Continuous Blood Pressure Estimation Research Articles

Non-Contact Blood Pressure Estimation From Radar Signals by a Stacked Deformable Convolution Network.

This study introduces a contactless blood pressure monitoring approach that combines conventional radar signal processing with novel deep learning architectures. During the preprocessing phase, datasets suitable for synchronization are created by integrating Kalman filtering, multiscale bandpass filters, and a periodic extraction method in the time domain. These data comprise data on chest micro variations, encapsulating a complex array of physiological and biomedical information reflective of cardiac micromotions. The Radar-based Stacked Deformable convolution Network (RSD-Net) integrates channel and spatial self attention mechanisms within a deformable convolutional framework to enhance feature extraction from radar signals. The network architecture systematically employs deformable convolutions for initial deep feature extraction from individual signals. Subsequently, continuous blood pressure estimation is conducted using self attention mechanisms on feature map from single source coupled with multi-feature map channel attention. The performance of model is corroborated via the open-source dataset procured using a non-invasive 24 GHz six-port continuous wave radar system. The dataset, encompassing readings from 30 healthy individuals subjected to diverse conditions including rest, the Valsalva maneuver, apnea, and tilt-table examinations. It serves to substantiate the validity and resilience of the proposed method in the non-contact assessment of continuous blood pressure. Evaluation metrics reveal Pearson correlation coefficients of 0.838 for systolic and 0.797 for diastolic blood pressure predictions. The Mean Error (ME) and Standard Deviation (SD) for systolic and diastolic blood pressure measurements are -0.32 ±6.14 mmHg and -0.20 ±5.50 mmHg, respectively. The ablation study assesses the contribution of different structural components of the RSD-Net, validating their significance in the overall of model performance.

Non-Invasive Continuous Blood Pressure Estimation from Single-Channel PPG Based on a Temporal Convolutional Network Integrated with an Attention Mechanism

Non-Invasive Continuous Blood Pressure Estimation from Single-Channel PPG Based on a Temporal Convolutional Network Integrated with an Attention Mechanism

Design and validation of dual-point time-differentiated photoplethysmogram (2PPG) wearable for cuffless blood pressure estimation

Background & objectivesMeasurement of blood pressure (BP) in ambulatory patients is crucial for at high-risk cardiovascular patients. A non-obtrusive, non-occluding device that continuously measures BP via photoplethysmography will enable long-term ambulatory assessment of BP. The aim of this study is to validate the metasense 2PPG cuffless wearable design for continuous BP estimation without ECG. MethodsA customized high-speed electronic optical sensor architecture with laterally spaced reflectance pulse oximetry was designed into a simple unobtrusive low-power wearable in the form of a watch. 78 volunteers with a mean age of 32.72 ± 7.4 years (21 to 64), 51% male, 49% female were recruited with ECG-2PPG signals acquired. The fiducial features of the 2PPG morphologies were then attributed to the estimator. A 9–1 K-fold cross-validation was applied in the ML. ResultsThe correlation for PTT-SBP was 0.971 and for PTT-DBP was 0.954. The mean absolute error was 3.167±1.636 mmHg for SBP and 6.4 ± 3.9 mm Hg for DBP. The ambulatory estimate for SBP and DBP for an individual over 3 days with 8-hour recordings was 0.70–0.81 for SBP and 0.42–0.51 for DBP with a ± 2.65 mmHg for SBP and ±2.02 mmHg for DBP. For SBP, 98% of metasense measurements were within 15 mm Hg and for DBP, 91% of metasense measurements were within 10 mmHg ConclusionsThe metasense device provides continuous, non-invasive BP estimations that are comparable to ambulatory BP meters. The portability and unobtrusiveness of this device, as well as the ability to continuously measure BP could one day enable long-term ambulatory BP measurement for precision cardiovascular therapeutic regimens.

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

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

Current Developments in Cuff-Free Non-invasive Continuous Blood Pressure Estimation Using Photoplethysmography

Current Developments in Cuff-Free Non-invasive Continuous Blood Pressure Estimation Using Photoplethysmography

Advancing Continuous Blood Pressure Estimation with Transformer on Photoplethysmography in Operation Room

Advancing Continuous Blood Pressure Estimation with Transformer on Photoplethysmography in Operation Room

Beat-to-beat continuous blood pressure estimation with optimal feature set of PPG and ECG signals using deep recurrent neural networks

Aim: Continuous blood pressure (BP) monitoring can provide invaluable information for cardiovascular disease (CVD) diagnosis. The purpose of this study is to develop a deep recurrent neural network (RNN) model with an optimal feature set of photoplethysmogram (PPG) and electrocardiogram (ECG) signals for continuous BP estimation. Methods: This paper presents a novel deep recurrent neural network (RNN), which consists of 2-layered bidirectional Long Short-term Memory (Bi-LSTM) and 6-layered LSTM networks. It is used to estimate BP based on the optimal feature set of PPG and ECG signals. In this work, the optimal feature set is determined using five different feature selection methods. Results: The proposed method is evaluated based on 660 subjects from the University of California Irvine (UCI) machine learning repository. The RNN model with optimal feature set achieved root mean square error (RMSE) of 3.223 and 1.781 mmHg for systolic BP (SBP) and diastolic BP (DBP), respectively. It also showed mean absolute error (MAE) of 2.514 and 1.383 mmHg for SBP and DBP, respectively. Regarding the British Hypertension Society (BHS) standard, the results attained grade A for the estimation of SBP and DBP. Conclusion: The experimental results suggest that the proposed deep RNN model with an optimal feature set can improve the performance of BP prediction. Thus, it is possible to further apply our proposed method to develop a wearable device for real-time BP monitoring.

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

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

Beat to Beat Continuous Blood Pressure Estimation using the Transmission Coefficient Waveforms out of On-body Continuous Wave Antennas

Blood pressure (BP) is a clear sign of hypertension, a condition that can be hazardous and even fatal. Regular blood pressure monitoring is therefore crucial, but many people dislike cuff-based devices, and they can only be used while at rest. To address this need, this paper investigates the use of micro strip patch antenna technology to obtain BP values through an innovative technique. The change of transmission coefficient EM waveforms emitted by the antennas will be analyzed and correlated with brachial artery thickness to radius ratios and hence, blood pressure assessment at different pressure points. The suggested morphology based on simulations and data algorithms performed at couple of instances of brachial artery diameters to mimic the condition of blood flow. A dedicated analysis software provided to calculate the systolic and diastolic pressure levels.

A mixed attention-gated U-Net for continuous cuffless blood pressure estimation

A mixed attention-gated U-Net for continuous cuffless blood pressure estimation

Comparison of seven shallow and deep regressors in continuous blood pressure and heart rate estimation using single-channel photoplethysmograms under three evaluation cases

Our objective is to compare seven blood pressure (BP) and heart rate (HR) estimation models based on shallow and deep regressors using single-channel photoplethysmograms (PPGs) under three evaluation cases. Non-invasive, cuffless, continuous, and simultaneous estimation of systolic BP, diastolic BP, and HR using only PPGs is valuable for routinely monitoring the health status using simple device configuration. However, the performances of regressors cannot be directly compared based on the metrics presented in their respective papers because they use different datasets and evaluation procedures. Therefore, we used a common dataset (containing 1811 patients) and prepared three evaluation cases in which the training and testing data included, partly included, and did not include data from the same subjects. Shallow models using hand-crafted features performed reasonably well, and when the training data contained data similar to the testing data, deep models using raw signals as input achieved accurate BP and HR estimation. The spectral–temporal residual network showed the highest performance, with modified PP-Net as the runner-up; this study revealed that they are strong candidates for non-invasive, cuffless, continuous, and simultaneous BP and HR estimation models using single-channel PPGs in clinical and daily-use scenarios. Furthermore, results showed that the estimation performances rely on the level of inclusion of same subjects’ data in the training and testing data. To address the discrepancy between pre-measured data domain and new user’s data domain, we will investigate effective transfer learning techniques in the future work. The training and testing datasets used in this study are made public (https://drive.google.com/drive/folders/14nSeg0V-metCIs3LHPBH9tolxiyCN8eA?usp=share_link).

Blood pressure estimation and its recalibration assessment using wrist cuff blood pressure monitor.

The rapid evolution of wearable technology in healthcare sectors has created the opportunity for people to measure their blood pressure (BP) using a smartwatch at any time during their daily activities. Several commercially-available wearable devices have recently been equipped with a BP monitoring feature. However, concerns about recalibration remain. Pulse transit time (PTT)-based estimation is required for initial calibration, followed by periodic recalibration. Recalibration using arm-cuff BP monitors is not practical during everyday activities. In this study, we investigated recalibration using PTT-based BP monitoring aided by a deep neural network (DNN) and validated the performance achieved with more practical wrist-cuff BP monitors. The PTT-based prediction produced a mean absolute error (MAE) of 4.746 ± 1.529 mmHg for systolic blood pressure (SBP) and 3.448 ± 0.608 mmHg for diastolic blood pressure (DBP) when tested with an arm-cuff monitor employing recalibration. Recalibration clearly improved the performance of both DNN and conventional linear regression approaches. We established that the periodic recalibration performed by a wrist-worn BP monitor could be as accurate as that obtained with an arm-worn monitor, confirming the suitability of wrist-worn devices for everyday use. This is the first study to establish the potential of wrist-cuff BP monitors as a means to calibrate BP monitoring devices that can reliably substitute for arm-cuff BP monitors. With the use of wrist-cuff BP monitoring devices, continuous BP estimation, as well as frequent calibrations to ensure accurate BP monitoring, are now feasible.

Noninvasive continuous blood pressure estimation with fewer parameters based on RA-ReliefF feature selection and MPGA-BPN models

Photoplethysmography (PPG) and electrocardiogram (ECG) are closely related to the cardiovascular system and have substantial waveform parameters associated with blood pressure (BP), so extracting their waveform parameters to estimate BP has been widely studied. However, the more parameters, the more complex the model is, and the more factors affect the estimation results, which further affects the application of the BP estimation model to related equipment. Greatly reducing the number of parameters without reducing the accuracy of BP prediction, so as to improve the availability, applicability and robustness of BP estimation model, is the key for the real application of this method. In this study, we used relativity analysis (RA) to fill the gap of ReliefF algorithm, so the same number of parameter dimensions selected by ReliefF contain more signal information. Furthermore, a BP prediction model based on multi-population genetic algorithm (MPGA) and back propagation network (BPN) was constructed. Using waveform parameters of PPG and ECG signals with a duration of 20 s and the measured BP as a set of data, we extracted 522 and 23,608 sets of data from 25 healthy subjects and the UCI_BP dataset respectively. Three, five, seven and all parameters were selected by ReliefF for training in MPGA-BPN model and the results were compared. The comparison shows that five-parameters dataset can performs as all- parameters in both healthy individuals and UCI_BP, which confirms the validity of the feature selection in this study and lays the roots for BP estimation with fewer parameters and high accuracy.

The pre-ejection period is a highly stress dependent parameter of paramount importance for pulse-wave-velocity based applications.

The pulse-wave-velocity, is used for indirect, cuff-less, continuous blood pressure estimation. It is commonly detected by measuring the time delay between a defined point in an ECG and the arrival of the peripheral pulse wave (e.g., oxygen saturation sensor). The period between electrical stimulation of the heart (ECG) and actual blood ejection from the heart is called the pre-ejection period (PEP). This study aims at characterizing the PEP under mental and physical stress with focus on its relations to other cardiovascular parameters such as heart rate and importance for blood pressure (BP) estimation. We measured the PEP in 71 young adults at rest, under mental (TSST) and physical stress (ergometer) via impedance-cardiography. The PEP is highly dependent on mental and physical load. It is strongly correlated with indicators of sympathetic strain (p < 0.001). At rest (mean 104.5 ms), the PEP shows a high interindividual variability but small intraindividual variability. Mental stress decreases the PEP by 16% (mean 90.0 ms) while physical stress halves PEP (mean 53.9 ms). The PEP does correlate differently with heart rate under differing circumstances (rest: R 2 0.06, mental stress: R 2 0.29, physical stress: R 2 0.65). Subsequently, using PEP and heart rate enables the discrimination of rest, mental and physical strain with a positive predictive value of 93%. The PEP is a cardiovascular parameter with large interindividual variability at rest and subject-depended dynamic under load which is of great importance for ECG-based pulse-wave-velocity (PWV) determination. Considering its variability and large impact on the pulse arrival time, PEP is a crucial factor in PWV based BP estimation.

LSTM Multi-Stage Transfer Learning for Blood Pressure Estimation Using Photoplethysmography

Considerable research has been devoted to developing machine-learning models for continuous Blood Pressure (BP) estimation. A challenging problem that arises in this domain is the selection of optimal features with interpretable models for medical professionals. The aim of this study was to investigate evidence-based physiologically motivating features based on a solid physiological background of BP determinants. A powerful and compact set of features encompassing six physiologically oriented features was extracted in addition to another set of features consisting of six commonly used features for comparison purposes. In this study, we proposed a BP predictive model using Long Short-Term Memory (LSTM) networks with multi-stage transfer learning approach. The proposed model topology consists of three cascaded stages. First, a BP classification stage. Second, a Mean Arterial Pressure (MAP) regression stage to further approximate a quantity proportional to Vascular Resistance (VR) using the extracted Cardiac Output (CO) from the PPG signal. Third, the main BP estimation stage. The final stage (final BP prediction) is able to exploit embedded correlations between BP and the proposed features along with derived outputs carrying hemodynamic characteristics through the sub-sequence stages. We also constructed traditional single-stage Artificial Neural Network (ANN) and LSTM-based models to appraise the performance gain of our proposed model. The models were tested and evaluated on 40 subjects from the MIMIC II database. The LSTM-based multi-stage model attained a MAE ± SD of 2.03 ± 3.12 for SBP and 1.18 ± 1.70 mmHg for DBP. The proposed set of features resulted in drastic error reduction, of up to 86.21%, compared to models trained on the commonly used features. The superior performance of the proposed multi-stage model provides confirmatory evidence that the selected transferable features among the stages coupled with the high-performing multi-stage topology enhance blood pressure estimation accuracy using PPG signals. This indicates the compelling nature and sufficiency of the proposed efficient features set.

Continuous Blood Pressure Estimation in Wearable Devices Using Photoplethysmography: A Review

Cardiovascular diseases (CVD) are among those with the highest mortality rates, and various wearable devices for continuous monitoring are emerging as a complement to medical procedures. Blood pressure (BP) monitoring in wearable devices, in order to be continuous, must be performed noninvasively, thus involving photoplethysmography (PPG), a technology that has been widely studied in recent years as a non-invasive solution for BP estimation. However, continuous data acquisition in a wearable system is still a challenge, one of the reasons being the noise caused by movement, the correct use of the PPG signal, and the estimation method to be used. This paper reviews the advances in blood pressure estimation based on photoplethysmography, focusing on the analysis of the preprocessing (ICA, FIR, adaptive filters) of the signals. Among the filters reviewed, the most suitable for dealing with Motion Artifacts (MA) of a wearable system are the adaptive filters, because conventional filters are limited to work only in the band for which they are designed, which does not always cover the spectrum of the MA. A review of the estimation methods is also carried out, among them machine learning stands out because it shows greater growth due to the new proposals that use more signals and obtain better results in terms of accuracy. The objective is to know and analyze the appropriate preprocessing filters and estimation methods from the perspective of wearable systems using PPG sensors affected by AM. Keywords— Blood Pressure Estimation, PAT, PTT, Machine Learning, Photoplethysmography, adaptive filtering.

Evaluating and Visualizing the Contribution of ECG Characteristic Waveforms for PPG-Based Blood Pressure Estimation.

Non-invasive continuous blood pressure monitoring is of great significance for the preventing, diagnosing, and treating of cardiovascular diseases (CVDs). Studies have demonstrated that photoplethysmogram (PPG) and electrocardiogram (ECG) signals can effectively and continuously predict blood pressure (BP). However, most of the BP estimation models focus on the waveform features of the PPG signal, while the peak value of R-wave in ECG is only used as a time reference, and few references investigated the ECG waveforms. This paper aims to evaluate the influence of three characteristic waveforms in ECG on the improvement of BP estimation. PPG is the primary signal, and five input combinations are formed by adding ECG, P wave, QRS complex, T wave, and none. We employ five common convolutional neural networks (CNN) to validate the consistency of the contribution. Meanwhile, with the visualization of Gradient-weighted class activation mapping (Grad-CAM), we generate the heat maps and further visualize the distribution of CNN’s attention to each waveform of PPG and ECG. The heat maps show that networks pay more attention to the QRS complex and T wave. In the comparison results, the QRS complex and T wave have more contribution to minimizing errors than P wave. By separately adding P wave, QRS complex, and T wave, the average MAE of these networks reaches 7.87 mmHg, 6.57 mmHg, and 6.21 mmHg for systolic blood pressure (SBP), and 4.27 mmHg, 3.65 mmHg, and 3.73 mmHg, respectively, for diastolic blood pressure (DBP). The results of the experiment show that QRS complex and T wave deserves more attention and feature extraction like PPG waveform features in the continuous BP estimation.

Joint Regression Network and Window Function-Based Piecewise Neural Network for Cuffless Continuous Blood Pressure Estimation Only Using Single Photoplethesmogram

The blood pressure (BP) is generally measured using a cuff based sphygmomanometer. However, it is inconvenient to be used. Recently, an alternative solution only using the photoplethesmograms (PPGs) was proposed. In this case, the continuous BP estimation could be performed. First, the features were extracted from the PPGs. Then, a regression network was employed to estimate the BP values. Nevertheless, the accuracy of this approach was not so high. In order to improve the estimation accuracy, this paper proposes to cascade a two layer piecewise neural network to the output of the existing regression network to correct the estimation error. In particular, the overall system is a three layer network. The first layer of the network is the existing regression network. It generates the initial estimated BP values. The second layer of the network consists of the window functions. It segments the range of the BP values into various regions for the further processing. The final layer of the network performs the estimation correction. The performance of our proposed network is evaluated via two practical datasets and three common regression networks including the three layer artificial neural network (ANN) based regression network, the random forest (RF) based regression network and the support vector regression (SVR) based network. For the first dataset, our proposed method with the RF model and the piecewise neural network achieves the systolic BP (SBP) estimation error and the diastolic BP (DBP) estimation error at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.01{\pm }2.22$ </tex-math></inline-formula> mmHg with the correlation coefficient at 0.926 and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.43{\pm }3.37$ </tex-math></inline-formula> mmHg with the correlation coefficient at 0.935, respectively. On the other hand, the conventional RF model without the piecewise neural network achieves the SBP estimation error and the DBP estimation error at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.34{\pm }4.08$ </tex-math></inline-formula> mmHg with the correlation coefficient at 0.740 and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.89{\pm }4.98$ </tex-math></inline-formula> mmHg with the correlation coefficient at 0.840, respectively. For the second dataset, our proposed method with the RF model and the piecewise neural network achieves the SBP estimation error and the DBP estimation error at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.91{\pm }8.06$ </tex-math></inline-formula> mmHg with the correlation coefficient at 0.876 and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.47{\pm }5.59$ </tex-math></inline-formula> mmHg with the correlation coefficient at 0.859, respectively. On the other hand, the conventional RF model without the piecewise neural network achieves the SBP estimation error and the DBP estimation error at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9.77{\pm }9.01$ </tex-math></inline-formula> mmHg with the correlation coefficient at 0.805 and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.08{\pm }5.55$ </tex-math></inline-formula> mmHg with the correlation coefficient at 0.612, respectively. It can be seen that our proposed network yields the estimated BP values highly correlated to the reference BP values. Also, our proposed method yields the higher accuracies compared to the existing networks. This demonstrates the effectiveness of our proposed network.

PPG-based blood pressure estimation can benefit from scalable multi-scale fusion neural networks and multi-task learning

The continuous measurement of blood pressure (BP) plays an important role in preventing cardiovascular diseases. However, common cuff-based devices with cumbersome operations are difficult to realize continuous BP monitoring. We propose a continuous BP estimation method based on the end-to-end multi-scale fusion and multi-task learning neural network (MSF-MTLNet). It can use the 8-second PPG segment and its derivatives as input and automatically learn their multi-scale features to improve BP accuracy. The proposed method implements feature extraction by adaptively fusing multiple convolution kernels with different sizes through the attention mechanism. Simultaneously, multi-task learning was introduced to discover task-relevant features. A total of 277,600 segments from 1825 patients in the University of California, Irvine database are used for experiments. They are divided into the independent training set, validation set, and test set according to the ratio of 8:1:1 in an inter-subject manner. The mean and standard deviation of estimation error for systolic blood pressure and diastolic blood pressure are 0.97 ± 8.87 mmHg and 0.55 ± 4.23 mmHg, respectively, which outperforms the current cutting-edge method. Our approach almost meets the Association for the Advancement of Medical Instrumentation standard. Considering the British Hypertension Society standards, it achieves an 'A' grade for DBP estimation and a 'B' grade for SBP estimation. Experimental results show that MSF-MTLNet can obtain promising precision in cuffless and continuous BP estimation.

Continuous Blood Pressure Estimation Based on Multi-Scale Feature Extraction by the Neural Network With Multi-Task Learning.

In this article, a novel method for continuous blood pressure (BP) estimation based on multi-scale feature extraction by the neural network with multi-task learning (MST-net) has been proposed and evaluated. First, we preprocess the target (Electrocardiograph; Photoplethysmography) and label signals (arterial blood pressure), especially using peak-to-peak time limits of signals to eliminate the interference of the false peak. Then, we design a MST-net to extract multi-scale features related to BP, fully excavate and learn the relationship between multi-scale features and BP, and then estimate three BP values simultaneously. Finally, the performance of the developed neural network is verified by using a public multi-parameter intelligent monitoring waveform database. The results show that the mean absolute error ± standard deviation for systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) with the proposed method against reference are 4.04 ± 5.81, 2.29 ± 3.55, and 2.46 ± 3.58 mmHg, respectively; the correlation coefficients of SBP, DBP, and MAP are 0.96, 0.92, and 0.94, respectively, which meet the Association for the Advancement of Medical Instrumentation standard and reach A level of the British Hypertension Society standard. This study provides insights into the improvement of accuracy and efficiency of a continuous BP estimation method with a simple structure and without calibration. The proposed algorithm for BP estimation could potentially enable continuous BP monitoring by mobile health devices.