Cuffless Blood Pressure Estimation Research Articles

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

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

The Effects of Filtering PPG Signal on Pulse Arrival Time-Systolic Blood Pressure Correlation.

Pulse arrival time (PAT), evaluated from electro-cardiogram (ECG) and photoplethysmogram (PPG) signals, has been widely used for cuff-less blood pressure (BP) estimation due to its high correlation with BP. However, the question of whether filtering the PPG signal impacts the extracted PAT values and consequently, the correlation between PAT and BP, has not been investigated before. In this paper, using data from 18 subjects, changes in the PAT values, and in the subject-specific PAT-systolic BP (SBP) correlation caused by filtering the PPG signal with variable cutoff frequencies in the range of 2 to 15 Hz are studied. For PAT extraction, three PPG characteristic points (foot, maximum slope and systolic peak) are considered. Results show that differences in the cutoff frequency can shift the PAT values and introduce a worst-case error of over 8.2 mmHg for SBP estimation, indicating that PPG signal filter settings can impact PAT-based BP estimations. Our study suggests that extracting the PAT from the maximum slope point of PPG signal filtered at 10 Hz provides the most stable correlation with SBP.

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.

Cuff-Less Blood Pressure Estimation From Photoplethysmography via Visibility Graph and Transfer Learning.

This paper presents a new solution that enables the use of transfer learning for cuff-less blood pressure (BP) monitoring via short duration of photoplethysmogram (PPG). The proposed method estimates BP with low computational budget by 1) creating images from segments of PPG via visibility graph (VG), hence, preserving the temporal information of the PPG waveform, 2) using pre-trained deep convolutional neural network (CNN) to extract feature vectors from VG images, and 3) solving for the weights and bias between the feature vectors and the reference BPs with ridge regression. Using the University of California Irvine (UCI) database consisting of 348 records, the proposed method achieves a best error performance of 0.00±8.46 mmHg for systolic blood pressure (SBP), and -0.04±5.36 mmHg for diastolic blood pressure (DBP), respectively, in terms of the mean error (ME) and the standard deviation (SD) of error, ranking grade B for SBP and grade A for DBP under the British Hypertension Society (BHS) protocol. Our novel data-driven method offers a computationally-efficient end-to-end solution for rapid and user-friendly cuff-less PPG-based BP estimation.

Improving Cuff-Less Continuous Blood Pressure Estimation with Linear Regression Analysis

In this work, the authors investigate the cuff-less estimation of continuous BP through pulse transit time (PTT) and heart rate (HR) using regression techniques, which is intended as a first step towards continuous BP estimation with a low error, according to AAMI guidelines. Hypertension (the ‘silent killer’) is one of the main risk factors for cardiovascular diseases (CVDs), which are the main cause of death worldwide. Its continuous monitoring can offer a valid tool for patient care, as blood pressure (BP) is a significant indicator of health and, using it together with other parameters, such as heart and breath rates, could strongly improve prevention of CVDs. The novelties introduced in this work are represented by the implementation of pre-processing and by the innovative method for features research and features processing to continuously monitor blood pressure in a non-invasive way. Currently, invasive methods are the only reliable methods for continuous monitoring, while non-invasive techniques measure the values every few minutes. The proposed approach can be considered the first step for the integration of these types of algorithms on wearable devices, in particular on those developed for the SINTEC project.

A new deep learning framework based on blood pressure range constraint for continuous cuffless BP estimation

Blood pressure (BP) is known as an indicator of human health status, and regular measurement is helpful for early detection of cardiovascular diseases. Traditional techniques for measuring BP are either invasive or cuff-based and thus are not suitable for continuous measurement. Aiming at the deficiencies in existing studies, a novel cuffless BP estimation framework of Receptive Field Parallel Attention Shrinkage Network (RFPASN) and BP range constraint is proposed. Firstly, RFPASN uses the multi-scale large receptive field convolution module to capture the long-term dynamics in the photoplethysmography (PPG) signal without using long short-term memory (LSTM). On this basis, the features acquired by the parallel mixed domain attention module are used as thresholds, and the soft threshold function is used to screen the input features to enhance the discriminability and robustness of features, which can significantly improve the prediction accuracy of diastolic blood pressure (DBP) and systolic blood pressure (SBP). Finally, in order to prevent large fluctuations in the prediction results of RFPASN, RFPASN based on BP range constraint is proposed to make the prediction results of RFPASN more accurate and reasonable. The performance of the proposed method is demonstrated on a publically available MIMIC-II database. The database contains normal, hypertensive and hypotensive people. We have achieved MAE of 1.63/1.59 (DBP) and 2.26/2.15 (SBP) mmHg for BP on total population of 1562 subjects. A comparative study shows that the proposed algorithm is more promising than the state-of-the-art.

Concatenated convolutional neural network model for cuffless blood pressure estimation using fuzzy recurrence properties of photoplethysmogram signals

Due to the importance of continuous monitoring of blood pressure (BP) in controlling hypertension, the topic of cuffless BP estimation has been widely studied in recent years. A most important approach is to explore the nonlinear mapping between the recorded peripheral signals and the BP values which is usually conducted by deep neural networks. Because of the sequence-based pseudo periodic nature of peripheral signals such as photoplethysmogram (PPG), a proper estimation model needed to be equipped with the 1-dimensional (1-D) and recurrent layers. This, in turn, limits the usage of 2-dimensional (2-D) layers adopted in convolutional neural networks (CNN) for embedding spatial information in the model. In this study, considering the advantage of chaotic approaches, the recurrence characterization of peripheral signals was taken into account by a visual 2-D representation of PPG in phase space through fuzzy recurrence plot (FRP). FRP not only provides a beneficial framework for capturing the spatial properties of input signals but also creates a reliable approach for embedding the pseudo periodic properties to the neural models without using recurrent layers. Moreover, this study proposes a novel deep neural network architecture that combines the morphological features extracted simultaneously from two upgraded 1-D and 2-D CNNs capturing the temporal and spatial dependencies of PPGs in systolic and diastolic BP estimation. The model has been fed with the 1-D PPG sequences and the corresponding 2-D FRPs from two separate routes. The performance of the proposed framework was examined on the well-known public dataset, namely, multi-parameter intelligent in Intensive Care II. Our scheme is analyzed and compared with the literature in terms of the requirements of the standards set by the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). The proposed model met the AAMI requirements, and it achieved a grade of A as stated by the BHS standard. In addition, its mean absolute errors and standard deviation for both systolic and diastolic blood pressure estimations were considerably low, 3.05 ± 5.26 mmHg and 1.58 ± 2.6 mmHg, in turn.

High Fidelity MZI-BCG Sensor With Homodyne Demodulation for Unobtrusive HR and BP Monitoring

An all-optical active homodyne detection aided fiber-optic Mach-Zehnder interferometer (MZI) vital signs monitoring system is proposed and investigated. The active homodyne detection is mainly realized by employing a wavelength-tunable vertical-cavity surface-emitting laser (VCSEL) and automatic closed-loop control system (CLCS). It can effectively maintain the MZI sensor to operate around the quadrature point and successfully eliminate the fading effect of output signal induced by low frequency drift. Therefore, high fidelity and high stableness of the ballistocardiograph (BCG) signal is guaranteed. The results show that according to the Bland-Altman and the confidence ellipse analysis, the heart rate (HR) monitoring performance reveals high accuracy and good consistence. What’ more, a multiple linear regression-based blood pressure (BP) estimation model is presented, which shows that the BP is highly correlated with I-J-K complex oriented features, including IJ interval, JK interval and JK amplitude. It indeed offers a potential approach to achieve cuff-less BP estimation merely based on the high-fidelity BCG signal. In conclusion, the proposed MZI-BCG monitoring system is simple to use, and it shows great potential in future unobtrusive cardiac monitoring both in home and hospital.

Cuffless Blood Pressure Estimation Based on Monte Carlo Simulation Using Photoplethysmography Signals.

Blood pressure measurements are one of the most routinely performed medical tests globally. Blood pressure is an important metric since it provides information that can be used to diagnose several vascular diseases. Conventional blood pressure measurement systems use cuff-based devices to measure the blood pressure, which may be uncomfortable and sometimes burdensome to the subjects. Therefore, in this study, we propose a cuffless blood pressure estimation model based on Monte Carlo simulation (MCS). We propose a heterogeneous finger model for the MCS at wavelengths of 905 nm and 940 nm. After recording the photon intensities from the MCS over a certain range of blood pressure values, the actual photoplethysmography (PPG) signals were used to estimate blood pressure. We used both publicly available and self-made datasets to evaluate the performance of the proposed model. In case of the publicly available dataset for transmission-type MCS, the mean absolute errors are 3.32 ± 6.03 mmHg for systolic blood pressure (SBP), 2.02 ± 2.64 mmHg for diastolic blood pressure (DBP), and 1.76 ± 2.8 mmHg for mean arterial pressure (MAP). The self-made dataset is used for both transmission- and reflection-type MCSs; its mean absolute errors are 2.54 ± 4.24 mmHg for SBP, 1.49 ± 2.82 mmHg for DBP, and 1.51 ± 2.41 mmHg for MAP in the transmission-type case as well as 3.35 ± 5.06 mmHg for SBP, 2.07 ± 2.83 mmHg for DBP, and 2.12 ± 2.83 mmHg for MAP in the reflection-type case. The estimated results of the SBP and DBP satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standards and are within Grade A according to the British Hypertension Society (BHS) standards. These results show that the proposed model is efficient for estimating blood pressures using fingertip PPG signals.

Cuff-Less Non-Invasive Blood Pressure Measurement Using Various Machine Learning Regression Techniques and Analysis

This paper proposes a new approach for non-invasive cuff-less arterial Blood Pressure (BP) estimation using pulse transit time (PTT). The ECG and PPG signals were acquired at a sampling rate of 500Hz. Standard cuff based Sphygmomanometer used to take reference BP and heart rate simultaneously. The hardware for the acquiring the ECG and PPG signals were designed and fabricated and were made and study was carried out with 60 subject during various activities. The objective of this work is to estimate the Systolic BP and Diastolic BP using PTT techniques and to apply regression analysis using machine learning methods for estimating the BP, compare the results with recording simultaneously carried out using the standard devices. The proposed work concludes that AdaBoost algorithm has highest accuracy in estimating systolic and diastolic BP values. The readings obtained are in accordance with the AHA standards and are in acceptable limits and can be used for measuring BP in wearable devices.

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).

Estimation of Beat-by-Beat Blood Pressure and Heart Rate From ECG and PPG Using a Fine-Tuned Deep CNN Model

Given that current cuffless blood pressure (BP) measurement technologies feature acceptable overall accuracy, this paper proposed a sufficiently accurate cuffless BP estimation method based on photoplethysmography (PPG) and electrocardiography (ECG) signals. This study used single-channel PPG and ECG signals to estimate heart rate (HR), diastolic BP (DBP), and systolic BP (SBP). A modified long-term recurrent convolutional network comprising a multi-scale convolution network and a long short-term memory (LSTM) network was used to develop a deep learning model for accurately estimating BP and HR. The PPG and ECG signal data of 1551 patients were obtained from the Data Sets-UCI Machine Learning Repository of the University of California, Irvine. The study dataset comprised ECG, PPG, and arterial BP (ABP) signals from the PhysioNet MIMIC II dataset. The original signals were processed by removing noise and artifacts. The aforementioned dataset contains 12,000 records in a hierarchical data format, with each record containing three signals, namely 125-Hz ECG signals from channel II (ECG lead II), 125-Hz PPG signals from the fingertip, and 125-Hz invasive ABP signals. To validate the stability and performance of the developed model, ten-fold cross-validation was conducted. The mean absolute error (MAE) (standard deviation (SD)) values of the developed model for predicting SBP, DBP, and HR were 2.24 mmHg (3.59 mmHg), 1.40 mmHg (2.56 mmHg), and 0.84 bpm (2.23 bpm), respectively. In addition, the estimated SBP and DBP values satisfied the standards of the British Hypertension Society and the Association for the Advancement of Medical Instrumentation. Compared with the methods proposed in other studies, the deep learning model developed in this study required a lower number of layers to provide accurate SBP, DBP, and HR estimations. The results of this study confirmed the effectiveness of the proposed deep learning architecture.

Novel Cuffless Blood Pressure Estimation Method Using a Bayesian Hierarchical Model.

Continuous blood pressure (BP) monitoring is important for the prevention and early diagnosis of cardiovascular diseases. Cuffless BP estimation using pulse arrival time (PAT) via a mathematical model which enables continuous BP measurement has recently become a popular research topic. In this study, simultaneous biomedical signals from ten healthy subjects were acquired by electrocardiogram (ECG) and photoplethysmogram (PPG) sensors and the continuous reference BP data were collected by a cuff-based Finometer PRO BP monitor. A hierarchical model was applied to estimate the parameters of a nonlinear model which in turn is used to estimate systolic blood pressure (SBP) using PAT with few calibration measurements. The mean absolute difference (MAD) between the estimated SBP and reference SBP is 4.35±1.43 mmHg using the proposed hierarchical model with three calibration measurements and is 4.36±1.17 mmHg with a single calibration measurement.

Cuff-Less Blood Pressure Estimation via Small Convolutional Neural Networks.

Deep learning-based cuff-less blood pressure (BP) estimation methods have recently gained increased attention as they can provide accurate BP estimation with only one physiological signal as input. In this paper, we present a simple and effective method for cuff-less BP estimation by training a small-scale convolutional neural network (CNN), modified from LeNet-5, with images created from short segments of the photoplethysmogram (PPG) signal via visibility graph (VG). Results show that the trained modified LeNet-5 model achieves an error performance of 0.184±7.457 mmHg for the systolic BP (SBP), and 0.343±4.065 mmHg for the diastolic BP (DBP) in terms of the mean error (ME) and the standard deviation (SD) of error between the estimated and reference BP. Both the SBP and the DBP accuracy rank grade A under the British Hypertension Society (BHS) protocol, demonstrating that our proposed method is an accurate way for cuff-less BP estimation.

Recurrent Neural Network Models for Blood Pressure Monitoring Using PPG Morphological Features.

Continuous non-invasive Blood Pressure (BP) monitoring is vital for the early detection and control of hypertension. However, this is yet not possible as all current non-invasive BP devices are cuff-based devices and hence precluding continuous monitoring. Several methods have been proposed to overcome this challenge, one of which utilises the Photoplethysmograph (PPG) signal in an effort to predict reliable BP values from this signal using various computational approaches. Although, good performance has been reported in the literature, it was mainly achieved on a small inadequate sample size using conventional models that are unable to account for the temporal variations in the input vector. To address these limitations, this paper proposes cuff-less and continuous blood pressure estimation using Long Short-term Memory (LSTM) and Gated Recurrent Units (GRU). The models were evaluated on 942 patients acquired from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) dataset. The proposed models produced superior results in comparison with conventional artificial neural network. In particular, the best performance was achieved by the GRU, with mean absolute error and standard deviation of 5.77 ± 8.52 mmHg and 3.33±5.02 mmHg for systolic (SBP) and diastolic blood pressure (DBP), respectively. Furthermore, the results comply with the international standards for cuff-less blood pressure estimation.

Blood pressure-independent neurogenic effect on conductance and resistance vessels: a consideration for cuffless blood pressure measurement?

Pulse transit time (PTT) and pulse arrival time (PAT) are promising measures for cuffless arterial blood pressure (BP) estimation given the intrinsic arterial stiffness-BP relationship. However, arterial stiffness (and PTT) is altered by autonomically-driven smooth muscle tension changes, potentially independent of BP. This would limit PTT or PAT as accurate BP correlates, more so in resistance vessels than conductance arteries. To quantify if there is a measurable neurogenic effect on PAT measured using photoplethysmography (PPG) (path includes resistance vessels) and radial artery tonometry (path includes only conductance vessels) during physiologically induced BP changes. PATs were measured continuously in participants (n=15, 35±15 years, 9 male) using an electrocardiogram and, simultaneously, a Finometer® PRO finger sensor, a finger PPG sensor and radial artery tonometer during seated rest, cold pressor test, cycling and isometric handgrip (IHG) exercise. ΔBP/ΔPAT was calculated for each sensor and each condition. All interventions significantly increased BP. A significant difference was observed in ΔBP/ΔPAT between cycling and both the cold pressor test and IHG exercise (p<0.05). ΔBP/ΔPAT did not differ whether measured via PPG or tonometry. Under the conditions tested, autonomic function does not have a BP-independent effect on PAT where the path includes resistance vessels (PPG signal), likely due to the speed of the wave and the short path length of resistance vessels. Autonomic function therefore does not limit the ability for use of PPG as a signal for potentially estimating BP without a cuff.

PulseLab: An Integrated and Expandable Toolbox for Pulse Wave Velocity-based Blood Pressure Estimation.

In this paper, we introduce PulseLab, a comprehensive MATLAB toolbox that enables estimating the blood pressure (BP) from electrocardiogram (ECG) and photoplethysmogram (PPG) signals using pulse wave velocity (PWV)-based models. This universal framework consists of 6 sequential modules, covering end-to-end procedures that are needed for estimating BP from raw PPG/ECG data. These modules are "dataset formation", "signal pre-processing", "segmentation", "characteristic-points detection", "pulse transit time (PTT)/ pulse arrival time (PAT) calculation", and "model validation". The toolbox is expandable and its application programming interface (API) is built such that newly-derived PWV-BP models can be easily included. The toolbox also includes a user-friendly graphical user interface (GUI) offering visualization for step-by-step processing of physiological signals, position of characteristic points, PAT/PTT values, and the BP regression results. To the best of our knowledge, PulseLab is the first comprehensive toolbox that enables users to optimize their model by considering several factors along the process for obtaining the most accurate model for cuff-less BP estimation.

Introduction of Boosting Algorithms in Continuous Non-Invasive Cuff-less Blood Pressure Estimation using Pulse Arrival Time.

Blood Pressure (BP) is a critical biomarker for cardiorespiratory health. Conventional non-invasive BP measurement devices are mostly built on the principle of auscultation, oscillometry, or tonometry. The strong correlation between the Pulse Arrival Time (PAT) and BP has enabled unconstrained cuff-less BP monitoring. In this paper, we exploited that relationship for estimating Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Mean Arterial pressure (MAP) values. The proposed model involves extraction of PAT values by denoising the signals using advanced filtering techniques and finally employing machine learning algorithms to estimate cuff-less BP. The results are validated against Advancement of Medical Instrumentation (AAMI) standards and British Hypertension Society (BHS) protocols. The proposed method meets the AAMI standards in the context of estimating DBP and MAP values. The model's accuracy achieved Grade A for both MAP and DBP values using the CatBoost algorithm, whereas it achieved grade A for MAP and Grade B for DBP using the XGBoost algorithm based on the BHS standards.

An investigation of the individualized, two-point calibration method for cuffless blood pressure estimation using pulse arrival time: an historical perspective using the Casio BP-100 digital watch.

The use of wearable cuffless blood pressure (BP) devices is becoming commercially prevalent with little published validation information. Most devices rely, at least in part, on the relationship between pulse arrival time (PAT) and BP, a theoretical fundamental relationship that was first commercially exploited in 1993 with the release of the Casio BP-100 digital watch. This study explored the PAT method of BP estimation in a commercial device where it first began, the Casio BP-100 (Model No. 900) digital watch, which employs an individualized, two-point calibration method. Device accuracy was determined by comparison to a conventional cuff-based BP device measurements. Twenty participants (11 female, 9 male) had BP measured using both devices at rest, during a 5-minute isometric hand-grip exercise and at 1-minute post-exercise. Due to bidirectional scatter of BP estimation by the BP-100 device, there was no significant difference between the reference device and the BP-100. The devices showed poor correlation for both systolic BP (SBP) (R=0.36, p=0.13) and diastolic BP (DBP) (R=0.044, p=0.37). However, on average the watch was able to provide correct directional changes in SBP but not DBP with exercise. Despite being an industry first, the Casio BP-100 watch employed a method that gives a great chance of accuracy: a two point, individualized calibration method - more detailed than calibration methods in more modern devices. The watch, on average across a cohort, provided some information on BP directional change but was uncorrelated with cuff-based BP measurement. If the utility of beat-by-beat BP estimation is to be utilized, limitations of this method need to be addressed.

Simultaneous Electrical Bio-Impedance Plethysmography at Different Body Parts: Continuous and Non-Invasive Monitoring of Pulse Wave Velocity.

A simultaneous and time-synchronized electrical bio-impedance plethysmography (BPG) sensor system is implemented for long-term, continuous, and non-invasive measurement of arterial pulse wave velocity (PWV). The proposed BPG sensor system electrically separates each ground plane of two BPG channels and controller, and the two different BPG channels are time-synchronized by the controller transmitting periodic pulse signal to the two BPG channels. Furthermore, net parasitic capacitance between the ground planes is minimized by removing isolated DC-DC converter, limiting the number of digital capacitive isolators, and adopting optimal layout of the ground planes. The proposed sensor system is integrated on 278cm2 printed circuit board. The sensor system consumes 0.35 W/channel, and outstanding channel-to-channel isolation is expected by coupling factor performance of -77.7 dB. In addition, modified electrode configuration for BPG at chest drastically reduces baseline wandering by respiratory motion artifact, thereby further facilitating long-term, continuous, and non-invasive PWV measurement. As a result, long-term, continuous, and non-invasive PWV measurement more than 95 minutes is successfully performed to pave the way for developing pulse transit time (PTT)-based cuff-less blood pressure (BP) estimation technique.