Cuffless Blood Pressure Research Articles

Cuffless Blood Pressure Estimation Model Based on Prior Information of Physiological Data and LSTM

Cuffless Blood Pressure Estimation Model Based on Prior Information of Physiological Data and LSTM

RU-Net, Multi-Scale Feature Fusion and Transfer Learning: Unlocking the Potential of Cuffless Blood Pressure Monitoring with PPG and ECG.

This study introduces an innovative deep-learning model for cuffless blood pressure estimation using PPG and ECG signals, demonstrating state-of-the-art performance on the largest clean dataset, PulseDB. The rU-Net architecture, a fusion of U-Net and ResNet, enhances both generalization and feature extraction accuracy. Accurate multi-scale feature capture is facilitated by short-time Fourier transform (STFT) time-frequency distributions and multi-head attention mechanisms, allowing data-driven feature selection. The inclusion of demographic parameters as supervisory information further elevates performance. On the calibration-based dataset, our model excels, achieving outstanding accuracy (SBP MAE ± std: 4.49 ± 4.86 mmHg, DBP MAE ± std: 2.69 ± 3.10 mmHg), surpassing AAMI standards and earning a BHS Grade A rating. Addressing the challenge of calibration-free data, we propose a fine-tuning-based transfer learning approach. Remarkably, with only 10% data transfer, our model attains exceptional accuracy (SBP MAE ± std: 4.14 ± 5.01 mmHg, DBP MAE ± std: 2.48 ± 2.93 mmHg). This study sets the stage for the development of highly accurate and reliable wearable cuffless blood pressure monitoring devices.

Accurate and 30-plus days reliable cuffless blood pressure measurements with 9-minutes personal photoplethysmograph data and mixed deduction learning

Cuffless blood pressure (BP) measurements have long been anticipated, and the PPG (Photoplethysmography)-only method is the most promising one since already embedded in many wearable devices. To further meet the clinical accuracy requirements, PPG-only BP predictions with personalized modeling for overcoming personal deviations have been widely studied, but all required tens to hundreds of minutes of personal PPG measurements for training. Moreover, their accurate test periods without calibration practice were not reported. In this work, we collected records of PPG data from our recruited subjects in real-life scenarios instead of relying on the openly available MIMIC dataset obtained from intensive care unit (ICU) patients. Since our objective is commercial application and a substantial reduction in training data, we tailored our model training to closely mimic real-world usage. To achieve this, we developed a training approach that only requires 9-minutes of personal PPG signal recordings and mixed with other PPG data from our recruited 364 subjects. The modeling is conducted with two-channel paired inputs to the convolutional neural network (CNN)-based model, which we called Mixed Deduction Learning (MDL). The test results of 88 samples from 15 subjects, under testing period up to 30-plus days without extra calibration, revealed that MDL meets most of the standards of AAMI, BHS, and IEEE 1708–2014 (for static test only) for BP measurement devices, which indicates MDL’s long-term stability and consistency. Furthermore, we found that the model with two-channel inputs presents a trend of improving performance as the pool of mixed training data increased, while the conventional one-channel input revealed degraded performance. The outperformance of MDL is attributed to many significant features remained in the first CNN layer even when mixing personal 9-minutes data with the other 364 subjects. Consequently, PPG-only with MDL introduces a new avenue for overcoming challenges in training due to personal physiological variations. Given our consideration of real-life usage, this technology can be seamlessly translated to commercial applications.

A highly elastic, moisturizing, and adhesion conductive hydrogel designed for cuffless blood pressure measurement

Real-time monitoring of blood pressure in athletes during exercise is of great importance, however, conventional methods pose challenges in achieving portable and instantaneous measurements. While, the electrodes used in emerging wearable blood pressure monitoring instruments still have certain defects. In this study, a novel cuffless blood pressure monitoring system was developed using a self-developed polyacrylamide/trehalose/LiCl (PAM/Trehalose/LiCl) conductive hydrogel as the electrode material. The hydrogel electrode has high tensile (strain: 3568 %) and conductive properties (7.57 S/m), while also exhibiting low toughness, moisture retention, and adhesion. While ensuring the comfort of the monitoring system, it can also accurately detect strain over a wide range of deformations (GF value of up to 6.31 in the strain interval of 2000–3400 %). Finally, this system accurately extracts relevant features of PPG and ECG signals and predicts cuff-less blood pressure using the BP Transformer-XL model. The main control chip, ECG detection module, Bluetooth module, and PPG detection module are integrated, and band-pass filtering and data feature extraction techniques are combined. The study found that the average errors in systolic and diastolic readings were 2.09 mmHg and 1.57 mmHg, respectively, compared to those measured by an electronic sphygmomanometer. Therefore, this study successfully combined hydrogel electrodes with a cuffless blood pressure monitoring system to provide technical support for the development of personalized training programs for future athletes and the guarantee of long-term competitive performance.

Wearable cuffless blood pressure monitoring devices: a commentary.

Hypertension is a growing public challenge as a leading risk factor for cardiovascular disease and all-cause mortality. Reducing overall cardiovascular risk through early screening, initiation of treatment and ongoing monitoring remains a priority in the comprehensive management of hypertension and its complications. Community nurses are ideally positioned to play a crucial role in the early detection of hypertension and providing support for its management. Wearable cuffless devices have the potential for continuous remote blood pressure monitoring. However, there is not enough literature on the validity and usability of wearable cuffless blood pressure devices to justify their use in clinical practice. This commentary critically appraises a systematic review designed to assess the validity, features and clinical usability of wearable cuffless devices, and expands on its findings and their relevance to community nursing and future research.

BP-Diff: a conditional diffusion model for cuffless continuous BP waveform estimation using U-Net.

Continuous monitoring of blood pressure (BP) is crucial for daily healthcare. Although invasive methods provide accurate continuous BP measurements, they are not suitable for routine use. Photoplethysmography (PPG), a non-invasive technique that detects changes in blood volume within the microcirculation using light, shows promise for BP measurement. The primary goal of this study is to develop a novel cuffless method based on PPG for accurately estimating continuous BP.
Approach. We introduce BP-Diff, an end-to-end method for cuffless continuous BP waveform estimation utilizing a conditional diffusion probability model combined with a U-Net architecture. This approach takes advantage of the stochastic properties of diffusion models and the strong feature representation capabilities of U-Net. It integrates the continuous BP waveform as the initial status and uses the PPG signal and its derivatives as conditions to guide the training and sampling process.
Main results. BP-Diff was evaluated using both uncalibrated and calibrated schemes. The results indicate that, when uncalibrated, BP-Diff can accurately track BP dynamics, including peak and valley positions, as well as timing. After calibration, BP-Diff achieved highly accurate BP estimations. The mean absolute error (MAE) of the estimated BP waveforms, along with the systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) from the calibrated BP-Diff model, were 2.99 mmHg, 2.6 mmHg, 1.4 mmHg, and 1.44 mmHg, respectively. Consistency tests, including Bland-Altman analysis and Pearson correlation, confirmed its high reliability compared to reference BP. BP-Diff meets the American Association for Medical Instrumentation (AAMI) standards and has achieved a Grade A from the British Hypertension Society (BHS).
Significance. This study utilizes PPG signals to develop a novel cuffless continuous BP measurement method, demonstrating superiority over existing approaches. The method is suitable for integration into wearable devices, providing a practical solution for continuous BP monitoring in everyday healthcare.

Detection of hypertension using a target spectral camera: a prospective clinical study

Hypertension is a significant contributor to premature mortality, and the regular monitoring of blood pressure (BP) enables the early detection of hypertension and cardiovascular disease. There is an urgent need for the development of highly accurate cuffless BP devices. We examined BP measurements based on a target spectral camera’s recordings and evaluated their accuracy. Images of 215 adults’ palms and faces were recorded, and BP was measured. The camera captured RGB wavelength data at 640 × 480 pixels and 150 frames per second (fps). These recordings were analyzed to extract pulse transit time (PTT) values between the face and palm, a key parameter for estimating BP. Continuous BP measurements were taken using a CNAPmonitor500 for validation. Three frequency wavelengths were measured from video images. A machine learning model was constructed to determine hypertension, defined as a systolic BP of 130 mmHg or higher or a diastolic BP of 80 mmHg or higher, using the visualized data. The discrimination between hypertension and normal BP was 95.0% accurate within 30 s and 90.3% within 5 s, based on the captured images. The results of heartbeat-by-heartbeat analyses can be used to determine hypertension based on only one second of camera footage or one heartbeat. The data extracted from a video recorded by a target spectral camera enabled accurate hypertension diagnoses, suggesting the potential for simplified BP monitoring.

A paralleled CNN and Transformer network for PPG-based cuff-less blood pressure estimation

Cuff-less blood pressure measurement plays a pivotal part in hypertension diagnosis and prevention. By employing deep learning algorithms, blood pressure estimation applying photoplethysmography (PPG) signals has achieved remarkable improvements and attracted growing attention. However, most existing methods utilize the PPG signal emphasized on localized feature learning, often neglecting the global features in the temporal data. To tackle the issue, we propose a novel architecture. It leverages a parallel processing approach by using Convolutional Neural Network for extracting local features and simultaneously employing Transformer to capture global features. The feature fusion block integrates the features using spatial and channel attention. The blood pressure values are regressed by two fully connected layers. To verify the performance of our method, we conduct evaluations on the Medical Information Mart for Intensive Care (MIMIC) database. On the MIMIC database, our algorithm demonstrates exceptional performance. For Diastolic Pressure, Mean Arterial Pressure, and Systolic Pressure, the mean absolute errors are 2.36 mmHg, 2.03 mmHg, and 4.44 mmHg, respectively. This performance aligns with the rigorous criteria required by an Association for the Advancement of Medical Instrumentation (AAMI) and receives a Grade A rating in accordance with the British Hypertension Society (BHS) standard.

O20 CURRENT 24-HOUR AMBULATORY BLOOD PRESSURE TECHNOLOGIES SHOULD BE RECONSIDERED AS THE REFERENCE STANDARD FOR VALIDATION OF CUFFLESS BLOOD PRESSURE DEVICES

Background and Objective: Recommendations for the validation of cuffless blood pressure (BP) devices were recently released by the European Society of Hypertension (ESH). In this recommendation, 24-hour ambulatory BP monitoring (ABPM) is the reference standard for a newly proposed ‘awake-asleep’ accuracy test. However, this reference standard may be inappropriate because ABPM is subject to measurement insecurities, and it is unknown if it would pass the awake-asleep test. Therefore, the aim of this study was to determine if ABPM itself would pass the awake-asleep test recommended for validation of cuffless BP devices. Methods: Dual arm, simultaneous ABPM were recorded using two identical cuff-based devices (TM-2430, A&D, Japan) among 46 healthy adults (22±2.3 years; 54% female). The awake-asleep test was defined as the difference between the left arm ABPM minus right arm ABPM for assessing the change in BP between awake-asleep time (ESH recommendations: mean difference ± SD ≤5±8 mmHg, correlation r>0.7 for test and reference values; awake-asleep BP components calculated based on personalized sleeping times). Measurement artefacts were identified by analysis of the cuff deflation pressure curves and hydrostatic pressure differences (body position) between the cuffs. Results: ABPM did not pass the awake-asleep test due to higher than acceptable measurement variability for systolic BP, -0.1±8.6 mmHg, r=0.69 and partially met requirements for diastolic BP, -0.7±5.9 mmHg, r=0.63. After correction for measurement artefacts and hydrostatic pressure differences, ABPM passed the awake-asleep test for systolic BP (-1.7±5.0 mmHg, r=0.76) but still did not meet requirements for diastolic BP due to insufficient correlation (r=0.64). Conclusion: In this analysis, ABPM itself did not meet the awake-asleep test criteria recommended by the ESH. Using current ABPM technologies as a reference standard for cuffless BP validation protocols may need to be reconsidered and they need improvement before use for this purpose. Improving ABPM through better measurement artefact detection could be an initial solution which would also improve its value for clinical decision making.

O18 UNCERTAINTY QUANTIFICATION OF WEARABLE CUFFLESS BLOOD PRESSURE MEASUREMENT

Background and Objective: Due to aleatoric and epistemic uncertainty, most cuffless blood pressure (BP) estimation models struggle to provide reliable and accurate BP estimations. The purpose of this study is to quantify the uncertainty of wearable cuffless BP estimation so as to reduce the impact of uncertainty on the accuracy of model estimation, and in the meanwhile to provide an estimated uncertainty interval (UI) in addition to the point estimation. Methods: We developed a gradient boosting regression tree (GBRT) model to estimate ambulatory BP and the estimation UI with eight noninvasive features extracted from wearable photoplethysmogram (PPG) and electrocardiogram (ECG) signals. We validated the proposed method with the Microsoft Aurora dataset that was originally collected for the Aurora-BP study. We identified 483 subjects (247 males) with wearable watches collecting PPG, ECG, and other signals, while ambulatory BP was monitored hourly using an oscillometric BP device. We trained the model with quantile loss on 60% of subjects (2954 samples), then calibrated the estimated UI with conformal predication with 24% (2148 samples) of subjects and tested the model with 16% (1658 samples) of the subjects. Results: The mean absolute difference (MAD) in systolic BP (SBP) and diastolic BP (DBP) estimated by the GBRT model were 13.96 mmHg and 9.89 mmHg, respectively. Then, with implementation of conformal prediction with error rates of ϵ=0.05, for the test set samples, the percentage of the estimated UI covering the reference BP during the daytime and nighttime phases can reach 91.67% and 95.89%, respectively. Conclusions: The combination of the GBRT model with conformal predication can quantify the uncertainty in cuffless BP estimations. In addition, the estimated uncertainty quantification interval together with the point estimation is potentially more reliable than single-point estimation, which could benefit better diagnosis and treatment of hypertension.

O84 BLOOD PRESSURE MEASUREMENT VIA IMPEDANCE CARDIOGRAPHY: AN ARTIFICIAL INTELLIGENCE BASED PROOF-OF-CONCEPT

Objective: Accurate blood pressure (BP) measurement is crucial for the diagnosis, risk assessment, treatment decision-making, and monitoring of cardiovascular diseases. Unfortunately, the gold-standard, cuff-based BP measurement suffer from inaccuracies and discomfort. As a result, continuous, cuff-less, and non-invasive BP assessment methods are merging as promising alternatives. This study is the first to comprehensively assess impedance cardiography (ICG) for Machine Learning enabled BP measurement. Methods: We analysed ICG data from 71 young and healthy adults. Nine different Machine Learning algorithms were evaluated for their BP estimation performance. Accuracy measures comprised mean difference, standard deviation, Limits of Agreement, mean absolute error, and root mean squared error. Additionally, the B-Score was calculated to evaluate the “true”, relative performance of the best-performing model. Results: The Multi-Linear Regression model showed the highest performance (mean difference = -0.01, r = 0.82) for systolic BP. The Support Vector Regressor model achieved the best results (mean difference = 0.15, r = 0.51) for diastolic BP. Both models exhibited positive B-Scores, indicative of their predictive capability. All tested models’ estimations correlated with both systolic and diastolic reference BP (r > 0.72 / r > 0.34, p < 0.001). Conclusion: The study highlights the potential of ICG based Machine Learning algorithms for accurately estimating arterial BP. Such algorithms can provide accurate and continuous BP data. This offers the possibility of enhancing patient care, timely diagnostics, and treatment decision-making. Our approach is especially promising for post-surgical and intensive care environments.

Nonlinear Viscoelastic Modeling of Finger Arteries: Toward Smartphone-Based Blood Pressure Monitoring via the Oscillometric Finger Pressing Method.

Oscillometric finger pressing is a smartphone-based blood pressure (BP) monitoring method. Finger photoplethysmography (PPG) oscillations and pressure are measured during a steady increase in finger pressure, and an algorithm computes systolic BP (SP) and diastolic BP (DP) from the measurements. The objective was to assess the impact of finger artery viscoelasticity on the BP computation. Nonlinear viscoelastic models relating transmural pressure (finger BP - applied pressure) to PPG oscillations during finger pressing were developed. The output of each model to a measured transmural pressure input was fitted to measured PPG oscillations from 15 participants. A parametric sensitivity analysis was performed via model simulations to elucidate the viscoelastic effect on the derivative-based BP computation algorithm. A Wiener viscoelastic model comprising a first-order transfer function followed by a static sigmoidal function fitted the measured PPG oscillations better than an elastic model containing only the static function (median (IQR) error of 30.5% (25.6%-34.0%) vs 50.9% (46.7%-53.7%); p<0.01). In Wiener model simulations, the derivative algorithm underestimated SP, especially with high pulse pressure and low transfer function cutoff frequency (i.e., greater viscoelasticity). The mean of the normalized PPG waveform at the maximum oscillation beat was found to correlate with the cutoff frequency (r = -0.8) and could thus possibly be used to compensate for viscoelasticity. Finger artery viscoelasticity negatively impacts oscillometric BP computation algorithms but can potentially be compensated for using available measurements. These findings may help in converting smartphones into truly cuffless BP monitors for improving hypertension awareness and control.

A new method to compute the blood flow equations using the physics-informed neural operator

Using the Navier-Stokes equation for real blood flow simulations in patients is a challenging task. Among many of the challenges, initial and boundary conditions are not directly measurable. Many parameters for the flow model are also not directly measurable and differ from patient to patient and over time. New machine learning techniques, like physics-informed neural networks (PINN), offer some new ways to handle these difficulties. In this work, we aim to learn the operator that maps some easily measurable physiological signals to the solution of the blood flow equation. We use our proposed model based on Navier-Stokes equation and PINN to fit real data on blood pressure. A Windkessel boundary condition is used to produce physically correct reflection waves. A time-periodic condition is used to capture the periodicity of blood flow and enables our model to simulate the blood flow without initial and boundary conditions. Furthermore, we allow the periods of each instance of solution to be different, which makes the training of neural operators computationally expensive, but more accuracy and physical correct towards real blood pressure data. Further more, we also propose an efficient implementation to incorporate the periodic condition into our model. Estimating the hyper-parameters in the Navier-Stokes equation is also difficult. We then introduce a hyper-parameter network to estimate these parameters during the training process as well. The blood flow data contains useful information for disease detection and diagnosis, but directly measuring the entire blood flow remains a significant challenge. We apply our proposed method to cuffless blood pressure estimation. More specifically, we aim to predict the blood pressure waveform (continuous blood pressure and velocity in both time and space) from Electrocardiogram (ECG) and photoplethysmogram (PPG) signals, which can be easily measured using wearable devices. Compared to other methods, our method is the first one that can predict blood flow continuously, both with location and time which are valuable for cardio-vascular medical treatments and diagnoses.

Assessment of blood pressure variability: characteristics and comparison of blood pressure measurement methods.

Previous studies have reported that blood pressure variability (BPV) is associated with the risk of cardiovascular events independent of blood pressure (BP) levels. While there is little evidence from intervention trials examining whether suppressing BPV is useful in preventing cardiovascular disease, it is suggested that detection of abnormally elevated BPV may be useful in reducing cardiovascular events adding by complementing management of appropriate BP levels. Cuffless BP devices can assess beat-to-beat BPV. Although cuffless BP monitoring devices have measurement accuracy issues that need to be resolved, this is an area of research where the evidence is accumulating rapidly, with many publications on beat-to-beat BPV over several decades. Ambulatory BP monitoring (ABPM) can assess 24-hour BPV and nocturnal dipping patterns. Day-to-day BPV and visit-to-visit BPV are assessed by self-measured BP monitoring at home and office BP measurement, respectively. 24 h, day-to-day, and visit-to-visit BPV have been reported to be associated with cardiovascular prognosis. Although there have been several studies comparing whether ABPM and self-measured BP monitoring at home is the superior measurement method of BPV, no strong evidence has been accumulated that indicates whether ABPM or self-measured home BP is superior. ABPM and self-measured BP monitoring have their own advantages and complement each other in the assessment of BPV.

High-Detectivity All-Polymer Photodiode Empowers Smart Vitality Surveillance and Computational Imaging Rivaling Silicon Diodes.

Near-infrared (NIR) organic photodetectors (OPDs), particularly all-polymer-based ones, hold substantial commercial promise in the healthcare and imaging sectors. However, the process of optimizing their active layer composition to achieve highly competitive figures of merit lacks a clear direction and methodology. In this work, celebrity polymer acceptor PY-IT into a more NIR absorbing host system PBDB-T:PZF-V, to significantly enhance the photodetection competence, is introduced. The refined all-polymer ternary broadband photodetector demonstrates superior performance metrics, including experimentally measured noise current as low as 6 fA Hz-1/2, specific detectivity reaching 8×1012 Jones, linear dynamic range (LDR) of 145 dB, and swift response speed surpassing 200 kHz, striking a fair balance between sensitivity and response speed. Comprehensive morphological and photophysical characterizations elucidate the mechanisms behind the observed performance enhancements in this study, which include reduced trap density, enhanced charge transport, diminished charge recombination, and balanced electron/hole mobilities. Moreover, the practical deployment potential of the proof-of-concept device in self-powered mode is demonstrated through their application in a machine learning-based cuffless blood pressure (BP) estimation system and in high-resolution computational imaging across complex environments, where they are found to quantitatively rival commercial silicon diodes.

A review: Blood pressure monitoring based on PPG and circadian rhythm.

The demand for ambulatory blood pressure monitoring (ABPM) is increasing due to the global rise in cardiovascular disease patients. However, conventional ABPM methods are discontinuous and can disrupt daily activities and sleep patterns. Photoplethysmography (PPG) is gaining attention from researchers due to its simplicity, portability, affordability, and ease of signal acquisition. This paper critically examines the advancements achieved in the technology of PPG-guided noninvasive blood pressure (BP) monitoring and explores future opportunities. We have performed a literature search using the Web of Science and PubMed search engines, from January 2018 to October 2023, for PPG signal quality assessment (SQA), cuffless BP estimation using single PPG, and associations between circadian rhythm and BP. Based on this foundation, we first examine the impact of PPG signal quality on blood pressure estimation results while focusing on methods for assessing PPG signal quality. Subsequently, the methods documented for estimating cuff-free BP from PPG signals are summarized. Furthermore, the study examines how individual differences affect the accuracy of BP estimation, incorporating the factors that influence arterial blood pressure (ABP) and elucidating the impact of circadian rhythm on blood pressure. Finally, there will be a summary of the study's findings and suggestions for future research directions.

A novel cuffless blood pressure monitoring device in hypertension care

Background: Hypertension management is challenged by the dynamic nature of blood pressure (BP) fluctuations. These fluctuations are influenced by factors such as physical activity, emotional changes, and sleep patterns. Traditional guidelines focus on static measurements taken in clinical settings. However, these readings may not accurately represent an individual’s typical BP levels. This approach can lead to misdiagnoses, such as white coat or masked hypertension, resulting in either unnecessary treatment or missed diagnoses.Current Concepts: Recent global hypertension guidelines emphasize the importance of home BP monitoring and 24-hour ambulatory BP measurements for more accurate diagnoses and treatment plans. However, several barriers exist, such as the high cost of ambulatory BP devices and practical challenges associated with home BP monitoring, which limit their widespread utilization. Despite these challenges, integrating BP measurements into mobile devices offers a promising approach to monitoring BP outside the clinical setting. Various innovative approaches have been developed, including smartwatches utilizing photoplethysmography sensors and machine learning algorithms. However, their accuracy and the need for periodic calibration are areas of concern.Discussion and Conclusion: Mobile device BP monitoring represents a significant advancement in hypertension management, offering the potential for continuous, convenient, and less intrusive monitoring. Despite these potential benefits, challenges such as device accuracy verification, large-scale data interpretation, and calibration must be addressed. The future of hypertension management will likely rely on these technologies, along with traditional methods, to provide better insight into individual BP profiles. This integration promises to improve hypertension diagnosis, management, and ultimately patient outcomes, although further research and development are necessary to overcome the current limitations.

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 ( ) 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.

Optimizing time-in-target-range assessment for blood pressure: insights from a large-scale study with continual cuffless monitoring.

Blood pressure (BP) time-in-target-range (TTR) is an emerging predictor of cardiovascular risk. Conventional BP methods are fundamentally unable to provide an optimal assessment of TTR, using irregular measurements separated by lengthy intervals. We investigated the optimal duration and frequency for reliable, practical TTR assessment in clinical settings using continual monitoring. This retrospective study analyzed 2.3 million BP readings from 5,189 European home users (55 ± 11 years, 82% male, BMI 28.0 ± 5.8) using a cuffless BP monitor (Aktiia SA). Systolic BP (SBP) data over 15 consecutive days were assessed (29 ± 11 readings/subject/24-h; 434 + 132 readings/subject/15-day). Subjects were classified into risk-related TTR groups based on 15-day SBP data (24-h, target 90-125 mmHg; ≥6 daytime readings). Various measurement frequencies and durations (1-14 days; 24-h/daytime; 2, 4 or ≥ 6 readings/day) were compared to this reference. Two specific configurations paralleling ambulatory ("One-Day-24 h") and home ("One-Week-Daytime") BP monitoring were selected for detailed analysis. The reference TTR classified 63.0% of the subjects as high risk, 19.0% intermediate, and 18.0% low. "One-Day-24 h" schedule inaccurately classified 26% of subjects compared to the reference TTR, and "One-Week-Daytime" schedule inaccurately classified 45%. Classification accuracy with both schedules was high for subjects with very low or very high reference TTR, but poor otherwise. Accuracy of ≥90% in TTR classification only occurred with 7 days of continual 24-h monitoring. For the first time, with the benefit of a cuffless device that measures BP with sufficient frequency and duration, practical use of TTR is enabled as a potentially enhanced metric to manage hypertension.