPG quality is relaxed by selecting beats during intervals of the highest signal strength. Based on the experimental results, the proposed system outperforms the state-of-the-art systems in the sense of mean absolute error (MAE) and standard deviation (STD). STD for the test data is decreased to 5.4782 and 3.8539 for SBP and DBP, respectively. Also, MAE decreased to 2.3453 and 1.6854 for SBP and DBP, respectively. Moreover, the results for BP estimation from real video reveal that the STD reaches 8.027882 and 6.013052 for SBP and DBP, respectively. Also, MAE for the estimated BP from real videos reaches 7.052803 and 5.616028 for SBP and DBP, respectively.Graphical abstractProposed camera-based blood pressure monitoring system

Monitoring blood pressure and cardiac function without positioning via a deep learning-assisted strain sensor array.

Continuous and reliable monitoring of blood pressure and cardiac function is of great importance for diagnosing and preventing cardiovascular diseases. However, existing cardiovascular monitoring approaches are bulky and costly, limiting their wide applications for early diagnosis. Here, we developed an intelligent blood pressure and cardiac function monitoring system based on a conformal and flexible strain sensor array and deep learning neural networks. The sensor has a variety of advantages, including high sensitivity, high linearity, fast response and recovery, and high isotropy. Experiments and simulation synergistically verified that the sensor array can acquire high-precise and feature-rich pulse waves from the wrist without precise positioning. By combining high-quality pulse waves with a well-trained deep learning model, we can monitor blood pressure and cardiac function parameters. As a proof of concept, we further constructed an intelligent wearable system for real-time and long-term monitoring of blood pressure and cardiac function, which may contribute to personalized health management, precise and early diagnosis, and remote treatment.

Blood Pressure Monitoring Based on Flexible Encapsulated Sensors

Blood pressure monitoring is a significant concern in the field of healthcare, and the utilization of flexible encapsulated sensors presents a promising solution for achieving noninvasive and comfortable monitoring. This paper presents a study on the flexible encapsulation of MEMS pressure sensors and the development of an enhanced arterial tonometry method for blood pressure measurement, ultimately leading to the realization of a blood pressure monitoring system based on flexible encapsulated sensors. To improve wearer comfort and acquire reliable pulse signals, a flexible encapsulation sensor combining parylene and PDMS materials was fabricated. Additionally, to address the issue of low accuracy in blood pressure measurement, various machine learning algorithms were compared and analyzed, leading to the identification of the random forest model as the optimal regressor. Consequently, a blood pressure monitoring system based on the improved arterial tension method was designed and implemented. The experimental results demonstrate that the proposed system achieved a significant enhancement of 31.4% and 21% in the accuracy of systolic and diastolic blood pressure measurements, respectively, compared with the arterial tension method.

Clinical Validation of Wearable Piezoelectric Blood Pressure Sensor for Continuous Health Monitoring.

Wearable blood pressure sensors have recently attracted attention as healthcare devices for continuous non-invasive arterial pressure (CNAP) monitoring. However, the accuracy of wearable blood pressure (BP) monitoring devices has been controversial due to the low signal quality of sensors, the absence of an accurate transfer function to convert the sensor signals into BP values, and the lack of clinical validation regarding measurement precision. Here, we report a wearable piezoelectric blood pressure sensor (WPBPS), which achieved a high normalized sensitivity (0.062 kPa-1 ), fast response time (23ms) for CNAP monitoring. The transfer function of a linear regression model was designed, offering a simple solution to convert the flexible piezoelectric sensor signals into BP values. In order to verify the measurement accuracy of WPBPS, clinical trials were performed on 35 subjects aged from 20s to 80s after screening. The mean difference between the WPBPS and a commercial sphygmomanometer of 175 BP data pairs was -0.89 ± 6.19mmHg and -0.32 ± 5.28mmHg for systolic blood pressure (SBP) and diastolic blood pressure (DBP), respectively. By building a WPBPS-embedded wristwatch, we demonstrated the potentially promising use of a convenient, portable continuous BP monitoring system for cardiovascular disease diagnosis in this study. This article is protected by copyright. All rights reserved.

Neutrophil and monocyte ratios to high-density lipoprotein cholesterol as biomarkers in non-dipping hypertension

ABSTRACT Objective To investigate the level and significance of neutrophils to high-density lipoprotein cholesterol ratio (NHR) and monocytes to high-density lipoprotein cholesterol ratio (MHR) in patients with non-dipping hypertension. Methods A total of 228 patients were retrospectively enrolled in the study. They were divided into the dipping hypertension group (n = 76), the non-dipping hypertension group (n = 77) and the control group (n = 75) according to 24-h ambulatory blood pressure monitoring system (ABPM) recordings. NHR and MHR were calculated and compared statistically. Receiver operating characteristic (ROC) curve analyses were performed for NHR and MHR. Binary logistic regression analyses were introduced to investigate the independent associations of NHR and MHR with non-dipping hypertension. Results The NHR and MHR were significantly higher in the non-dipping hypertension group compared with the control group (p = .001, p < .001, respectively) and the dipping hypertension group (p = .039, p = .003, respectively). According to ROC curve analyses, NHR>73.35 and MHR>7.54 were regarded as high-risk groups. The area under the curve (AUC) was 0.642 (p < .001) for NHR and 0.718 (p < .001) for MHR. In multivariate analysis, compared with NHR, only MHR was still recognized as a marker for detection of non-dipping hypertension (odds ratio [OR]: 1.208, 95% confidence interval [CI]: 1.076 to 1.356, p = .001). Conclusions Our data indicated that not NHR but MHR as new composite marker of inflammation and lipid metabolism may predict non-dipping hypertension to some extent.

Masked pregnancy-associated hypertension as a predictor of adverse outcomes

Masked hypertension has been described in nonpregnant populations as elevated blood pressure in the home setting that is not reproduced on clinical assessment. Patients with masked hypertension have a greater risk of cardiovascular morbidity than patients who have blood pressures within normal range or those with white coat hypertension. This study aimed to determine whether masked pregnancy-associated hypertension detected on Connected Maternity Online Monitoring, a remote home blood pressure monitoring system, is associated with higher rates of hypertensive disorders of pregnancy during delivery admission and maternal and neonatal morbidities. This was a retrospective cohort study of all patients on Connected Maternity Online Monitoring who delivered at 6 hospitals in a single healthcare system between October 2016 and December 2020. Patients were classified as having either normal blood pressure or masked pregnancy-associated hypertension. Masked pregnancy-associated hypertension was defined as remotely detected systolic blood pressure of ≥140 mm Hg or diastolic blood pressure of ≥90 mm Hg after 20 weeks of gestation on 2 occasions before diagnosis in a clinical setting. The chi-square test and Student t test were used for demographic and outcomes comparisons. Logistic regression was used to adjust outcomes by race, insurance, and body mass index. A total of 2430 deliveries were included in our analysis, including 165 deliveries that met the criteria for masked pregnancy-associated hypertension. Clinically established pregnancy-associated hypertension, defined at the time of delivery, was more common in the masked pregnancy-associated hypertension group than in the normotensive group (66% vs 10%; adjusted odds ratio, 17.2; 95% confidence interval, 11.91-24.81). Patients with masked pregnancy-associated hypertension had higher rates of preeclampsia with severe features on delivery admission than normotensive patients (28% vs 2%; adjusted odds ratio, 23.35; 95% confidence interval, 14.25-38.26). Preterm delivery (16% vs 7%; adjusted odds ratio, 2.47; 95% confidence interval, 1.55-3.94), cesarean delivery(38% vs 26%; adjusted odds ratio, 1.58; 95% confidence interval, 1.13-2.23), small for gestational age (11% vs 5%; adjusted odds ratio, 2.27; 95% confidence interval, 1.31-3.94), and neonatal intensive care unit admission (8% vs 4%; adjusted odds ratio, 2.20; 95% confidence interval, 1.18-4.09) were more common among patients with masked pregnancy-associated hypertension than among normotensive patients. With more outcomes research, remote blood pressure monitoring may prove to be an important tool in identifying pregnancies at risk of complications related to masked hypertension.

Remotely Programmable Deep Brain Stimulator Combined with an Invasive Blood Pressure Monitoring System for a Non-Tethered Rat Model in Hypertension Research.

The control circuits of blood pressure have a strong neural regulatory element important in the pathogenesis of essential drug-resistant hypertension. Targeting lower medullary neural control mechanisms of blood pressure by electrical stimulation could be beneficial, and therefore, a novel device is needed. This paper presents a remotely programmable deep brain stimulator with an invasive continuous blood pressure monitoring system in a non-tethered rat model. The device is designed for lower medullary deep brain stimulation research with minimal interference to a daily animal routine. Electrodes were implanted in the caudal ventrolateral medulla. Animal survivability, catheter patency rates, and device data drift were evaluated. Eight out of ten rats survived the surgery and testing period with no or mild temporary neurological compromise. The study revealed that carotid catheters filled with heparinized glycerol ensure better catheter patency rates and blood pressure transduction. There was no significant drift in the device's pressure sensitivity during the experiment. To our knowledge, this is the first experimental study to show considerable animal survival after lower medullary implantation. Combining the ability to measure and monitor invasive blood pressure with a closed-loop brain pulse generator in a single device could be of potential value in future hemodynamic animal research.

Validation of the BPLab® 24-Hour Blood Pressure Monitoring System According to the European Standard BS EN 1060-4:2004 and British Hypertension Society Protocol [Retraction].

[This retracts the article DOI: 10.2147/MDER.S25800.].

Innovation in Healthcare through Technology and Artificial Intelligence – Case Study: VitalView Blood Pressure Monitoring System

The rising prevalence of chronic diseases and the advancement of artificial intelligence and machine learning are driving significant shifts in the healthcare industry. Chronic diseases pose an urgent threat to the world’s healthcare systems due to the rising number of fatalities, making the demand for innovative approaches imperative. Applying VitalView, a blood pressure monitoring system, as a case study, this thesis explores the intersection of these fields. The system’s main goal is to employ AI and machine learning to offer personalized treatment for chronic diseases, especially blood pressure. To accomplish this, the study commences by examining the challenges faced by Albania’s healthcare system, in particular issues connected to access, costs, and service quality. The study then digs into the development of VitalView, an innovative solution that meets the needs of patients and healthcare professionals. The creation of an algorithm for data analysis and communication is at the core of this work. Autoregressive Integrated Moving Average (ARIMA), among other machine learning models, improves the platform’s ability to forecast blood pressure trends and simplify doctor-patient communication. The system also provides advice and recommendations for the patient’s general state of health. The case study methodology is employed with the objective of putting this system’s applications into action in real life situations. Its findings show the potential of AI and machine learning to boost communication, improve the management of chronic blood pressure diseases, and boost patient outcomes. This thesis demonstrates how innovative technologies could cope with pressing concerns of our time and contributes to the discussion on artificial intelligence in the healthcare industry. The VitalView system serves as an example of how innovation, data analysis, and user-centered design function together. It highlights how AI and machine learning have the potential to improve healthcare.

A standardized clinical assessment and management plan (SCAMP) reduces readmissions in postpartum hypertension

Postpartum hypertension is one of the most frequent reasons for emergency department (ED) visits and readmission. We aimed to evaluate the effect of a SCAMP on these metrics in the postpartum period. We conducted a prospective cohort study of patients with postpartum hypertension at a tertiary care center for 3 months before and after enacting a SCAMP. The prospective group (P) were compared with historical controls (C). Elements of the SCAMP included 1) initiation or uptitration of medication for any blood pressure (BP) >150/100 or 2 BPs >140/90 within a 24hr period with goal of achieving normotension and 2) enrollment in outpatient virtual BP monitoring with close follow-up by the study team on discharge. Primary outcome was postpartum ED visit or readmission. Secondary outcomes were medication side effects, hypotension, utilization of the BP monitoring system, and length of stay (LOS). Chi-square, t-test, and Mann-Whitney U test were used as appropriate to compare groups. IRB#14-001098. 176 patients in (P) cohort were compared to 156 historical controls (C). The primary outcome of ED visits significantly decreased from 7.69% (C) to 1.13% (P) (p=0.003), and readmission rates decreased from 7.05% (C) to 1.70% (P) (p=0.02). When combined, the rate of patients who had either an ED visit or readmission decreased from 14.74% (C) to 3.21% (P) (p< 0.001). More patients were prescribed anti-hypertensives on discharge in (P) cohort (33.0% P vs 21.8% C, p=0.02), of whom 3 (5.2%) had hypotension (BP < 100/60) and 4 (6.9%) had side effects. The (P) cohort had longer mean postpartum LOS that was statistically significant (2.4 1.0 days P vs 2.1 1.0 days C, p=0.04). Total 100 (56.8%) patients were compliant with virtual BP monitoring, with 18 (18%) medication adjustments made per protocol. Utilizing a SCAMP encompassing strict BP control in the postpartum period followed by intensive outpatient follow-up can significantly reduce postpartum ED visits and readmissions.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Overview of the verticalization of technology: history of the occurrence and relevance of application

Verticalization is a mobilization technique that is aimed at preventing and treating impaired gravitational gradient in patients who are on bed rest for 24 h, regardless of their motor and cognitive status, and is conducted actively and passively. Passive verticalization as a rehabilitation technique is recommended for patients who cannot stand up independently and keep themselves in an upright position because of the severity of the general condition.&#x0D; In the preparation of this review, mainly literature sources from highly rated publications were used. Preference was given to sources published in the last 10 years. Review articles and materials prepared by experts in the field of writing/editing scientific publications, including review articles, were selected for the review.&#x0D; In the aggregate of the verticalization methods analyzed, the common recommendations for all are the early start of rehabilitation measures, continuity, and careful monitoring of hemodynamic characteristics. The technique itself today has more than 50 years of experience, and the combination of verticalization technologies with a mechanotherapeutic simulator with electric motor support is a modern solution for their application. This equipment can be used in both intensive care in addition to the equipment of continuous respiratory support and rehabilitation and other wards.&#x0D; The verticalization procedure, if possible, should be applied at the earliest possible time after the traumatic event to improve the rehabilitation potential. The verticalization of patients with chronic impairment of consciousness has physiological features, and hemodynamic adaptation is recommended to reduce the risk of orthostatic cerebral ischemia. For patients with disorders of the central nervous system, preliminary adaptation should be conducted to subsequent verticalization by gradually lifting on a multifunctional bed under hemodynamic control, including the CNAP continuous blood pressure monitoring system (Drger systems).

A new noninvasive finger sensor (NICCI system) for continuous blood pressure and pulse pressure variation monitoring: A method comparison study in patients having neurosurgery.

The NICCI system (Getinge, Gothenburg, Sweden) is a new noninvasive haemodynamic monitoring system using a finger sensor. We aimed to investigate the performance of the NICCI system to measure blood pressure and pulse pressure variation compared with intra-arterial measurements. A prospective method comparison study. University Medical Center Hamburg-Eppendorf, Hamburg, Germany. Forty-seven neurosurgery patients. We performed a method comparison study in 47 neurosurgery patients to compare NICCI blood pressure measurements (BP NICCI ) with intra-arterial blood pressure measurements (BP ART ) (Bland-Altman analysis, four-quadrant plot, error grid analysis) and NICCI pulse pressure variation measurements (PPV NICCI ) with pulse pressure variation calculated manually from the intra-arterial blood pressure waveform (PPV ART ) (Bland-Altman analysis, predictive agreement, Cohen's kappa). The mean of the differences ± standard deviation (95% limits of agreement) between BP NICCI and BP ART was 11 ± 10 mmHg (-8 to 30 mmHg) for mean blood pressure (MBP), 3 ± 12 mmHg (-21 to 26 mmHg) for systolic blood pressure (SBP) and 12 ± 10 mmHg (-8 to 31 mmHg) for diastolic blood pressure (DBP). In error grid analysis, 54% of BP NICCI and BP ART MBP measurement pairs were classified as 'no risk', 43% as 'low risk', 3% as 'moderate risk' and 0% as 'significant risk' or 'dangerous risk'. The mean of the differences between PPV NICCI and PPV ART was 1 ± 3% (-4 to 6%). The predictive agreement between PPV NICCI and PPV ART was 80% and Cohen's kappa was 0.55. The absolute agreement between BP NICCI and BP ART was not clinically acceptable. We recommend not using the current version of the NICCI system for blood pressure monitoring during surgery. The absolute agreement between PPV NICCI and PPV ART was clinically acceptable with moderate predictive agreement regarding pulse pressure variation categories. The NICCI system needs to be further developed and re-evaluated when an improved version is available. The study was registered in the German Clinical Trials Register (DRKS00023188) on 2 October 2020.

Contactless Blood Pressure Estimation System Using a Computer Vision System

Blood pressure (BP) is one of the most common vital signs related to cardiovascular diseases. BP is traditionally measured by mercury, aneroid, or digital sphygmomanometers; however, these approaches are restrictive, inconvenient, and need a pressure cuff to be attached directly to the patient. Therefore, it is clinically important to develop an innovative system that can accurately measure BP without the need for any direct physical contact with the people. This work aims to create a new computer vision system that remotely measures BP using a digital camera without a pressure cuff. The proposed BP system extracts the optical properties of photoplethysmographic signals in two regions in the forehead captured by a digital camera and calculates BP based on specific formulas. The experiments were performed on 25 human participants with different skin tones and repeated at different times under ambient light conditions. Compared to the systolic/diastolic BP readings obtained from a commercial digital sphygmomanometer, the proposed BP system achieves an accuracy of 94.6% with a root mean square error (RMSE) of 9.2 mmHg for systolic BP readings and an accuracy of 95.4% with an RMSE of 7.6 mmHg for diastolic BP readings. Thus, the proposed BP system has the potential of being a promising tool in the upcoming generation of BP monitoring systems.

Association of Abdominal Arterial Calcification Score with Patients' Survival and Kidney Allograft Function after Kidney Transplant.

Abdominal arterial calcification (AAC) is common among candidates for kidney transplant. The aim of this study is to correlate AAC score value with post-kidney transplant outcomes. We modified the coronary calcium score by changing the intake data points and used it to quantitate the AAC. We conducted a retrospective clinical study of all adult patients who were transplanted at our center, between 2010 and 2013, and had abdominal computed tomography scan done before transplantation. Outcomes included mortality, pulse pressure (PP) measured by 24h ambulatory blood pressure monitoring system, and kidney allograft function measured by iothalamate clearance. For each 1000 increase of AAC score value, there is an associated 1.05 increase in the risk of death (95% CI 1.02, 1.08) (p < 0.001). Overall median AAC value for all patients was 1784; Kaplan-Meier curve showed reduced survival of all-cause mortality for patients with AAC score value above median and reduced survival among patients with cardiac related mortality. The iothalamate clearance was lower among patients with total AAC score value above the median. Patients with abnormal PP (< 40 or > 60mmHg) had an elevated median AAC score value at 4319.3 (IQR 1210.4, 11097.1) compared to patients with normal PP with AAC score value at 595.9 (IQR 9.9, 2959.9) (p < 0.001). We showed an association of AAC with patients' survival and kidney allograft function after kidney transplant. The AAC score value could be used as a risk stratification when patients are considered for kidney transplant.

Noninvasive, Wearable Multi Biosensors for Continuous, Long-term Monitoring of Blood Pressure via Internet of Things Applications

Continuous blood pressure (BP) monitoring through various physical stress tests is important for the prediction of hypertension and cardiovascular mortality. This study proposes a multimodal wearable biosensor device for continuous BP monitoring through various tests during exercise and internet of things (IoT) applications. We evaluated three pulse transit time (PTT)-BP estimation models to identify the best mode for predicting BP response to exercise. Moreover, the proposed BP monitoring system was designed to connect to the cloud server for health data processing and management. The efficacy of the proposed device was validated by monitoring the natural variability of BP in various cycling tests. Thus, BP estimation based on the impedance plethysmography waveform and arterial impedance offers superiority compared to other conventional PTT estimation methods. These results suggest the feasibility of the multimodal biosensor device for continuous, long-term BP monitoring in remote diagnosis and management with IoT-based healthcare applications.

Blood Pressure Estimation using a Single Channel Bio-Impedance Ring Sensor.

The demand for non-obtrusive, accurate, and continuous blood pressure (BP) monitoring systems is becoming more prevalent with the realization of its significance in preventable cardiovascular disease (CVD) globally. Current cuff-based standards are bulky, uncomfortable, and are limited to discrete recording periods. Wearable sensor technologies such as those using optical photoplethysmography (PPG) have been used to develop blood pressure estimation models through a variety of methods. However, this technology falls short as optical based systems have bias favoring lighter skin tones and lower body fat compositions. Bioimpedance (Bio-Z) is a capable modality of sensing arterial blood flow without implicit inadvertent bias towards individuals. In this paper we propose a ring-based bioimpedance system to capture arterial blood flow from the digital artery of the finger. The ring design provides a more compact wearable device utilizing only a single Bio-Z channel, making it a familiar fit to individuals. Post-processing the acquired Bio-Z signals, we extracted 9 frequency domain features from windowed beat cycles to train subject specific regression models. Results indicate the average mean absolute errors for systolic/diastolic BP to be 4.38/3.63mmHg, consistent with AAMI standards.

A Novel CNN-LSTM Model Based Non-Invasive Cuff-Less Blood Pressure Estimation System.

PPG (Photoplethysmography) and ECG (Electro-cardiogram) physiological signals have been known to have certain indicators for establishing blood pressure (BP) levels. Continuous monitoring of blood pressure (BP) is highly valuable for cardiovascular patients; however the existing non-invasive cuff-based blood pressure monitoring system is discreet and applies artificial pressure on patients' arms that is uncomfortable. The other invasive method is highly interventional in nature and is highly disturbing when the patient is recuperating in the hospital wards or elsewhere. A non-invasive cuff-less, non-disturbing, and continuous BP measurement system targeted toward surgical, clinical, and domestic usage are proposed in this work. A convolutional neural network (CNN) followed by a long short-term memory layer (LSTM) was designed and applied to ECG and PPG signals to present accurate systolic blood pressure (SBP), and diastolic blood pressure (DBP). For developing the CNN-LSTM layers, a novel and open-source dataset was compiled that consisted of PPG and ECG signals from 30 healthy participants and is made publicly available for further usage to the research community. The novel CNN-LSTM based cuff-less blood pressure evaluation system presented a mean absolute error (MAE) of 2.57 mmHg and 3.44 mmHg for SBP and DBP respectively with similar standard-deviation (SD) metrics. The characterized error metrics of the proposed system are the lowest to date when compared to other prior work. Clinical Relevance- A cuff-less BP estimation system allows patients to have easy access to blood pressure evaluation as well as aid in determining unsafe health ailments like hypertension. Ready access to such system will not only allow practitioners to continuously monitor BP in hospitals but also help patients to regularly monitor BP more frequently at their convenience.

Singlemode-Multimode-Singlemode Optical Fiber Sensor for Accurate Blood Pressure Monitoring

A dual-channel single-mode-multi-mode-single-mode (SMS) fiber optic sensor encapsulated by polydimethylsiloxane (PDMS) was proposed for the first time, for the simultaneous monitoring of the brachial and radial arteries for accurate blood pressure prediction. With the help of the machine learning algorithm Support Vector Regression (SVR), the SMS fiber sensor can continuously and accurately monitor the systolic and diastolic blood pressure. Commercial sphygmomanometers are used to calibrate the accuracy of blood pressure measurement. Compared with the single-channel system, this system can extract more pulse wave features for blood pressure prediction, such as radial artery transit time (RPTT), brachial artery transit time (BPTT), and the transit time difference between the radial artery and the brachial artery (DBRPTT). The results show that the performance of dual-channel blood pressure monitoring is more accurate than that of single-channel blood pressure monitoring in terms of the absolute value of the correlation coefficient (R) and the average value of the difference between SBP and DBP. In addition, both the single-channel and dual-channel blood pressure monitoring are in line with the Association for the Advancement of Medical Devices (AAMI), but the average deviation (DM, 0.06 mmHg) and standard deviation (SD, 1.54 mmHg) of dual-channel blood pressure monitoring are more accurate. The blood pressure monitoring system has the characteristics of low cost, high sensitivity, non-invasive and capability for remote real time monitoring, which can provide effective solution for intelligent health monitoring in the era of artificial intelligence in the future.

A non-invasive heart rate and blood pressure monitoring system using piezoelectric and photoplethysmographic sensors

In this work, a non-invasive heart rate (HR) and blood pressure (BP) monitoring system using a lead zirconate titanate (PZT) piezoelectric and photoplethysmographic (PPG) sensor has, preliminarily, been investigated. A modified pulse transit time (MPTT) is applied to demodulate HR, systolic blood pressure (SBP), and diastolic blood pressure (DBP) from obtained BP waveforms. In addition, an engineering application software written by LabView programming is, also, developed for dynamic signal monitoring and processing of desired parameters. Consequently, HR and BP measurements on 15 volunteers are performed and validated against a reference digital sphygmomanometer. The results show equivalent HR from both sensors with a mean absolute difference (MAD) of 1.78 beats per minute. Meanwhile, MADs of SBP and DBP measurements are 2.62 and 1.36 mmHg, complying with AAMI-BHS accuracy of “Grade A”, respectively. Therefore, PZT and PPG-based monitoring system could lead to development of accurate, non-invasive, and low-cost HR and BP devices.