DNN-BP: a novel framework for cuffless blood pressure measurement from optimal PPG features using deep learning model

Continuous blood pressure (BP) provides essential information for monitoring one's health condition. However, BP is currently monitored using uncomfortable cuff-based devices, which does not support continuous BP monitoring. This paper aims to introduce a blood pressure monitoring algorithm based on only photoplethysmography (PPG) signals using the deep neural network (DNN). The PPG signals are obtained from 125 unique subjects with 218 records and filtered using signal processing algorithms to reduce the effects of noise, such as baseline wandering, and motion artifacts. The proposed algorithm is based on pulse wave analysis of PPG signals, extracted various domain features from PPG signals, and mapped them to BP values. Four feature selection methods are applied and yielded four feature subsets. Therefore, an ensemble feature selection technique is proposed to obtain the optimal feature set based on major voting scores from four feature subsets. DNN models, along with the ensemble feature selection technique, outperformed in estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP) compared to previously reported approaches that rely only on the PPG signal. The coefficient of determination ( R 2 ) and mean absolute error (MAE) of the proposed algorithm are 0.962 and 2.480 mmHg, respectively, for SBP and 0.955 and 1.499 mmHg, respectively, for DBP. The proposed approach meets the Advancement of Medical Instrumentation standard for SBP and DBP estimations. Additionally, according to the British Hypertension Society standard, the results attained Grade A for both SBP and DBP estimations. It concludes that BP can be estimated more accurately using the optimal feature set and DNN models. The proposed algorithm has the potential ability to facilitate mobile healthcare devices to monitor continuous BP.

HGCTNet: Handcrafted Feature-Guided CNN and Transformer Network for Wearable Cuffless Blood Pressure Measurement

Biosignals collected by wearable devices, such as electrocardiogram and photoplethysmogram, exhibit redundancy and global temporal dependencies, posing a challenge in extracting discriminative features for blood pressure (BP) estimation. To address this challenge, we propose HGCTNet, a handcrafted feature-guided CNN and transformer network for cuffless BP measurement based on wearable devices. By leveraging convolutional operations and self-attention mechanisms, we design a CNN-Transformer hybrid architecture to learn features from biosignals that capture both local information and global temporal dependencies. Then, we introduce a handcrafted feature-guided attention module that utilizes handcrafted features extracted from biosignals as query vectors to eliminate redundant information within the learned features. Finally, we design a feature fusion module that integrates the learned features, handcrafted features, and demographics to enhance model performance. We validate our approach using two large wearable BP datasets: the CAS-BP dataset and the Aurora-BP dataset. Experimental results demonstrate that HGCTNet achieves an estimation error of 0.9 ± 6.5 mmHg for diastolic BP (DBP) and 0.7 ± 8.3 mmHg for systolic BP (SBP) on the CAS-BP dataset. On the Aurora-BP dataset, the corresponding errors are -0.4 ± 7.0 mmHg for DBP and -0.4 ± 8.6 mmHg for SBP. Compared to the current state-of-the-art approaches, HGCTNet reduces the mean absolute error of SBP estimation by 10.68% on the CAS-BP dataset and 9.84% on the Aurora-BP dataset. These results highlight the potential of HGCTNet in improving the performance of wearable cuffless BP measurements.

Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload

Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilitate tracking blood pressure fluctuations in various conditions. In this work, data-driven photoplethysmograph features extracted from the brachial and digital arteries of 28 healthy subjects were used to feed a random forest classifier in an attempt to develop a system capable of tracking blood pressure. We evaluated the behavior of this latter classifier according to the different sizes of the training set and degrees of personalization used. Aggregated accuracy, precision, recall, and F1-score were equal to 95.1%, 95.2%, 95%, and 95.4% when 30% of a target subject's pulse waveforms were combined with five randomly selected source subjects available in the dataset. Experimental findings illustrated that incorporating a pre-training stage with data from different subjects made it viable to discern morphological distinctions in beat-to-beat pulse waveforms under conditions of cognitive or physical workload.

History of the development of isolated heart perfusion experimental model and its pioneering role in understanding heart physiology

The isolated heart perfusion model, a fundamental tool in cardiovascular research, has evolved significantly since its inception in the late 19th century. This review traces the development of the isolated heart model, from its early adaptations by pioneers such as Langendorff and Starling to modern advancements by researchers like Morgan and Neely. We discuss the various applications of the model in pharmacological testing, disease modeling, and educational settings, emphasizing its crucial role in understanding cardiac function and disease mechanisms. Recent technological enhancements, including high-resolution imaging, integration with bioengineering, and advanced genomic and proteomic analyses, have significantly broadened the capabilities of these models. Looking forward, we explore potential future developments such as the integration of precision medicine, stem cell research, and artificial intelligence, which promise to revolutionize the use of isolated heart perfusion models. This review highlights the model's crucial role in bridging experimental research and clinical applications.

Flexible Strain Sensors Based on an Interlayer Synergistic Effect of Nanomaterials for Continuous and Noninvasive Blood Pressure Monitoring

The continuous, noninvasive monitoring of human blood pressure (BP) through the accurate detection of pulse waves has extremely stringent requirements on the sensitivity and stability of flexible strain sensors. In this study, a new ultrasensitive flexible strain sensor based on the interlayer synergistic effect was fabricated through drop-casting and drying silver nanowires and graphene films on polydimethylsiloxane substrates and was further successfully applied for continuous monitoring of BP. This strain sensor exhibited ultrahigh sensitivity with a maximum gauge factor of 34357.2 (∼700% sensitivity enhancement over other major sensors), satisfactory response time (∼85 ms), wide strange range (12%), and excellent stability. An interlayer fracture mechanism was proposed to elucidate the working principle of the strain sensor. The real-time BP values can be obtained by analyzing the relationship between the BP and the pulse transit time. To verify our strain sensor for real-time BP monitoring, our strain sensor was compared with a conventional electrocardiogram-photoplethysmograph method and a commercial cuff-based device and showed similar measurement results to BP values from both methods, with only minor differences of 0.693, 0.073, and 0.566 mmHg in the systolic BP, diastolic BP, and mean arterial pressure, respectively. Furthermore, the reliability of the strain sensors was validated by testing 20 human subjects for more than 50 min. This ultrasensitive strain sensor provides a new pathway for continuous and noninvasive BP monitoring.

CiGNN: A Causality-Informed and Graph Neural Network Based Framework for Cuffless Continuous Blood Pressure Estimation

Causalityholds profound potentials to dissipate confusion and improve accuracy in cuffless continuous blood pressure (BP) estimation, an area often neglected in current research. In this study, we propose a two-stage framework, CiGNN, that seamlessly integrates causality and graph neural network (GNN) for cuffless continuous BP estimation. The first stage concentrates on the generation of a causal graph between BP and wearable features from the the perspective of causal inference, so as to identify features that are causally related to BP variations. This stage is pivotal for the identification of novel causal features from the causal graph beyond pulse transit time (PTT). We found these causal features empower better tracking in BP changes compared to PTT. For the second stage, a spatio-temporal GNN (STGNN) is utilized to learn from the causal graph obtained from the first stage. The STGNN can exploit both the spatial information within the causal graph and temporal information from beat-by-beat cardiac signals for refined cuffless continuous BP estimation. We evaluated the proposed method with three datasets that include 305 subjects (102 hypertensive patients) with age ranging from 20-90 and BP at different levels, with the continuous Finapres BP as references. The mean absolute difference (MAD) for estimated systolic blood pressure (SBP) and diastolic blood pressure (DBP) were 3.77 mmHg and 2.52 mmHg, respectively, which outperformed comparison methods. In all cases including subjects with different age groups, while doing various maneuvers that induces BP changes at different levels and with or without hypertension, the proposed CiGNN method demonstrates superior performance for cuffless continuous BP estimation. These findings suggest that the proposed CiGNN is a promising approach in elucidating the causal mechanisms of cuffless BP estimation and can substantially enhance the precision of BP measurement.

Calibration-free blood pressure estimation based on a convolutional neural network

In this study, we conducted research on a deep learning-based blood pressure (BP) estimation model suitable for wearable environments. To measure BP while wearing a wearable watch, it needs to be considered that computing power for signal processing is limited and the input signals are subject to noise interference. Therefore, we employed a convolutional neural network (CNN) as the BP estimation model and utilized time-series electrocardiogram (ECG) and photoplethysmogram (PPG) signals, which are quantifiable in a wearable context. We generated periodic input signals and used differential and thresholding methods to decrease noise in the preprocessing step. We then applied a max-pooling technique with filter sizes of 2 × 1 and 5 × 1 within a 3-layer convolutional neural network to estimate BP. Our method was trained, validated, and tested using 2.4 million data samples from 49 patients in the intensive care unit. These samples, totaling 3.1 GB were obtained from the publicly accessible MIMIC database. As a result of a test with 480,000 data samples, the average root mean square error in BP estimation was 3.41, 5.80, and 2.78 mm Hg in the prediction of pulse pressure, systolic BP (SBP), and diastolic BP (DBP), respectively. The cumulative error percentage less than 5 mm Hg was 68% and 93% for SBP and DBP, respectively. In addition, the cumulative error percentage less than 15 mm Hg was 98% and 99% for SBP and DBP. Subsequently, we evaluated the impact of changes in input signal length (1 cycle vs. 30 s) and the introduction of noise on BP estimation results. The experimental results revealed that the length of the input signal did not significantly affect the performance of CNN-based analysis. When estimating BP using noise-added ECG signals, the mean absolute error (MAE) for SBP and DBP was 9.72 and 6.67 mm Hg, respectively. Meanwhile, when using noise-added PPG signals, the MAE for SBP and DBP was 26.85 and 14.00 mm Hg, respectively. Therefore, this study confirmed that using ECG signals rather than PPG signals is advantageous for noise reduction in a wearable environment. Besides, short sampling frames without calibration can be effective as input signals. Furthermore, it demonstrated that using a model suitable for information extraction rather than a specialized deep learning model for sequential data can yield satisfactory results in BP estimation.

A Survey on Blood Pressure Measurement Technologies: Addressing Potential Sources of Bias

Regular blood pressure (BP) monitoring in clinical and ambulatory settings plays a crucial role in the prevention, diagnosis, treatment, and management of cardiovascular diseases. Recently, the widespread adoption of ambulatory BP measurement devices has been predominantly driven by the increased prevalence of hypertension and its associated risks and clinical conditions. Recent guidelines advocate for regular BP monitoring as part of regular clinical visits or even at home. This increased utilization of BP measurement technologies has raised significant concerns regarding the accuracy of reported BP values across settings. In this survey, which focuses mainly on cuff-based BP monitoring technologies, we highlight how BP measurements can demonstrate substantial biases and variances due to factors such as measurement and device errors, demographics, and body habitus. With these inherent biases, the development of a new generation of cuff-based BP devices that use artificial intelligence (AI) has significant potential. We present future avenues where AI-assisted technologies can leverage the extensive clinical literature on BP-related studies together with the large collections of BP records available in electronic health records. These resources can be combined with machine learning approaches, including deep learning and Bayesian inference, to remove BP measurement biases and provide individualized BP-related cardiovascular risk indexes.

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

Schrödinger spectrum based continuous cuff-less blood pressure estimation using clinically relevant features from PPG signal and its second derivative

The presented study estimates cuff-less blood pressure (BP) from photoplethysmography (PPG) signals using multiple machine-learning (ML) models and the semi-classical signal analysis (SCSA) technique. The study proposes a novel signal reconstruction algorithm, which optimizes the semi-classical constant of the SCSA approach and eliminates the trade-off between complexity and accuracy during signal reconstruction. It predicts BP values using regression algorithms by processing reconstructed PPG signals' spectral features, extracting clinically relevant PPG and its second derivative's (SDPPG) morphological features. The developed method was assessed using a virtual in-silico dataset with more than 4000 subjects and the Multi-Parameter Intelligent Monitoring in Intensive Care Units (MIMIC-II) dataset. Results showed that the method attained a mean absolute error (MAE) of 5.37 and 2.96 mmHg for systolic and diastolic BP, respectively, using the CatBoost algorithm. This approach met the Association for the Advancement of Medical Instrumentation's standard and achieved Grade A for all BP categories in the British Hypertension Society protocol. The proposed framework performs well even when applied to a combined clinically relevant database originating from MIMIC-III and the Queensland dataset. The proposed method's performance is further evaluated in a non-clinical setting with noisy and deformed PPG signals to validate the efficacy of the SCSA method. The noise stress tests further showed that the algorithm maintained its key feature detection, signal reconstruction capability, and estimation accuracy up to a 10 dB SNR ratio. The proposed cuff-less BP estimation technique has the potential to perform well in mobile healthcare devices due to its straightforward implementation approach.

Cuffless Blood Pressure Measurement Using Smartwatches: A Large-Scale Validation Study

This study aimed to evaluate the performance of cuffless blood pressure (BP) measurement techniques in a large and diverse cohort of participants. We enrolled 3077 participants (aged 18-75, 65.16% women, 35.91% hypertensive participants) and conducted followed-up for approximately 1 month. Electrocardiogram, pulse pressure wave, and multiwavelength photoplethysmogram signals were simultaneously recorded using smartwatches; dual-observer auscultation systolic BP (SBP) and diastolic BP (DBP) reference measurements were also obtained. Pulse transit time, traditional machine learning (TML), and deep learning (DL) models were evaluated with calibration and calibration-free strategy. TML models were developed using ridge regression, support vector machine, adaptive boosting, and random forest; while DL models using convolutional and recurrent neural networks. The best-performing calibration-based model yielded estimation errors of 1.33 ± 6.43 mmHg for DBP and 2.31 ± 9.57 mmHg for SBP in the overall population, with reduced SBP estimation errors in normotensive (1.97 ± 7.85 mmHg) and young (0.24 ± 6.61 mmHg) subpopulations. The best-performing calibration-free model had estimation errors of -0.29 ± 8.78 mmHg for DBP and -0.71 ± 13.04 mmHg for SBP. We conclude that smartwatches are effective for measuring DBP for all participants and SBP for normotensive and younger participants with calibration; performance degrades significantly for heterogeneous populations including older and hypertensive participants. The availability of cuffless BP measurement without calibration is limited in routine settings. Our study provides a large-scale benchmark for emerging investigations on cuffless BP measurement, highlighting the need to explore additional signals or principles to enhance the accuracy in large-scale heterogeneous populations.

Temporal complexity in photoplethysmography and its influence on blood pressure

Objective: The temporal complexity of photoplethysmography (PPG) provides valuable information about blood pressure (BP). In this study, we aim to interpret the stochastic PPG patterns with a model-based simulation, which may help optimize the BP estimation algorithms. Methods: The classic four-element Windkessel model is adapted in this study to incorporate BP-dependent compliance profiles. Simulations are performed to generate PPG responses to pulse and continuous stimuli at various timescales, aiming to mimic sudden or gradual hemodynamic changes observed in real-life scenarios. To quantify the temporal complexity of PPG, we utilize the Higuchi fractal dimension (HFD) and autocorrelation function (ACF). These measures provide insights into the intricate temporal patterns exhibited by PPG. To validate the simulation results, continuous recordings of BP, PPG, and stroke volume from 40 healthy subjects were used. Results: Pulse simulations showed that central vascular compliance variation during a cardiac cycle, peripheral resistance, and cardiac output (CO) collectively contributed to the time delay, amplitude overshoot, and phase shift of PPG responses. Continuous simulations showed that the PPG complexity could be generated by random stimuli, which were subsequently influenced by the autocorrelation patterns of the stimuli. Importantly, the relationship between complexity and hemodynamics as predicted by our model aligned well with the experimental analysis. HFD and ACF had significant contributions to BP, displaying stability even in the presence of high CO fluctuations. In contrast, morphological features exhibited reduced contribution in unstable hemodynamic conditions. Conclusion: Temporal complexity patterns are essential to single-site PPG-based BP estimation. Understanding the physiological implications of these patterns can aid in the development of algorithms with clear interpretability and optimal structures.

Effects of Pulse Transit Time and Pulse Arrival Time on Cuff-less Blood Pressure Estimation: A Comparison Study with Multiple Experimental Interventions

Pulse transit time (PTT) has shown a correlation with blood pressure (BP), and it is considered as a potential marker for cuff-less BP estimation. However, pulse arrival time (PAT) including pre-ejection period (PEP) has been utilized more widely because of its convenience to acquisition and calculation. In spite of this, whether PAT can surrogate PTT has been a controversial topic for many years. In this study, we designed an experiment on 55 subjects with multiple interventions, those may cause the changes in BP and PEP. We analyzed the linear and nonlinear correlations between BP and PTT/PAT, and also assessed the performances of PTT-based and PAT-based models on tracking the BP variation. Five typical BP estimation models were used for comparison. We found that PEP could change rapidly in response to the interventions related with physical stress. Although PTT had a better linear correlation with BP, most of the PAT-based models showed more accuracy than PTT-based models in all of the interventions, especially for the calibrated models. It is suggested that PAT has the potential to predict BP, and the inclusion of PEP in the measurement of PAT is necessary.

Wearable sensing, big data technology for cardiovascular healthcare: current status and future prospective

ABSTRACT

Wearable technology, which can continuously and remotely monitor physiological and behavioral parameters by incorporated into clothing or worn as an accessory, introduces a new era for ubiquitous health care. With big data technology, wearable data can be analyzed to help long-term cardiovascular care. This review summarizes the recent developments of wearable technology related to cardiovascular care, highlighting the most common wearable devices and their accuracy. We also examined the application of these devices in cardiovascular healthcare, such as the early detection of arrhythmias, measuring blood pressure, and detecting prevalent diabetes. We provide an overview of the challenges that hinder the widespread application of wearable devices, such as inadequate device accuracy, data redundancy, concerns associated with data security, and lack of meaningful criteria, and offer potential solutions. Finally, the future research direction for cardiovascular care using wearable devices is discussed.

Wearable Continuous Blood Pressure Monitoring Devices Based on Pulse Wave Transit Time and Pulse Arrival Time: A Review

Continuous blood pressure (BP) monitoring is of great significance for the real-time monitoring and early prevention of cardiovascular diseases. Recently, wearable BP monitoring devices have made great progress in the development of daily BP monitoring because they adapt to long-term and high-comfort wear requirements. However, the research and development of wearable continuous BP monitoring devices still face great challenges such as obvious motion noise and slow dynamic response speeds. The pulse wave transit time method which is combined with photoplethysmography (PPG) waves and electrocardiogram (ECG) waves for continuous BP monitoring has received wide attention due to its advantages in terms of excellent dynamic response characteristics and high accuracy. Here, we review the recent state-of-art wearable continuous BP monitoring devices and related technology based on the pulse wave transit time; their measuring principles, design methods, preparation processes, and properties are analyzed in detail. In addition, the potential development directions and challenges of wearable continuous BP monitoring devices based on the pulse wave transit time method are discussed.

A novel method for non-invasive blood pressure estimation based on continuous pulse transit time: An observational study

Unlike traditional pulse transit time (PTT), continuous PTT (CPTT) can be used to calculate PTT from all samples within the cardiac cycle. It has the potential to be utilized for continuous blood pressure (BP) estimation. This study evaluated the feasibility of CPTT as a non-invasive consecutive blood pressure estimation method in 20 volunteers. The CPTT was calculated with a time delay in all discrete samples of photoplethysmograms measured at two different body sites. BP was then calculated with a regression equation. For comparative evaluation, BP based on PTT was also estimated. Continuous blood pressure was measured using a non-invasive volume clamp BP monitoring device. Four types of BP measurement, systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP), were estimated using PTT and CPTT. Correlation coefficients and root-mean-squared-error (RMSE) were used for evaluating BP estimation performance. For estimating SBP, DBP, PP, and MAP, PTT-based BP estimation showed correlations of .407, .373, .410, and .286, respectively, and CPTT-based BP estimation showed correlations of .436, .446, .506, and .097, respectively. With PTT-based estimation, the RMSE between the estimated BP and the baseline BP was 5.44 ± 1.56 mmHg for SBP, 3.14 ± 0.46 mmHg for DBP, 3.66 ± 0.70 mmHg for MAP, and 3.73 ± 1.31 mmHg for PP. The estimated BP using CPTT showed RMSE of 5.36 ± 1.39 mmHg for SBP, 3.02 ± 0.49 mmHg for SBP, 3.44 ± 0.63 mmHg for MAP, and 3.91 ± 1.41 mmHg for PP.

Advancement in the Cuffless and Noninvasive Measurement of Blood Pressure: A Review of the Literature and Open Challenges

Hypertension is a chronic condition that is one of the prominent reasons behind cardiovascular disease, brain stroke, and organ failure. Left unnoticed and untreated, the deterioration in a health condition could even result in mortality. If it can be detected early, with proper treatment, undesirable outcomes can be avoided. Until now, the gold standard is the invasive way of measuring blood pressure (BP) using a catheter. Additionally, the cuff-based and noninvasive methods are too cumbersome or inconvenient for frequent measurement of BP. With the advancement of sensor technology, signal processing techniques, and machine learning algorithms, researchers are trying to find the perfect relationships between biomedical signals and changes in BP. This paper is a literature review of the studies conducted on the cuffless noninvasive measurement of BP using biomedical signals. Relevant articles were selected using specific criteria, then traditional techniques for BP measurement were discussed along with a motivation for cuffless measurement use of biomedical signals and machine learning algorithms. The review focused on the progression of different noninvasive cuffless techniques rather than comparing performance among different studies. The literature survey concluded that the use of deep learning proved to be the most accurate among all the cuffless measurement techniques. On the other side, this accuracy has several disadvantages, such as lack of interpretability, computationally extensive, standard validation protocol, and lack of collaboration with health professionals. Additionally, the continuing work by researchers is progressing with a potential solution for these challenges. Finally, future research directions have been provided to encounter the challenges.

Wearable blood pressure measurement devices and new approaches in hypertension management: the digital era

Out-of-office blood pressure (BP) measurement is considered an integral component of the diagnostic algorithm and management of hypertension. In the era of digitalization, a great deal of wearable BP measuring devices has been developed. These digital blood pressure monitors allow frequent BP measurements with minimal annoyance to the patient while they do promise radical changes regarding the diagnostic accuracy, as the importance of making an accurate diagnosis of hypertension has become evident. By increasing the number of BP measurements in different conditions, these monitors allow accurate identification of different clinical phenotypes, such as masked hypertension and pathological BP variability, that seem to have a negative impact on cardiovascular prognosis. Frequent measurements of BP and the incorporation of new features in BP variability, both enable well-rounded interpretation of BP data in the context of real-life settings. This article is a review of all different technologies and wearable BP monitoring devices.

Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring

Blood pressure (BP) determines whether a person has hypertension and offers implications as to whether he or she could be affected by cardiovascular disease. Cuff-based sphygmomanometers have traditionally provided both accuracy and reliability, but they require bulky equipment and relevant skills to obtain precise measurements. BP measurement from photoplethysmography (PPG) signals has become a promising alternative for convenient and unobtrusive BP monitoring. Moreover, the recent developments in remote photoplethysmography (rPPG) algorithms have enabled new innovations for contactless BP measurement. This paper illustrates the evolution of BP measurement techniques from the biophysical theory, through the development of contact-based BP measurement from PPG signals, and to the modern innovations of contactless BP measurement from rPPG signals. We consolidate knowledge from a diverse background of academic research to highlight the importance of multi-feature analysis for improving measurement accuracy. We conclude with the ongoing challenges, opportunities, and possible future directions in this emerging field of research.

Real-Time Cuffless Continuous Blood Pressure Estimation Using 1D Squeeze U-Net Model: A Progress toward mHealth

Measuring continuous blood pressure (BP) in real time by using a mobile health (mHealth) application would open a new door in the advancement of the healthcare system. This study aimed to propose a real-time method and system for measuring BP without using a cuff from a digital artery. An energy-efficient real-time smartphone-application-friendly one-dimensional (1D) Squeeze U-net model is proposed to estimate systolic and diastolic BP values, using only raw photoplethysmogram (PPG) signal. The proposed real-time cuffless BP prediction method was assessed for accuracy, reliability, and potential usefulness in the hypertensive assessment of 100 individuals in two publicly available datasets: Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-I) and Medical Information Mart for Intensive Care (MIMIC-III) waveform database. The proposed model was used to build an android application to measure BP at home. This proposed deep-learning model performs best in terms of systolic BP, diastolic BP, and mean arterial pressure, with a mean absolute error of 4.42, 2.25, and 2.56 mmHg and standard deviation of 4.78, 2.98, and 3.21 mmHg, respectively. The results meet the grade A performance requirements of the British Hypertension Society and satisfy the AAMI error range. The result suggests that only using a short-time PPG signal is sufficient to obtain accurate BP measurements in real time. It is a novel approach for real-time cuffless BP estimation by implementing an mHealth application and can measure BP at home and assess hypertension.

Bayesian Model Averaging for Improving the Accuracy of Cuffless Blood Pressure Estimation

In recent decades, many researches have proposed various models for continuous, cuffless blood pressure (BP) estimation. However, due to aleatoric uncertainty and epistemic uncertainty existing in the problem, it is very challenging to evaluate cuffless BP with acceptable accuracy. This paper innovatively proposes a cuffless BP ensemble estimation model based on Bayesian Model Average (BMA) method to reduce the epistemic uncertainty. We combine four most frequently cited physiological models and four regression models based on Photoplethysmogram (PPG) and Electrocardiogram (ECG) signals, and use the BMA method to assign weights to each model to achieve accurate cuffless BP prediction. The proposed method was validated on 17 healthy and 13 hypertensive subjects with continuous Finometer BP as a reference. The results showed that the error mean ± SD (standard deviations) of both SBP and DBP predicted by the proposed method were 2.13 ± 5.68 mmHg and 1.42 ± 5.11 mmHg, respectively, which were both lower than each of the model. And the MAE was 6% and 8% lower than the best member of the model ensemble. We also analyzed the relationship between the number of training epochs and model prediction performance. When 15 cardiac cycles were choosed for training, it could get a good balance between model prediction accuracy and algorithm complexity. Therefore, the proposed BMA method can solve the model uncertainty problem, providing robust and deterministic BP prediction. Clinical relevance- This paper proposes a new method for wearable BP estimation which enables BP monitoring in both clinical settings and home settings. It offers a stable way to monitor BP to help patients detect disease early.

Cuffless blood pressure measuring devices: review and statement by the European Society of Hypertension Working Group on Blood Pressure Monitoring and Cardiovascular Variability

BACKGROUND

Many cuffless blood pressure (BP) measuring devices are currently on the market claiming that they provide accurate BP measurements. These technologies have considerable potential to improve the awareness, treatment, and management of hypertension. However, recent guidelines by the European Society of Hypertension do not recommend cuffless devices for the diagnosis and management of hypertension.

OBJECTIVE

This statement by the European Society of Hypertension Working Group on BP Monitoring and Cardiovascular Variability presents the types of cuffless BP technologies, issues in their validation, and recommendations for clinical practice.

STATEMENTS

Cuffless BP monitors constitute a wide and heterogeneous group of novel technologies and devices with different intended uses. Cuffless BP devices have specific accuracy issues, which render the established validation protocols for cuff BP devices inadequate for their validation. In 2014, the Institute of Electrical and Electronics Engineers published a standard for the validation of cuffless BP devices, and the International Organization for Standardization is currently developing another standard. The validation of cuffless devices should address issues related to the need of individual cuff calibration, the stability of measurements post calibration, the ability to track BP changes, and the implementation of machine learning technology. Clinical field investigations may also be considered and issues regarding the clinical implementation of cuffless BP readings should be investigated.

CONCLUSION

Cuffless BP devices have considerable potential for changing the diagnosis and management of hypertension. However, fundamental questions regarding their accuracy, performance, and implementation need to be carefully addressed before they can be recommended for clinical use.

Cuffless Blood Pressure Measurement

Cuffless blood pressure (BP) measurement has become a popular field due to clinical need and technological opportunity. However, no method has been broadly accepted hitherto. The objective of this review is to accelerate progress in the development and application of cuffless BP measurement methods. We begin by describing the principles of conventional BP measurement, outstanding hypertension/hypotension problems that could be addressed with cuffless methods, and recent technological advances, including smartphone proliferation and wearable sensing, that are driving the field. We then present all major cuffless methods under investigation, including their current evidence. Our presentation includes calibrated methods (i.e., pulse transit time, pulse wave analysis, and facial video processing) and uncalibrated methods (i.e., cuffless oscillometry, ultrasound, and volume control). The calibrated methods can offer convenience advantages, whereas the uncalibrated methods do not require periodic cuff device usage or demographic inputs. We conclude by summarizing the field and highlighting potentially useful future research directions. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 24 is June 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Piezoelectric Dynamics of Arterial Pulse for Wearable Continuous Blood Pressure Monitoring

Piezoelectric arterial pulse wave dynamics are traditionally considered to be similar to those of typical blood pressure waves. However, achieving accurate continuous blood pressure wave monitoring based on arterial pulse waves remains challenging, because the correlation between piezoelectric pulse waves and their related blood pressure waves is unclear. To address this, the correlation between piezoelectric pulse waves and blood pressure waves is first elucidated via theoretical, simulation, and experimental analysis of these dynamics. Based on this correlation, the authors develop a wireless wearable continuous blood pressure monitoring system, with better portability than conventional systems that are based on the pulse wave velocity between multiple sensors. They explore the feasibility of achieving wearable continuous blood pressure monitoring without motion artifacts, using a single piezoelectric sensor. These findings eliminate the controversy over the arterial pulse wave piezoelectric response, and can potentially be used to develop a portable wearable continuous blood pressure monitoring device for the early prevention and daily control of hypertension.

Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: a review from VascAgeNet

The photoplethysmogram (PPG) signal is widely measured by clinical and consumer devices, and it is emerging as a potential tool for assessing vascular age. The shape and timing of the PPG pulse wave are both influenced by normal vascular aging, changes in arterial stiffness and blood pressure, and atherosclerosis. This review summarizes research into assessing vascular age from the PPG. Three categories of approaches are described: 1) those which use a single PPG signal (based on pulse wave analysis), 2) those which use multiple PPG signals (such as pulse transit time measurement), and 3) those which use PPG and other signals (such as pulse arrival time measurement). Evidence is then presented on the performance, repeatability and reproducibility, and clinical utility of PPG-derived parameters of vascular age. Finally, the review outlines key directions for future research to realize the full potential of photoplethysmography for assessing vascular age.

Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review

Recent research indicates that Photoplethysmography (PPG) signals carry more information than oxygen saturation level (SpO2) and can be utilized for affordable, fast, and noninvasive healthcare applications. All these encourage the researchers to estimate its feasibility as an alternative to many expansive, time-wasting, and invasive methods. This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 2020. To this aim, papers from 1981 to date are reviewed and categorized in terms of the healthcare application domain. Along with consolidated research areas, recent topics that are growing in popularity are also discovered. We also highlight the potential impact of using PPG signals on an individual's quality of life and public health. The state-of-the-art studies suggest that in the years to come PPG wearables will become pervasive in many fields of medical practices, and the main domains include cardiology, respiratory, neurology, and fitness. Main operation challenges, including performance and robustness obstacles, are identified.

Wearable Photoplethysmography for Cardiovascular Monitoring

Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD.

Using noninvasive adjusted pulse transit time for tracking beat-to-beat systolic blood pressure during ventricular arrhythmia

Tracking beat-to-beat blood pressure noninvasively during ventricular arrhythmia (VA) is of great importance but rarely reported. The goal of our study was to investigate the potential utility of the adjusted pulse transit time (APTT) to track beat-to-beat femoral systolic blood pressure (SBP) during VA. Patients who underwent radiofrequency ablation for arrhythmias at Fuwai Hospital were enrolled. Electrocardiograms (ECGs), finger photoplethysmograms, and femoral arterial blood pressure were recorded simultaneously during VA. The APTT was calculated as the ratio between the square of the conventional pulse transit time (cPTT) and the RR interval of the ECG waveform. Forty-five patients were enrolled in our study, and 22,849 beats were collected during their VA. The inverse of the APTT showed a good correlation with femoral SBP during VA (r = 0.70 ± 0.18). The APTT-derived SBP demonstrated acceptable accuracy in terms of the mean difference ± standard deviation (-0.01 ± 10.54 mmHg) from the invasive femoral SBP. The area under the receiver operating characteristic (ROC) curve for the ability of the APTT to detect ≥30% decreases in femoral SBP was 0.903 (95% confidential interval, 0.895-0.911). In addition, the APTT performed better than the cPTT and RR interval in the above analysis (all P < 0.05). Therefore, the APTT has acceptable accuracy in tracking beat-to-beat femoral SBP and could detect substantially decreased femoral SBP. These findings indicate that the APTT may be a promising noninvasive surrogate for invasive femoral SBP during VA. A multiparameter model combining APTT and other parameters is needed to further improve the accuracy.

Cuffless blood pressure monitoring from a wristband with calibration-free algorithms for sensing location based on bio-impedance sensor array and autoencoder

Continuous monitoring of blood pressure (BP) is essential for the prediction and the prevention of cardiovascular diseases. Cuffless BP methods based on non-invasive sensors integrated into wearable devices can translate blood pulsatile activity into continuous BP data. However, local blood pulsatile sensors from wearable devices suffer from inaccurate pulsatile activity measurement based on superficial capillaries, large form-factor devices and BP variation with sensor location which degrade the accuracy of BP estimation and the device wearability. This study presents a cuffless BP monitoring method based on a novel bio-impedance (Bio-Z) sensor array built in a flexible wristband with small-form factor that provides a robust blood pulsatile sensing and BP estimation without calibration methods for the sensing location. We use a convolutional neural network (CNN) autoencoder that reconstructs an accurate estimate of the arterial pulse signal independent of sensing location from a group of six Bio-Z sensors within the sensor array. We rely on an Adaptive Boosting regression model which maps the features of the estimated arterial pulse signal to systolic and diastolic BP readings. BP was accurately estimated with average error and correlation coefficient of 0.5 ± 5.0 mmHg and 0.80 for diastolic BP, and 0.2 ± 6.5 mmHg and 0.79 for systolic BP, respectively.

A Life Well Lived: In Memory of Máximo E. Valentinuzzi

Throughout his career, Dr. Máximo (Max) E. Valentinuzzi worked long and hard for the development of the BME profession locally, regionally, and internationally. His accomplishments were numerous and valuable, including his editorship of the IEEE Pulse special column known as "Retrospectroscope" for more than ten years. A highlight and strength of the magazine over the past decade, this special column was built largely on the efforts and contributions from Dr. Valentinuzzi (Figure 1).

Blood pressure assessment with in-ear photoplethysmography

Objective. In this study, we aimed to estimate blood pressure (BP) from in-ear photoplethysmography (PPG). This novel implementation provided an unobtrusive and steady way of recording PPG, whereas previous PPG measurements were mostly performed at the wrist, finger, or earlobe.Methods. The time between forward and reflected PPG waves was very short at the ear site. To minimize errors introduced by feature extraction, a multi-Gaussian decomposition of in-ear PPG was performed. Both hand-crafted and whole-based features were extracted and the best combination of features was selected using a backward-search wrapper method and evaluated by the Akaike information criteria. Hemodynamic parameters such as compliance and inertance were estimated from a four-element Windkessel (WK4) model, which was used to pre-classify PPG signals and generate different BP estimation algorithms. Calibration was done by using previous measurements from the same class. To validate this novel approach, 53 subjects were recruited for a one-month follow-up study, and 17 subjects were recruited for a two-month follow-up study. Calibrated systolic BP estimation accuracy was significantly improved with inertance-based pre-classification, while diastolic BP showed less improvement.Results. With proper feature selection, pre-classification and calibration, we have achieved a mean absolute error of 5.35 mmHg for SBP estimation, compared to 6.16 mmHg if no pre-classification was carried out. The performance did not deteriorate in two months, showing a decent BP trend-tracking ability.Conclusion. The study demonstrated the feasibility of in-ear PPG to reliably measure BP, which represents an important technological advancement in terms of unobtrusiveness and steadiness.

Feasibility Study of Pulse Width at Half Amplitude of Camera PPG for Contactless Blood Pressure Estimation

Non-contact blood pressure (BP) estimation with imaging photoplethysmogram (PPG) that can be acquired by camera is a promising alternative to cuff-based technology because of its nature of pervasive, low-cost, and being continuous. Most of the non-contact BP estimation methods are based on the principle of pulse transit time (PTT) as being used for wearable cuffless BP measurement. However, PTT-based method on the one hand requires simultaneous capture of images of multiple skin sites with the sites being at a distance from each other; and on the other hand, it can only partially reflect BP changes according to previous studies. In this paper, we propose to use a different camera PPG feature that has not yet been fully studied - pulse width at half amplitude (PWHA) for the evaluation of BP in a non-contact way. PWHA can be obtained from a single-site camera PPG, and it can indicate BP changes. The relationship of PWHA and BP was analyzed on 16 healthy subjects with BP changes induced by deep breathing and stepping exercise. The results showed that beat-to-beat PWHA can well track dynamic BP changes, and it is inversely related to BP across the sampled population and within each individual with about 80% individuals having high correlations. The findings suggest that PWHA can reflect the dynamic changes in cardiovascular characteristics and thereby BP changes, demonstrating the feasibility of imaging PWHA for non-contact BP estimation beyond the PTT method.Clinical Relevance- This provides a potential new method for non-contact BP, which allows BP monitoring at home, clinical setting, and public places in a pervasive manner. It reduces contacts between persons during a pandemic and offers an ever-present way to monitor BP.

Estimation of resting blood pressure using facial thermal images by separating acute stress variations

The increasing number of people with hypertension worldwide has become a matter of grave concern. Blood pressure monitoring using a non-contact measurement technique is expected to detect and control this medical condition. Previous studies have estimated blood pressure variations following an acute stress response based on facial thermal images obtained from infrared thermography devices. However, a non-contact resting blood pressure estimation method is required because blood pressure is generally measured in the resting state without inducing acute stress. Day-long blood pressure variations include short-term variations due to acute stress and long-term variations in circadian rhythms. The aim of this study is to estimate resting blood pressure from facial thermal images by separating and excluding short-term variations related to acute stress. To achieve this, short-term blood pressure variations components related to acute stress on facial thermal images were separated using independent component analysis. Resting blood pressure was estimated with the extracted independent components excluding the short-term components using multiple regression analysis. The results show that the proposed approach can accurately estimate resting blood pressure from facial thermal images, with a 9.90 mmHg root mean square error. In addition, features related to resting blood pressure were represented in the nose, lip, and cheek regions.

Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning

Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we use this parameterization to train NNs with a larger PPG dataset and carry out a systematic evaluation of the predicted blood pressure. The analysis revealed a strong systematic increase of the prediction error towards less frequent BP values across NN architectures. Moreover, we tested different train/test set split configurations which underpin the importance of a careful subject-aware dataset assignment to prevent overly optimistic results. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply different personalization techniques and retrain our NNs with subject-specific data for both the PPG-only and rPPG case. Whilst the particular technique is less important, personalization reduces the prediction errors significantly.

Noninvasive Continuous Blood Pressure Estimation from Pulse Transit Time: A review of the calibration models

Noninvasive continuous blood pressure estimation is a promising alternative to minimally invasive blood pressure measurement using cuff and invasive catheter measurement, because it opens the way to both long-term and continuous blood pressure monitoring in ecological situation. The most current estimation algorithm is based on pulse transit time measurement where at least two measured signals need to be acquired. From the pulse transit time values, it is possible to estimate the continuous blood pressure for each cardiac cycle. This measurement highly depends on arterial properties which are not easily accessible with common measurement techniques; but these properties are needed as input for the estimation algorithm. With every change of input arterial properties, the error in the blood pressure estimation rises, thus a periodic calibration procedure is needed for error minimization. Recent research is focused on simplified constant arterial properties which are not constant over time and uses only linear model based on initial measurement. The elaboration of continuous calibration procedures, independent of recalibration measurement, is the key to improving the accuracy and robustness of noninvasive continuous blood pressure estimation. However, most models in literature are based on linear approximation and we discuss here the need for more complete calibration models.

Measurement of pulse transit time using ultra-wideband radar

BACKGROUND: The pulse transit time is an important factor that can be used to estimate the blood pressure indirectly. In many studies, pressures in the artery near and far from the heart are measured or the electrocardiogram and photoplethysmography are used to calculate the pulse transit time. In other words, the so-called contact measurements have been mainly used in these studies. OBJECTIVE: In this paper, a new method based on radar technology to measure the pulse transit time in a non-contact manner is proposed. METHODS: Radar pulses were simultaneously emitted to the chest and the wrist, and the reflected pulses were accumulated. Heartbeats were extracted by performing principal component analysis on each time series belonging to the accumulated pulses. Then, the matched heartbeat pairs were found among the heartbeats obtained from the chest and wrist and the time delay between them, i.e. the pulse transit time, was obtained. RESULTS: By comparing the pulse transit times obtained by the proposed method with those obtained by conventional methods, it is confirmed that the proposed method using the radar can be used to obtain the pulse transit time in a non-contact manner.

PCA-Based Multi-Wavelength Photoplethysmography Algorithm for Cuffless Blood Pressure Measurement on Elderly Subjects

The prevalence of hypertension has made blood pressure (BP) measurement one of the most wanted functions in wearable devices for convenient and frequent self-assessment of health conditions. The widely adopted principle for cuffless BP monitoring is based on arterial pulse transit time (PTT), which is measured with electrocardiography and photoplethysmography (PPG). To achieve cuffless BP monitoring with more compact wearable electronics, we have previously conceived a multi-wavelength PPG (MWPPG) strategy to perform BP estimation from arteriolar PTT, requiring only a single sensing node. However, challenges remain in decoding the compounded MWPPG signals consisting of both heterogeneous physiological information and motion artifact (MA). In this work, we proposed an improved MWPPG algorithm based on principal component analysis (PCA) which matches the statistical decomposition results with the arterial pulse and capillary pulse. The arteriolar PTT is calculated accordingly as the phase shift based on the entire waveforms, instead of local peak lag time, to enhance the feature robustness. Meanwhile, the PCA-derived MA component is employed to identify and exclude the MA-contaminated segments. To evaluate the new algorithm, we performed a comparative experiment (N = 22) with a cuffless MWPPG measurement device and used double-tube auscultatory BP measurement as a reference. The results demonstrate the accuracy improvement enabled by the PCA-based operations on MWPPG signals, yielding errors of 1.44 ± 6.89 mmHg for systolic blood pressure and -1.00 ± 6.71 mm Hg for diastolic blood pressure. In conclusion, the proposed PCA-based method can improve the performance of MWPPG in wearable medical devices for cuffless BP measurement.

Bio-Impedance Measurement Optimization for High-Resolution Carotid Pulse Sensing

Continuous hemodynamic monitoring is important for long-term cardiovascular healthcare, especially in hypertension. The impedance plethysmography (IPG) based carotid pulse sensing is a non-invasive diagnosis technique for measuring pulse signals and further evaluating the arterial conditions of the patient such as continuous blood pressure (BP) monitoring. To reach the high-resolution IPG-based carotid pulse detection for cardiovascular applications, this study provides an optimized measurement parameter in response to obvious pulsation from the carotid artery. The influence of the frequency of excitation current, electrode cross-sectional area, electrode arrangements, and physiological site of carotid arteries on IPG measurement resolution was thoroughly investigated for optimized parameters. In this study, the IPG system was implemented and installed on the subject’s neck above the carotid artery to evaluate the measurement parameters. The measurement results within 6 subjects obtained the arterial impedance variation of 2137 mΩ using the optimized measurement conditions, including excitation frequency of 50 kHz, a smaller area of 2 cm², electrode spacing of 4 cm and 1.7 cm for excitation and sensing functions, and location on the left side of the neck. The significance of this study demonstrates an optimized measurement methodology of IPG-based carotid pulse sensing that greatly improves the measurement quality in cardiovascular monitoring.

Pulse Arrival Time Segmentation Into Cardiac and Vascular Intervals - Implications for Pulse Wave Velocity and Blood Pressure Estimation

OBJECTIVE

This study demonstrates a novel method for pulse arrival time (PAT) segmentation into cardiac isovolumic contraction (IVC) and vascular pulse transit time to approximate central pulse wave velocity (PWV).

METHODS

10 subjects (38 ± 10 years, 121 ± 12 mmHg SBP) ranging from normotension to hypertension were repeatedly measured at rest and with induced changes in blood pressure (BP), and thus PWV. ECG was recorded simultaneously with ultrasound-based carotid distension waveforms, a photoplethysmography-based peripheral waveform, noninvasive continuous and intermittent cuff BP. Central PAT was segmented into cardiac and vascular time intervals using a fiducial point in the carotid distension waveform that reflects the IVC onset. Central and peripheral PWVs were computed from (segmented) intervals and estimated arterial path lengths. Correlations with Bramwell-Hill PWV, systolic and diastolic BP (SBP/DBP) were analyzed by linear regression.

RESULTS

Central PWV explained more than twice the variability (R²) in Bramwell-Hill PWV compared to peripheral PWV (0.56 vs. 0.27). SBP estimated from central PWV undercuts the IEEE mean absolute deviation threshold of 5 mmHg, significantly lower than peripheral PWV or PAT (4.2 vs. 7.1 vs. 10.1 mmHg).

CONCLUSION

Cardiac IVC onset signaled in carotid distension waveforms enables PAT segmentation to obtain unbiased vascular pulse transit time. Corresponding PWV estimates provide the basis for single-site assessment of central arterial stiffness, confirmed by significant correlations with Bramwell-Hill PWV and SBP.

SIGNIFICANCE

In a small-scale cohort, we present proof-of-concept for a novel method to estimate central PWV and BP, bearing potential to improve the practicality of cardiovascular risk assessment in clinical routines.

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Coronavirus disease 2019 (COVID-19) has emerged as a pandemic with serious clinical manifestations including death. A pandemic at the large-scale like COVID-19 places extraordinary demands on the world's health systems, dramatically devastates vulnerable populations, and critically threatens the global communities in an unprecedented way. While tremendous efforts at the frontline are placed on detecting the virus, providing treatments and developing vaccines, it is also critically important to examine the technologies and systems for tackling disease emergence, arresting its spread and especially the strategy for diseases prevention. The objective of this article is to review enabling technologies and systems with various application scenarios for handling the COVID-19 crisis. The article will focus specifically on 1) wearable devices suitable for monitoring the populations at risk and those in quarantine, both for evaluating the health status of caregivers and management personnel, and for facilitating triage processes for admission to hospitals; 2) unobtrusive sensing systems for detecting the disease and for monitoring patients with relatively mild symptoms whose clinical situation could suddenly worsen in improvised hospitals; and 3) telehealth technologies for the remote monitoring and diagnosis of COVID-19 and related diseases. Finally, further challenges and opportunities for future directions of development are highlighted.

Electrical Impedance Plethysmography Versus Tonometry To Measure the Pulse Wave Velocity in Peripheral Arteries in Young Healthy Volunteers: a Pilot Study

The leading cause of health loss and deaths worldwide are cardiovascular diseases. A predictor of cardiovascular diseases and events is the arterial stiffness. The pulse wave velocity (PWV) can be used to estimate arterial stiffness non-invasively. The tonometer is considered as the gold standard for measuring PWV. This approach requires manual probe fixation above the artery and depends on the skills of the operator. Electrical impedance plethysmography (IPG) is an interesting alternative using skin surface sensing electrodes, that is miniaturizable, cost-effective and allows measurement of deeper arteries. The aim of this pilot study was to explore if IPG can be a suitable technique to measure pulse wave velocity in legs as an alternative for the tonometer technique. The PWV was estimated by differences in the ECG-gated pulse arrival times (PAT) at the a. femoralis, a. popliteal, a. tibialis dorsalis and a. dorsalis pedis in nine healthy young adults using IPG and the SphygmoCor tonometer as a reference. The estimated PWV results from bioimpedance and the tonometer were fairly in agreement, and the beat-to-beat variability in PAT was similar. This pilot study indicates that the use of IPG may be a good alternative for estimating PWV in the legs.

An instrument for measuring blood pressure and assessing cardiovascular health from the fingertip

Despite blood pressure being one the leading modifiable risk factors for cardiovascular disease and death, it is severely under-monitored. For this challenge we propose a finger artery non-invasive tono-oscillometric monitor (FANTOM) which is an automated low-cost instrument for measuring blood pressure and hemodynamic parameters from the fingertip. The sensing technology is highly scalable and could be integrated to a pulse oximeter probe for increased patient comfort. A tonometric cuff-less mechatronic system is used to apply pressure on the fingertip for (i) measuring oscillometric blood pressure, (ii) recording arterial waveform and for (iii) constructing central blood pressure (CBP) waveform. Clinical study on volunteers (n = 33) was performed against a commercially available arm cuff device yielding systolic and diastolic readings ((mean±SD) mmHg) of (-0.9 ± 7.3) mmHg and (-3.3 ± 6.6) mmHg respectively. The results comply with the Association for the Advancement of Medical Instrumentation (AAMI) standard for non-invasive blood pressure monitors. The arterial pulse recording morphology was compared against a volume clamp device (CNSystems CNAP 500) (n = 3) resulting in similar performance. Comparison of CBP against a pulse wave analysis (PWA) device (Atcor Medical Sphygmocor XCEL) (n = 5) revealed central aortic systolic pulse (CASP) and central augmentation index (cAIx) estimates with precision and accuracy of (2.0 ± 3.7) mmHg and (1.4 ± 6.2)% respectively. In conclusion, the results indicate that the proposed technology could be useful in the development of new portable or wearable blood pressure monitors. The sensing technology is highly scalable and could be integrated to a pulse oximeter probe for increased patient comfort.

Machine Learning Approaches For Improved Continuous, Non-occlusive Arterial Pressure Monitoring Using Photoplethysmography

Arterial pressure (AP) is a crucial biomarker for cardiovascular disease prevention and management. Photoplethysmography (PPG) could provide a novel, paradigm-shifting approach for continuous, non-obtrusive AP monitoring, comfortably integrated in wearable and mobile devices; yet, it still faces challenges in accuracy and robustness. In this work, we sought to integrate machine learning (ML) techniques into a previously established, clinically-validated classical approach (oBPM^®) to develop new accurate AP estimation tools based on PPG, and at the same time improve our understanding of the underlying physiological parameters. In this novel approach, oBPM^® was used to pre-process PPG signals and robustly extract physiological features, and ML models were trained on these features to estimate systolic AP (SAP). A feature relevance analysis showed that reference (calibration) information, followed by various morphological parameters of the PPG pulse wave, comprised the most important features for SAP estimation. A performance analysis then revealed that LASSO-regularized linear regression, Gaussian process regression and support vector regression are effective for SAP estimation, particularly when operating on reduced feature sets previously obtained with e.g. LASSO. These approaches yielded substantial reductions in error standard deviation of 9-15% relative to conventional oBPM^®. Altogether, these results indicate that ML approaches are well-suited, and promising tools to help overcoming the challenges of ubiquitous AP monitoring.

Cuffless Blood Pressure Estimation Using Features Extracted from Carotid Dual-Diameter Waveforms

The major challenges in deep learning approaches to cuffless blood pressure estimation is selecting the most appropriate representative of the blood pulse waveform and extraction of relevant features for data collection. This paper performs an analysis of a novel dataset consisting of 71 features from the carotid dual-diameter waveforms and 4 blood pressure parameters. In particular, the analysis uses gradient boosting and graph-theoretic algorithms to determine (1) features with high predictive power and (2) potential to be pruned. Identifying such features and understanding their physiological significance is important for building blood pressure estimation models using machine learning that is robust across diverse clinical environments and patient sets.

Comparing blood pressure measurements between a photoplethysmography-based and a standard cuff-based manometry device

Repeated blood pressure (BP) measurements allow better control of hypertension. Current measurements rely on cuff-based devices. The aim of the present study was to compare BP measurements using a novel cuff-less photoplethysmography-based device to a standard sphygmomanometer device. Males and females were recruited from within the general population who arrived at a public BP screening station. One to two measurements were taken from each using a sphygmomanometer-based and the photoplethysmography-based devices. Devices were considered equal if the mean difference between paired measurements was below 5 mmHg and the Standard Deviation (SD) was no greater than 8 mmHg. Agreement and reliability analyses were also performed. 1057 subjects were included in the study analysis. There were no adverse events during the study. The mean (± SD) difference between paired measurements for all subjects was -0.1 ± 3.6 mmHg for the systolic and 0.0 ± 3.5 mmHg for the diastolic readings. We found 96.31% agreement in identifying hypertension and an Interclass Correlation Coefficient of 0.99 and 0.97 for systolic and diastolic measurements, respectively. The photoplethysmography-based device was found similar to the gold-standard sphygmomanometer-based device with high agreement and reliability levels. The device might enable a reliable, more convenient method for repeated BP monitoring.

Kouzani A, Mosadegh B, Gharaie S. Blood Pressure Sensors: Materials, Fabrication Methods, Performance Evaluations and Future Perspectives

Advancements in materials science and fabrication techniques have contributed to the significant growing attention to a wide variety of sensors for digital healthcare. While the progress in this area is tremendously impressive, few wearable sensors with the capability of real-time blood pressure monitoring are approved for clinical use. One of the key obstacles in the further development of wearable sensors for medical applications is the lack of comprehensive technical evaluation of sensor materials against the expected clinical performance. Here, we present an extensive review and critical analysis of various materials applied in the design and fabrication of wearable sensors. In our unique transdisciplinary approach, we studied the fundamentals of blood pressure and examined its measuring modalities while focusing on their clinical use and sensing principles to identify material functionalities. Then, we carefully reviewed various categories of functional materials utilized in sensor building blocks allowing for comparative analysis of the performance of a wide range of materials throughout the sensor operational-life cycle. Not only this provides essential data to enhance the materials' properties and optimize their performance, but also, it highlights new perspectives and provides suggestions to develop the next generation pressure sensors for clinical use.

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

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