Cuffless Blood Pressure Research Articles

53. Clinical Uses of Cuffless Blood Pressure Monitoring Devices Using Photoplethysmography as A Promising Ambulatory Telemonitoring Tool: A Systematic Review and Meta-Analysis

Introduction: As check-ups in healthcare facilities are much arduous during the pandemic including blood pressure (BP) control, an alternative is urgently needed to replace the use of disturbing cuff-based office and ambulatory BP monitoring (BPM) devices. With the advancement of telemedicine, real-time checking and reporting of blood pressure may be potentially achieved using photoplethysmography (PPG) technology in cuffless devices. Therefore, this study evaluated the accuracy of these devices compared to the cuff-based BPM devices. Methods: This systematic review and meta-analysis was conducted based on the PRISMA 2020 guideline through multiple databases using Rayyan according to the prearranged inclusion and exclusion criteria, yielding six clinical studies to be included in the final review and analysis. Results: Overall fixed-effect meta-analysis of all studies (total of 319 subjects) presented small differences between cuffless and cuff-based devices, showing promising accuracy according to the current medical instrumentation guideline both in measuring systolic BP (SMD: 0.23 mmHg [95% CI: 0.07–0.39], p = 0.004; I2 = 0%, p = 0.55) and diastolic BP (SMD: 0.27 mmHg [95% CI: 0.11–0.43], p = 0.0007; I2 = 39%, p = 0.14). Discussion: PPG itself is a noninvasive technology, consisting of an infrared-emitting light source and a photodetector to measure the blood-reflected light intensity. Despite its ease in equipment, it measures BP accurately without being influenced by various positions and activities. Moreover, the data can be accessed real-time by both users and healthcare providers. Conclusion: In summary, cuffless PPG BPM devices have the potential in becoming a telemonitoring device for ambulatory patients for its accuracy. Its presence may be the answer to current restriction towards healthcare access during the COVID-19 pandemic. Therefore, in order to further confirm our findings, more clinical studies with various settings are encouraged to be held.

A Multi-Parameter Fusion Method for Cuffless Continuous Blood Pressure Estimation Based on Electrocardiogram and Photoplethysmogram

Blood pressure (BP) is an essential physiological indicator to identify and determine health status. Compared with the isolated BP measurement conducted by traditional cuff approaches, cuffless BP monitoring can reflect the dynamic changes in BP values and is more helpful to evaluate the effectiveness of BP control. In this paper, we designed a wearable device for continuous physiological signal acquisition. Based on the collected electrocardiogram (ECG) and photoplethysmogram (PPG), we proposed a multi-parameter fusion method for noninvasive BP estimation. An amount of 25 features were extracted from processed waveforms and Gaussian copula mutual information (MI) was introduced to reduce feature redundancy. After feature selection, random forest (RF) was trained to realize systolic BP (SBP) and diastolic BP (DBP) estimation. Moreover, we used the records in public MIMIC-III as the training set and private data as the testing set to avoid data leakage. The mean absolute error (MAE) and standard deviation (STD) for SBP and DBP were reduced from 9.12 ± 9.83 mmHg and 8.31 ± 9.23 mmHg to 7.93 ± 9.12 mmHg and 7.63 ± 8.61 mmHg by feature selection. After calibration, the MAE was further reduced to 5.21 mmHg and 4.15 mmHg. The result showed that MI has great potential in feature selection during BP prediction and the proposed multi-parameter fusion method can be used for long-term BP monitoring.

Continuous cuffless blood pressure monitoring with a wearable ring bioimpedance device

Smart rings provide unique opportunities for continuous physiological measurement. They are easy to wear, provide little burden in comparison to other smart wearables, are suitable for nocturnal settings, and can be sized to provide ideal contact between the sensors and the skin at all times. Continuous measuring of blood pressure (BP) provides essential diagnostic and prognostic value for cardiovascular health management. However, conventional ambulatory BP measurement devices operate using an inflating cuff that is bulky, intrusive, and impractical for frequent or continuous measurements. We introduce ring-shaped bioimpedance sensors leveraging the deep tissue sensing ability of bioimpedance while introducing no sensitivity to skin tones, unlike optical modalities. We integrate unique human finger finite element model with exhaustive experimental data from participants and derive optimum design parameters for electrode placement and sizes that yields highest sensitivity to arterial volumetric changes, with no discrimination against varying skin tones. BP is constructed using machine learning algorithms. The ring sensors are used to estimate arterial BP showing peak correlations of 0.81, and low error (systolic BP: 0.11 ± 5.27 mmHg, diastolic BP: 0.11 ± 3.87 mmHg) for >2000 data points and wide BP ranges (systolic: 89–213 mmHg and diastolic: 42–122 mmHg), highlighting the significant potential use of bioimpedance ring for accurate and continuous estimation of BP.

A Novel Cuffless Blood Pressure Prediction: Uncovering New Features and New Hybrid ML Models.

This paper investigates new feature extraction and regression methods for predicting cuffless blood pressure from PPG signals. Cuffless blood pressure is a technology that measures blood pressure without needing a cuff. This technology can be used in various medical applications, including home health monitoring, clinical uses, and portable devices. The new feature extraction method involves extracting meaningful features (time and chaotic features) from the PPG signals in the prediction of systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. These extracted features are then used as inputs to regression models, which are used to predict cuffless blood pressure. The regression model performances were evaluated using root mean squared error (RMSE), R2, mean square error (MSE), and the mean absolute error (MAE). The obtained RMSE was 4.277 for systolic blood pressure (SBP) values using the Matérn 5/2 Gaussian process regression model. The obtained RMSE was 2.303 for diastolic blood pressure (DBP) values using the rational quadratic Gaussian process regression model. The results of this study have shown that the proposed feature extraction and regression models can predict cuffless blood pressure with reasonable accuracy. This study provides a novel approach for predicting cuffless blood pressure and can be used to develop more accurate models in the future.

An Unobtrusive, Wireless and Wearable Single-Site Blood Pressure Monitor Based on an Armband Using Electrocardiography (ECG) and Reflectance Photoplethysmography (PPG) Signal Processing

Wearable medical devices (WMDs) for healthcare applications have become ubiquitous, allowing remote, at-home, and real-time chronic monitoring that have significantly decongested clinics. These WMDs permitted the monitoring of several physiological parameters, such as heart and respiration rates, SPO2, temperature, and energy expenditure during activities of daily living (ADLs) or fitness activities. While the measurement of these parameters has become common, full noninvasive, unobtrusive, and real-time blood pressure (BP) monitoring remains elusive owing to BP’s complex dynamics. To bring this into fruition, several works have been conducted combining different biosignals to indirectly extract BP by using PTT. Unlike previous works, we considered PTT variability by averaging it over discrete durations to account for BP variability for a more accurate estimation. PTTs were obtained using electrocardiograph (ECG) and reflective photoplethysmograph (rPPG) signals extracted by a wearable device attached to a single site on the upper arm. Our results show a significant correlation between average PTT and the BP measured using auscultation in a trial study. The developed system has potential for chronic, noninvasive, and cuff-less blood pressure monitors (BPMs) for localized and single-site implementations. Meanwhile, real-time data from the wearable device may be accessed via a remote desktop or a mobile phone application.

Cuff-Less Blood Pressure Prediction Based on Photoplethysmography and Modified ResNet.

Cardiovascular disease (CVD) has become a common health problem of mankind, and the prevalence and mortality of CVD are rising on a year-to-year basis. Blood pressure (BP) is an important physiological parameter of the human body and also an important physiological indicator for the prevention and treatment of CVD. Existing intermittent measurement methods do not fully indicate the real BP status of the human body and cannot get rid of the restraining feeling of a cuff. Accordingly, this study proposed a deep learning network based on the ResNet34 framework for continuous prediction of BP using only the promising PPG signal. The high-quality PPG signals were first passed through a multi-scale feature extraction module after a series of pre-processing to expand the perceptive field and enhance the perception ability on features. Subsequently, useful feature information was then extracted by stacking multiple residual modules with channel attention to increase the accuracy of the model. Lastly, in the training stage, the Huber loss function was adopted to stabilize the iterative process and obtain the optimal solution of the model. On a subset of the MIMIC dataset, the errors of both SBP and DBP predicted by the model met the AAMI standards, while the accuracy of DBP reached Grade A of the BHS standard, and the accuracy of SBP almost reached Grade A of the BHS standard. The proposed method verifies the potential and feasibility of PPG signals combined with deep neural networks in the field of continuous BP monitoring. Furthermore, the method is easy to deploy in portable devices, and it is more consistent with the future trend of wearable blood-pressure-monitoring devices (e.g., smartphones and smartwatches).

An Ultra‐Sensitive Flexible Resistive Sensor with Double Strain Layer and Crack Inspired by the Physical Structure of Human Epidermis: Design, Fabrication, and Cuffless Blood Pressure Monitoring Application

AbstractThe cuffless blood pressure (BP) monitoring device has attracted much attention because of its comfortable and real‐time monitoring. Inspired by the physical structure of the human epidermis, an ultra‐sensitive flexible resistive sensor with a double strain layer and crack structure is prepared by screen printing in this study. The strain material of the sensor is mainly prepared by blending Poly(3,4‐ethylenedioxythiophene) :poly(styrenesulfonate) (PEDOT:PSS) with waterborne polyurethane and reduced graphene oxide. The sensor exhibits a high gauge factor (GF, ≈1682), fast response (≈48 ms), and long‐term stability (> 2000 cycles) at low strain (≈0–5%). The sensor is placed on the human radial artery and can accurately measure the pulse wave. The features of the pulse wave are automatically extracted using a convolutional neural network. Then the features are corrected using a long short‐term memory neural network, and the current BP value is predicted using a fully connected network layer. The BP result meets the A‐level standards of the Association for the Advancement of Medical Instrumentation and the British Hypertension Society. This study provides an efficient device and method for continuous and non‐invasive BP monitoring.

A Machine Learning Approach to the Non-Invasive Estimation of Continuous Blood Pressure Using Photoplethysmography

Blood pressure is an important vital sign that sometimes requires continuous measurement. The current methods include cuff measurements (manual auscultation and oscillometric techniques) for non-continuous measurement and invasive arterial cannulation for continuous measurement. The use of photoplethysmography as a cuffless, non-invasive, and continuous blood pressure measurement system is investigated through the use of four neural networks. These predict the systolic blood pressure, diastolic blood pressure, mean arterial blood pressure, and waveform shape. The models are trained on 890 h of data from 1669 patients in the MIMIC-III database. Feature-trained artificial neural networks predict the systolic blood pressure to 5.26 ± 6.53 mmHg (mean error ± standard deviation), the diastolic blood pressure to 2.96 ± 3.31 mmHg, and the mean arterial pressure to 3.27 ± 3.55 mmHg. These are used to shift and scale the predicted waveform, allowing the waveform prediction neural network to optimise for the wave shape rather than the amplitude. The waveform prediction has 86.4% correlation with the actual arterial blood pressure waveform. All results meet international clinical blood pressure measurement standards and could potentially change how blood pressure is measured in both clinical and research settings. However, more data from healthy individuals and analysis of the models’ biases based on clinical features is required.

Scenario Adaptive Cuffless Blood Pressure Estimation by Integrating Cardiovascular Coupling Effects.

Adding cuffless blood pressure (BP) measurement function to wearable devices is of great value in the fight against hypertension. The widely used arterial pulse transit time (PTT)-based method for BP monitoring relies primarily on vascular status-determined BP models and typically exhibits degraded performance over time and is sensitive to measurement procedures. Developing alternative methods with improved accuracy and adaptability to various application scenarios is highly desired for cuffless BP measurement. In this work, we proposed a pattern-fusion (PF) method that incorporates cardiovascular coupling effects in the vascular model by combining three calculation modules - cardiac parameter extraction module, cardiac parameter-to-BP mapping module, and BP regulation module. Specifically, the first module combines feedforward, feedback, and propagation modes to model different modulation functions of a cardiovascular system and is responsible for extracting BP-related features from electrocardiography (ECG) and photoplethysmography (PPG) signals; the cardiac parameter-to-BP mapping module is used to map cardiac parameters into mean blood pressure (MBP) by fusing different features; finally, the BP regulation module recovers accurate systolic BP (SBP) and diastolic BP (DBP) from given MBP. With the concerted use of these three modules, the pattern fusion method consistently demonstrates excellent BP prediction accuracy in a variety of measurement scenarios and durations, exhibiting SBP/DBP mean absolute error (MAE) of 3.65/4.56 mmHg for the short-term (<10 mins) continuous measurement dataset, SBP/DBP MAE of 6.84/3.81 mmHg for the medium-term (avg. > 20 hours) continuous measurement dataset, and SBP/DBP MAE of 6.24/3.65 mmHg for the long-term (> 1 month) intermittent measurement dataset.

An Easy-to-Use Hand-to-Hand Impedance-Based Sensor to Obtain Carotid Pulse Arrival Time

Pulse arrival times (PATs) are time intervals between the electrocardiogram (ECG) and an arterial pulse wave (APW) widely used to obtain cuffless blood pressure (BP). Although some distal APWs can be easily measured using, for example, a photoplethysmography (PPG) sensor on a finger, proximal APWs, such as the one of the carotid in the neck, are much more difficult to obtain for unskilled users. To overcome this, we propose an impedance-based sensor using only two electrodes in contact with each upper extremity (one to inject current and one to measure voltage at each hand or wrist), which can measure both ECG and an APW [an impedance plethysmogram, (IPG)] to obtain the PAT. Since the injected current flows from hand to hand across the upper torso, we hypothesize that the measured IPG should be sensitive to the arrival of the APW near the heart and, therefore, that the obtained PAT could be used as a surrogate of a proximal PAT even when measured on the hands. We have verified this by comparing the hand-to-hand impedance PAT obtained with the sensor with a gold-standard proximal PAT obtained in the carotid by tonometry in a cohort of 84 volunteers aged 20–61 years, showing a correlation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${r} =0.90$ </tex-math></inline-formula> between the two. These results support the feasibility of future inclusion of these proximal PAT measurements in easy-to-use devices to obtain cuffless BP or other cardiovascular information with improved performance by nonspecialist users outside clinical settings.

Upper-Arm Photoplethysmographic Sensor with One-Time Calibration for Long-Term Blood Pressure Monitoring.

Wearable cuffless photoplethysmographic blood pressure monitors have garnered widespread attention in recent years; however, the long-term performance values of these devices are questionable. Most cuffless blood pressure monitors require initial baseline calibration and regular recalibrations with a cuffed blood pressure monitor to ensure accurate blood pressure estimation, and their estimation accuracy may vary over time if left uncalibrated. Therefore, this study assessed the accuracy and long-term performance of an upper-arm, cuffless photoplethysmographic blood pressure monitor according to the ISO 81060-2 standard. This device was based on a nonlinear machine-learning model architecture with a fine-tuning optimized method. The blood pressure measurement protocol followed a validation procedure according to the standard, with an additional four weekly blood pressure measurements over a 1-month period, to assess the long-term performance values of the upper-arm, cuffless photoplethysmographic blood pressure monitor. The results showed that the photoplethysmographic signals obtained from the upper arm had better qualities when compared with those measured from the wrist. When compared with the cuffed blood pressure monitor, the means ± standard deviations of the difference in BP at week 1 (baseline) were -1.36 ± 7.24 and -2.11 ± 5.71 mmHg for systolic and diastolic blood pressure, respectively, which met the first criterion of ≤5 ± ≤8.0 mmHg and met the second criterion of a systolic blood pressure ≤ 6.89 mmHg and a diastolic blood pressure ≤ 6.84 mmHg. The differences in the uncalibrated blood pressure values between the test and reference blood pressure monitors measured from week 2 to week 5 remained stable and met both criteria 1 and 2 of the ISO 81060-2 standard. The upper-arm, cuffless photoplethysmographic blood pressure monitor in this study generated high-quality photoplethysmographic signals with satisfactory accuracy at both initial calibration and 1-month follow-ups. This device could be a convenient and practical tool to continuously measure blood pressure over long periods of time.

Blood pressure stratification using photoplethysmography and light gradient boosting machine.

Introduction: Globally, hypertension (HT) is a substantial risk factor for cardiovascular disease and mortality; hence, rapid identification and treatment of HT is crucial. In this study, we tested the light gradient boosting machine (LightGBM) machine learning method for blood pressure stratification based on photoplethysmography (PPG), which is used in most wearable devices. Methods: We used 121 records of PPG and arterial blood pressure (ABP) signals from the Medical Information Mart for Intensive Care III public database. PPG, velocity plethysmography, and acceleration plethysmography were used to estimate blood pressure; the ABP signals were used to determine the blood pressure stratification categories. Seven feature sets were established and used to train the Optuna-tuned LightGBM model. Three trials compared normotension (NT) vs. prehypertension (PHT), NT vs. HT, and NT + PHT vs. HT. Results: The F1 scores for these three classification trials were 90.18%, 97.51%, and 92.77%, respectively. The results showed that combining multiple features from PPG and its derivative led to a more accurate classification of HT classes than using features from only the PPG signal. Discussion: The proposed method showed high accuracy in stratifying HT risks, providing a noninvasive, rapid, and robust method for the early detection of HT, with promising applications in the field of wearable cuffless blood pressure measurement.

Using Ballistocardiogram and Impedance Plethysmogram for Minimal Contact Measurement of Blood Pressure Based on a Body Weight-Fat Scale.

Electronic health (eHealth) is a strategy to improve the physical and mental condition of a human, collecting daily physiological data and information from digital apparatuses. Body weight and blood pressure (BP) are the most popular and important physiological data. The goal of this study is to develop a minimal contact BP measurement method based on a commercial body weight-fat scale, capturing biometrics when users stand on it. The pulse transit time (PTT) is extracted from the ballistocardiogram (BCG) and impedance plethysmogram (IPG), measured by four strain gauges and four footpads of a commercial body weight-fat scale. Cuffless BP measurement using the electrocardiogram (ECG) and photoplethysmogram (PPG) serves as the reference method. The BP measured by a commercial BP monitor is considered the ground truth. Twenty subjects participated in this study. By the proposed model, the root-mean-square errors and correlation coefficients (r2s) of estimated systolic blood pressure and diastolic blood pressure are 7.3 ± 2.1 mmHg and 4.5 ± 1.8 mmHg, and 0.570 ± 0.205 and 0.284 ± 0.166, respectively. This accuracy level achieves the C grade of the corresponding IEEE standard. Thus, the proposed method has the potential benefit for eHealth monitoring in daily application.

Photoplethysmography Signal Wavelet Enhancement and Novel Features Selection for Non-Invasive Cuff-Less Blood Pressure Monitoring.

In this paper, new features relevant to blood pressure (BP) estimation using photoplethysmography (PPG) are presented. A total of 195 features, including the proposed ones and those already known in the literature, have been calculated on a set composed of 50,000 pulses from 1080 different patients. Three feature selection methods, namely Correlation-based Feature Selection (CFS), RReliefF and Minimum Redundancy Maximum Relevance (MRMR), have then been applied to identify the most significant features for BP estimation. Some of these features have been extracted through a novel PPG signal enhancement method based on the use of the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the enhanced signal leads to a reliable identification of the characteristic points of the PPG signal (e.g., systolic, diastolic and dicrotic notch points) by simple means, obtaining results comparable with those from purposely defined algorithms. For systolic points, mean and std of errors computed as the difference between the locations obtained using a purposely defined already known algorithm and those using the MODWT enhancement are, respectively, 0.0097 s and 0.0202 s; for diastolic points they are, respectively, 0.0441 s and 0.0486 s; for dicrotic notch points they are 0.0458 s and 0.0896 s. Hence, this study leads to the selection of several new features from the MODWT enhanced signal on every single pulse extracted from PPG signals, in addition to features already known in the literature. These features can be employed to train machine learning (ML) models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) in a non-invasive way, which is suitable for telemedicine health-care monitoring.

Combining Gaussian Process with Hybrid Optimal Feature Decision in Cuffless Blood Pressure Estimation.

Noninvasive blood pressure estimation is crucial for cardiovascular and hypertension patients. Cuffless-based blood pressure estimation has received much attention recently for continuous blood pressure monitoring. This paper proposes a new methodology that combines the Gaussian process with hybrid optimal feature decision (HOFD) in cuffless blood pressure estimation. First, we can choose one of the feature selection methods: robust neighbor component analysis (RNCA), minimum redundancy, maximum relevance (MRMR), and F-test, based on the proposed hybrid optimal feature decision. After that, a filter-based RNCA algorithm uses the training dataset to obtain weighted functions by minimizing the loss function. Next, we combine the Gaussian process (GP) algorithm as the evaluation criteria, which is used to determine the best feature subset. Hence, combining GP with HOFD leads to an effective feature selection process. The proposed combining Gaussian process with the RNCA algorithm shows that the root mean square errors (RMSEs) for the SBP (10.75 mmHg) and DBP (8.02 mmHg) are lower than those of the conventional algorithms. The experimental results represent that the proposed algorithm is very effective.

PulseDB: A large, cleaned dataset based on MIMIC-III and VitalDB for benchmarking cuff-less blood pressure estimation methods.

There has been a growing interest in developing cuff-less blood pressure (BP) estimation methods to enable continuous BP monitoring from electrocardiogram (ECG) and/or photoplethysmogram (PPG) signals. The majority of these methods have been evaluated using publicly-available datasets, however, there exist significant discrepancies across studies with respect to the size, the number of subjects, and the applied pre-processing steps for the data that is eventually used for training and testing the models. Such differences make conducting performance comparison across models largely unfair, and mask the generalization capability of various BP estimation methods. To fill this important gap, this paper presents "PulseDB," the largest cleaned dataset to date, for benchmarking BP estimation models that also fulfills the requirements of standardized testing protocols. PulseDB contains 1) 5,245,454 high-quality -s segments of ECG, PPG, and arterial BP (ABP) waveforms from 5,361 subjects retrieved from the MIMIC-III waveform database matched subset and the VitalDB database; 2) subjects' identification and demographic information, that can be utilized as additional input features to improve the performance of BP estimation models, or to evaluate the generalizability of the models to data from unseen subjects; and 3) positions of the characteristic points of the ECG/PPG signals, making PulseDB directly usable for training deep learning models with minimal data pre-processing. Additionally, using this dataset, we conduct the first study to provide insights about the performance gap between calibration-based and calibration-free testing approaches for evaluating generalizability of the BP estimation models. We expect PulseDB, as a user-friendly, large, comprehensive and multi-functional dataset, to be used as a reliable source for the evaluation of cuff-less BP estimation methods.

Development of Real-Time Cuffless Blood Pressure Measurement Systems with ECG Electrodes and a Microphone Using Pulse Transit Time (PTT).

Research has shown that pulse transit time (PTT), which is the time delay between the electrocardiogram (ECG) signal and the signal from a photoplethysmogram (PPG) sensor, can be used to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) without the need for a cuff. However, the LED of the PPG sensor requires the precise adjustment of both light intensity and light absorption rates according to the contact status of the light-receiving element. This results in the need for regular calibration. In this study, we propose a cuffless blood pressure monitor that measures real-time blood pressure using a microphone instead of a PPG sensor. The blood pulse wave is measured in the radial artery of the wrist using a microphone that can directly measure the sound generated by a body rather than sending energy inside the body and receiving a returning signal. Our blood pressure monitor uses the PTT between the R-peak of the ECG signal and two feature points of the blood pulse wave in the radial artery of the wrist. ECG electrodes and circuits were fabricated, and a commercial microelectromechanical system (MEMS) microphone was used as the microphone to measure blood pulses. The peak points of the blood pulse from the microphone were clear, so the estimated SBP and DBP could be obtained from each ECG pulse in real time, and the resulting estimations were similar to those made by a commercial cuff blood pressure monitor. Since neither the ECG electrodes nor the microphone requires calibration over time, the real-time cuffless blood pressure monitor does not require calibration. Using the developed device, blood pressure was measured three times daily for five days, and the mean absolute error (MAE) and standard deviation (SD) of the SBP and DBP were found to be 2.72 ± 3.42 mmHg and 2.29 ± 3.53 mmHg, respectively. As a preliminary study for proof-of-concept, these results were obtained from one subject. The next step will be a pilot study on a large number of subjects.

Features from the photoplethysmogram and the electrocardiogram for estimating changes in blood pressure

There is a growing emphasis being placed on the potential for cuffless blood pressure (BP) estimation through modelling of morphological features from the photoplethysmogram (PPG) and electrocardiogram (ECG). However, the appropriate features and models to use remain unclear. We investigated the best features available from the PPG and ECG for BP estimation using both linear and non-linear machine learning models. We conducted a clinical study in which changes in BP (DeltaBP) were induced by an infusion of phenylephrine in 30 healthy volunteers (53.8% female, 28.0 (9.0) years old). We extracted a large and diverse set of features from both the PPG and the ECG and assessed their individual importance for estimating DeltaBP through Shapley additive explanation values and a ranking coefficient. We trained, tuned, and evaluated linear (ordinary least squares, OLS) and non-linear (random forest, RF) machine learning models to estimate DeltaBP in a nested leave-one-subject-out cross-validation framework. We reported the results as correlation coefficient (rho _p), root mean squared error (RMSE), and mean absolute error (MAE). The non-linear RF model significantly (p<0.05) outperformed the linear OLS model using both the PPG and the ECG signals across all performance metrics. Estimating DeltaSBP using the PPG alone (rho _p = 0.86 (0.23), RMSE = 5.66 (4.76) mmHg, MAE = 4.86 (4.29) mmHg) performed significantly better than using the ECG alone (rho _p = 0.69 (0.45), RMSE = 6.79 (4.76) mmHg, MAE = 5.28 (4.57) mmHg), all p < 0.001. The highest ranking features from the PPG largely modelled increasing reflected wave interference driven by changes in arterial stiffness. This finding was supported by changes observed in the PPG waveform in response to the phenylephrine infusion. However, a large number of features were required for accurate BP estimation, highlighting the high complexity of the problem. We conclude that the PPG alone may be further explored as a potential single source, cuffless, blood pressure estimator. The use of the ECG alone is not justified. Non-linear models may perform better as they are able to incorporate interactions between feature values and demographics. However, demographics may not adequately account for the unique and individualised relationship between the extracted features and BP.

Cuffless Blood Pressure Estimation with Confidence Intervals using Hybrid Feature Selection and Decision Based on Gaussian Process

Cuffless blood pressure (BP) monitoring is crucial for patients with cardiovascular disease and hypertension. However, conventional BP monitors provide only single-point estimates without confidence intervals. Therefore, the statistical variability in the estimates is indistinguishable from the intrinsic variability caused by physiological processes. This study introduced a novel method for improving the reliability of BP and confidence intervals (CIs) estimations using a hybrid feature selection and decision method based on a Gaussian process. F-test and robust neighbor component analysis were applied as feature selection methods for obtaining a set of highly weighted features to estimate accurate BP and CIs. Akaike’s information criterion algorithm was used to select the best feature subset. The performance of the proposed algorithm was confirmed through experiments. Comparisons with conventional algorithms indicated that the proposed algorithm provided the most accurate BP and CIs estimates. To the best of the authors’ knowledge, the proposed method is currently the only one that provides highly reliable BP and CIs estimates. Therefore, the proposed algorithm may be robust for concurrently estimating BP and CIs.

PS-BPC03-1: DEVELOPMENT OF CUFFLESS AND CALIBRATION-LESS BLOOD PRESSURE MEASUREMENTS

Objective: The characteristics of dynamic short-term blood pressure variability caused by numerous triggers vary greatly among individuals. Wearable devices that continuously and noninvasively capture beat-by-beat blood pressure values are a necessity to further study blood pressure. The development of cuffless blood pressure measurements, which, unlike sphygmomanometers, do not require pressurization and depressurization time, has attracted much attention in recent years for its potential to capture beat-by-beat blood pressure values. However, cuffless blood pressure measurements approved in Europe and the United States require calibration before use. This indicates that the function is limited to estimation of blood pressure variation from the time of calibration. In this study, we conducted a measurement test aiming at the practical application of the cuffless and calibration-less blood pressure measurements. Design and Method: In this study, we recruited adults aged 20 years and older to evaluate whether a wearable device without pre-calibration could meet the standards of BHS, ESH, ISO81060–2:2018 and IEEE1708a-2019 and ensure the same accuracy as existing sphygmomanometers. For each measurement test, the cuff was wrapped around the subject's upper left arm and a wearable device was attached to the ipsilateral wrist, and the subject was asked to maintain a sitting posture. Measurements were taken with the reference sphygmomanometer every 3 minutes for 7 times. To minimize the effect of the venous congestion on the measurements, the measurement period with the wearable device was from 30 seconds after the completion of the cuff depressurization until the start of the next pressurization. The blood pressure value measured by the wearable device was evaluated against the average of the values measured by the reference sphygmomanometer before and after. Results: 219 participants were included in the analysis. Participants with systolic blood pressure values ranging from 80 to 204 and diastolic blood pressure values ranging from 46 to 140 were included. The mean and SD of the differences between the estimates and the reference were -1.27 ± 4.97 mmHg for SBP and 0.74 ± 2.55 mmHg for DBP. Conclusion: The overall accuracy of the cuffless and calibration-less blood pressure measurements device satisfied the criteria shown in ISO81060–2 against various adults in the sitting posture. This study showed the strong potential of the cuffless and calibration-less blood pressure measurements.