Cuffless Blood Pressure in clinical practice: challenges, opportunities and current limits.

Background: Cuffless blood pressure measurement technologies have attracted significant attention for their potential to transform cardiovascular monitoring.Methods: This updated narrative review thoroughly examines the challenges, opportunities, and limitations associated with the implementation of cuffless blood pressure monitoring systems.Results: Diverse technologies, including photoplethysmography, tonometry, and ECG analysis, enable cuffless blood pressure measurement and are integrated into devices like smartphones and smartwatches. Signal processing emerges as a critical aspect, dictating the accuracy and reliability of readings. Despite its potential, the integration of cuffless technologies into clinical practice faces obstacles, including the need to address concerns related to accuracy, calibration, and standardization across diverse devices and patient populations. The development of robust algorithms to mitigate artifacts and environmental disturbances is essential for extracting clear physiological signals. Based on extensive research, this review emphasizes the necessity for standardized protocols, validation studies, and regulatory frameworks to ensure the reliability and safety of cuffless blood pressure monitoring devices and their implementation in mainstream medical practice. Interdisciplinary collaborations between engineers, clinicians, and regulatory bodies are crucial to address technical, clinical, and regulatory complexities during implementation. In conclusion, while cuffless blood pressure monitoring holds immense potential to transform cardiovascular care. The resolution of existing challenges and the establishment of rigorous standards are imperative for its seamless incorporation into routine clinical practice.Conclusion: The emergence of these new technologies shifts the paradigm of cardiovascular health management, presenting a new possibility for non-invasive continuous and dynamic monitoring. The concept of cuffless blood pressure measurement is viable and more finely tuned devices are expected to enter the market, which could redefine our understanding of blood pressure and hypertension.

This review explores cuffless blood pressure technologies and their impact on clinical practice, highlighting innovative devices that offer non-invasive, continuous and non-continuous monitoring without a cuff. Signal processing is essential for ensuring accurate readings, as it filters out unwanted artifacts and environmental disturbances which could make the reading inaccurate. While these advancements show great potential for transforming cardiovascular care, there are still several challenges to overcome, including the need for standardized protocols and validation studies to ensure their reliability and safety in clinical settings. Collaborative efforts between engineers, clinicians, and regulatory bodies are needed to address the technical and regulatory complexities surrounding the implementation of these technologies. These cuffless blood pressure measurement devices have the potential to reshape how we understand and manage blood pressure and hypertension.

Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload.

Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilitate tracking blood pressure fluctuations in various conditions. In this work, data-driven photoplethysmograph features extracted from the brachial and digital arteries of 28 healthy subjects were used to feed a random forest classifier in an attempt to develop a system capable of tracking blood pressure. We evaluated the behavior of this latter classifier according to the different sizes of the training set and degrees of personalization used. Aggregated accuracy, precision, recall, and F1-score were equal to 95.1%, 95.2%, 95%, and 95.4% when 30% of a target subject's pulse waveforms were combined with five randomly selected source subjects available in the dataset. Experimental findings illustrated that incorporating a pre-training stage with data from different subjects made it viable to discern morphological distinctions in beat-to-beat pulse waveforms under conditions of cognitive or physical workload.

Wearable cuffless blood pressure monitoring devices: a commentary.

Hypertension is a growing public challenge as a leading risk factor for cardiovascular disease and all-cause mortality. Reducing overall cardiovascular risk through early screening, initiation of treatment and ongoing monitoring remains a priority in the comprehensive management of hypertension and its complications. Community nurses are ideally positioned to play a crucial role in the early detection of hypertension and providing support for its management. Wearable cuffless devices have the potential for continuous remote blood pressure monitoring. However, there is not enough literature on the validity and usability of wearable cuffless blood pressure devices to justify their use in clinical practice. This commentary critically appraises a systematic review designed to assess the validity, features and clinical usability of wearable cuffless devices, and expands on its findings and their relevance to community nursing and future research.

Cuffless Blood Pressure Measurement.

Cuffless blood pressure (BP) measurement has become a popular field due to clinical need and technological opportunity. However, no method has been broadly accepted hitherto. The objective of this review is to accelerate progress in the development and application of cuffless BP measurement methods. We begin by describing the principles of conventional BP measurement, outstanding hypertension/hypotension problems that could be addressed with cuffless methods, and recent technological advances, including smartphone proliferation and wearable sensing, that are driving the field. We then present all major cuffless methods under investigation, including their current evidence. Our presentation includes calibrated methods (i.e., pulse transit time, pulse wave analysis, and facial video processing) and uncalibrated methods (i.e., cuffless oscillometry, ultrasound, and volume control). The calibrated methods can offer convenience advantages, whereas the uncalibrated methods do not require periodic cuff device usage or demographic inputs. We conclude by summarizing the field and highlighting potentially useful future research directions.

Performance of the Aktiia optical blood pressure measurement device in the elderly: a comparison with double blinded auscultation in different body positions.

BACKGROUND: Accurate blood pressure (BP) measurement is essential for the correct diagnosis and management of hypertension (HTN) especially in the elderly population. As with of all BP devices, the accuracy of cuffless devices must be verified. This study (NCT04027777) aimed to evaluate the performance of a wrist cuffless optical BP device in an elderly population cohort in different body positions with auscultation as the reference measurement. DESIGN AND METHODS: Patients aged 65-85 years with different BP categories but without diabetes were recruited. After an initial calibration based on auscultatory measurements, BP estimation from the Aktiia Bracelet (Aktiia SA, Switzerland) were compared to reference double-blinded auscultatory measurements in sitting, standing and lying positions on four separate visits distributed over one month. In the absence of a universal standard for cuffless BP device at the time of the study, modified ISO81060-2 criteria were used for performance analysis. RESULTS: Thirty-five participants were included in the analysis fulfilling the inclusion requirements of ISO 81060-2. A total of 469 paired measurements were obtained with overall 83% acceptance rate. Differences (mean ± SD)   between Aktiia Bracelet and auscultation for systolic BP were -0.26 ± 9.96 mmHg for all body positions aggregated (sitting 1.23 ± 7.88 mmHg, standing -1.81 ± 11.11 mmHg, lying -1.8 ± 9.96 mmHg). Similarly, differences for diastolic BP were -0.75 ± 7.0 mmHg (0.2 ± 5.55 mmHg, -5.35 ± 7.75 mmHg and -0.94 ± 7.47 mmHg, respectively). Standard deviation of the averaged differences per subject for systolic/diastolic BP was 3.8/2.5 mmHg in sitting and 4.4/3.7 mmHg for all body positions aggregated. CONCLUSIONS: Overall, this study demonstrates a similar performance of the Aktiia Bracelet compared to auscultation in an elderly population in body positions representative of daily activities. The use of more comfortable, non-invasive, and non-occlusive BP monitors during long periods may facilitate e-health and may contribute to better management of HTN, including diagnosis and treatment of HTN, in the elderly.

Accuracy of blood pressure measurements is essential in the diagnosis and the follow-up of patients with high blood pressure. As with any blood pressure measuring device, a validation is necessary. In this study including a elderly population, we compared values obtained by the cuffless Aktiia Bracelet (Aktiia SA, Switzerland) after an initial calibration with the reference auscultatory method during four separate study days distributed over one month. We show that the accuracy of the Aktiia Bracelet is similar to auscultation. The accuracy varies depending on the position in which the measurement is performed. Overall, the accuracy is not modified by a higher age category. The use of a cuffless device in the elderly population characterized by high prevalence of hypertension may facilitate the follow-up of blood pressure with more comfort and minimal constraints.

Accuracy and User Acceptability of 24-hour Ambulatory Blood Pressure Monitoring by a Prototype Cuffless Multi-Sensor Device Compared to a Conventional Oscillometric Device.

OBJECTIVE: 24-hour ambulatory blood pressure monitoring (24ABPM) is state of the art in out-of-office blood pressure (BP) monitoring. Due to discomfort and technical limitations related to cuff-based 24ABPM devices, methods for non-invasive and continuous estimation of BP without the need for a cuff have gained interest. The main aims of the present study were to compare accuracy of a pulse arrival time (PAT) based BP-model and user acceptability of a prototype cuffless multi-sensor device (cuffless device), developed by Aidee Health AS, with a conventional cuff-based oscillometric device (ReferenceBP) during 24ABPM. METHODS: Ninety-five normotensive and hypertensive adults underwent simultaneous 24ABPM with the cuffless device on the chest and a conventional cuff-based oscillometric device on the non-dominant arm. PAT was calculated using the electrocardiogram (ECG) and photoplethysmography (PPG) sensors incorporated in the chest-worn device. The cuffless device recorded continuously, while ReferenceBP measurements were taken every 20 minutes during daytime and every 30 minutes during nighttime. Two-minute PAT-based BP predictions corresponding to the ReferenceBP measurements were compared with ReferenceBP measurements using paired t-tests, bias, and limits of agreement. RESULTS: Mean (SD) of ReferenceBP compared to PAT-based daytime and nighttime systolic BP (SBP) were 129.7 (13.8) mmHg versus 133.6 (20.9) mmHg and 113.1 (16.5) mmHg versus 131.9 (23.4) mmHg. Ninety-five % limits of agreements were [-26.7, 34.6 mmHg] and [-20.9, 58.4 mmHg] for daytime and nighttime SBP respectively. The cuffless device was reported to be significantly more comfortable and less disturbing than the ReferenceBP device during 24ABPM. CONCLUSIONS: In the present study, we demonstrated that a general PAT-based BP model had unsatisfactory agreement with ambulatory BP during 24ABPM, especially during nighttime. If sufficient accuracy can be achieved, cuffless BP devices have promising potential for clinical assessment of BP due to the opportunities provided by continuous BP measurements during real-life conditions and high user acceptability.

What is the context?Hypertension is a major risk factor for cardiovascular and cerebrovascular end-organ damage, morbidity, and mortality world-wide.Accurate measurement of blood pressure is essential for the diagnosis and management of hypertension.What is new?Cuffless blood pressure devices that allow measurement of blood pressure without a pressure cuff is a promising and novel method of blood pressure estimation.The objective of this study is to assess whether pulse arrival time alone can be used to estimate blood pressure accurately during 24-hour ambulatory blood pressure monitoring, using a prototype cuffless device placed on the chest.Our analysis shows that a general model based on pulse arrival time overestimated ambulatory blood pressure, especially during nighttime.User acceptability was higher with the cuffless device compared to a conventional cuff-based oscillometric device during 24-hour ambulatory blood pressure monitoring.What is the impact?This study provides further evidence that accurate blood pressure estimations cannot be achieved by using pulse arrival time alone as a surrogate for blood pressure measurements.

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

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

Current and Developing Technologies for BP Monitoring.

PURPOSE OF REVIEW: To discuss new and emerging technologies for blood pressure measurement and monitoring, including the limitations of current blood pressure measurement techniques, hopes for new device technologies, and the current barriers impeding change in this space. RECENT FINDINGS: A number of new cuffless devices are being developed and poised to emerge on the marketplace in coming years. There are several different types of technologies and sensors currently under study. New guidelines for validation of cuffless blood pressure devices have recently been developed in anticipation of this change. The current standards for blood pressure device validation are specific to cuff-based technology and are insufficient for validating devices with cuffless-based technologies. In anticipation of a number of new cuffless technologies coming to market in the coming years, three sets of standards have been developed and published in recent years to address this gap.

Current evidence suggests that estimating blood pressure from convenient ECG waveforms alone is not viable.

Cuffless blood pressure (BP) measurement could improve hypertension awareness and control and is being widely pursued. Some have proposed to estimate BP from the electrocardiogram (ECG) alone despite little physiological basis. In this minireview, we extracted the most relevant articles related to ECG-based BP estimation. Our findings suggest that, as expected, estimating BP from ECG does not appear to be viable. Most notably, we have not found any evidence that ECG features can track BP changes. At best, certain ECG features may indicate heart disease and thus correlate with high BP, but this may not be clinically useful.

Development of Real-Time Cuffless Blood Pressure Measurement Systems with ECG Electrodes and a Microphone Using Pulse Transit Time (PTT).

Research has shown that pulse transit time (PTT), which is the time delay between the electrocardiogram (ECG) signal and the signal from a photoplethysmogram (PPG) sensor, can be used to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) without the need for a cuff. However, the LED of the PPG sensor requires the precise adjustment of both light intensity and light absorption rates according to the contact status of the light-receiving element. This results in the need for regular calibration. In this study, we propose a cuffless blood pressure monitor that measures real-time blood pressure using a microphone instead of a PPG sensor. The blood pulse wave is measured in the radial artery of the wrist using a microphone that can directly measure the sound generated by a body rather than sending energy inside the body and receiving a returning signal. Our blood pressure monitor uses the PTT between the R-peak of the ECG signal and two feature points of the blood pulse wave in the radial artery of the wrist. ECG electrodes and circuits were fabricated, and a commercial microelectromechanical system (MEMS) microphone was used as the microphone to measure blood pulses. The peak points of the blood pulse from the microphone were clear, so the estimated SBP and DBP could be obtained from each ECG pulse in real time, and the resulting estimations were similar to those made by a commercial cuff blood pressure monitor. Since neither the ECG electrodes nor the microphone requires calibration over time, the real-time cuffless blood pressure monitor does not require calibration. Using the developed device, blood pressure was measured three times daily for five days, and the mean absolute error (MAE) and standard deviation (SD) of the SBP and DBP were found to be 2.72 ± 3.42 mmHg and 2.29 ± 3.53 mmHg, respectively. As a preliminary study for proof-of-concept, these results were obtained from one subject. The next step will be a pilot study on a large number of subjects.

A PPG-Based Calibration-Free Cuffless Blood Pressure Estimation Method Using Cardiovascular Dynamics.

Traditional cuff-based sphygmomanometers for measuring blood pressure can be uncomfortable and particularly unsuitable to use during sleep. A proposed alternative method uses dynamic changes in the pulse waveform over short intervals and replaces calibration with information from photoplethysmogram (PPG) morphology to provide a calibration-free approach using a single sensor. Results from 30 patients show a high correlation of 73.64% for systolic blood pressure (SBP) and 77.72% for diastolic blood pressure (DBP) between blood pressure estimated with the PPG morphology features and the calibration method. This suggests that the PPG morphology features could replace the calibration stage for a calibration-free method with similar accuracy. Applying the proposed methodology on 200 patients and testing on 25 new patients resulted in a mean error (ME) of -0.31 mmHg, a standard deviation of error (SDE) of 4.89 mmHg, a mean absolute error (MAE) of 3.32 mmHg for DBP and an ME of -4.02 mmHg, an SDE of 10.40 mmHg, and an MAE of 7.41 mmHg for SBP. These results support the potential for using a PPG signal for calibration-free cuffless blood pressure estimation and improving accuracy by adding information from cardiovascular dynamics to different methods in the cuffless blood pressure monitoring field.

Nocturnal Blood Pressure Estimation from Sleep Plethysmography Using Machine Learning.

BACKGROUND: Elevated nocturnal blood pressure (BP) is a risk factor for cardiovascular disease (CVD) and mortality. Cuffless BP assessment aided by machine learning could be a desirable alternative to traditional cuff-based methods for monitoring BP during sleep. We describe a machine-learning-based algorithm for predicting nocturnal BP using single-channel fingertip plethysmography (PPG) in healthy adults. METHODS: Sixty-eight healthy adults with no apparent sleep or CVD (53% male), with a median (IQR) age of 29 (23-46 years), underwent overnight polysomnography (PSG) with fingertip PPG and ambulatory blood pressure monitoring (ABPM). Features based on pulse morphology were extracted from the PPG waveforms. Random forest models were used to predict night-time systolic blood pressure (SBP) and diastolic blood pressure (DBP). RESULTS: Our model achieved the highest out-of-sample performance with a window length of 7 s across window lengths explored (60 s, 30 s, 15 s, 7 s, and 3 s). The mean absolute error (MAE ± STD) was 5.72 ± 4.51 mmHg for SBP and 4.52 ± 3.60 mmHg for DBP. Similarly, the root mean square error (RMSE ± STD) was 6.47 ± 1.88 mmHg for SBP and 4.62 ± 1.17 mmHg for DBP. The mean correlation coefficient between measured and predicted values was 0.87 for SBP and 0.86 for DBP. Based on Shapley additive explanation (SHAP) values, the most important PPG waveform feature was the stiffness index, a marker that reflects the change in arterial stiffness. CONCLUSION: Our results highlight the potential of machine learning-based nocturnal BP prediction using single-channel fingertip PPG in healthy adults. The accuracy of the predictions demonstrated that our cuffless method was able to capture the dynamic and complex relationship between PPG waveform characteristics and BP during sleep, which may provide a scalable, convenient, economical, and non-invasive means to continuously monitor blood pressure.

Accuracy of blood pressure measurement across BMI categories using the OptiBP™ mobile application.

PURPOSE: Obesity is a clear risk factor for hypertension. Blood pressure (BP) measurement in obese patients may be biased by cuff size and upper arm shape which may affect the accuracy of measurements. This study aimed to assess the accuracy of the OptiBP smartphone application for three different body mass index (BMI) categories (normal, overweight and obese). MATERIALS AND METHODS: Participants with a wide range of BP and BMI were recruited at Lausanne University Hospital's hypertension clinic in Switzerland. OptiBP estimated BP by recording an optical signal reflecting light from the participants' fingertips into a smartphone camera. Age, sex and BP distribution were collected to fulfil the AAMI/ESH/ISO universal standards. Both auscultatory BP references and OptiBP BP were measured and compared using the simultaneous opposite arms method, as described in the 81060-2:2018 ISO norm. Subgroup analyses were performed for each BMI category. RESULTS: We analyzed 414 recordings from 95 patients: 34 were overweight and 15 were obese. The OptiBP application had a performance acceptance rate of 82%. The mean and standard deviation (SD) differences between the optical BP estimations and the auscultatory reference rates (criterion 1) were respected in all subgroups: SBP mean value was 2.08 (SD 7.58); 1.32 (6.44); -2.29 (5.62) respectively in obese, overweight and normal weight subgroup. For criterion 2, which investigates the precision errors on an individual level, the threshold for systolic BP in the obese group was slightly above the requirement for this criterion. CONCLUSION: This study demonstrated that the OptiBP application is easily applicable to overweight and obese participants. Differences between the reference measure and the OptiBP estimation were within ISO limits (criterion 1). In obese participants, the SD of mean error was outside criterion 2 limits. Whether auscultatory measurement, due to arm morphology or the OptiBP is associated with increasing bias in obese still needs to be studied.

What is the context? • Hypertension and obesity have a major impact on population health and costs. • Obesity is a chronic disease characterized by abnormal or excessive fat accumulation. • Obesity, in combination with other diseases like hypertension, is a major risk factor for cardiovascular and total death. • In Europe, the obesity rate is 21.5% for men and 24.5% for women. • Hypertension, which continues to increase in the population, is a factor that can be modified when well managed. • Blood pressure measurement by the usual method may be complicated in obese patients due to fat accumulation and the shape of the arm and can lead to measurement errors. In addition, the non-invasive blood pressure measurement can be constraining and uncomfortable.What is new? • Smartphone apps are gradually appearing and allow the measurement of blood pressure without a pressure cuff using photoplethysmography. • OptiBP is a smartphone application that provides an estimate of blood pressure that has been evaluated in the general population. • The objective of this study is to assess whether OptiBP is equally effective in obese and overweight patients.What is the impact? • The use of smartphones to estimate BP in overweight and obese patients may be a solution to the known bias associated with cuff measurement. • The acquisition of more and more data with a larger number of patients will allow the continuous improvement of the application's algorithm.

High-frame-rate A-mode ultrasound for calibration-free cuffless carotid pressure: feasibility study using lower body negative pressure intervention.

PURPOSE: Existing technologies to measure central blood pressure (CBP) intrinsically depend on peripheral pressure or calibration models derived from it. Pharmacological or physiological interventions yielding different central and peripheral responses compromise the accuracy of such methods. We present a high-frame-rate ultrasound technology for cuffless and calibration-free evaluation of BP from the carotid artery. The system uses a pair of single-element ultrasound transducers to capture the arterial diameter and local pulse wave velocity (PWV) for the evaluation of beat-by-beat BP employing a novel biomechanical model. MATERIALS AND METHODS: System's functionality assessment was conducted on eight male subjects (26 ± 4 years, normotensive and no history of cardiovascular risks) by perturbing pressure via short-term moderate lower body negative pressure (LBNP) intervention (-40 mmHg for 1 min). The ability of the system to capture dynamic responses of carotid pressure to LBNP was investigated and compared against the responses of peripheral pressure measured using a continuous BP monitor. RESULTS: While the carotid pressure manifested trends similar to finger measurements during LBNP, the system also captured the differential carotid-to-peripheral pressure response, which corroborates the literature. The carotid diastolic and mean pressures agreed with the finger pressures (limits-of-agreement within ±7 mmHg) and exhibited acceptable uncertainty (mean absolute errors were 2.4 ± 3.5 and 2.6 ± 4.0 mmHg, respectively). Concurrent to the literature, the carotid systolic and pulse pressures (PPs) were significantly lower than those of the finger pressures by 11.1 ± 9.4 and 11.3 ± 8.2 mmHg, respectively (p < .0001). CONCLUSIONS: The study demonstrated the method's potential for providing cuffless and calibration-free pressure measurements while reliably capturing the physiological aspects, such as PP amplification and dynamic pressure responses to intervention.

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.

Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism.

Arterial blood pressure (ABP) is an important vital sign from which it can be extracted valuable information about the subject's health. After studying its morphology it is possible to diagnose cardiovascular diseases such as hypertension, so ABP routine control is recommended. The most common method of controlling ABP is the cuff-based method, from which it is obtained only the systolic and diastolic blood pressure (SBP and DBP, respectively). This paper proposes a cuff-free method to estimate the morphology of the average ABP pulse (ABPM¯) through a deep learning model based on a seq2seq architecture with attention mechanism. It only needs raw photoplethysmogram signals (PPG) from the finger and includes the capacity to integrate both categorical and continuous demographic information (DI). The experiments were performed on more than 1100 subjects from the MIMIC database for which their corresponding age and gender were consulted. Without allowing the use of data from the same subjects to train and test, the mean absolute errors (MAE) were 6.57 ± 0.20 and 14.39 ± 0.42 mmHg for DBP and SBP, respectively. For ABPM¯, R correlation coefficient and the MAE were 0.98 ± 0.001 and 8.89 ± 0.10 mmHg. In summary, this methodology is capable of transforming PPG into an ABP pulse, which obtains better results when DI of the subjects is used, potentially useful in times when wireless devices are becoming more popular.

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

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

Blood Pressure Continuous Measurement through a Wearable Device: Development and Validation of a Cuffless Method.

The present study aims to develop and validate a cuffless method for blood pressure continuous measurement through a wearable device. The goal is achieved according to the time-delay method, with the guiding principle of the time relation it takes for a blood volume to travel from the heart to a peripheral site. Inversely proportional to the blood pressure, this time relation is obtained as the time occurring between the R peak of the electrocardiographic signal and a marker point on the photoplethysmographic wave. Such physiological signals are recorded by using L.I.F.E. Italia's wearable device, made of a sensorized shirt and wristband. A linear regression model is implemented to estimate the corresponding blood pressure variations from the obtained time-delay and other features of the photoplethysmographic wave. Then, according to the international standards, the model performance is assessed, comparing the estimates with the measurements provided by a certified digital sphygmomanometer. According to the standards, the results obtained during this study are notable, with 85% of the errors lower than 10 mmHg and a mean absolute error lower than 7 mmHg. In conclusion, this study suggests a time-delay method for continuous blood pressure estimates with good performance, compared with a reference device based on the oscillometric technique.

Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning.

Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we use this parameterization to train NNs with a larger PPG dataset and carry out a systematic evaluation of the predicted blood pressure. The analysis revealed a strong systematic increase of the prediction error towards less frequent BP values across NN architectures. Moreover, we tested different train/test set split configurations which underpin the importance of a careful subject-aware dataset assignment to prevent overly optimistic results. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply different personalization techniques and retrain our NNs with subject-specific data for both the PPG-only and rPPG case. Whilst the particular technique is less important, personalization reduces the prediction errors significantly.

Beat-to-Beat Continuous Blood Pressure Estimation Using Bidirectional Long Short-Term Memory Network.

Continuous blood pressure (BP) monitoring is important for patients with hypertension. However, BP measurement with a cuff may be cumbersome for the patient. To overcome this limitation, various studies have suggested cuffless BP estimation models using deep learning algorithms. A generalized model should be considered to decrease the training time, and the model reproducibility should be taken into account in multi-day scenarios. In this study, a BP estimation model with a bidirectional long short-term memory network is proposed. The features are extracted from the electrocardiogram, photoplethysmogram, and ballistocardiogram. The leave-one-subject-out (LOSO) method is incorporated to generalize the model and fine-tuning is applied. The model was evaluated using one-day and multi-day tests. The proposed model achieved a mean absolute error (MAE) of 2.56 and 2.05 mmHg for the systolic and diastolic BP (SBP and DBP), respectively, in the one-day test. Moreover, the results demonstrated that the LOSO method with fine-tuning was more compatible in the multi-day test. The MAE values of the model were 5.82 and 5.24 mmHg for the SBP and DBP, respectively.