Smartphone-Based Blood Pressure Monitoring via the Oscillometric Finger Pressing Method: Analysis of Oscillation Width Variations Can Improve Diastolic Pressure Computation

Cuffless Blood Pressure Measurement: Where Do We Actually Stand?

Cuffless blood pressure (BP) measurement offers considerable potential for clinical practice but is a challenging technological field. Many are investigating pulse wave analysis with or without pulse arrival time in which machine learning is applied to pulsatile waveforms obtained with mobile devices (eg, wristbands, smartphones) to estimate BP. These methods generally require individual user calibration with cuff BP measurements or demographics (eg, age, sex). This calibration makes it difficult to evaluate the method's accuracy, and many studies claiming accuracy used inadequate testing procedures. Yet, publications and regulatory-cleared devices continue to rise, seemingly implying technological advancements. An update is provided on the flurry of activity in cuffless BP technologies over the last 2 to 3 years, covering the clinical need, the latest devices, recent publications based on pulse wave analysis and pulse arrival time, progress in developing validation standards for cuffless BP devices, and recent publications on other cuffless BP measurement principles. Despite the high volume of research and development, to date, there is no compelling evidence that pulse wave analysis and pulse arrival time can provide significant added value in BP measurement accuracy beyond the cuff BP or demographic data for calibration. Thus, it is reasonable to at least be skeptical of published and future studies on pulse wave analysis and pulse arrival time for cuffless BP measurement with uncertain testing procedures. It is important to focus on establishing robust validation standards for cuffless BP devices requiring individual user calibration and also pursuing cuffless and calibration-free BP measurement methodologies going forward.

Beat-to-Beat Oscillometric Blood Pressure Estimation: A Bayesian Approach With System Identification

OBJECTIVE

Our study aims to advance noninvasive blood pressure (BP) monitoring through the introduction of innovative beat-to-beat oscillometric BP estimation methods. We aim to overcome current device limitations by delivering continuous and accurate BP estimates, utilizing physiologically based mathematical models.

METHODS

We developed novel beat-to-beat oscillometric BP estimation methods based on physiologically grounded mathematical models of intra-arterial BP and the arterial system effect. Our approach includes a recursive Bayesian method for parameter estimation and a new system identification technique to refine initial parameter estimates. We tested our methods through simulations and real-world experiments involving 10 individuals.

RESULTS

Mean errors for systolic and diastolic BP were as low as -1.26 mmHg and 2.03 mmHg, respectively, with standard deviations of errors at 5.95 mmHg and 4.16 mmHg. Furthermore, our methods enabled the estimation of additional cardiovascular parameters such as heart rate, respiration rate, and mean arterial pressure.

CONCLUSION

Our novel beat-to-beat oscillometric BP estimation methods offer a significant advancement in noninvasive BP monitoring technology, addressing the limitations of current devices by providing continuous beat-to-beat BP estimates.

SIGNIFICANCE

Our approach represents a promising direction for improving the reliability and comprehensiveness of cardiovascular parameter estimation in noninvasive BP monitoring devices, facilitating more effective patient care and monitoring.

Wearable Blood Pressure Monitoring Devices: Understanding Heterogeneity in Design and Evaluation

OBJECTIVE

Rapid advances in cuffless blood pressure (BP) monitoring have the potential to radically transform clinical care for cardiovascular health. However, due to the large heterogeneity in device design and evaluation, it is difficult to critically and quantitatively evaluate research progress. In this two-part manuscript, we provide a principled way of describing and accounting for heterogeneity in device and study design.

METHODS

We first provide an overview of foundational elements and design principles of three critical aspects: 1) sensors and systems, 2) pre-processing and feature extraction, and 3) BP estimation algorithms. Then, we critically analyze the state-of-the-art methods via a systematic review.

RESULTS

First, we find large heterogeneity in study designs, making fair comparisons extremely challenging. Moreover, many study designs have data leakage and are underpowered. We suggest a first open-contribution BP estimation benchmark for standardization. Next, we observe that BP distribution in the study sample and the time between calibration and test in emerging personalized devices confound BP estimation error. We suggest accounting for these using a convenient metric coined "explained deviation". Finally, we complement this manuscript with a website, https://wearablebp.github.io, containing a bibliography, meta-analysis results, datasets, and benchmarks, providing a timely plaWorm to understand state-of-the-art devices.

CONCLUSION

There is large heterogeneity in device and study design, which should be carefully accounted for when designing, comparing, and contrasting studies.

SIGNIFICANCE

Our findings will allow readers to parse out the heterogeneous literature and move toward promising directions for safer and more reliable devices in clinical practice and beyond.

Photoplethysmography Features Correlated with Blood Pressure Changes

Blood pressure measurement is a key indicator of vascular health and a routine part of medical examinations. Given the ability of photoplethysmography (PPG) signals to provide insights into the microvascular bed and their compatibility with wearable devices, significant research has focused on using PPG signals for blood pressure estimation. This study aimed to identify specific clinical PPG features that vary with different blood pressure levels. Through a literature review of 297 publications, we selected 16 relevant studies and identified key time-dependent PPG features associated with blood pressure prediction. Our analysis highlighted the second derivative of PPG signals, particularly the b/a and d/a ratios, as the most frequently reported and significant predictors of systolic blood pressure. Additionally, features from the velocity and acceleration photoplethysmograms were also notable. In total, 29 features were analyzed, revealing novel temporal domain features that show promise for further research and application in blood pressure estimation.

Nonlinear Viscoelastic Modeling of Finger Arteries: Toward Smartphone-Based Blood Pressure Monitoring via the Oscillometric Finger Pressing Method

OBJECTIVE

Oscillometric finger pressing is a smartphone-based blood pressure (BP) monitoring method. Finger photoplethysmography (PPG) oscillations and pressure are measured during a steady increase in finger pressure, and an algorithm computes systolic BP (SP) and diastolic BP (DP) from the measurements. The objective was to assess the impact of finger artery viscoelasticity on the BP computation.

METHODS

Nonlinear viscoelastic models relating transmural pressure (finger BP - applied pressure) to PPG oscillations during finger pressing were developed. The output of each model to a measured transmural pressure input was fitted to measured PPG oscillations from 15 participants. A parametric sensitivity analysis was performed via model simulations to elucidate the viscoelastic effect on the derivative-based BP computation algorithm.

RESULTS

A Wiener viscoelastic model comprising a first-order transfer function followed by a static sigmoidal function fitted the measured PPG oscillations better than an elastic model containing only the static function (median (IQR) error of 30.5% (25.6%-34.0%) vs 50.9% (46.7%-53.7%); p<0.01). In Wiener model simulations, the derivative algorithm underestimated SP, especially with high pulse pressure and low transfer function cutoff frequency (i.e., greater viscoelasticity). The mean of the normalized PPG waveform at the maximum oscillation beat was found to correlate with the cutoff frequency (r = -0.8) and could thus possibly be used to compensate for viscoelasticity.

CONCLUSION

Finger artery viscoelasticity negatively impacts oscillometric BP computation algorithms but can potentially be compensated for using available measurements.

SIGNIFICANCE

These findings may help in converting smartphones into truly cuffless BP monitors for improving hypertension awareness and control.

A smartphone application toward detection of systolic hypertension in underserved populations

High systolic blood pressure (BP) is the most important modifiable risk factor for cardiovascular disease. Managing systolic hypertension is especially difficult in underserved populations wherein access to cuff BP devices is limited. We showed that ubiquitous smartphones without force sensing can be converted into absolute pulse pressure (PP) monitors. The concept is for the user to perform guided thumb and hand maneuvers with the phone to induce cuff-like actuation and allow built-in sensors to make cuff-like measurements for computing PP. We developed an Android smartphone PP application. The 'app' could be learned by volunteers and yielded PP with total error < 8 mmHg against cuff PP (N = 24). We also analyzed a large population-level database comprising adults less than 65 years old to show that PP plus other basic information can detect systolic hypertension with ROC AUC of 0.9. The smartphone PP app could ultimately help reduce the burden of systolic hypertension in underserved populations and thus health disparities.

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.

History and evolution of blood pressure measurement

Hypertension is the leading cause of morbidity and mortality worldwide. Hypertension mostly accompanies no symptoms, and therefore blood pressure (BP) measurement is the only way for early recognition and timely treatment. Methods for BP measurement have a long history of development and improvement. Invasive method via arterial cannulation was first proven possible in the 1800's. Subsequent scientific progress led to the development of the auscultatory method, also known as Korotkoff' sound, and the oscillometric method, which enabled clinically available BP measurement. However, hypertension management status is still poor. Globally, less than half of adults are aware of their hypertension diagnosis, and only one-third of them being treated are under control. Novel methods are actively investigated thanks to technological advances such as sensors and machine learning in addition to the clinical needs for easier and more convenient BP measurement. Each method adopts different technologies with its own specific advantages and disadvantages. Promises of novel methods include comprehensive information on out-of-office BP capturing dynamic short-term and long-term fluctuations. However, there are still pitfalls such as the need for regular calibration since most novel methods capture relative BP changes rather than an absolute value. In addition, there is growing concern on their accuracy and precision as conventional validation protocols are inappropriate for cuffless continuous methods. In this article, we provide a comprehensive overview of the past and present of BP measurement methods. Novel and emerging technologies are also introduced with respect to their potential applications and limitations.