Continuous blood pressure measurement using pulse transit time

Optimizing the input feature sets and machine learning algorithms for reliable and accurate estimation of continuous, cuffless blood pressure

The advent of mobile devices, wearables and digital healthcare has unleashed a demand for accurate, reliable, and non-interventional ways to measure continuous blood pressure (BP). Many consumer products claim to measure BP with a cuffless device, but their lack of accuracy and reliability limit clinical adoption. Here, we demonstrate how multimodal feature datasets, comprising: (i) pulse arrival time (PAT); (ii) pulse wave morphology (PWM), and (iii) demographic data, can be combined with optimized Machine Learning (ML) algorithms to estimate Systolic BP (SBP), Diastolic BP (DBP) and Mean Arterial Pressure (MAP) within a 5 mmHg bias of the gold standard Intra-Arterial BP, well within the acceptable limits of the IEC/ANSI 80601-2-30 (2018) standard. Furthermore, DBP's calculated using 126 datasets collected from 31 hemodynamically compromised patients had a standard deviation within 8 mmHg, while SBP's and MAP's exceeded these limits. Using ANOVA and Levene's test for error means and standard deviations, we found significant differences in the various ML algorithms but found no significant differences amongst the multimodal feature datasets. Optimized ML algorithms and key multimodal features obtained from larger real-world data (RWD) sets could enable more reliable and accurate estimation of continuous BP in cuffless devices, accelerating wider clinical adoption.

The pre-ejection period is a highly stress dependent parameter of paramount importance for pulse-wave-velocity based applications

Purpose

The pulse-wave-velocity, is used for indirect, cuff-less, continuous blood pressure estimation. It is commonly detected by measuring the time delay between a defined point in an ECG and the arrival of the peripheral pulse wave (e.g., oxygen saturation sensor). The period between electrical stimulation of the heart (ECG) and actual blood ejection from the heart is called the pre-ejection period (PEP). This study aims at characterizing the PEP under mental and physical stress with focus on its relations to other cardiovascular parameters such as heart rate and importance for blood pressure (BP) estimation.

Methods

We measured the PEP in 71 young adults at rest, under mental (TSST) and physical stress (ergometer) via impedance-cardiography.

Results

The PEP is highly dependent on mental and physical load. It is strongly correlated with indicators of sympathetic strain (p < 0.001). At rest (mean 104.5 ms), the PEP shows a high interindividual variability but small intraindividual variability. Mental stress decreases the PEP by 16% (mean 90.0 ms) while physical stress halves PEP (mean 53.9 ms). The PEP does correlate differently with heart rate under differing circumstances (rest: R ² 0.06, mental stress: R ² 0.29, physical stress: R ² 0.65). Subsequently, using PEP and heart rate enables the discrimination of rest, mental and physical strain with a positive predictive value of 93%.

Conclusion

The PEP is a cardiovascular parameter with large interindividual variability at rest and subject-depended dynamic under load which is of great importance for ECG-based pulse-wave-velocity (PWV) determination. Considering its variability and large impact on the pulse arrival time, PEP is a crucial factor in PWV based BP estimation.

Measuring Suite for Vascular Response Monitoring during Hyperbaric Oxygen Therapy by Means of Pulse Transit Time (PTT) Analysis

The efficacy of hyperbaric oxygen therapy in treating wound healing disorders is well established. The obvious explanation is the presence of elevated oxygen tissue tensions during the high-pressure oxygen exposure. This explanation omits that the effective agent, elevated oxygen tension, is only present for 6.25% of the time. To investigate possible prevailing vascular changes caused by HBOT, the presented device monitors the vascular response during therapy by Pulse-Transit-Time analysis. The device allows synchronous 1 kHz ECG and PPG measurements. The data are stored in a 1 GBit flash drive and retrieved post-therapy. Normoxic measurements on the authors with and without nicotine validate the device's functionality. Measurements during HBO therapy have been successfully performed.

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.

An evaluation of a video magnification-based system for respiratory rate monitoring in an acute mental health setting

CONTEXT

One of the most important goals of inpatient psychiatric care is to provide a safe and therapeutic environment for both patients and staff. A small number of aggressive or agitated patients are difficult to sedate, even after multiple doses of sedating antipsychotics. Adverse effects can result in harm to the patient and staff and that observations are conducted without touching the patient.

AIM

This study aims to determine if motion magnification can improve the feasibility of non-contact respirations monitoring over a video feed.

METHODS

Registered nurses were invited to view seven pairs of pre-recorded footage of healthy volunteers and count the number of breaths that they observe over a period of one minute for each. One of the paired videos was unprocessed and the other magnified the motion of chest rise and fall.

RESULTS

Nursing observation of respirations showed an improvement in reduction of count error from 15.7 % to 1.5 % after video magnification of respiratory movement. Nurses also stated that viewing the processed video was much easier to make their observations from.

CONCLUSION

It is possible to use magnified video to monitor respirations of patients during circumstances where it is potentially difficult to obtain. Further observational studies should be conducted on a larger scale with this type of technique and is urgently needed to inform practice.

Blood pressure reaction to negative stimuli: Insights from continuous recording and analysis

Individuals with a tendency toward abnormally enhanced cardiovascular responses to stress are at greater risk of developing essential hypertension later in life. Accurate profiling of continuous blood pressure (BP) reactions in healthy populations is crucial for understanding normal and abnormal emotional reaction patterns. To this end, we examined the continuous time course of BP reactions to aversive pictures among healthy participants. In two experiments, we showed participants negative and neutral pictures while simultaneously measuring their continuous BP and heart rate (HR) reactions. In this study, BP reactions were analyzed continuously, in contrast to previous studies, in which BP responses were averaged across blocks. To compare time points along a temporal continuum, we applied a multi-level B-spline model, which is innovative in the context of BP analysis. Additionally, HR was similarly analyzed in order to examine its correlation with BP. Both experiments revealed a similar pattern of BP reactivity and association with HR. In line with previous studies, a decline in BP and HR levels was found in response to negative pictures compared to neutral pictures. In addition, in both conditions, we found an unexpected elevation of BP toward the end of the stimuli exposure period. These findings may be explained by the recruitment of attention resources in the presence of negative stimuli, which is alleviated toward the end of the stimulation. This study highlights the importance of continuous measurement and analysis for characterizing the time course of BP reactivity to emotional stimuli.

Multimodal Monitoring of Cardiovascular Responses to Postural Changes

Background

In the poorly understood relationship between orthostatic hypotension and falls, next to blood pressure (BP), baroreflex sensitivity (BRS) and cerebral autoregulation (CAR) may be key measures. The posture- and movement dependency of orthostatic hypotension requires continuous and unobtrusive monitoring. This may be possible using simultaneous photoplethysmography (PPG), electrocardiography (ECG), and near-infrared spectroscopy (NIRS) signal recordings, from which pulse wave velocity (PWV; potentially useful for BP estimation), BRS and CAR can be derived. The PPG, NIRS and PWV signal correlation with BP and BRS/CAR reliability and validity need to be addressed.

Methods

In 34 healthy adults (mean age 25 years, inter quartile range 22–45; 10 female), wrist and finger PPG, ECG, bifrontal NIRS (oxygenated and deoxygenated hemoglobin) and continuous BP were recorded during sit to stand and supine to stand movements. Sixteen participants performed slow and rapid supine to stand movements; eighteen other participants performed a 1-min squat movement. Pulse wave velocity (PWV) was defined as the inverse of the ECG R-peak to PPG pulse delay; PPG, NIRS and PWV signal correlation with BP as their Pearson correlations with mean arterial pressure (MAP) within 30 s after the postural changes; BRS as inter beat interval drop divided by systolic BP (SBP) drop during the postural changes; CAR as oxygenated hemoglobin drop divided by MAP drop. BRS and CAR were separately computed using measured and estimated (linear regression) BP. BRS/CAR reliability was defined by the intra class correlation between repeats of the same postural change; validity as the Pearson correlation between BRS/CAR values based on measured and estimated BP.

Results

The highest correlation with MAP was found for finger PPG and oxygenated hemoglobin, ranging from 0.75–0.79 (sit to stand), 0.66–0.88 (supine to stand), and 0.82–0.94 (1-min squat). BRS and CAR reliability was highest during the different supine to stand movements, ranging from 0.17 – 0.49 (BRS) and 0.42-0.75 (CAR); validity was highest during rapid supine to stand movements, 0.54 and 0.79 respectively.

Conclusion

PPG-ECG-NIRS recordings showed high correlation with BP and enabled computation of reliable and valid BRS and CAR estimates, suggesting their potential for continuous unobtrusive monitoring of orthostatic hypotension key measures.

Non-invasive continuous blood pressure monitoring systems: current and proposed technology issues and challenges

High blood pressure (BP) or hypertension is the single most crucial adjustable risk factor for cardiovascular diseases (CVDs) and monitoring the arterial blood pressure (ABP) is an efficient way to detect and control the prevalence of the cardiovascular health of patients. Therefore, monitoring the regulation of BP during patients' daily life plays a critical role in the ambulatory setting and the latest mobile health technology. In recent years, many studies have been conducted to explore the feasibility and performance of such techniques in the health care system. The ultimate aim of these studies is to find and develop an alternative to conventional BP monitoring by using cuff-less, easy-to-use, fast, and cost-effective devices for controlling and lowering the physical harm of CVDs to the human body. However, most of the current studies are at the prototype phase and face a range of issues and challenges to meet clinical standards. This review focuses on the description and analysis of the latest continuous and cuff-less methods along with their key challenges and barriers. Particularly, most advanced and standard technologies including pulse transit time (PTT), ultrasound, pulse arrival time (PAT), and machine learning are investigated. The accuracy, portability, and comfort of use of these technologies, and the ability to integrate to the wearable healthcare system are discussed. Finally, the future directions for further study are suggested.

Non-invasive continuous blood pressure measurement based on mean impact value method, BP neural network, and genetic algorithm

BACKGROUND

Non-invasive continuous blood pressure monitoring can provide an important reference and guidance for doctors wishing to analyze the physiological and pathological status of patients and to prevent and diagnose cardiovascular diseases in the clinical setting. Therefore, it is very important to explore a more accurate method of non-invasive continuous blood pressure measurement.

OBJECTIVE

To address the shortcomings of existing blood pressure measurement models based on pulse wave transit time or pulse wave parameters, a new method of non-invasive continuous blood pressure measurement - the GA-MIV-BP neural network model - is presented.

METHOD

The mean impact value (MIV) method is used to select the factors that greatly influence blood pressure from the extracted pulse wave transit time and pulse wave parameters. These factors are used as inputs, and the actual blood pressure values as outputs, to train the BP neural network model. The individual parameters are then optimized using a genetic algorithm (GA) to establish the GA-MIV-BP neural network model.

RESULTS

Bland-Altman consistency analysis indicated that the measured and predicted blood pressure values were consistent and interchangeable.

CONCLUSIONS

Therefore, this algorithm is of great significance to promote the clinical application of a non-invasive continuous blood pressure monitoring method.

Particle Filtering and Sensor Fusion for Robust Heart Rate Monitoring Using Wearable Sensors

This paper describes a novel methodology leveraging particle filters for the application of robust heart rate monitoring in the presence of motion artifacts. Motion is a key source of noise that confounds traditional heart rate estimation algorithms for wearable sensors due to the introduction of spurious artifacts in the signals. In contrast to previous particle filtering approaches, we formulate the heart rate itself as the only state to be estimated, and do not rely on multiple specific signal features. Instead, we design observation mechanisms to leverage the known steady, consistent nature of heart rate variations to meet the objective of continuous monitoring of heart rate using wearable sensors. Furthermore, this independence from specific signal features also allows us to fuse information from multiple sensors and signal modalities to further improve estimation accuracy. The signal processing methods described in this work were tested on real motion artifact affected electrocardiogram and photoplethysmogram data with concurrent accelerometer readings. Results show promising average error rates less than 2 beats/min for data collected during intense running activities. Furthermore, a comparison with contemporary signal processing techniques for the same objective shows how the proposed implementation is also computationally more efficient for comparable performance.

Advances in Photopletysmography Signal Analysis for Biomedical Applications

Heart Rate Variability (HRV) is an important tool for the analysis of a patient’s physiological conditions, as well a method aiding the diagnosis of cardiopathies. Photoplethysmography (PPG) is an optical technique applied in the monitoring of the HRV and its adoption has been growing significantly, compared to the most commonly used method in medicine, Electrocardiography (ECG). In this survey, definitions of these technique are presented, the different types of sensors used are explained, and the methods for the study and analysis of the PPG signal (linear and nonlinear methods) are described. Moreover, the progress, and the clinical and practical applicability of the PPG technique in the diagnosis of cardiovascular diseases are evaluated. In addition, the latest technologies utilized in the development of new tools for medical diagnosis are presented, such as Internet of Things, Internet of Health Things, genetic algorithms, artificial intelligence and biosensors which result in personalized advances in e-health and health care. After the study of these technologies, it can be noted that PPG associated with them is an important tool for the diagnosis of some diseases, due to its simplicity, its cost⁻benefit ratio, the easiness of signals acquisition, and especially because it is a non-invasive technique.

A novel Blood Pressure estimation method combing Pulse Wave Transit Time model and neural network model

Blood Pressure (BP) measurement can assist doctors to assess patients' cardiovascular status and diagnose heart diseases. Pulse Wave Transit Time (PWTT) model is one frequently used BP estimation method to monitor BP continuously in clinics. However, individual variations may influence the measurement accuracy of PWTT model. Focusing on above promble, this paper proposes a novel BP estimation method combining a classical PWTT model and a neural network model. The novel method is composed of five steps: signal pre-processing, feature extraction, initial PWTT model selection, model correction by neural network model, and final PWTT model identification. A validation experiment based on 10 patients from Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) database showed that the BP estimation results by our method had a minimum mean of error readout value 5 mmHg with a standard deviation of error readout value ±8mmHg. As a result, both the diastolic blood pressure and systolic blood pressure estimation by our method can meet clinical requirements.

BioWatch - a wrist watch based signal acquisition system for physiological signals including blood pressure

A wrist watch based system, which can measure electrocardiogram (ECG) and photoplethysmogram (PPG), is presented in this work. By using both ECG and PPG we also measure pulse transit time (PTT), which studies show to correlate well with blood pressure (BP). The system is also capable of monitoring heart rate using either ECG or PPG and can monitor blood oxygenation by easily replacing the PPG sensors with a different set. In this work, we investigate methods to train a fitting function to convert a PTT measurement to its corresponding systolic BP. We also validate measurements on different postures and show the value of calibrating the device for each posture. This system, called BioWatch, can potentially facilitate continuous and ubiquitous monitoring of ECG, PPG, heart rate, blood oxygenation and BP.

BioWatch: A Noninvasive Wrist-Based Blood Pressure Monitor That Incorporates Training Techniques for Posture and Subject Variability

Noninvasive continuous blood pressure (BP) monitoring is not yet practically available for daily use. Challenges include making the system easily wearable, reducing noise level and improving accuracy. Variations in each person's physical characteristics, as well as the possibility of different postures, increase the complexity of continuous BP monitoring, especially outside the hospital. This study attempts to provide an easily wearable solution and proposes training to specific posture and individual for further improving accuracy. The wrist watch-based system we developed can measure electrocardiogram and photoplethysmogram. From these two signals, we measure pulse transit time through which we can obtain systolic and diastolic blood pressure through regression techniques. In this study, we investigate various functions to perform the training to obtain blood pressure. We validate measurements on different postures and subjects, and show the value of training the device to each posture and each subject. We observed that the average RMSE between the measured actual systolic BP and calculated systolic BP is between 7.83 to 9.37 mmHg across 11 subjects. The corresponding range of error for diastolic BP is 5.77 to 6.90 mmHg. The system can also automatically detect the arm position of the user using an accelerometer with an average accuracy of 98%, to make sure that the sensor is kept at the proper height. This system, called BioWatch, can potentially be a unified solution for heart rate, SPO2 and continuous BP monitoring.

Validation of new and existing decision rules for the estimation of beat-to-beat pulse transit time

Pulse transit time (PTT) is a pivotal marker of vascular stiffness. Because the actual PTT duration in vivo is unknown and the complicated variation in waveform may occur, the robust determination of characteristic point is still a very difficult task in the PTT estimation. Our objective is to devise a method for real-time estimation of PTT duration in pulse wave. It has an ability to reduce the interference caused by both high- and low-frequency noise. The reproducibility and performance of these methods are assessed on both artificial and clinical pulse data. Artificial data are generated to investigate the reproducibility with various signal-to-noise ratios. For all artificial data, the mean biases obtained from all methods are less than 1 ms; collectively, this newly proposed method has minimum standard deviation (SD, <1 ms). A set of data from 33 participants together with the synchronously recorded continuous blood pressure data are used to investigate the correlation coefficient (CC). The statistical analysis shows that our method has maximum values of mean CC (0.5231), sum of CCs (17.26), and median CC (0.5695) and has the minimum SD of CCs (0.1943). Overall, the test results in this study indicate that the newly developed method has advantages over traditional decision rules for the PTT measurement.

A survey on signals and systems in ambulatory blood pressure monitoring using pulse transit time

Blood pressure monitoring based on pulse transit or arrival time has been the focus of much research in order to design ambulatory blood pressure monitors. The accuracy of these monitors is limited by several challenges, such as acquisition and processing of physiological signals as well as changes in vascular tone and the pre-ejection period. In this work, a literature survey covering recent developments is presented in order to identify gaps in the literature. The findings of the literature are classified according to three aspects. These are the calibration of pulse transit/arrival times to blood pressure, acquisition and processing of physiological signals and finally, the design of fully integrated blood pressure measurement systems. Alternative technologies as well as locations for the measurement of the pulse wave signal should be investigated in order to improve the accuracy during calibration. Furthermore, the integration and validation of monitoring systems needs to be improved in current ambulatory blood pressure monitors.

SMART DOCS: a new patient-centered outcomes and coordinated-care management approach for the future practice of sleep medicine

ABSTRACT

The practice of medicine is currently undergoing a transformation to become more efficient, cost-effective, and patient centered in its delivery of care. The aim of this article is to stimulate discussion within the sleep medicine community in addressing these needs by our approach as well as other approaches to sleep medicine care. The primary goals of the Sustainable Methods, Algorithms, and Research Tools for Delivering Optimal Care Study (SMART DOCS) are: (1) to introduce a new Patient-Centered Outcomes and Coordinated-Care Management (PCCM) approach for the future practice of sleep medicine, and (2) to test the PCCM approach against a Conventional Diagnostic and Treatment Outpatient Medical Care (CONV) approach in a randomized, two-arm, single-center, long-term, comparative effectiveness trial. The PCCM approach is integrated into a novel outpatient care delivery model for patients with sleep disorders that includes the latest technology, allowing providers to obtain more accurate and rapid diagnoses and to make evidence-based treatment recommendations, while simultaneously enabling patients to have access to personalized medical information and reports regarding their diagnosis and treatment so that they can make more informed health care decisions. Additionally, the PCCM approach facilitates better communication between patients, referring primary care physicians, sleep specialists, and allied health professionals so that providers can better assist patients in achieving their preferred outcomes. A total of 1,506 patients 18 y or older will be randomized to either the PCCM or CONV approach and will be followed for at least 1 y with endpoints of improved health care performance, better health, and cost control.

CLINICAL TRIALS NUMBER

http://www.clinicaltrials.gov, NCT02037438.