British Hypertension Society Research Articles

Personalized blood pressure estimation using multiview fusion information of wearable physiological signals and transfer learning

Continuous blood pressure (BP) monitoring is crucial for individual health management, yet the significant inter-individual variations among patients pose challenges to achieving precision medicine. In response to this issue, we propose a parallel cross-hybrid architecture that integrates a convolutional neural network backbone and a Mix-Transformer backbone. This model, grounded in multi-view physiological signals and personalized fine-tuning strategies, aims to estimate BP, facilitating the capture of physiological information across diverse receptive fields and enhancing network expressive capabilities. Our proposed architecture exhibits superior performance in estimating systolic blood pressure and diastolic blood pressure, with average absolute errors of 3.94 mmHg and 2.24 mmHg, respectively. These results surpass existing baseline models and align with the standards set by the British Hypertension Society, the Association for the Advancement of Medical Instrumentation, and the Institute of Electrical and Electronics Engineers for BP measurement. Additionally, this study explores a personalized model fine-tuning strategy by adjusting specific layers and incorporating individual information, presenting an optimal solution. The model's generalization ability is validated through transfer learning across databases (public and self-made). To enhance the proposed architecture's usability in wearable devices, this study employs a knowledge distillation strategy for model lightweighting, with preliminary application in our designed real-time BP estimation system. This study provides an efficient and accurate solution for personalized BP estimation, exhibiting broad potential applications.

WITHDRAWN: Non-Contact Blood Pressure Monitoring from Radar Signals by a Temporal-Spatial Feature Fusion Network

Non-contact blood pressure (BP) monitoring offers a comfortable and uninterrupted means of BP assessment, free from the constraints of physical contact. This capability is particularly better for the routine surveillance of BP for individuals with hypertension, burn injuries, skin sensitivities, and other pertinent conditions. In this study, we proposed a Temporal-Spatial Feature Fusion Network (TSFN), a radar-based, non-contact approach for BP monitoring. The TSFN architecture combines Residual Networks (ResNet) for the meticulous extraction of spatial features from radar signals, Gated Recurrent Unit (GRU) for the discernment of temporal dynamics, and Multiple Head Attention (MHA) to selectively emphasize crucial information. These components collectively ensure precise BP detection from radar signals. To enhance the model's robustness, a Pseudo-Huber loss function was employed to refine the optimization process, providing a smoother gradient transition and improved stability. Evaluations demonstrated impressive accuracies, with mean errors of 0.24±6.78 mmHg for systolic blood pressure (SBP) and 0.25±5.13 mmHg for diastolic blood pressure (DBP). These outcomes meet the standards set by the British Hypertension Society (BHS) for grade ‘A’ benchmarks for SBP and DBP measurements. Notably, the TSFN model avoids the need for complex feature engineering, demonstrating its effectiveness in monitoring BP fluctuations across diverse physiological states at 2s intervals. This feature highlights its potential applicability in real-time monitoring systems, offering a promising solution for continuous, non-contact BP monitoring.

Are previously validated blood pressure self-measurement devices accepted under the Universal Standard? A systematic review.

This study aimed to analyze whether oscillometric blood pressure devices validated for the general population may be considered approved under Universal Standard criteria. A systematic review was conducted, with searches in nine databases, up to September 2023, including 32 validation studies of noninvasive arm cuff devices for self-measurement. The British Hypertension Society protocol was most common (68%), followed by the Association for the Advancement of Medical Instrumentation (40%). Most devices met Universal Standard criterion 1, but only 17 (53%) met criterion 2. Few studies contained details about the choice of cuffs, the number of participants by arm circumference, or the differences between methods by cuff subgroup. Due to the considerable differences between validation protocols, 53% of the devices analyzed were approved under the Universal Standard. The study contributes to expanding the validated pool of self-measurement devices under the Universal Standard.

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

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

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

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

Continuous blood pressure monitoring based on transformer encoders and stacked attention gated recurrent units

Continuous blood pressure monitoring (CBPM) is critical to support the accurate prevention and reliable treatment of cardiovascular diseases. To achieve efficient multi-information interaction and further improve the monitoring performance, this research proposes an intelligent model based on transformer encoders and stacked attention gated recurrent units (TE-SAGRU) for CBPM. Long-term multi-source feature sequences with rich information are initially extracted from photoplethysmography (PPG) and electrocardiography (ECG) signals. The paralleled transformer encoders are constructed for different source feature sequences to obtain high-level feature representations and preserve respective long-term independence. The multiple stacked attention gated recurrent units are cross-connected for multi-interactive feature fusion and promoting complementarity effects of multi-source features on CBPM. Comprehensive comparison experiments are carried out to validate the effectiveness of the TE-SAGRU model, using the dataset with 1000 subjects derived from the MIMIC-III database. The continuous monitoring errors of the TE-SAGRU model for systolic blood pressure (SBP) and diastolic blood pressure (DBP) are 3.91 ± 5.65 mmHg and 2.29 ± 3.01 mmHg. The monitored results pass the requirement of the Association for the Advancement of Medical Instrumentation (AAMI) standard and achieve Grade A of the British Hypertension Society (BHS) protocol.

Photoplethysmography-based non-invasive blood pressure monitoring via ensemble model and imbalanced dataset processing.

Photoplethysmography, a widely embraced tool for non-invasive blood pressure (BP) monitoring, has demonstrated potential in BP prediction, especially when machine learning techniques are involved. However, predictions with a singular model often fall short in terms of accuracy. In order to counter this issue, we propose an innovative ensemble model that utilizes Light Gradient Boosting Machine (LightGBM) as the base estimator for predicting systolic and diastolic BP. This study included 115 women and 104 men, with experimental results indicating mean absolute errors of 5.63 mmHg and 9.36 mmHg for diastolic and systolic BP, in line with level B and C standards set by the British Hypertension Society. Additionally, our research confronts data imbalance in medical research which can detrimentally affect classification. Here we demonstrate an effective use for the Synthetic Minority Over-sampling Technique (SMOTE) with three nearest neighbors for handling moderate imbalanced datasets. The application of this method outperformed other methods in the field, achieving an F1 score of 81.6% and an AUC value of 0.895, emphasizing the potential value of SMOTE for addressing imbalanced datasets in medical research.

First, a seat; then, an upgrade.

The Sir Stanley Peart Essay Competition is an annual event run by the British and Irish Hypertension Society to encourage Early Career Researchers to continue the ethos of Sir Stanley Peart. Sir Stanley Peart was a clinician and clinical researcher who made a major contribution to our understanding of blood pressure regulation. He was the first to demonstrate the release of noradrenaline in response to sympathetic nerve stimulation. He was also the first to purify, and determine the structure of, angiotensin and he later isolated the enzyme, renin, and carried out many important investigations of the factors controlling its release in the body. This year, the essay topic was "Do we need new classes of antihypertensive drugs?". In his prize-winning essay, "First, a seat; then, an upgrade", Dr Sathyanarayanan argues that we do not need new classes of antihypertensive drugs, instead we should focus our attention on addressing the factors that lead to high blood pressure in the first place and use our existing drug classes more effectively.

DNN-BP: a novel framework for cuffless blood pressure measurement from optimal PPG features using deep learning model.

Continuous blood pressure (BP) provides essential information for monitoring one's health condition. However, BP is currently monitored using uncomfortable cuff-based devices, which does not support continuous BP monitoring. This paper aims to introduce a blood pressure monitoring algorithm based on only photoplethysmography (PPG) signals using the deep neural network (DNN). The PPG signals are obtained from 125 unique subjects with 218 records and filtered using signal processing algorithms to reduce the effects of noise, such as baseline wandering, and motion artifacts. The proposed algorithm is based on pulse wave analysis of PPG signals, extracted various domain features from PPG signals, and mapped them to BP values. Four feature selection methods are applied and yielded four feature subsets. Therefore, an ensemble feature selection technique is proposed to obtain the optimal feature set based on major voting scores from four feature subsets. DNN models, along with the ensemble feature selection technique, outperformed in estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP) compared to previously reported approaches that rely only on the PPG signal. The coefficient of determination ( ) and mean absolute error (MAE) of the proposed algorithm are 0.962 and 2.480 mmHg, respectively, for SBP and 0.955 and 1.499 mmHg, respectively, for DBP. The proposed approach meets the Advancement of Medical Instrumentation standard for SBP and DBP estimations. Additionally, according to the British Hypertension Society standard, the results attained Grade A for both SBP and DBP estimations. It concludes that BP can be estimated more accurately using the optimal feature set and DNN models. The proposed algorithm has the potential ability to facilitate mobile healthcare devices to monitor continuous BP.

Highly Sensitive Flexible Capacitive Pressure Sensor Based on a Multicross-Linked Dual-Network Ionic Hydrogel for Blood Pressure Monitoring Applications.

Flexible capacitive pressure sensors based on ionic hydrogels (IHs) have garnered significant attention in the field of wearable technology. However, the vulnerability of traditional single-network hydrogels to mechanical damage and the complexity associated with preparing double-network hydrogels present challenges in developing a highly sensitive, easily prepared, and durable IH-based flexible capacitive pressure sensor. This study introduces a novel multicross-linked dual-network IH achieved through the physical and chemical cross-linking of polymers polyvinyl alcohol (PVA) and chitosan (CS), ionic solution H3PO4, and cross-linking agent gum arabic. Flexible capacitive pressure sensors, characterized by high sensitivity and a broad pressure range, are fabricated by employing mesh as templates to design cut-corner cube microstructures with high uniformity and controllability on the IHs. The sensor exhibits high sensitivity across a wide pressure range (0-290 kPa) and with excellent features such as high resolution (∼1.3 Pa), fast response-recovery time (∼11 ms), and repeatable compression stability at 25 kPa (>2000 cycles). The IHs as a dielectric layer demonstrate long-term water retention properties, enabling exposure to air for up to 100 days. Additionally, the developed sensor shows the ability to accurately measure the pulse wave within the small pressure range. By combining the pulse wave acquired by the sensor with a trained neural network model, we achieve successful blood pressure (BP) prediction, meeting the standards set by the Association for the Advancement of Medical Instrumentation and the British Hypertension Society. Ultimately, the sensor proposed in this study holds promising prospects for broad applications in high-precision wearable medical electronic devices.

Ultra-sensitive flexible resistive sensor based on modified PEDOT: PSS inspired by earthworm

Flexible resistive sensors currently face challenges such as low sensitivity and poor stability. In this work, inspired by the skin structure of earthworm, a double strain layer with annular cracks aiming to achieve precise construction of conductive pathways in strain materials is proposed. This design strategy enhances the sensitivity, detection range, stability, and consistency of flexible resistive sensor. The sensing materials utilized primarily consist of mechanically and electrically modified PEDOT:PSS. The sensor showcases high sensitivity (with a gauge factor up to 1973.83), a wide detection range (0 ∼ 10 %), rapid response time (60–70 ms), and exceptional repeatability (1000 cycles). The sensor is integrated into finger clips for measurement of pulse wave. By analyzing features extracted from pulse wave, neural networks are employed for the prediction of encompassing coronary heart disease (CHD), atrial septal defect (ASD), atrial fibrillation (AF) and blood pressure levels. The classification accuracy of CHD, ASD and AF surpasses 96 %. The average prediction errors for systolic blood pressure, diastolic blood pressure and mean arterial pressure are found to be −0.3455, −0.2025 and −0.0319, respectively, meeting the standards set by the Association for the Advancement of Medical Instrumentation and the British Hypertension Society Class A criteria.

Abstracts from the 2023 Annual Scientific Meeting of the British and Irish Hypertension Society (BIHS).

Abstracts from the 2023 Annual Scientific Meeting of the British and Irish Hypertension Society (BIHS).

A Continuous Non-Invasive Blood Pressure Prediction Method Based on Deep Sparse Residual U-Net Combined with Improved Squeeze and Excitation Skip Connections.

Arterial blood pressure (ABP) serves as a pivotal clinical metric in cardiovascular health assessments, with the precise forecasting of continuous blood pressure assuming a critical role in both preventing and treating cardiovascular diseases. This study proposes a novel continuous non-invasive blood pressure prediction model, DSRUnet, based on deep sparse residual U-net combined with improved SE skip connections, which aim to enhance the accuracy of using photoplethysmography (PPG) signals for continuous blood pressure prediction. The model first introduces a sparse residual connection approach for path contraction and expansion, facilitating richer information fusion and feature expansion to better capture subtle variations in the original PPG signals, thereby enhancing the network's representational capacity and predictive performance and mitigating potential degradation in the network performance. Furthermore, an enhanced SE-GRU module was embedded in the skip connections to model and weight global information using an attention mechanism, capturing the temporal features of the PPG pulse signals through GRU layers to improve the quality of the transferred feature information and reduce redundant feature learning. Finally, a deep supervision mechanism was incorporated into the decoder module to guide the lower-level network to learn effective feature representations, alleviating the problem of gradient vanishing and facilitating effective training of the network. The proposed DSRUnet model was trained and tested on the publicly available UCI-BP dataset, with the average absolute errors for predicting systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean blood pressure (MBP) being 3.36 ± 6.61 mmHg, 2.35 ± 4.54 mmHg, and 2.21 ± 4.36 mmHg, respectively, meeting the standards set by the Association for the Advancement of Medical Instrumentation (AAMI), and achieving Grade A according to the British Hypertension Society (BHS) Standard for SBP and DBP predictions. Through ablation experiments and comparisons with other state-of-the-art methods, the effectiveness of DSRUnet in blood pressure prediction tasks, particularly for SBP, which generally yields poor prediction results, was significantly higher. The experimental results demonstrate that the DSRUnet model can accurately utilize PPG signals for real-time continuous blood pressure prediction and obtain high-quality and high-precision blood pressure prediction waveforms. Due to its non-invasiveness, continuity, and clinical relevance, the model may have significant implications for clinical applications in hospitals and research on wearable devices in daily life.

'Legacy publication of a 2009 validation of the Riester Big Ben Square Desk aneroid device for blood pressure measurement according to the European Society of Hypertension International Protocol for validation of blood pressure measuring devices in adults (2002)'.

To report a validation of the Riester Big Ben Square Desk Aneroid Sphygmomanometer according to the international protocol developed by the Working Group on Blood Pressure Monitoring of the European Society of Hypertension 2002 (ESH-IP 2002) in the interest of transparency. This legacy publication is intended to assure users that the device satisfied the requirements in place at that time. Performance of the device was assessed by participants' age, sex, arm circumference and entry SBP/DBP. Validation was performed in 33 participants. The sphygmomanometer was assessed according to the ESH-IP, which defines zones of accuracy compared to the mercury standard as ≤5, ≤10, ≤15 mmHg or more. The mean (± SD) age was 50.5 ± 13.0 years, range 29-71 years, entry SBP 142.6 ± 23.7 mmHg, entry DBP 89.0 ± 17.8 mmHg. The device passed all the requirements listed and the validation protocol. The Riester Big Ben Square Desk aneroid sphygmomanometer slightly underestimated the observer-measured SBP, yet slightly overestimated DBP. The observer-device disagreement was -0.8 ± 6.4 mmHg SBP and +0.6 ± 4.0 mmHg DBP. These data show that the Riester Big Ben Square Desk aneroid sphygmomanometer fulfilled the ESH-IP 2002 requirements for the validation of BP monitors. It was on this basis that the British and Irish Hypertension Society recommended it for clinical use in the adult population.

A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithms

Background and objectiveHypertension is a potentially dangerous health condition that can be detected by measuring blood pressure (BP). Blood pressure monitoring and measurement are essential for preventing and treating cardiovascular diseases. Cuff-based devices, on the other hand, are uncomfortable and prevent continuous BP measurement. MethodsIn this study, a new non-invasive and cuff-less method for estimating Systolic Blood Pressure (SBP), Mean Arterial Pressure (MAP), and Diastolic Blood Pressure (DBP) has been proposed using characteristic features of photoplethysmogram (PPG) signals and nonlinear regression algorithms. PPG signals were collected from 219 participants, which were then subjected to preprocessing and feature extraction steps. Analyzing PPG and its derivative signals, a total of 46 time, frequency, and time-frequency domain features were extracted. In addition, the age and gender of each subject were also included as features. Further, correlation-based feature selection (CFS) and Relief F feature selection (ReliefF) techniques were used to select the relevant features and reduce the possibility of over-fitting the models. Finally, support vector regression (SVR), K-nearest neighbour regression (KNR), decision tree regression (DTR), and random forest regression (RFR) were established to develop the BP estimation model. Regression models were trained and evaluated on all features as well as selected features. The best regression models for SBP, MAP, and DBP estimations were selected separately. ResultsThe SVR model, along with the ReliefF-based feature selection algorithm, outperforms other algorithms in estimating the SBP, MAP, and DBP with the mean absolute error of 2.49, 1.62 and 1.43 mmHg, respectively. The proposed method meets the Advancement of Medical Instrumentation standard for BP estimations. Based on the British Hypertension Society standard, the results also fall within Grade A for SBP, MAP, and DBP. ConclusionThe findings show that the method can be used to estimate blood pressure non-invasively, without using a cuff or calibration, and only by utilizing the PPG signal characteristic features.

Assessing heart rate and blood pressure estimation from image photoplethysmography using a digital blood pressure meter

This study presents a non-contact approach to measuring heart rate and blood pressure using an image photoplethysmography (iPPG) signal, and compares the results to those from an oscillometric blood pressure meter. Facial videos of 100 subjects were recorded via a webcam under ambient lighting conditions to extract iPPG signals. The results revealed a strong correlation between the heart rate derived from iPPG and that obtained from an oscillometric blood pressure meter. In addition, a continuous wavelet transform images with a 6-s duration were used as input for a custom convolutional neural network model, providing the most accurate blood pressure estimation. The proposed method received a grade A for diastolic and grade B for systolic blood pressure based on the British Hypertension Society's criteria. It also met the standards set by the Association for the Advancement of Medical Instrumentation. This non-contact framework shows promising potential for efficient screening purposes.

Blood Pressure Estimation Based on PPG and ECG Signals Using Knowledge Distillation.

Easy access bio-signals are useful for alleviating the shortcomings and difficulties associated with cuff-based and invasive blood pressure (BP) measurement techniques. This study proposes a deep learning model, trained using knowledge distillation, based on photoplethysmographic (PPG) and electrocardiogram (ECG) signals to estimate systolic and diastolic blood pressures. The estimation model comprises convolutional layers followed by one bidirectional recurrent layer and attention layers. The training approach involves knowledge distillation, where a smaller model (student model) is trained by leveraging information from a larger model (teacher model). The proposed multistage model was evaluated on 1205 subjects from Medical Information Mart for Intensive Care (MIMIC) III database using the Association for the Advancement of Medical Instrumentation (AAMI) and the standards of the British Hypertension Society (BHS). The results revealed that our model performance achieved grade A in estimating both systolic blood pressure (SBP) and diastolic blood pressure (DBP) and met the requirements of the AAMI standard. After training with knowledge distillation (KD), the model achieved a mean absolute error and standard deviation of 2.94 ± 5.61mmHg for SBP and 2.02 ± 3.60mmHg for DBP. Our results demonstrate the benefits of the knowledge distillation training method in reducing the number of parameters and improving the predictive accuracy of the blood pressure regression model.

Continuous Patient-Independent Estimation of Respiratory Rate and Blood Pressure Using Robust Spectro-Temporal Features Derived From Photoplethysmogram Only.

Objective: A patient-independent approach for continuous estimation of vital signs using robust spectro-temporal features derived from only photoplethysmogram (PPG) signal. Methods: In the pre-processing stage, we remove baseline shifts and artifacts of the PPG signal using Incremental Merge Segmentation with adaptive thresholding. From the cleaned PPG, we extract multiple parameters independent of individual patient PPG morphology for both Respiration Rate (RR) and Blood Pressure (BP). In addition, we derived a set of novel spectral and statistical features strongly correlated to BP. We proposed robust correlation-based feature selection methods for accurate RR estimates. For fewer computations and accurate measurements of BP, the most significant features are selected using correlation and mutual information measures in the feature engineering part. Finally, RR and BP are estimated using breath counting and a neural network regression model, respectively. Results: The proposed approach outperforms the current state-of-the-art in both RR and BP. The RR algorithm results in mean absolute errors (median, 25th-75th percentiles) of 0.4 (0.1-0.7) for CapnoBase dataset and 0.5(0.3-2.8) for BIDMC dataset without discarding any data window. Similarly, BP approach has been validated on a large dataset derived from MIMIC-II ([Formula: see text]1700 records) which has errors (mean absolute, standard deviation) of 5.0(6.3) and 3.0(4.0) for systolic and diastolic BP, respectively. The results meet the American Association for the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS) Class A criteria. Conclusion: By using robust features and feature selection methods, we alleviated patient dependency to have reliable estimates of vitals.

Continuous non-invasive arterial blood pressure monitoring with photoplethysmography via SE-MSResUNet network

ObjectiveArterial blood pressure (ABP) waveforms include physiological parameters closely related to BP values, while only a few studies predict BP values through the monitoring ABP waveforms. Current ABP waveforms monitoring methods are limited by its high computing cost and information redundancy of multi-channel input signals. The aim of our work is to propose a new end-to-end method for monitoring the ABP waveforms using single-channel photoplethysmography (PPG) signals by constructing a lightweight Multi-Scale Residual U-Net fused with Squeeze-and-Excitation module (SE-MSResUNet) model. MethodsFirstly, the PPG and ABP signals are preprocessed with denoising, segmentation, and eliminating phase difference. Subsequently, the SE-MSResUNet model is constructed and trained using training data to achieve the mapping relationship between PPG and ABP signals. Then, the BP values are extracted from the monitored ABP waveforms. Finally, the performance of the SE-MSResUNet in ABP waveform monitoring and BP estimation is investigated. ResultsThe ABP waveforms monitored by our proposed SE-MSResUNet network with PPG signals only are highly consistent with the reference ABP waveforms. The MAE ± SD of estimated diastolic blood pressure (DBP), mean arterial pressure (MAP) and systolic blood pressure (SBP) compared with reference values are 2.16 ± 3.75, 2.29 ± 3.72, and 3.88 ± 6.17 mmHg, respectively. The proposed method meets the Association for the Advancement of Medical Instrumentation (AAMI) standard and reach A level of the British Hypertension Society (BHS) standard. ConclusionsOur proposed method outperforms current state-of-the-art methods for non-invasive blood pressure monitoring from PPG signals. The algorithm provides a potential possibility for continuous non-invasive blood pressure monitoring through portable wearable devices.

Development of continuous cuffless blood pressure prediction platform using enhanced 1-D SENet–LSTM

In this study, we propose a blood pressure estimation that employs a combined 1-D squeeze and excitation network (SENet-LSTM) architecture. Combining photoplethysmography (PPG) and electrocardiogram (ECG) signals can provide more helpful feature information. The original signals were processed by removing the noise and artifacts. By introducing SE block, the ability to learn features is enhanced, and an end-to-end approach is used to realize the automatic learning process. Additionally, the algorithm has the ability to recover arterial blood pressure (ABP) waveforms from blood pressure readings. The results of this study met the Grade A standard for diastolic blood pressure (DBP), systolic blood pressure (SBP), and mean arterial pressure (MAP) set by the British Hypertension Society (BHS) and were also in accordance with the Association for the Advancement of Medical Instrumentation (AAMI) standard. According to the DBP and SBP ranges, blood pressure is divided into the following categories: normal blood pressure, prehypertension, and hypertension. For the classification of Hypertension, Normotension, and Prehypertension using SBP, we achieved accuracy of 94% and F1 scores of 0.92, 0.94, and 0.85. For the results obtained using DBP classification the overall accuracy was 91%, with F1 scores of 0.85, 0.98, and 0.78. When compared to other studies, the classifier results generated by SBP and DBP estimates based on the 1-D SENet-LSTM method improved accuracy by 2% and 11%, respectively.