Cuffless Blood Pressure Monitoring Research Articles

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.

Validation of deep learning models for cuffless blood pressure estimation on a large benchmarking dataset

Objective: This study aims to evaluate the effectiveness of deep learning techniques in estimating cuffless blood pressure (BP) across a diverse patient population in intensive care units (ICUs). Methods: A comprehensive ICU benchmarking dataset encompassing 2,154 patients with a wide demographic range (18-97 years old) and varied cardiovascular status was employed to validate several deep learning models in predicting continuous BP waveforms. Three methods were developed to enhance the model's generalizability to this heterogeneous dataset. Ten-fold subject-independent cross-validation was performed and the model performance was assessed through mean absolute error (MAE), Pearson’s correlation coefficient (PCC), and compared with significance analysis. Results: The UTransBPNet_Demo_In model, which incorporated demographic and physiological signals as inputs, achieved a PCC of 0.89 and a MAE of 10.38 mmHg in predicting arterial BP waveforms, demonstrating the highest tracking capability among all models. Notably, the performance of UTransBPNet_Demo_In remained robust across variations in demographic and cardiovascular status. Conclusion: The UTransBPNet_Demo_In model demonstrates robust predictive capabilities across a broad spectrum of demographics and cardiovascular conditions. Although the performance still needs further improvement, this study offers a benchmark in the field of cuffless BP monitoring in critical care settings for future studies.

Ability of a 24-h ambulatory cuffless blood pressure monitoring device to track blood pressure changes in clinical practice.

There is an increasing number of cuffless blood pressure (BP) measurement (BPM) devices. Despite promising results when comparing single measurements, the ability of these devices to track changes in BP levels over 24 h related to an initial calibration BP (CalibBP) is unknown. Our aim was to analyse this ability in a cuffless device using pulse transit time. We prospectively enrolled 166 participants for simultaneously performed cuffless (Somnotouch-NIBP) and cuff-based (Spacelabs 90217A/IEM Mobil-O-graph) 24 h BPM. As CalibBP for the cuffless device, first cuff-based BP was used. As surrogate for changes in BP levels after the CalibBP, we used the difference between the CalibBP and mean 24 h, awake and asleep BP measured by the two devices. In addition, we analysed the relationship between the difference of the CalibBP and the cuff-based BPM versus the difference between the cuff-based and the cuffless BPM devices. Mean(SD) difference between the CalibBP and mean 24hBP by the cuff-based or cuffless BP device were 7.4 (13.2) versus 1.8 (8.3) mmHg for systolic ( P < 0.0001) and 6.6 (6.8) versus 1.6 (5.8) mmHg for diastolic ( P < 0.0001). A near linear relationship was seen among the difference between the CalibBP and the cuff-based BPM values and the difference between the cuff-based and cuffless BPM device. Our data indicate a lower ability of the cuffless BPM device to track changes of BP levels after CalibBP. In addition, cuffless device accuracy was associated with the changes in BP levels after the initial CalibBP - the larger the BP level change, the larger the difference between the devices. https://www.clinicaltrials.gov ; Unique identifier: NCT03054688; NCT03975582.

Predicting Blood Pressures for Pregnant Women by PPG and Personalized Deep Learning.

Blood pressure (BP) is predicted by this effort based on photoplethysmography (PPG) data to provide effective pre-warning of possible preeclampsia of pregnant women. Towards frequent BP measurement, a PPG sensor device is utilized in this study as a solution to offer continuous, cuffless blood pressure monitoring frequently for pregnant women. PPG data were collected using a flexible sensor patch from the wrist arteries of 194 subjects, which included 154 normal individuals and 40 pregnant women. Deep-learning models in 3 stages were built and trained to predict BP. The first stage involves developing a baseline deep-learning BP model using a dataset from common subjects. In the 2nd stage, this model was fine-tuned with data from pregnant women, using a 1-Dimensional Convolutional Neural Network (1D-CNN) with Convolutional Block Attention Module (CBAMs), followed by bi-directional Gated Recurrent Units (GRUs) layers and attention layers. The fine-tuned model results in a mean error (ME) of -1.40 ± 7.15 (standard deviation, SD) for systolic blood pressure (SBP) and -0.44 (ME) ± 5.06 (SD) for diastolic blood pressure (DBP). At the final stage is the personalization for individual pregnant women using transfer learning again, enhancing further the model accuracy to -0.17 (ME) ± 1.45 (SD) for SBP and 0.27 (ME) ± 0.64 (SD) for DBP showing a promising solution for continuous, non-invasive BP monitoring in precision by the proposed 3-stage of modeling, fine-tuning and personalization.

Performance of a cuffless blood pressure monitor in the adult and elderly population - a comparative validation study

Abstract Background Cuffless blood pressure (BP) monitoring devices are emerging as powerful tools for BP monitoring and management of hypertension, particularly in the elderly, in whom prevalence of hypertension is high. Some cuffless BP monitors allow the hourly and daily tracking of blood pressure. It remains unclear, however, whether cuffless devices yield equivalent performance across age categories. Purpose To compare the performance of BP measurements conducted by a cuffless BP monitor in different body positions in adult and elderly participants with a wide range of BP levels. Methods 2 cohorts of patients (adult, 21-65 years; elderly, 65-85 years) were recruited for a prospective clinical study (NCT04027777) between August/2019–March/2022. During 4 visits over a month, systolic/diastolic BP (SBP, DBP) measurements obtained by the investigated cuffless wrist bracelet were compared to the reference measurement – double auscultation – in a sitting, standing and lying position after a unique initial calibration. The two ISO 81060-2 criteria were used for the comparison of BP measurements by the cuffless monitor and auscultation (BP-reading-level – criterion 1; subject-level – criterion 2) in the sitting position (there are no ISO criteria for other body positions). The mean differences on SBP and DBP between cohorts both at BP-reading-level and at subject-level were compared (unpaired t-test). Results 96 adult (38±12 years)/35 elderly (73±6 years) were included in the study. Of those, 84 adult/35 elderly patients were investigated in sitting position; 91 adult/35 elderly in all body positions. SBP and DBP were significantly higher in the elderly cohort (SBP 116.4±20.3 vs. 133.9±17.7 mmHg, P≤0.0001; DBP 74.5±12.0 vs. 77.9±11.7 mmHg, P≤0.0001). The results for the analyses at BP-reading-level and subject-level are shown in Table 1. For the analyses on sitting position at BP-reading-level, SBP and DBP for both cohorts were within ISO targets for mean error and SD. At subject-level, the SD of the mean paired differences per subject were within individual ISO targets for SBP and DBP on both cohorts. Statistically significant differences between cohorts were found only at BP-reading-level for all body positions, although the magnitude of differences were not clinically relevant (Table 1, bold and *). Figure 1 highlights the biases and correlations of SBP and DBP readings between the cuffless BP monitor and auscultation for both adult and elderly cohorts. Conclusions The cuffless BP monitor investigated in the present study yielded equivalent BP measurements compared to auscultation, with similar performance in the adult and elderly populations.Table 1Figure 1

Daytime blood pressure measurements performed by a cuffless blood pressure monitor compared to 24-hour ambulatory blood pressure monitor: preliminary comparative results of a single-centre study

Abstract Background Ambulatory blood pressure (BP) monitors (ABPM) measure systolic and diastolic BP (SBP, DBP), limited to 24-48-hour snapshots, using uncomfortable cuff-based oscillometric sphygmomanometers. Some cuffless BP devices overcome these limitations, supporting continual BP monitoring. There is currently intense debate whether cuffless devices generate data comparable to ABPM. Purpose Here, we present preliminary results of daytime BP measurements performed by a validated and commercially available cuffless BP monitor compared to 24-hour ABPM. Methods 52 out of 63 estimated total patients (NCT04548986, age 53.2±7.1 years, 18.9% female, arm circumference 28.5±2 cm) had been enrolled in a 12-week cardiac rehabilitation (CR) program. The patients used a validated, CE-marked, over-the-counter optical cuffless wrist BP monitor during the entire CR program, while 24-hour cuff-based ABPM was performed on the first and last day of the CR program. For each patient, average daytime (9am-9pm) SBP and DBP were calculated – and compared – for 1 day of ABPM and 7 days of cuffless monitor (session 1: ABPM first day vs. cuffless monitor first week; session 2: ABPM last day vs. cuffless monitor last week). Only sessions that had 20 or more valid daytime measurements on both modalities were included in the study. Results 50 patients (35 in session 1, 15 in session 2) had 20 or more valid daytime measurements on both modalities, and were included in the study. Bias between the cuffless monitor and ABPM for daytime DBP was (mean ± SD [95% CI] ) -2.2±8.0 [-4.5, 0.1] mmHg, R2 = 0.66 (Figure 1A), with agreements of 78% and 96% within ±10 mmHg (dark grey) and ±15 mmHg (light grey) regions of interest (ROI), respectively. Bias between the cuffless monitor and ABPM for daytime SBP was 1.6±10.5 [-1.5, 4.6] mmHg, R2 = 0.70 (Figure 1B), with agreements of 60% and 84% within the ±10/±15 mmHg ROI, respectively. Conclusions The results suggest that 1-week monitoring with the investigated cuffless monitor yields equivalent daytime BP values to 24-h ABPM, which supports the use of cuffless BP monitor for long-term, continual daytime BP monitoring. The present preliminary study is limited by the partially recruited population and daytime measurements only. Night-time analysis will be covered in subsequent studies.Figure 1

Pearls and pitfalls of cuffless blood pressure monitoring devices

Cuffless blood pressure measuring devices are emerging as alternatives for out-of-office assessment of patients with hypertension. This novel technology promises to deliver continuous or beat-to-beat blood pressure measurement and resolve some of the inconveniences associated with conventional cuff-based devices such as improper cuff fit, discomfort, low adherence to monitoring, and technical challenges. Blood pressure can be estimated based on phenomena related to cardiovascular activity: pulse wave velocity, pulse wave analysis, tonometry, and volume clamping. In this narrative review, we seek to summarize current approaches for nontraditional measurement of blood pressure and discuss some of the benefits and limitations of this technology.

The Promise and Illusion of Continuous, Cuffless Blood Pressure Monitoring.

Blood pressure (BP) fluctuations outside of clinic are increasingly recognized for their role in the development of cardiovascular disease, syncope, and premature death and as a promising target for tailored hypertension treatment. However, current cuff-based BP devices, including home and ambulatory devices, are unable to capture the breadth of BP variability across human activities, experiences, and contexts. Cuffless, wearable BP devices offer the promise of beat-to-beat, continuous, noninvasive measurement of BP during both awake and sleep periods with minimal patient inconvenience. Importantly, cuffless BP devices can characterize BP variability, allowing for the identification of patient-specific triggers of BP surges in the home environment. Unfortunately, the pace of evidence, regulation, and validation testing has lagged behind the pace of innovation and direct consumer marketing. We provide an overview of the available technologies and devices for cuffless BP monitoring, considerations for the calibration and validation of these devices, and the promise and pitfalls of the cuffless BP paradigm.

Noninvasive Cuffless Blood Pressure Monitoring. How Mechanism-Driven and Data-Driven Models Can Help in Clinical Practice

Continuous noninvasive cuffless blood pressure (BP) monitoring is essential for early detection and treatment of hypertension. In this paper, we provide an overview of the recent advancements in cuffless BP sensors. These include contact wearable sensors such as electrocardiography (ECG), photoplethysmography (PPG), contact non-wearable sensors such as ballistocardiography (BCG), and contactless sensors such as video plethysmography (VPG). These sensors employ different measuring mechanisms such as pulse arrival time (PAT), pulse transit time (PTT), and pulse wave analysis (PWA) to estimate BP. However, challenges exist in the effective use and interpretation of signal features to obtain clinically reliable BP measurements. The correlations between signal features and BP are obtained by mechanism-driven models which use physiological principles to identify mathematical correlations, and data-driven models which use machine learning algorithms to analyze observational data to identify multidimensional correlations. On the one hand, applying mechanism-driven models to non-linear scenarios and incomplete or noisy data is challenging On the other hand, data-driven models require a large amount of data in order to prevent physically inconsistent predictions, resulting in poor generalization. From this perspective, this paper proposes to combine the strengths of mechanism-driven and data-driven approaches to obtain a more comprehensive approach, the physiology-informed machine-learning approach, with the goal of enhancing the accuracy, interpretability, and scalability of continuous cuffless BP monitoring. This holds promise for personalized clinical applications and the advancement of hypertension management.

AN EQUIVALENT MEASUREMENT PERFORMANCE OF THE AKTIIA BRACELET IS OBSERVED IN THE ADULT AND ELDERLY POPULATIONS

Objective: It remains unclear whether non-occlusive blood pressure (BP) monitoring devices yield equivalent performance across different age categories. We sought to compare the performance of BP measurements conducted by the Aktiia cuffless monitor in the adult and elderly populations, in different body positions. Design and method: Two cohorts of patients (adult, 21-65 years; elderly, 65-85 years) were recruited for a prospective clinical study (NCT04027777, Lausanne University Hospital, Switzerland, August/2019–March/2022). During 4 visits spread over a month, systolic/diastolic BP (SBP, DBP) measurements obtained by the cuffless Aktiia G1 Bracelet (Aktiia SA, Switzerland) were compared to double-blinded auscultation in sitting, standing and lying positions after initial calibration. The two ISO 81060-2 criteria were used to compare BP measurements by Aktiia and auscultation (BP-reading-level – criterion 1; subject-level – criterion 2) in the sitting position – as no ISO criteria exists so far for other body positions. Results: 96 adult (38±12 years)/35 elderly (73±6 years) were included in the study. Of those, 84 adult/35 elderly patients were investigated in sitting position; 91 adult/35 elderly in all body positions. Average SBP and DBP were higher in the elderly cohort (SBP 133.9±17.7 vs. 116.4±20.3 mmHg, P&lt;0.0001; DBP 77.9±11.7 vs. 74.5±12.0 mmHg, P&lt;0.0001). On sitting position at BP-reading-level, SBP and DBP for both cohorts were within ISO targets for mean error and SD (Table 1). At subject-level, the SD of the mean paired differences per subject were within individual ISO targets for SBP and DBP on both cohorts. Statistically significant differences between cohorts were found only at BP-reading-level for all body positions (SBP: P = 0.0127; DBP: P = 0.0199). Figure 1 highlights the biases and correlations of SBP and DBP readings between the cuffless BP monitor and auscultation for both adult and elderly cohorts. Conclusions: The investigated cuffless BP monitor yielded equivalent BP measurements compared to auscultation, with similar performance in the adult and elderly populations.

Feasibility, credence, and usefulness of out-of-office cuffless blood pressure monitoring using smartwatch: a population survey

BackgroundCuffless blood pressure (BP) measurement, enabled by recent advances in wearable devices, allows for BP monitoring in daily life. This study aims to evaluate the feasibility, cresdence, and usefulness of cuffless BP monitoring through a population survey.MethodsDuring the "Daily BP Measurement with Your Galaxy Watch" campaign held by the Korean Society of Hypertension, participants were asked to share their experiences with cuffless BP measurement using a smartwatch application through an online survey. The questionnaire included questions about age, underlying medical conditions, smartwatch utilization, experience with BP calibration, the reliability of BP values measured by a smartwatch, and willingness to use the BP monitoring function in the future.ResultsA total of 1071 participants responded to the survey. The largest age group (decile) was 50-59 years old (33.3%), followed by 40-49 years old (29.9%). Although nearly half of the participants (47.5%) had no chronic diseases, 40.1% reported having hypertension. BP monitoring was the most frequently utilized smartwatch function (95.8%), followed by heart rate measurement (87.1%). 31.8% of participants reported that BP values measured by the smartphone application were "very accurate and helpful," while 63.5% rated them as "slightly lower (44.4%)" or "higher (19.1%)" compared to the standard home BP monitoring device. 93% of the participants reported utilizing the BP monitoring function at least once a week. Regarding the BP calibration process, most participants (93.9%) calibrated the BP measurement application themselves, and 50.8% rated the difficulty level as "very easy."ConclusionCuffless BP measurement using a smartwatch application was feasible in the general population, including the self-calibration process. However, the satisfaction level in terms of accuracy is still modest, indicating a need for further development.

FEASIBILITY AND MEASUREMENT STABILITY OF SMARTWATCH-BASED CUFFLESS BLOOD PRESSURE MONITORING

Objective: Cuffless wearable devices are currently being developed for long-term monitoring of blood pressure (BP) in patients with hypertension and in apparently healthy people. This study evaluated the feasibility and measurement stability of smartwatch-based cuffless BP monitoring in real-world conditions. Design and method: Users of the first smartwatch-based cuffless BP monitor approved in Korea (Samsung Galaxy Watch) were invited to upload their data from using the device for 4 weeks post calibration. Results: A total of 760 participants (mean age 43.7±11.9, 80.3% men) provided 35,797 BP readings (average monitoring 22±4 days [SD]; average readings 47±42 per participant [median 36]). Each participant obtained 1.5±1.3 readings/day and 19.7% of the participants obtained measurements every day. BP showed considerable variability, mainly depending on the day and time of the measurement. There was a trend towards higher BP levels on Mondays than on other days of the week and on workdays than in weekends. BP readings taken between 00:00 and 04:00 tended to be the lowest, whereas those between 12:00 and 16:00 the highest. The average pre-post calibration error for systolic BP (difference in 7-day BP before and after calibration), was 6.8±5.6 mmHg, and was increased with higher systolic BP levels before calibration. Conclusions: Smartwatch-based cuffless BP monitoring is feasible for out-of-office monitoring in the real-world setting. The stability of BP measurement post calibration and the standardization and optimal time interval for recalibration need further investigation.

Continuous cuffless blood pressure monitoring using photoplethysmography-based PPG2BP-net for high intrasubject blood pressure variations

Continuous, comfortable, convenient (C3), and accurate blood pressure (BP) measurement and monitoring are needed for early diagnosis of various cardiovascular diseases. To supplement the limited C3 BP measurement of existing cuff-based BP technologies, though they may achieve reliable accuracy, cuffless BP measurement technologies, such as pulse transit/arrival time, pulse wave analysis, and image processing, have been studied to obtain C3 BP measurement. One of the recent cuffless BP measurement technologies, innovative machine-learning and artificial intelligence-based technologies that can estimate BP by extracting BP-related features from photoplethysmography (PPG)-based waveforms have attracted interdisciplinary attention of the medical and computer scientists owing to their handiness and effectiveness for both C3 and accurate, i.e., C3A, BP measurement. However, C3A BP measurement remains still unattainable because the accuracy of the existing PPG-based BP methods was not sufficiently justified for subject-independent and highly varying BP, which is a typical case in practice. To circumvent this issue, a novel convolutional neural network(CNN)- and calibration-based model (PPG2BP-Net) was designed by using a comparative paired one-dimensional CNN structure to estimate highly varying intrasubject BP. To this end, approximately 70%, 20%, and 10% of 4185 cleaned, independent subjects from 25,779 surgical cases were used for training, validating, and testing the proposed PPG2BP-Net, respectively and exclusively (i.e., subject-independent modelling). For quantifying the intrasubject BP variation from an initial calibration BP, a novel ‘standard deviation of subject-calibration centring (SDS)’ metric is proposed wherein high SDS represents high intrasubject BP variation from the calibration BP and vice versa. PPG2BP-Net achieved accurately estimated systolic and diastolic BP values despite high intrasubject variability. In 629-subject data acquired after 20 minutes following the A-line (arterial line) insertion, low error mean and standard deviation of 0.209pm 7.509 and 0.150pm 4.549;textrm{mmHg} for highly varying A-line systolic and diastolic BP values, respectively, where their SDSs are 15.375 and 8.745. This study moves one step forward in developing the C3A cuffless BP estimation devices that enable the push and agile pull services.

Add-on soft electronic interfaces for continuous cuffless blood pressure monitoring.

Continuous monitoring of arterial blood pressure is clinically important for the diagnosis and management of cardiovascular diseases. Soft electronic devices with skin-like properties show promise in a wide range of applications, including the human-machine interface, the Internet of things, and health monitoring. Here, we report the use of add-on soft electronic interfaces to address the connection challenges between soft electrodes and rigid data acquisition circuitry for bioimpedance monitoring of cardiac signals, including heart rate and cuffless blood pressure. Nanocomposite films in add-on electrodes provide robust electrical and mechanical contact with the skin and the rigid circuitry. We demonstrate bioimpedance sensors composed of add-on electrodes for continuous blood pressure monitoring with high accuracy. Specifically, the bioimpedance collected with add-on nanocomposite electrodes shows a signal-to-noise ratio of 37.0 dB, higher than the ratio of 25.9 dB obtained with standard silver/silver chloride (Ag/AgCl gel) electrodes. Although the sample set is low, the continuously measured systolic and diastolic blood pressure offer accuracy of -2.0 ± 6.3 mmHg and -4.3 ± 3.9 mmHg, respectively, confirming the grade A performance based on the IEEE standard. These results show promise in bioimpedance measurements with add-on soft electrodes for cuffless blood pressure monitoring.

Method-comparison study between a watch-like sensor and a cuff-based device for 24-h ambulatory blood pressure monitoring

The use of 24-h ambulatory blood pressure monitoring (ABPM) has been continuously increasing over the last decades. However, cuff-based devices may cause discomfort, particularly at night, leading to potentially non-representative blood pressure (BP) values. We investigated the feasibility of a cuff-less BP monitoring solution in 67 subjects undergoing conventional 24-h ABPM. A watch-like optical sensor was attached at the upper arm or wrist at the contralateral side of the cuff. Systolic (SBP) and diastolic BP (DBP) values were estimated from the measured optical signals by pulse wave analysis. Average 24-h, daytime and nighttime BP values were compared between the conventional monitor and the cuff-less sensor. The differences between both methods—expressed as mean ± standard deviation (95% limits of agreement)—were of − 1.8 ± 6.2 mmHg (− 13.9, 10.3) on SBP and − 2.3 ± 5.4 mmHg (− 13.0, 8.3) on DBP for 24-h averages, of − 1.5 ± 6.6 mmHg (− 14.4, 11.4) on SBP and − 1.8 ± 5.9 mmHg (− 13.4, 9.9) on DBP for daytime averages, and of 0.4 ± 7.5 mmHg (− 14.4, 15.1) on SBP and − 1.3 ± 6.8 mmHg (− 14.7, 12.0) on DBP for nighttime averages. These results encouragingly suggest that cuff-less 24-h ABPM may soon become a clinical possibility.

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

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

Continuous cuffless blood pressure monitoring with a wearable ring bioimpedance device

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

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

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

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

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

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

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