Ballistocardiogram as Proximal Timing Reference for Pulse Transit Time Measurement: Potential for Cuffless Blood Pressure Monitoring

Using a Bodily Weight-Fat Scale for Cuffless Blood Pressure Measurement Based on the Edge Computing System

Blood pressure (BP) measurement is a major physiological information for people with cardiovascular diseases, such as hypertension, heart failure, and atherosclerosis. Moreover, elders and patients with kidney disease and diabetes mellitus also are suggested to measure their BP every day. The cuffless BP measurement has been developed in the past 10 years, which is comfortable to users. Now, ballistocardiogram (BCG) and impedance plethysmogram (IPG) could be used to perform the cuffless BP measurement. Thus, the aim of this study is to realize edge computing for the BP measurement in real time, which includes measurements of BCG and IPG signals, digital signal process, feature extraction, and BP estimation by machine learning algorithm. This system measured BCG and IPG signals from a bodily weight-fat scale with the self-made circuits. The signals were filtered to reduce the noise and segmented by 2 s. Then, we proposed a flowchart to extract the parameter, pulse transit time (PTT), within each segment. The feature included two calibration-based parameters and one calibration-free parameter was used to estimate BP with XGBoost. In order to realize the system in STM32F756ZG NUCLEO development board, we limited the hyperparameters of XGBoost model, including maximum depth (max_depth) and tree number (n_estimators). Results show that the error of systolic blood pressure (SBP) and diastolic blood pressure (DBP) in server-based computing are 2.64 ± 9.71 mmHg and 1.52 ± 6.32 mmHg, and in edge computing are 2.2 ± 10.9 mmHg and 1.87 ± 6.79 mmHg. This proposed method significantly enhances the feasibility of bodily weight-fat scale in the BP measurement for effective utilization in mobile health applications.

Wearable Blood Pressure Monitoring Devices: Understanding Heterogeneity in Design and Evaluation

OBJECTIVE

Rapid advances in cuffless blood pressure (BP) monitoring have the potential to radically transform clinical care for cardiovascular health. However, due to the large heterogeneity in device design and evaluation, it is difficult to critically and quantitatively evaluate research progress. In this two-part manuscript, we provide a principled way of describing and accounting for heterogeneity in device and study design.

METHODS

We first provide an overview of foundational elements and design principles of three critical aspects: 1) sensors and systems, 2) pre-processing and feature extraction, and 3) BP estimation algorithms. Then, we critically analyze the state-of-the-art methods via a systematic review.

RESULTS

First, we find large heterogeneity in study designs, making fair comparisons extremely challenging. Moreover, many study designs have data leakage and are underpowered. We suggest a first open-contribution BP estimation benchmark for standardization. Next, we observe that BP distribution in the study sample and the time between calibration and test in emerging personalized devices confound BP estimation error. We suggest accounting for these using a convenient metric coined "explained deviation". Finally, we complement this manuscript with a website, https://wearablebp.github.io, containing a bibliography, meta-analysis results, datasets, and benchmarks, providing a timely plaWorm to understand state-of-the-art devices.

CONCLUSION

There is large heterogeneity in device and study design, which should be carefully accounted for when designing, comparing, and contrasting studies.

SIGNIFICANCE

Our findings will allow readers to parse out the heterogeneous literature and move toward promising directions for safer and more reliable devices in clinical practice and beyond.

Identifying Gravity-Related Artifacts on Ballistocardiography Signals by Comparing Weightlessness and Normal Gravity Recordings (ARTIFACTS): Protocol for an Observational Study

BACKGROUND

Modern ballistocardiography (BCG) and seismocardiography (SCG) use acceleration sensors to measure oscillating recoil movements of the body caused by the heartbeat and blood flow, which are transmitted to the body surface. Acceleration artifacts occur through intrinsic sensor roll, pitch, and yaw movements, assessed by the angular velocities of the respective sensor, during measurements that bias the signal interpretation.

OBJECTIVE

This observational study aims to generate hypotheses on the detection and elimination of acceleration artifacts due to the intrinsic rotation of accelerometers and their differentiation from heart-induced sensor accelerations.

METHODS

Multimodal data from 4 healthy participants (3 male and 1 female) using BCG-SCG and an electrocardiogram will be collected and serve as a basis for signal characterization, model modulation, and location vector derivation under parabolic flight conditions from µg to 1.8g. The data will be obtained during a parabolic flight campaign (3 times 30 parabolas) between September 24 and July 25 (depending on the flight schedule). To detect the described acceleration artifacts, accelerometers and gyroscopes (6-degree-of-freedom sensors) will be used for measuring acceleration and angular velocities attributed to intrinsic sensor rotation. Changes in acceleration and angular velocities will be explored by conducting descriptive data analysis of resting participants sitting upright in varying gravitational states.

RESULTS

A multimodal data set will serve as a basis for research into a noninvasive and gentle method of BCG-SCG with the aid of low-noise and synchronous 3D gyroscopes and 3D acceleration sensors. Hypotheses will be generated related to detecting and eliminating acceleration artifacts due to the intrinsic rotation of accelerometers and gyroscopes (6-degree-of-freedom sensors) and their differentiation from heart-induced sensor accelerations. Data will be collected entirely and exclusively during the parabolic flights, taking place between September 2024 and July 2025. Thus, as of June 2024, no data have been collected yet. The data will be analyzed until December 2025. The results are expected to be published by June 2026.

CONCLUSIONS

The study will contribute to understanding artificial acceleration bias to signal readings. It will be a first approach for a detection and elimination method.

TRIAL REGISTRATION

Deutsches Register Klinische Studien DRKS00034402; https://drks.de/search/en/trial/DRKS00034402.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/63306.

Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement

In recent decades, there has been ongoing development in the application of computer vision (CV) in the medical field. As conventional contact-based physiological measurement techniques often restrict a patient's mobility in the clinical environment, the ability to achieve continuous, comfortable and convenient monitoring is thus a topic of interest to researchers. One type of CV application is remote imaging photoplethysmography (rPPG), which can predict vital signs using a video or image. While contactless physiological measurement techniques have an excellent application prospect, the lack of uniformity or standardization of contactless vital monitoring methods limits their application in remote healthcare/telehealth settings. Several methods have been developed to improve this limitation and solve the heterogeneity of video signals caused by movement, lighting, and equipment. The fundamental algorithms include traditional algorithms with optimization and developing deep learning (DL) algorithms. This article aims to provide an in-depth review of current Artificial Intelligence (AI) methods using CV and DL in contactless physiological measurement and a comprehensive summary of the latest development of contactless measurement techniques for skin perfusion, respiratory rate, blood oxygen saturation, heart rate, heart rate variability, and blood pressure.

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.

Deep-learning-based blood pressure estimation using multi channel photoplethysmogram and finger pressure with attention mechanism

Recently, several studies have proposed methods for measuring cuffless blood pressure (BP) using finger photoplethysmogram (PPG) signals. This study presents a new BP estimation system that measures PPG signals under progressive finger pressure, making the system relatively robust to errors caused by finger position when using the cuffless oscillometric method. To reduce errors caused by finger position, we developed a sensor that can simultaneously measure multi-channel PPG and force signals in a wide field of view (FOV). We propose a deep-learning-based algorithm that can learn to focus on the optimal PPG channel from multi channel PPG using an attention mechanism. The errors (ME ± STD) of the proposed multi channel system were 0.43±9.35 mmHg and 0.21 ± 7.72 mmHg for SBP and DBP, respectively. Through extensive experiments, we found a significant performance difference depending on the location of the PPG measurement in the BP estimation system using finger pressure.

Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring

Cardiovascular diseases (CVDs) are a leading cause of death worldwide. For early diagnosis, intervention and management of CVDs, it is highly desirable to frequently monitor blood pressure (BP), a vital sign closely related to CVDs, during people's daily life, including sleep time. Towards this end, wearable and cuffless BP extraction methods have been extensively researched in recent years as part of the mobile healthcare initiative. This review focuses on the enabling technologies for wearable and cuffless BP monitoring platforms, covering both the emerging flexible sensor designs and BP extraction algorithms. Based on the signal type, the sensing devices are classified into electrical, optical, and mechanical sensors, and the state-of-the-art material choices, fabrication methods, and performances of each type of sensor are briefly reviewed. In the model part of the review, contemporary algorithmic BP estimation methods for beat-to-beat BP measurements and continuous BP waveform extraction are introduced. Mainstream approaches, such as pulse transit time-based analytical models and machine learning methods, are compared in terms of their input modalities, features, implementation algorithms, and performances. The review sheds light on the interdisciplinary research opportunities to combine the latest innovations in the sensor and signal processing research fields to achieve a new generation of cuffless BP measurement devices with improved wearability, reliability, and accuracy.

Wrist ballistocardiography and invasively recorded blood pressure in healthy volunteers during reclining bike exercise

Objective: Ballistocardiogram (BCG) features are of interest in wearable cardiovascular monitoring of cardiac performance. We assess feasibility of wrist acceleration BCG during exercise for estimating pulse transit time (PTT), enabling broader cardiovascular response studies during acute exercise and improved monitoring in individuals at risk for cardiovascular disease (CVD). We also examine the relationship between PTT, blood pressure (BP), and stroke volume (SV) during exercise and posture interventions. Methods: 25 participants underwent a bike exercise protocol with four incremental workloads (0 W, 50 W, 100 W, and 150 W) in supine and semirecumbent postures. BCG, invasive radial artery BP, tonometry, photoplethysmography (PPG) and echocardiography were recorded. Ensemble averages of BCG signals determined aortic valve opening (AVO) timings, combined with peripheral pulse wave arrival times to calculate PTT. We tested for significance using Wilcoxon signed-rank test. Results: BCG was successfully recorded at the wrist during exercise. PTT exhibited a moderate negative correlation with systolic BP (ρ_Sup = -0.65, ρ_SR = -0.57, ρ_All = -0.54). PTT differences between supine and semirecumbent conditions were significant at 0 W and 50 W (p < 0.001), less at 100 W (p = 0.0135) and 150 W (p = 0.031). SBP and DBP were lower in semirecumbent posture (p < 0.01), while HR was slightly higher. Echocardiography confirmed association of BCG features with AVO and indicated a positive relationship between BCG amplitude and SV (ρ = 0.74). Significance: Wrist BCG may allow convenient PTT and possibly SV tracking during exercise, enabling studies of cardiovascular response to acute exercise and convenient monitoring of cardiovascular performance.

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.

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.

Cuffless Blood Pressure Monitoring: Academic Insights and Perspectives Analysis

In recent decades, cuffless blood pressure monitoring technology has been a point of research in the field of health monitoring and public media. Based on the web of science database, this paper evaluated the publications in the field from 1990 to 2020 using bibliometric analysis, described the developments in recent years, and presented future research prospects in the field. Through the comparative analysis of keywords, citations, H-index, journals, research institutions, national authors and reviews, this paper identified research hotspots and future research trends in the field of cuffless blood pressure monitoring. From the results of the bibliometric analysis, innovative methods such as machine learning technologies related to pulse transmit time and pulse wave analysis have been widely applied in blood pressure monitoring. The 2091 articles related to cuffless blood pressure monitoring technology were published in 1131 journals. In the future, improving the accuracy of monitoring to meet the international medical blood pressure standards, and achieving portability and miniaturization will remain the development goals of cuffless blood pressure measurement technology. The application of flexible electronics and machine learning strategy in the field will be two major development directions to guide the practical applications of cuffless blood pressure monitoring technology.

A Novel CNN-LSTM Model Based Non-Invasive Cuff-Less Blood Pressure Estimation System

PPG (Photoplethysmography) and ECG (Electro-cardiogram) physiological signals have been known to have certain indicators for establishing blood pressure (BP) levels. Continuous monitoring of blood pressure (BP) is highly valuable for cardiovascular patients; however the existing non-invasive cuff-based blood pressure monitoring system is discreet and applies artificial pressure on patients' arms that is uncomfortable. The other invasive method is highly interventional in nature and is highly disturbing when the patient is recuperating in the hospital wards or elsewhere. A non-invasive cuff-less, non-disturbing, and continuous BP measurement system targeted toward surgical, clinical, and domestic usage are proposed in this work. A convolutional neural network (CNN) followed by a long short-term memory layer (LSTM) was designed and applied to ECG and PPG signals to present accurate systolic blood pressure (SBP), and diastolic blood pressure (DBP). For developing the CNN-LSTM layers, a novel and open-source dataset was compiled that consisted of PPG and ECG signals from 30 healthy participants and is made publicly available for further usage to the research community. The novel CNN-LSTM based cuff-less blood pressure evaluation system presented a mean absolute error (MAE) of 2.57 mmHg and 3.44 mmHg for SBP and DBP respectively with similar standard-deviation (SD) metrics. The characterized error metrics of the proposed system are the lowest to date when compared to other prior work. Clinical Relevance- A cuff-less BP estimation system allows patients to have easy access to blood pressure evaluation as well as aid in determining unsafe health ailments like hypertension. Ready access to such system will not only allow practitioners to continuously monitor BP in hospitals but also help patients to regularly monitor BP more frequently at their convenience.

ResNet-BiLSTM: A Multiscale Deep Learning Model for Heartbeat Detection Using Ballistocardiogram Signals

As the heartbeat detection from ballistocardiogram (BCG) signals using force sensors is interfered by respiratory effort and artifact motion, advanced signal processing algorithms are required to detect the J-peak of each BCG signal so that beat-to-beat interval can be identified. However, existing methods generally rely on rule-based detection of a fixed size, without considering the rhythm features in a large time scale covering multiple BCG signals. Methods. This paper develops a deep learning framework based on ResNet and bidirectional long short-term memory (BiLSTM) to conduct beat-to-beat detection of BCG signals. Unlike the existing methods, the proposed network takes multiscale features of BCG signals as the input and, thus, can enjoy the complementary advantages of both morphological features of one BCG signal and rhythm features of multiple BCG signals. Different time scales of multiscale features for the proposed model are validated and analyzed through experiments. Results. The BCG signals recorded from 21 healthy subjects are conducted to verify the performance of the proposed heartbeat detection scheme using leave-one-out cross-validation. The impact of different time scales on the detection performance and the performance of the proposed model for different sleep postures are examined. Numerical results demonstrate that the proposed multiscale model performs robust to sleep postures and achieves an averaged absolute error (E abs) and an averaged relative error (E rel) of the heartbeat interval relative to the R-R interval of 9.92 ms and 2.67 ms, respectively, which are superior to those of the state-of-the-art detection protocol. Conclusion. In this work, a multiscale deep-learning model for heartbeat detection using BCG signals is designed. We demonstrate through the experiment that the detection with multiscale features of BCG signals can provide a superior performance to the existing works. Further study will examine the ultimate performance of the multiscale model in practical scenarios, i.e., detection for patients suffering from cardiovascular disorders with night-sleep monitoring.

A Novel Clustering-Based Algorithm for Continuous and Noninvasive Cuff-Less Blood Pressure Estimation

Extensive research has been performed on continuous and noninvasive cuff-less blood pressure (BP) measurement using artificial intelligence algorithms. This approach involves extracting certain features from physiological signals, such as ECG, PPG, ICG, and BCG, as independent variables and extracting features from arterial blood pressure (ABP) signals as dependent variables and then using machine-learning algorithms to develop a blood pressure estimation model based on these data. The greatest challenge of this field is the insufficient accuracy of estimation models. This paper proposes a novel blood pressure estimation method with a clustering step for accuracy improvement. The proposed method involves extracting pulse transit time (PTT), PPG intensity ratio (PIR), and heart rate (HR) features from electrocardiogram (ECG) and photoplethysmogram (PPG) signals as the inputs of clustering and regression, extracting systolic blood pressure (SBP) and diastolic blood pressure (DBP) features from ABP signals as dependent variables, and finally developing regression models by applying gradient boosting regression (GBR), random forest regression (RFR), and multilayer perceptron regression (MLP) on each cluster. The method was implemented using the MIMIC-II data set with the silhouette criterion used to determine the optimal number of clusters. The results showed that because of the inconsistency, high dispersion, and multitrend behavior of the extracted features vectors, the accuracy can be significantly improved by running a clustering algorithm and then developing a regression model on each cluster and finally weighted averaging of the results based on the error of each cluster. When implemented with 5 clusters and GBR, this approach yielded an MAE of 2.56 for SBP estimates and 2.23 for DBP estimates, which were significantly better than the best results without clustering (DBP: 6.27, SBP: 6.36).

Pulse arrival time as a surrogate of blood pressure

Various models have been proposed for the estimation of blood pressure (BP) from pulse transit time (PTT). PTT is defined as the time delay of the pressure wave, produced by left ventricular contraction, measured between a proximal and a distal site along the arterial tree. Most researchers, when they measure the time difference between the peak of the R-wave in the electrocardiogram signal (corresponding to left ventricular depolarisation) and a fiducial point in the photoplethysmogram waveform (as measured by a pulse oximeter attached to the fingertip), describe this erroneously as the PTT. In fact, this is the pulse arrival time (PAT), which includes not only PTT, but also the time delay between the electrical depolarisation of the heart's left ventricle and the opening of the aortic valve, known as pre-ejection period (PEP). PEP has been suggested to present a significant limitation to BP estimation using PAT. This work investigates the impact of PEP on PAT, leading to a discussion on the best models for BP estimation using PAT or PTT. We conducted a clinical study involving 30 healthy volunteers (53.3% female, 30.9 ± 9.35 years old, with a body mass index of 22.7 ± 3.2 kg/m[Formula: see text]). Each session lasted on average 27.9 ± 0.6 min and BP was varied by an infusion of phenylephrine (a medication that causes venous and arterial vasoconstriction). We introduced new processing steps for the analysis of PAT and PEP signals. Various population-based models (Poon, Gesche and Fung) and a posteriori models (inverse linear, inverse squared and logarithm) for estimation of BP from PTT or PAT were evaluated. Across the cohort, PEP was found to increase by 5.5 ms ± 4.5 ms from its baseline value. Variations in PTT were significantly larger in amplitude, - 16.8 ms ± 7.5 ms. We suggest, therefore, that for infusions of phenylephrine, the contribution of PEP on PAT can be neglected. All population-based models produced large BP estimation errors, suggesting that they are insufficient for modelling the complex pathways relating changes in PTT or PAT to changes in BP. Although PAT is inversely correlated with systolic blood pressure (SBP), the gradient of this relationship varies significantly from individual to individual, from - 2946 to - 470.64 mmHg/s in our dataset. For the a posteriori inverse squared model, the root mean squared errors (RMSE) for systolic and diastolic blood pressure (DBP) estimation from PAT were 5.49 mmHg and 3.82 mmHg, respectively. The RMSEs for SBP and DBP estimation by PTT were 4.51 mmHg and 3.53 mmHg, respectively. These models take into account individual calibration curves required for accurate blood pressure estimation. The best performing population-based model (Poon) reported error values around double that of the a posteriori inverse squared model, and so the use of population-based models is not justified.

The Latest Progress and Development Trend in the Research of Ballistocardiography (BCG) and Seismocardiogram (SCG) in the Field of Health Care

The current status of the research of Ballistocardiography (BCG) and Seismocardiogram (SCG) in the field of medical treatment, health care and nursing was analyzed systematically, and the important direction in the research was explored, to provide reference for the relevant researches. This study, based on two large databases, CNKI and PubMed, used the bibliometric analysis method to review the existing documents in the past 20 years, and made analyses on the literature of BCG and SCG for their annual changes, main countries/regions, types of research, frequently-used subject words, and important research subjects. The results show that the developed countries have taken a leading position in the researches in this field, and have made breakthroughs in some subjects, but their research results have been mainly gained in the area of research and development of the technologies, and very few have been actually industrialized into commodities. This means that in the future the researchers should focus on the transformation of BCG and SCG technologies into commercialized products, and set up quantitative health assessment models, so as to become the daily tools for people to monitor their health status and manage their own health, and as the main approaches of improving the quality of life and preventing diseases for individuals.

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.

Non-Invasive Heart Rate Estimation From Ballistocardiograms Using Bidirectional LSTM Regression

Non-invasive heart rate estimation is of great importance in daily monitoring of cardiovascular diseases. In this paper, a bidirectional long short term memory (bi-LSTM) regression network is developed for non-invasive heart rate estimation from the ballistocardiograms (BCG) signals. The proposed deep regression model provides an effective solution to the existing challenges in BCG heart rate estimation, such as the mismatch between the BCG signals and ground-truth reference, multi-sensor fusion and effective time series feature learning. Allowing label uncertainty in the estimation can reduce the manual cost of data annotation while further improving the heart rate estimation performance. Compared with the state-of-the-art BCG heart rate estimation methods, the strong fitting and generalization ability of the proposed deep regression model maintains better robustness to noise (e.g., sensor noise) and perturbations (e.g., body movements) in the BCG signals and provides a more reliable solution for long term heart rate monitoring.

Posture-Dependent Variability in Wrist Ballistocardiogram-Photoplethysmogram Pulse Transit Time: Implication to Cuff-Less Blood Pressure Tracking

OBJECTIVE

Toward the ultimate goal of robust cuff-less blood pressure (BP) tracking with wrist wearables against postural changes, the goal of this work was to investigate posture-dependent variability in pulse transit time (PTT) measured with ballistocardiogram (BCG) and photoplethysmogram (PPG) signal pair at the wrist.

METHODS

BCG and PPG signals were acquired from 25 subjects under the combination of 3 body (standing, sitting, and supine) and 3 arm (vertical in head-to-foot direction, placed on the chest, and holding a shoulder) postures. PTT was computed as the time interval between the BCG J wave and the PPG foot, and the impact of the 9 postures on PTT was analyzed by invoking an array of possible physical mechanisms.

RESULTS

Our work suggests that (i) wrist BCG-PPG PTT is consistent under standing and sitting postures with vertically held arms; and (ii) changes in wrist orientation and height as well as restrictions in body and arm movement may alter wrist BCG-PPG PTT via distortions in the wrist BCG and PPG waveforms. The results indicate that wrist BCG-PPG PTT varies with respect to postures even when BP remains constant.

CONCLUSION

The potential of cuff-less BP tracking via wrist BCG-PPG PTT demonstrated under standing posture with arms vertically down in the head-to-foot direction may not generalize to other body and arm postures.

SIGNIFICANCE

Understanding the physical mechanisms responsible for posture-induced BCG-PPG PTT variability may increase the versatility of the wrist BCG for cuff-less BP tracking.

A Wearable Ballistocardiography Device for Estimating Heart Rate During Positive Airway Pressure Therapy: Investigational Study Among the General Population

BACKGROUND

Obstructive sleep apnea (OSA) is a condition in which a person's airway is obstructed during sleep, thus disturbing their sleep. People with OSA are at a higher risk of developing heart problems. OSA is commonly treated with a positive airway pressure (PAP) therapy device, which is used during sleep. The PAP therapy setup provides a good opportunity to monitor the heart health of people with OSA, but no simple, low-cost method is available for the PAP therapy device to monitor heart rate (HR).

OBJECTIVE

This study aims to develop a simple, low-cost device to monitor the HR of people with OSA during PAP therapy. This device was then tested on a small group of participants to investigate the feasibility of the device.

METHODS

A low-cost and simple device to monitor HR was created by attaching a gyroscope to a PAP mask, thus integrating HR monitoring into PAP therapy. The gyroscope signals were then analyzed to detect heartbeats, and a Kalman filter was used to produce a more accurate and consistent HR signal. In this study, 19 participants wore the modified PAP mask while the mask was connected to a PAP device. Participants lay in 3 common sleeping positions and then underwent 2 different PAP therapy modes to determine if these affected the accuracy of the HR estimation.

RESULTS

Before the PAP device was turned on, the median HR error was <5 beats per minute, although the HR estimation error increased when participants lay on their side compared with when participants lay on their back. Using the different PAP therapy modes did not significantly increase the HR error.

CONCLUSIONS

These results show that monitoring HR from gyroscope signals in a PAP mask is possible during PAP therapy for different sleeping positions and PAP therapy modes, suggesting that long-term HR monitoring of OSA during PAP therapy may be possible.

Differential cardiopulmonary monitoring system for artifact-canceled physiological tracking of athletes, workers, and COVID-19 patients

Soft, skin-integrated electronic sensors can provide continuous measurements of diverse physiological parameters, with broad relevance to the future of human health care. Motion artifacts can, however, corrupt the recorded signals, particularly those associated with mechanical signatures of cardiopulmonary processes. Design strategies introduced here address this limitation through differential operation of a matched, time-synchronized pair of high-bandwidth accelerometers located on parts of the anatomy that exhibit strong spatial gradients in motion characteristics. When mounted at a location that spans the suprasternal notch and the sternal manubrium, these dual-sensing devices allow measurements of heart rate and sounds, respiratory activities, body temperature, body orientation, and activity level, along with swallowing, coughing, talking, and related processes, without sensitivity to ambient conditions during routine daily activities, vigorous exercises, intense manual labor, and even swimming. Deployments on patients with COVID-19 allow clinical-grade ambulatory monitoring of the key symptoms of the disease even during rehabilitation protocols.

Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram and Convolutional Neural Networks

Hypertension is a chronic disease that kills 7.6 million people worldwide annually. A continuous blood pressure monitoring system is required to accurately diagnose hypertension. Here, a chair-shaped ballistocardiogram (BCG)-based blood pressure estimation system was developed with no sensors attached to users. Two experimental sessions were conducted with 30 subjects. In the first session, two-channel BCG and blood pressure data were recorded for each subject. In the second session, the two-channel BCG and blood pressure data were recorded after running on a treadmill and then resting on the newly developed system. The empirical mode decomposition algorithm was used to remove noise in the two-channel BCG, and the instantaneous phase was calculated by applying a Hilbert transform to the first intrinsic mode functions. After training a convolutional neural network regression model that predicts the systolic and diastolic blood pressures (SBP and DBP) from the two-channel BCG phase, the results of the first session (rest) and second session (recovery) were compared. The results confirmed that the proposed model accurately estimates the rapidly rising blood pressure in the recovery state. Results from the rest sessions satisfied the Association for the Advancement of Medical Instrumentation (AAMI) international standards. The standard deviation of the SBP results in the recovery session exceeded 0.7.

Bed-Based Ballistocardiography: Dataset and Ability to Track Cardiovascular Parameters

BACKGROUND

The goal of this work was to create a sharable dataset of heart-driven signals, including ballistocardiograms (BCGs) and time-aligned electrocardiograms (ECGs), photoplethysmograms (PPGs), and blood pressure waveforms.

METHODS

A custom, bed-based ballistocardiographic system is described in detail. Affiliated cardiopulmonary signals are acquired using a GE Datex CardioCap 5 patient monitor (which collects ECG and PPG data) and a Finapres Medical Systems Finometer PRO (which provides continuous reconstructed brachial artery pressure waveforms and derived cardiovascular parameters).

RESULTS

Data were collected from 40 participants, 4 of whom had been or were currently diagnosed with a heart condition at the time they enrolled in the study. An investigation revealed that features extracted from a BCG could be used to track changes in systolic blood pressure (Pearson correlation coefficient of 0.54 +/- 0.15), dP/dtmax (Pearson correlation coefficient of 0.51 +/- 0.18), and stroke volume (Pearson correlation coefficient of 0.54 +/- 0.17).

CONCLUSION

A collection of synchronized, heart-driven signals, including BCGs, ECGs, PPGs, and blood pressure waveforms, was acquired and made publicly available. An initial study indicated that bed-based ballistocardiography can be used to track beat-to-beat changes in systolic blood pressure and stroke volume.

SIGNIFICANCE

To the best of the authors' knowledge, no other database that includes time-aligned ECG, PPG, BCG, and continuous blood pressure data is available to the public. This dataset could be used by other researchers for algorithm testing and development in this fast-growing field of health assessment, without requiring these individuals to invest considerable time and resources into hardware development and data collection.

Cuffless Blood Pressure Monitoring: Promises and Challenges

Current BP measurements are on the basis of traditional BP cuff approaches. Ambulatory BP monitoring, at 15- to 30-minute intervals usually over 24 hours, provides sufficiently continuous readings that are superior to the office-based snapshot, but this system is not suitable for frequent repeated use. A true continuous BP measurement that could collect BP passively and frequently would require a cuffless method that could be worn by the patient, with the data stored electronically much the same way that heart rate and heart rhythm are already done routinely. Ideally, BP should be measured continuously and frequently during diverse activities during both daytime and nighttime in the same subject by means of novel devices. There is increasing excitement for newer methods to measure BP on the basis of sensors and algorithm development. As new devices are refined and their accuracy is improved, it will be possible to better assess masked hypertension, nocturnal hypertension, and the severity and variability of BP. In this review, we discuss the progression in the field, particularly in the last 5 years, ending with sensor-based approaches that incorporate machine learning algorithms to personalized medicine.

Multimodal Photoplethysmography-Based Approaches for Improved Detection of Hypertension

Elevated blood pressure (BP) is a major cause of death, yet hypertension commonly goes undetected. Owing to its nature, it is typically asymptomatic until later in its progression when the vessel or organ structure has already been compromised. Therefore, noninvasive and continuous BP measurement methods are needed to ensure appropriate diagnosis and early management before hypertension leads to irreversible complications. Photoplethysmography (PPG) is a noninvasive technology with waveform morphologies similar to that of arterial BP waveforms, therefore attracting interest regarding its usability in BP estimation. In recent years, wearable devices incorporating PPG sensors have been proposed to improve the early diagnosis and management of hypertension. Additionally, the need for improved accuracy and convenience has led to the development of devices that incorporate multiple different biosignals with PPG. Through the addition of modalities such as an electrocardiogram, a final measure of the pulse wave velocity is derived, which has been proved to be inversely correlated to BP and to yield accurate estimations. This paper reviews and summarizes recent studies within the period 2010–2019 that combined PPG with other biosignals and offers perspectives on the strengths and weaknesses of current developments to guide future advancements in BP measurement. Our literature review reveals promising measurement accuracies and we comment on the effective combinations of modalities and success of this technology.

Novel Deep Convolutional Neural Network for Cuff-less Blood Pressure Measurement Using ECG and PPG Signals

Cuff-less blood pressure (BP) is a potential method for BP monitoring because it is undisturbed and continuous monitoring. Existing cuff-less estimation methods are easily influenced by signal noise and non-ideal signal morphology. In this study we propose a novel well-designed Convolutional Neural Network (CNN) model named Deep-BP for BP estimation task. The structure of Deep-BP can help to capture more underlying data features associated with BP than handcrafted features, thus improving the robustness and estimation accuracy. We carry out experiments with and without calibration procedure in training stage to evaluate the performance of new method in different application scenarios. The experiment results show that the Deep-BP model achieves high accuracy and outperforms existing methods, in the experiments both with and without calibration.

End-to-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism

Blood pressure (BP) is a vital sign that provides fundamental health information regarding patients. Continuous BP monitoring is important for patients with hypertension. Various studies have proposed cuff-less BP monitoring methods using pulse transit time. We propose an end-to-end deep learning architecture using only raw signals without the process of extracting features to improve the BP estimation performance using the attention mechanism. The proposed model consisted of a convolutional neural network, a bidirectional gated recurrent unit, and an attention mechanism. The model was trained by a calibration-based method, using the data of each subject. The performance of the model was compared to the model that used each combination of the three signals, and the model with the attention mechanism showed better performance than other state-of-the-art methods, including conventional linear regression method using pulse transit time (PTT). A total of 15 subjects were recruited, and electrocardiogram, ballistocardiogram, and photoplethysmogram levels were measured. The 95% confidence interval of the reference BP was [86.34, 143.74] and [51.28, 88.74] for systolic BP (SBP) and diastolic BP (DBP), respectively. The R 2 values were 0.52 and 0.49, and the mean-absolute-error values were 4.06 ± 4.04 and 3.33 ± 3.42 for SBP and DBP, respectively. In addition, the results complied with global standards. The results show the applicability of the proposed model as an analytical metric for BP estimation.

A Revised Point-to-Point Calibration Approach with Adaptive Errors Correction to Weaken Initial Sensitivity of Cuff-Less Blood Pressure Estimation

Initial calibration is a great challenge for cuff-less blood pressure (BP) measurement. The traditional one point-to-point (oPTP) calibration procedure only uses one sample/point to obtain unknown parameters of a specific model in a calm state. In fact, parameters such as pulse transit time (PTT) and BP still have slight fluctuations at rest for each subject. The conventional oPTP method had a strong sensitivity in the selection of initial value. Yet, the initial sensitivity of calibration has not been reported and investigated in cuff-less BP motoring. In this study, a mean point-to-point (mPTP) paring calibration method through averaging and balancing calm or peaceful states was proposed for the first time. Thus, based on mPTP, a factor point-to-point (fPTP) paring calibration method through introducing the penalty factor was further proposed to improve and optimize the performance of BP estimation. Using the oPTP, mPTP, and fPTP methods, a total of more than 100,000 heartbeat samples from 21 healthy subjects were tested and validated in the PTT-based BP monitoring technologies. The results showed that the mPTP and fPTP methods significantly improved the performance of estimating BP compared to the conventional oPTP method. Moreover, the mPTP and fPTP methods could be widely popularized and applied, especially the fPTP method, on estimating cuff-less diastolic blood pressure (DBP). To this extent, the fPTP method weakens the initial calibration sensitivity of cuff-less BP estimation and fills in the ambiguity for individualized calibration procedure.

A chair based ballistocardiogram time interval measurement with cardiovascular provocations

The objective of this study was to measure ballistocardiogram (BCG) based time intervals and compare them with systolic blood pressure values. Electrocardiogram (ECG) and BCG signals of six subjects sitting in a chair were measured with a ferroelectret film sensor. Time intervals between ECG R peak and BCG I and J waves were calculated to obtain RJ, RI and IJ intervals. The time intervals were modified with two cardiovascular provocations, controlled breathing and Valsalva maneuver. The controlled breathing changed all the time intervals (RJ, RI and IJ) whereas the Valsalva maneuver mainly caused variations in the RJ and RI intervals. The calculated time intervals were compared with reference arterial blood pressure values. Correlation coefficients of r = -0.61 and r = -0.78 were found between the RJ and RI time intervals and systolic blood pressure during Valsalva maneuver, respectively.

Phyjama

Unobtrusive and continuous monitoring of cardiac and respiratory rhythm, especially during sleeping, can have significant clinical utility. An exciting new possibility for such monitoring is the design of textiles that use all-textile sensors that can be woven or stitched directly into a textile or garment. Our work explores how we can make such monitoring possible by leveraging something that is already familiar, such as pyjama made of cotton/silk fabric, and imperceptibly adapt it to enable sensing of physiological signals to yield natural fitting, comfortable, and less obtrusive smart clothing.

We face several challenges in enabling this vision including requiring new sensor design to measure physiological signals via everyday textiles and new methods to deal with the inherent looseness of normal garments, particularly sleepwear like pyjamas. We design two types of textile-based sensors that obtain a ballistic signal due to cardiac and respiratory rhythm ---the first a novel resistive sensor that leverages pressure between the body and various surfaces and the second is a triboelectric sensor that leverages changes in separation between layers to measure ballistics induced by the heart. We then integrate several instances of such sensors on a pyjama and design a signal processing pipeline that fuses information from the different sensors such that we can robustly measure physiological signals across a range of sleep and stationary postures. We show that the sensor and signal processing pipeline has high accuracy by benchmarking performance both under restricted settings with twenty one users as well as more naturalistic settings with seven users.

The Potential of Wearable Limb Ballistocardiogram in Blood Pressure Monitoring via Pulse Transit Time

The goal of this study was to investigate the potential of wearable limb ballistocardiography (BCG) to enable cuff-less blood pressure (BP) monitoring, by investigating the association between wearable limb BCG-based pulse transit time (PTT) and BP. A wearable BCG-based PTT was calculated using the BCG and photoplethysmogram (PPG) signals acquired by a wristband as proximal and distal timing reference (called the wrist PTT). Its efficacy as surrogate of BP was examined in comparison with PTT calculated using the whole-body BCG acquired by a customized weighing scale (scale PTT) as well as pulse arrival time (PAT) using the experimental data collected from 22 young healthy participants under multiple BP-perturbing interventions. The wrist PTT exhibited close association with both diastolic (group average r = 0.79; mean absolute error (MAE) = 5.1 mmHg) and systolic (group average r = 0.81; MAE = 7.6 mmHg) BP. The efficacy of the wrist PTT was superior to scale PTT and PAT for both diastolic and systolic BP. The association was consistent and robust against diverse BP-perturbing interventions. The wrist PTT showed superior association with BP when calculated with green PPG rather than infrared PPG. In sum, wearable limb BCG has the potential to realize convenient cuff-less BP monitoring via PTT.

Unobtrusive Estimation of Cardiovascular Parameters with Limb Ballistocardiography

This study investigates the potential of the limb ballistocardiogram (BCG) for unobtrusive estimation of cardiovascular (CV) parameters. In conjunction with the reference CV parameters (including diastolic, pulse, and systolic pressures, stroke volume, cardiac output, and total peripheral resistance), an upper-limb BCG based on an accelerometer embedded in a wearable armband and a lower-limb BCG based on a strain gauge embedded in a weighing scale were instrumented simultaneously with a finger photoplethysmogram (PPG). To standardize the analysis, the more convenient yet unconventional armband BCG was transformed into the more conventional weighing scale BCG (called the synthetic weighing scale BCG) using a signal processing procedure. The characteristic features were extracted from these BCG and PPG waveforms in the form of wave-to-wave time intervals, wave amplitudes, and wave-to-wave amplitudes. Then, the relationship between the characteristic features associated with (i) the weighing scale BCG-PPG pair and (ii) the synthetic weighing scale BCG-PPG pair versus the CV parameters, was analyzed using the multivariate linear regression analysis. The results indicated that each of the CV parameters of interest may be accurately estimated by a combination of as few as two characteristic features in the upper-limb or lower-limb BCG, and also that the characteristic features recruited for the CV parameters were to a large extent relevant according to the physiological mechanism underlying the BCG.

Feasibility of Fingertip Oscillometric Blood Pressure Measurement: Model-Based Analysis and Experimental Validation

The most commonly used oscillometric upper-arm (UA) blood pressure (BP) monitors are not convenient enough for ambulatory BP monitoring, given the large size of the arm cuff and the compression of UA during the measurement. Finger-worn oscillometric BP devices featuring miniaturized finger cuff have been developed and researched as an alternative solution to the UA-based measurement, yet the reliability of the finger-based measurement is still questioned. To investigate the feasibility of oscillometric BP measurements at the finger position, we performed model-based analysis and experimental validation to explore the underlying issues associated with extending the cuff-based oscillometric approach from UA to other alternative sites. The simulation results revealed that a larger bone-to-tissue volume ratio produced a lower pressure transmission efficiency, which can account for the inter-site measurement discrepancies of mean blood pressure (MBP). We also experimentally compared the oscillometric MBP measurements at UA, middle forearm, wrist, finger proximal phalanx, and finger distal phalanx (FD) of 20 young adults, and each position was matched with a cuff of appropriate size and kept at the same height with the heart. The experimental results demonstrated that FD could be a superior alternative position for oscillometric BP measurement, as it requires the smallest cuff size while providing the most consistent MBP with the UA. Our analysis also suggested that further study is demanded to identify the appropriate oscillometric algorithm for reliable systolic blood pressure and diastolic blood pressure measurements at FD.

Physiological Association between Limb Ballistocardiogram and Arterial Blood Pressure Waveforms: A Mathematical Model-Based Analysis

By virtue of its direct association with the cardiovascular (CV) functions and compatibility to unobtrusive measurement during daily activities, the limb ballistocardiogram (BCG) is receiving an increasing interest as a viable means for ultra-convenient CV health and disease monitoring. However, limited insights on its physical implications have hampered disciplined interpretation of the BCG and systematic development of the BCG-based approaches for CV health monitoring. In this study, a mathematical model that can predict the limb BCG in responses to the arterial blood pressure (BP) waves in the aorta was developed and experimentally validated. The validated mathematical model suggests that (i) the limb BCG waveform reveals the timings and amplitudes associated with the aortic BP waves; (ii) mechanical filtering exerted by the musculoskeletal properties of the body can obscure the manifestation of the arterial BP waves in the limb BCG; and (iii) the limb BCG exhibits meaningful morphological changes in response to the alterations in the CV risk predictors. The physical insights garnered by the analysis of the mathematical model may open up new opportunities toward next generation of the BCG-based CV healthcare techniques embedded with transparency, interpretability, and robustness against the external variability.

Mitigation of Instrument-Dependent Variability in Ballistocardiogram Morphology: Case Study on Force Plate and Customized Weighing Scale

The objective of this study was to investigate the measurement instrument-dependent variability in the morphology of the ballistocardiogram (BCG) waveform in human subjects and computational methods to mitigate the variability. The BCG was measured in 22 young healthy subjects using a high-performance force plate and a customized commercial weighing scale under upright standing posture. The timing and amplitude features associated with the major I, J, K waves in the BCG waveforms were extracted and quantitatively analyzed. The results indicated that 1) the I, J, K waves associated with the weighing scale BCG exhibited delay in the timings within the cardiac cycle relative to the ECG R wave as well as attenuation in the absolute amplitudes than the respective force plate counterparts, whereas 2) the time intervals between the I, J, K waves were comparable. Then, two alternative computational methods were conceived in an attempt to mitigate the discrepancy between force plate versus weighing-scale BCG: a transfer function and an amplitude-phase correction. The results suggested that both methods effectively mitigated the discrepancy in the timings and amplitudes associated with the I, J, K waves between the force plate and weighing-scale BCG. Hence, signal processing may serve as a viable solution to the mitigation of the instrument-induced morphological variability in the BCG, thereby facilitating the standardized analysis and interpretation of the timing and amplitude features in the BCG across wide-ranging measurement platforms.

A Chair-Based Unconstrained/Nonintrusive Cuffless Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram

Hypertension is a well-known chronic disease that causes complications such as cardiovascular diseases or stroke, and thus needs to be continuously managed by using a simple system for measuring blood pressure. The existing method for measuring blood pressure uses a wrapping cuff, which makes measuring difficult for patients. To address this problem, cuffless blood pressure measurement methods that detect the peak pressure via signals measured using photoplethysmogram (PPG) and electrocardiogram (ECG) sensors and use it to calculate the pulse transit time (PTT) or pulse wave velocity (PWV) have been studied. However, a drawback of these methods is that a user must be able to recognize and establish contact with the sensor. Furthermore, the peak of the PPG or ECG cannot be detected if the signal quality drops, leading to a decrease in accuracy. In this study, a chair-type system that can monitor blood pressure using polyvinylidene fluoride (PVDF) films in a nonintrusive manner to users was developed. The proposed method also uses instantaneous phase difference (IPD) instead of PTT as the feature value for estimating blood pressure. Experiments were conducted using a blood pressure estimation model created via an artificial neural network (ANN), which showed that IPD could estimate more accurate readings of blood pressure compared to PTT, thus demonstrating the possibility of a nonintrusive blood pressure monitoring system.

In-Home Cardiovascular Monitoring System for Heart Failure: Comparative Study

Background

There is a pressing need to reduce the hospitalization rate of heart failure patients to limit rising health care costs and improve outcomes. Tracking physiologic changes to detect early deterioration in the home has the potential to reduce hospitalization rates through early intervention. However, classical approaches to in-home monitoring have had limited success, with patient adherence cited as a major barrier. This work presents a toilet seat–based cardiovascular monitoring system that has the potential to address low patient adherence as it does not require any change in habit or behavior.

Objective

The objective of this work was to demonstrate that a toilet seat–based cardiovascular monitoring system with an integrated electrocardiogram, ballistocardiogram, and photoplethysmogram is capable of clinical-grade measurements of systolic and diastolic blood pressure, stroke volume, and peripheral blood oxygenation.

Methods

The toilet seat–based estimates of blood pressure and peripheral blood oxygenation were compared to a hospital-grade vital signs monitor for 18 subjects over an 8-week period. The estimated stroke volume was validated on 38 normative subjects and 111 subjects undergoing a standard echocardiogram at a hospital clinic for any underlying condition, including heart failure.

Results

Clinical grade accuracy was achieved for all of the seat measurements when compared to their respective gold standards. The accuracy of diastolic blood pressure and systolic blood pressure is 1.2 (SD 6.0) mm Hg (N=112) and –2.7 (SD 6.6) mm Hg (N=89), respectively. Stroke volume has an accuracy of –2.5 (SD 15.5) mL (N=149) compared to an echocardiogram gold standard. Peripheral blood oxygenation had an RMS error of 2.3% (N=91).

Conclusions

A toilet seat–based cardiovascular monitoring system has been successfully demonstrated with blood pressure, stroke volume, and blood oxygenation accuracy consistent with gold standard measures. This system will be uniquely positioned to capture trend data in the home that has been previously unattainable. Demonstration of the clinical benefit of the technology requires additional algorithm development and future clinical trials, including those targeting a reduction in heart failure hospitalizations.

Reply to Comment on 'New photoplethysmogram indicators for improving cuffless and continuous blood pressure estimation accuracy'

OBJECTIVE

This article was written by the invitation of the editorial board of Physiological Measurement. It is a Reply to the Comment regarding our recently published paper entitled 'New photoplethysmogram indicators for improving cuffless and continuous blood pressure estimation accuracy' (Lin et al 2018 Physiol. Meas. 29 025005).

APPROACH

We appreciate van Helmond and Joseph's (2018 Physiol. Meas. 098001) interests and comments on our previous paper. In the Comment, they discussed in detail the physiology underlying the pulse arrive time (PAT)-based methods for blood pressure (BP) measurement, and concluded that there are inherent physiological reasons precluding the development of an accurate continuous cuffless BP measurement using PAT-based methods. We could agree with the comments of van Helmond and Joseph about the physiology underlying PAT-based methods for BP measurement. It may be difficult to minimize the confounding effects of physiological factors such as pre-ejection period and smooth muscle tone, etc. However, in this Reply, we discuss some potential solutions to deal with these problems from an engineering point of view.

MAIN RESULTS

When heart rate, more photoplethysmogram (PPG) features, PAT, robust machine learning models, and other techniques were adopted for BP estimation, it is promising for improving the accuracy of BP estimation to an acceptable range that can meet professional standards (e.g. Advancement of Medical Instrumentation (AAMI) standard, British Hypertension Society (BHS) protocol).

SIGNIFICANCE

PAT- and/or PPG-based methods may be a promising technique for continuous and unobtrusive blood pressure measurement.

Capturing Electrocardiogram Signals from Chairs by Multiple Capacitively Coupled Unipolar Electrodes

A prototype of an electrocardiogram (ECG) signal acquisition system with multiple unipolar capacitively coupled electrodes is designed and experimentally tested. Capacitively coupled electrodes made of a standard printed circuit board (PCB) are used as the sensing electrodes. Different from the conventional measurement schematics, where one single lead ECG signal is acquired from a pair of sensing electrodes, the sensing electrodes in our approaches operate in a unipolar mode, i.e., the biopotential signals picked up by each sensing electrodes are amplified and sampled separately. Four unipolar electrodes are mounted on the backrest of a regular chair and therefore four channel of signals containing ECG information are sampled and processed. It is found that the qualities of ECG signal contained in the four channel are different from each other. In order to pick up the ECG signal, an index for quality evaluation, as well as for aggregation of multiple signals, is proposed based on phase space reconstruction. Experimental tests are carried out while subjects sitting on the chair and clothed. The results indicate that the ECG signals can be reliably obtained in such a unipolar way.

Data mining investigation of the association between a limb ballistocardiogram and blood pressure

OBJECTIVE

To investigate the association between a limb ballistocardiogram (BCG) and blood pressure (BP) based on data mining.

APPROACH

During four BP-perturbing interventions, the BCG and reference BP were measured from 23 young, healthy volunteers using a custom-manufactured wristband equipped with a MEMS accelerometer and a commercial continuous BP measurement device. Both timing and amplitude features in the wrist BCG waveform were extracted, and significant features predictive of diastolic (DP) and systolic (SP) BP were selected using stepwise linear regression analysis. The selected features were further compressed using principal component analysis to yield a small set of DP and SP predictors. The association between the predictors thus obtained and BP was investigated by multivariate linear regression analysis.

MAIN RESULTS

The predictors exhibited a meaningful association with BP. When three most significant predictors were used for DP and SP, a correlation coefficient of r  =  0.75  ±  0.03 (DP) and r  =  0.75  ±  0.03 (SP), a root-mean-squared error (RMSE) of 7.4  ±  0.6 mmHg (DP) and 10.3  ±  0.8 mmHg (SP), and a mean absolute error (MAE) of 6.0  ±  0.5 mmHg (DP) and 8.3  ±  0.7 mmHg (SP) were obtained across all interventions (mean  ±  SE). The association was consistent in all the individual interventions (r  ⩾  0.68, RMSE  ⩽  5.7 mmHg, and MAE  ⩽  4.5 mmHg for DP as well as r  ⩾  0.61, RMSE  ⩽  7.9 mmHg, and MAE  ⩽  6.4 mmHg for SP on the average). The minimum number of requisite predictors for robust yet practically realistic BP monitoring appeared to be three. The association between predictors and BP was maintained even under regularized calibration (r  =  0.63  ±  0.05, RMSE  =  9.3  ±  0.8 mmHg, and MAE  =  7.6  ±  0.7 mmHg for DP as well as r  =  0.60  ±  0.05, RMSE  =  14.7  ±  1.4 mmHg, and MAE  =  11.9  ±  1.1 mmHg for SP (mean  ±  SE)). The requisite predictors for DP and SP were distinct from each other.

SIGNIFICANCE

The results of this study may provide a viable basis for ultra-convenient BP monitoring based on a limb BCG alone.

Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring From Ballistocardiograms

A multiple instance dictionary learning approach, dictionary learning using functions of multiple instances (DL-FUMI), is used to perform beat-to-beat heart rate estimation and to characterize heartbeat signatures from ballistocardiogram (BCG) signals collected with a hydraulic bed sensor. DL-FUMI estimates a "heartbeat concept" that represents an individual's personal ballistocardiogram heartbeat pattern. DL-FUMI formulates heartbeat detection and heartbeat characterization as a multiple instance learning problem to address the uncertainty inherent in aligning BCG signals with ground truth during training. Experimental results show that the estimated heartbeat concept obtained by DL-FUMI is an effective heartbeat prototype and achieves superior performance over comparison algorithms.

Ballistocardiogram-Based Approach to Cuffless Blood Pressure Monitoring: Proof of Concept and Potential Challenges

OBJECTIVE

The goal was to propose and establish the proof of concept of an ultraconvenient cuffless blood pressure monitoring approach based on the ballistocardiogram.

METHODS

The proposed approach monitors blood pressure by exploiting two features in the whole-body head-to-foot ballistocardiogram measured using a force plate: the time interval between the first ("I") and second ("J") major waves ("I-J interval") for diastolic pressure and the amplitude between the J and third major ("K") waves ("J-K amplitude") for pulse pressure. The efficacy of the approach was examined in 22 young healthy volunteers by investigating the diastolic pressure monitoring performance of pulse transit time, pulse arrival time, and ballistocardiogram's I-J interval, and the systolic pressure monitoring performance of pulse transit time and I-J interval in conjunction with ballistocardiogram's J-K amplitude.

RESULTS

The I-J interval was comparable to pulse transit time and pulse arrival time in monitoring diastolic pressure, and the J-K amplitude could provide meaningful improvement to pulse transit time and I-J interval in monitoring systolic pressure.

CONCLUSION

The ballistocardiogram may contribute toward ultraconvenient and more accurate cuffless blood pressure monitoring.

SIGNIFICANCE

The proposed approach has potential to complement the pulse transit time technique for cuffless blood pressure monitoring in two ways. First, it may be integrated with pulse transit time to enable independent monitoring of diastolic and systolic pressures via the J-K amplitude. Second, it may even enable diastolic and systolic pressure monitoring from the ballistocardiogram alone.

Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Predictions on Maximum Calibration Period and Acceptable Error Limits

OBJECTIVE

Pulse transit time (PTT) is being widely pursued for ubiquitous blood pressure (BP) monitoring. PTT-based systems may require periodic cuff calibrations but can still be useful for hypertension screening by affording numerous out-of-clinic measurements that can be averaged. The objective was to predict the maximum calibration period that would not compromise accuracy and acceptable error limits in light of measurement averaging for PTT-based systems.

METHODS

Well-known mathematical models and vast BP data were leveraged. Models relating PTT, age, and gender to BP were employed to determine the maximum time period for the PTT-BP calibration curve to change by <1 mmHg over physiological BP ranges for each age and gender. A model of within-person BP variability was employed to establish the screening accuracy of the conventional cuff-based approach. These models were integrated to investigate the screening accuracy of the average of numerous measurements of a PTT-based system in relation to the accuracy of its individual measurements.

RESULTS

The maximum calibration period was about 1 year for a 30 year old and declined linearly to about 6 months for a 70 year old. A PTT-based system with a precision error of >12 mmHg for systolic BP could achieve the screening accuracy of the cuff-based approach via measurement averaging.

CONCLUSION

This theoretical study indicates that PTT-based BP monitoring is viable even with periodic calibration and seemingly high measurement errors.

SIGNIFICANCE

The predictions may help guide the implementation, evaluation, and application of PTT-based BP monitoring systems in practice.