Real-Time Blood Pressure Estimation From Force-Measured Ultrasound

Artificial intelligence-based evaluation of carotid artery compressibility via point-of-care ultrasound in determining the return of spontaneous circulation during cardiopulmonary resuscitation

AIM

This study introduces RealCAC-Net, an artificial intelligence (AI) system, to quantify carotid artery compressibility (CAC) and determine the return of spontaneous circulation (ROSC) during cardiopulmonary resuscitation.

METHODS

A prospective study based on data from a South Korean emergency department from 2022 to 2023 investigated carotid artery compressibility in adult patients with cardiac arrest using a novel AI model, RealCAC-Net. The data comprised 11,958 training images from 161 cases and 15,080 test images from 134 cases. RealCAC-Net processes images in three steps: TransUNet-based segmentation, the carotid artery compressibility measurement algorithm for improved segmentation and CAC calculation, and CAC-based classification from 0 (indicating a circular shape) to 1 (indicating high compression). The accuracy of the ROSC classification model was tested using metrics such as the dice similarity coefficient, intersection-over-union, precision, recall, and F1 score.

RESULTS

RealCAC-Net, which applied the carotid artery compressibility measurement algorithm, performed better than the baseline model in cross-validation, with an average dice similarity coefficient of 0.90, an intersection-over-union of 0.84, and a classification accuracy of 0.96. The test set achieved a classification accuracy of 0.96 and an F1 score of 0.97, demonstrating its efficacy in accurately identifying ROSC in cardiac arrest situations.

CONCLUSIONS

RealCAC-Net enabled precise CAC quantification for ROSC determination during cardiopulmonary resuscitation. Future research should integrate this AI-enhanced ultrasound approach to revolutionize emergency care.

Resonance sonomanometry for noninvasive, continuous monitoring of blood pressure

Cardiovascular disease is the leading cause of death worldwide. Existing methods for continuous, noninvasive blood pressure (BP) monitoring suffer from poor accuracy, uncomfortable form factors, or a need for frequent calibration, limiting their adoption. We introduce a new framework for continuous BP measurement that is noninvasive and calibration-free called resonance sonomanometry. The method uses ultrasound imaging to measure both the arterial dimensions and artery wall resonances that are induced by acoustic stimulation, which offers a direct measure of BP by a fully determined physical model. The approach and model are validated in vitro using arterial mock-ups and then in multiple arteries in human subjects. This approach offers the promise of robust continuous BP measurements, providing significant benefits for early diagnosis and treatment of cardiovascular disease.

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.

Contact pressure-guided wearable dual-channel bioimpedance device for continuous hemodynamic monitoring

Wearable devices for continuous monitoring of arterial pulse waves have the potential to improve the diagnosis, prognosis, and management of cardiovascular diseases. These pulse wave signals are often affected by the contact pressure between the wearable device and the skin, limiting the accuracy and reliability of hemodynamic parameter quantification. Here, we report a continuous hemodynamic monitoring device that enables the simultaneous recording of dual-channel bioimpedance and quantification of pulse wave velocity (PWV) used to calculate blood pressure (BP). Our investigations demonstrate the effect of contact pressure on bioimpedance and PWV. The pulsatile bioimpedance magnitude reached its maximum when the contact pressure approximated the mean arterial pressure of the subject. We employed PWV to continuously quantify BP while maintaining comfortable contact pressure for prolonged wear. The mean absolute error and standard deviation of the error compared to the reference value were determined to be 0.1 ± 3.3 mmHg for systolic BP, 1.3 ± 3.7 mmHg for diastolic BP, and -0.4 ± 3.0 mmHg for mean arterial pressure when measurements were conducted in the lying down position. This research demonstrates the potential of wearable dual-bioimpedance sensors with contact pressure guidance for reliable and continuous hemodynamic monitoring.

Development of a PPG-based hardware and software system deployable on elbow and thumb for real-time estimation of pulse transit time

Blood pressure (BP) is a vital parameter used by clinicians to diagnose issues in the human cardiovascular system. Cuff-based BP devices are currently the standard method for on-the-spot and ambulatory BP measurements. However, cuff-based devices are not comfortable and are not suitable for long-term BP monitoring. Many studies have reported a significant correlation between pulse transit time (PTT) with blood pressure. However, this relation is impacted by many internal and external factors which might lower the accuracy of the PTT method. In this paper, we present a novel hardware system consisting of two custom photoplethysmography (PPG) sensors designed particularly for the estimation of PTT. In addition, a software interface and algorithms have been implemented to perform a real-time assessment of the PTT and other features of interest from signals gathered between the brachial artery and the thumb. A preclinical study has been conducted to validate the system. Five healthy volunteer subjects were tested and the results were then compared with those gathered using a reference device. The analysis reports a mean difference among subjects equal to -3.75±7.28 ms. Moreover, the standard deviation values obtained for each individual showed comparable results with the reference device, proving to be a valuable tool to investigate the factors impacting the BP-PTT relationship.Clinical Relevance- The proposed system proved to be a feasible solution to detect blood volume changes providing good quality signals to be used in the study of BP-PTT relationship.

Advancement in the Cuffless and Noninvasive Measurement of Blood Pressure: A Review of the Literature and Open Challenges

Hypertension is a chronic condition that is one of the prominent reasons behind cardiovascular disease, brain stroke, and organ failure. Left unnoticed and untreated, the deterioration in a health condition could even result in mortality. If it can be detected early, with proper treatment, undesirable outcomes can be avoided. Until now, the gold standard is the invasive way of measuring blood pressure (BP) using a catheter. Additionally, the cuff-based and noninvasive methods are too cumbersome or inconvenient for frequent measurement of BP. With the advancement of sensor technology, signal processing techniques, and machine learning algorithms, researchers are trying to find the perfect relationships between biomedical signals and changes in BP. This paper is a literature review of the studies conducted on the cuffless noninvasive measurement of BP using biomedical signals. Relevant articles were selected using specific criteria, then traditional techniques for BP measurement were discussed along with a motivation for cuffless measurement use of biomedical signals and machine learning algorithms. The review focused on the progression of different noninvasive cuffless techniques rather than comparing performance among different studies. The literature survey concluded that the use of deep learning proved to be the most accurate among all the cuffless measurement techniques. On the other side, this accuracy has several disadvantages, such as lack of interpretability, computationally extensive, standard validation protocol, and lack of collaboration with health professionals. Additionally, the continuing work by researchers is progressing with a potential solution for these challenges. Finally, future research directions have been provided to encounter the challenges.

Automated Force-Coupled Ultrasound Method for Calibration-Free Carotid Artery Blood Pressure Estimation

We develop, automate and evaluate a calibration-free technique to estimate human carotid artery blood pressure from force-coupled ultrasound images. After acquiring images and force, we use peak detection to align the raw force signal with an optical flow signal derived from the images. A trained convolutional neural network selects a seed point within the carotid in a single image. We then employ a region-growing algorithm to segment and track the carotid in subsequent images. A finite-element deformation model is fit to the observed segmentation and force via a two-stage iterative non-linear optimization. The first-stage optimization estimates carotid artery wall stiffness parameters along with systolic and diastolic carotid pressures. The second-stage optimization takes the output parameters from the first optimization and estimates the carotid blood pressure waveform. Diastolic and systolic measurements are compared with those of an oscillometric brachial blood pressure cuff. In 20 participants, average absolute diastolic and systolic errors are 6.2 and 5.6 mm Hg, respectively, and correlation coefficients are r = 0.7 and r = 0.8, respectively. Force-coupled ultrasound imaging represents an automated, standalone ultrasound-based technique for carotid blood pressure estimation, which motivates its further development and expansion of its applications.

Recent Advances in Non-Invasive Blood Pressure Monitoring and Prediction Using a Machine Learning Approach

Blood pressure (BP) monitoring can be performed either invasively via arterial catheterization or non-invasively through a cuff sphygmomanometer. However, for conscious individuals, traditional cuff-based BP monitoring devices are often uncomfortable, intermittent, and impractical for frequent measurements. Continuous and non-invasive BP (NIBP) monitoring is currently gaining attention in the human health monitoring area due to its promising potentials in assessing the health status of an individual, enabled by machine learning (ML), for various purposes such as early prediction of disease and intervention treatment. This review presents the development of a non-invasive BP measuring tool called sphygmomanometer in brief, summarizes state-of-the-art NIBP sensors, and identifies extended works on continuous NIBP monitoring using commercial devices. Moreover, the NIBP predictive techniques including pulse arrival time, pulse transit time, pulse wave velocity, and ML are elaborated on the basis of bio-signals acquisition from these sensors. Additionally, the different BP values (systolic BP, diastolic BP, mean arterial pressure) of the various ML models adopted in several reported studies are compared in terms of the international validation standards developed by the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) for clinically-approved BP monitors. Finally, several challenges and possible solutions for the implementation and realization of continuous NIBP technology are addressed.

Real-Time Cuffless Continuous Blood Pressure Estimation Using 1D Squeeze U-Net Model: A Progress toward mHealth

Measuring continuous blood pressure (BP) in real time by using a mobile health (mHealth) application would open a new door in the advancement of the healthcare system. This study aimed to propose a real-time method and system for measuring BP without using a cuff from a digital artery. An energy-efficient real-time smartphone-application-friendly one-dimensional (1D) Squeeze U-net model is proposed to estimate systolic and diastolic BP values, using only raw photoplethysmogram (PPG) signal. The proposed real-time cuffless BP prediction method was assessed for accuracy, reliability, and potential usefulness in the hypertensive assessment of 100 individuals in two publicly available datasets: Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-I) and Medical Information Mart for Intensive Care (MIMIC-III) waveform database. The proposed model was used to build an android application to measure BP at home. This proposed deep-learning model performs best in terms of systolic BP, diastolic BP, and mean arterial pressure, with a mean absolute error of 4.42, 2.25, and 2.56 mmHg and standard deviation of 4.78, 2.98, and 3.21 mmHg, respectively. The results meet the grade A performance requirements of the British Hypertension Society and satisfy the AAMI error range. The result suggests that only using a short-time PPG signal is sufficient to obtain accurate BP measurements in real time. It is a novel approach for real-time cuffless BP estimation by implementing an mHealth application and can measure BP at home and assess hypertension.

A Review of Noninvasive Methodologies to Estimate the Blood Pressure Waveform

Accurate estimation of blood pressure (BP) waveforms is critical for ensuring the safety and proper care of patients in intensive care units (ICUs) and for intraoperative hemodynamic monitoring. Normal cuff-based BP measurements can only provide systolic blood pressure (SBP) and diastolic blood pressure (DBP). Alternatively, the BP waveform can be used to estimate a variety of other physiological parameters and provides additional information about the patient's health. As a result, various techniques are being proposed for accurately estimating the BP waveforms. The purpose of this review is to summarize the current state of knowledge regarding the BP waveform, three methodologies (pressure-based, ultrasound-based, and deep-learning-based) used in noninvasive BP waveform estimation research and the feasibility of employing these strategies at home as well as in ICUs. Additionally, this article will discuss the physical concepts underlying both invasive and noninvasive BP waveform measurements. We will review historical BP waveform measurements, standard clinical procedures, and more recent innovations in noninvasive BP waveform monitoring. Although the technique has not been validated, it is expected that precise, noninvasive BP waveform estimation will be available in the near future due to its enormous potential.

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. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 24 is June 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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.

Measurement of Blood Pressure by Ultrasound-The Applicability of Devices, Algorithms and a View in Local Hemodynamics

OBJECTIVE

Due to ongoing technical progress, the ultrasonic measurement of blood pressure (BP) as an alternative to oscillometric measurement (NIBP) or the continuous non-invasive arterial pressure method (CNAP) moves further into focus. The US method offers several advantages over NIBP and CNAP, such as deep tissue penetration and the utilization of different arterial locations.

APPROACH

Ten healthy subjects (six female, aged 30.9 ± 4.6 years) volunteered in our investigation. In the ultrasonic BP measurement, we differentiated between the directly measured (pulsatile diastolic and systolic vessel diameter) and indirectly calculated variables at three different artery locations on both arms, with two different ultrasound devices in the transversal and longitudinal directions of the transducer. Simultaneously, NIBP monitoring served as reference BP, while CNAP monitored the steady state condition of the arm under investigation. The Moens-Korteweg algorithm (MKE) and the algorithm of the working group of San Diego (SanD) were selected for the indirectly calculated ultrasonic BP data.

MAIN RESULTS

With US, we were able to measure the BP at each selected arterial position. Due to the investigation setup, we found small but significant interactions of the main effects. Bland and Altman analysis revealed that US-BP measurement was similar to NIBP, with superior accuracy when compared to the established CNAP method. In addition, US-BP measurement showed that the measurement accuracy of both arms can be regarded as identical. In a detailed comparison of the selected arterial vascular sections, systematic discrepancies between the right and left arm could be observed.

CONCLUSION

In our pilot study, we measured BP effectively and accurately by US using two different devices. Our findings suggest that ultrasonic BP measurement is an adequate alternative for live and continuous hemodynamic monitoring.

CNN-LSTM Prediction Method for Blood Pressure Based on Pulse Wave

Cardiovascular disease (CVD), which seriously threatens human health, can be prevented by blood pressure (BP) measurement. However, convenient and accurate BP measurement is a vital problem. Although the easily-collected pulse wave (PW)-based methods make it possible to monitor BP at all times and places, the current methods still require professional knowledge to process the medical data. In this paper, we combine the advantages of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to propose a CNN-LSTM BP prediction method based on PW data. In detailed, CNN first extract features from PW data, and then the features are input into LSTM for further training. The numerical results based on real-life data sets show that the proposed method can achieve high predicted accuracy of BP while saving training time. As a result, CNN-LSTM can achieve convenient BP monitoring in daily health.

Variation of Shear Wave Elastography With Preload in the Thyroid: Quantitative Validation

OBJECTIVES

Thyroid shear wave elastography (SWE) has been shown to have advantages compared to biopsy or other imaging modalities in the evaluation of thyroid nodules. However, studies show variability in its assessment. The objective of this study was to evaluate whether stiffness measurements of the normal thyroid, as estimated by SWE, varied due to preload force or the pressure applied between the transducer and the patient.

METHODS

In this study, a measurement system was attached to the ultrasound transducer to measure the applied load. Shear wave elastographic measurements were obtained from the left lobe of the thyroid at applied transducer forces between 2 and 10 N. A linear mixed-effects model was constructed to quantify the association between the preload force and stiffness while accounting for correlations between repeated measurements within each participant. The preload force effect on elasticity was modeled by both linear and quadratic terms to account for a possible nonlinear association between these variables.

RESULTS

Nineteen healthy volunteers without known thyroid disease participated in the study. The participants had a mean age ± SD of 36 ± 8 years; 74% were female; 74% had a normal body mass index; and 95% were white non-Hispanic/Latino. The estimated elastographic value at a 2-N preload force was 16.7 kPa (95% confidence interval, 14.1-19.3 kPa), whereas the value at 10 N was 29.9 kPa (95% confidence interval, 24.9-34.9 kPa).

CONCLUSIONS

The preload force was significantly and nonlinearly associated with SWE estimates of thyroid stiffness. Quantitative standardization of preload forces in the assessment of thyroid nodules using elastography is an integral factor for improving the accuracy of thyroid nodule evaluation.

Non-Invasive Evaluation of a Carotid Arterial Pressure Waveform Using Motion-Tolerant Ultrasound Measurements During the Valsalva Maneuver

This work details the non-invasive evaluation of a carotid arterial blood pressure (ABP) waveform during the Valsalva maneuver. Unfocused and wide acoustic beams are insonated on the carotid artery to achieve motion-tolerant measurements with a simple two-element ultrasound scanner. Arterial flow and distension waveforms are reliably estimated from spectral Doppler and M-mode ultrasound images whose qualities are consistently maintained in different phases of the maneuver despite possible displacements of the artery. A local pulse wave velocity is estimated using a flow-area method, and it is then combined with the distension waveform to produce the ABP waveform. Human subject validation on seven healthy subjects shows that the bias in pulse pressure estimates across subjects is 0.47 ± 13.1 mmHg. The average root mean square deviations of the ultrasonically measured waveform across subjects is 10.1 ± 2.43 mmHg, excluding the strain phase of the Valsalva maneuver, and 17.7 ± 6.30 mmHg in all phases. The mean correlation coefficient between the ultrasonically measured and reference waveform is calculated to be 0.92 ± 0.04 across subjects. Detailed morphological features and their changes across different phases are observed as reported. This uninterrupted central ABP waveform monitoring under hemodynamics changes supports the idea of a novel stress test to evaluate the health and dynamics of the cardiovascular system at a spot check in clinical settings.

An Instrumented Ultrasound Probe for Shear Wave Elastography With Uneven Force Distribution

An instrumented ultrasound probe system is designed to estimate the distribution of pressure applied across the probe face during image acquisition. The pressure distribution is used to investigate the effects of varying, and non-uniform, pressure distributions on shear wave elastography measurements in phantoms and ex vivo samples. Pressure distribution has a notable effect on shear wave elastography of ex vivo samples. The gradient in applied pressure across the probe face is mirrored in the gradient of elasticity measurements across the ultrasound elastogram image.

Multi-Sensor Fusion Approach for Cuff-Less Blood Pressure Measurement

Ambulatory blood pressure (BP) provides valuable information for cardiovascular risk assessment. The present cuff-based devices are intrusive for long-term BP monitoring, whereas cuff-less BP measurement methods based on pulse transit time or multi-parameter are inferior in robustness and reliability by using electrocardiogram (ECG) and photoplethysmogram signals. This study examined a multi-sensor fusion-based platform and algorithm for systolic BP (SBP), mean arterial pressure (MAP), and diastolic BP (DBP) estimation. The proposed multi-sensor platform was comprised of one ECG sensor and two pulse pressure wave sensors for simultaneous signal collection. After extracting 35 features from the collected signals, a weakly supervised feature selection method was proposed for dimension reduction because the reference oscillometric technique-based BP are intermittent and can be redeemed as coarse-grained labels. BP models were then established using a multi-instance regression algorithm. A total of 85 participants including 17 hypertensive and 12 hypotensive patients were enrolled. Experimental results showed that the proposed approach exhibited good accuracy for diverse population with an estimation error of 1.62 ± 7.76 mmHg for SBP, 1.53 ± 6.03 mmHg for MAP, and 1.49 ± 5.52 for DBP, which complied with the association for the advancement of medical instrumentation standards in BP estimation. Moreover, the estimation accuracy is with random daily fluctuations rather than long-term degradation through a maximum two-month follow-up period indicated good robustness performance. These results suggest that the proposed approach is with high reliability and robustness and thus provides a novel insight for cuff-less BP measurement.