Non-Invasive measurement of blood pressure - Why we should look at BP traces rather than listen to Korotkoff sounds

Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives

The measurement of physiologic pressure helps diagnose and prevent associated health complications. From typical conventional methods to more complicated modalities, such as the estimation of intracranial pressures, numerous invasive and noninvasive tools that provide us with insight into daily physiology and aid in understanding pathology are within our grasp. Currently, our standards for estimating vital pressures, including continuous BP measurements, pulmonary capillary wedge pressures, and hepatic portal gradients, involve the use of invasive modalities. As an emerging field in medical technology, artificial intelligence (AI) has been incorporated into analyzing and predicting patterns of physiologic pressures. AI has been used to construct models that have clinical applicability both in hospital settings and at-home settings for ease of use for patients. Studies applying AI to each of these compartmental pressures were searched and shortlisted for thorough assessment and review. There are several AI-based innovations in noninvasive blood pressure estimation based on imaging, auscultation, oscillometry and wearable technology employing biosignals. The purpose of this review is to provide an in-depth assessment of the involved physiologies, prevailing methodologies and emerging technologies incorporating AI in clinical practice for each type of compartmental pressure measurement. We also bring to the forefront AI-based noninvasive estimation techniques for physiologic pressure based on microwave systems that have promising potential for clinical practice.

The Effect of Three Different Cuff/Arm Circumference Ratios Ranging over 80% on Cuff Blood Pressure Measurements

We aimed to explore whether the cuff/arm (C/A) circumference ratio within the suggested range (> 80%) affects the accuracy of mercury cuff blood pressure (BP) measurement (cuff BP) using intrabrachial BP (IABP) as a reference.A total of 253 patients aged 62.42 ± 9.70 years were included. After coronary angiography, the catheter in the right arm was gradually withdrawn toward the cubital fossa, and the IABP was continuously recorded. The cuff BP of the right arm was measured based on the artery blood flow using a special method similar to the traditional mercury method. The cuff was replaced using another C/A ratio after one minute, and the test was performed again. We used three different cuffs for each participant to meet the C/A ratios of 80%-84%, 85%-89%, and 90%-100%. We calculated the percentage deviation degree (DD) between the cuff BP and IABP values: DD = difference/IABP × 100%. The agreement between the values was evaluated using the Bland-Altman method.The IABP values were 138.52 ± 16.89/79.67 ± 9.81 mmHg. The DD of the systolic BP (SBP), with a ratio of 80%-84% (3.06%), was the smallest. The DD of the diastolic BP (DBP) was lowest at a ratio of 85%-89% (2.47%). Men and women had the lowest DD of the SBP at a C/A ratio of 80%-84% and the lowest DD of the DBP at a C/A ratio of 85%-89%. Regardless of whether the participants had coronary heart disease, the DD of the SBP at a C/A ratio of 80%-84% was the lowest, and the DD of the DBP at a C/A ratio of 85%-89% was the lowest.Even in the suggested range of > 80%, when the C/A ratio was 80%-84%, the difference in the SBP between the cuff and IABP was the lowest, but when the C/A ratio was 85%-89%, the difference in the DBP was the lowest.

Measuring blood pressure from Korotkoff sounds as the brachial cuff inflates on average provides higher values than when the cuff deflates

Objective. In this study, we test the hypothesis that if, as demonstrated in a previous study, brachial arteries exhibit hysteresis as the occluding cuff is deflated and fail to open until cuff pressure (CP) is well below true intra-arterial blood pressure (IABP). Approach Estimating systolic (SBP) and diastolic blood pressure (DBP) from the presence of Korotkoff sounds as CPincreasesmay eliminate these errors and give more accurate estimates of SBP relative to IABP readings.Main Results.In 63 subjects of varying age 45.4 ± 19.9 years (range 21-76 years), including 44 men (45.2 ± 19.5, range 21-76 years) and 19 women (45.6 ± 21.4, range 21-75 years), there was a significant (p< 0.0001) increase in SBP from 124.4 ± 15.7 to 129.2 ± 16.3 mmHg and a significant (p< 0.0001) increase in DBP from 70.2 ± 10.7 to 73.6 ± 11.5 mmHg. Of the 63 subjects, 59 showed a positive increase in SBP (1-19 mmHg) and 5 subjects showed a reduction (-5 to -1 mmHg). The average differences for SBP estimates derived as the cuff inflates and estimates derived as the cuff deflates were 4.9 ± 4.7 mmHg, not dissimilar to the differences observed between IABP and NIBP measurements. Although we could not develop multiparameter linear or nonlinear models to explain this phenomenon we have clearly demonstrated through analysis of variance test that both body mass index (BMI) and pulse wave velocity are implicated, supporting the hypothesis that the phenomenon is associated with age, higher BMI and stiffer arteries.Significance. The implications of this study are potentially profound requiring the implementation of a new paradigm for NIBP measurement and a revision of the international standards for their calibration.

An investigation of the individualized, two-point calibration method for cuffless blood pressure estimation using pulse arrival time: an historical perspective using the Casio BP-100 digital watch

BACKGROUND

The use of wearable cuffless blood pressure (BP) devices is becoming commercially prevalent with little published validation information. Most devices rely, at least in part, on the relationship between pulse arrival time (PAT) and BP, a theoretical fundamental relationship that was first commercially exploited in 1993 with the release of the Casio BP-100 digital watch.

OBJECTIVE

This study explored the PAT method of BP estimation in a commercial device where it first began, the Casio BP-100 (Model No. 900) digital watch, which employs an individualized, two-point calibration method. Device accuracy was determined by comparison to a conventional cuff-based BP device measurements.

METHODS

Twenty participants (11 female, 9 male) had BP measured using both devices at rest, during a 5-minute isometric hand-grip exercise and at 1-minute post-exercise.

RESULTS

Due to bidirectional scatter of BP estimation by the BP-100 device, there was no significant difference between the reference device and the BP-100. The devices showed poor correlation for both systolic BP (SBP) (R=0.36, p=0.13) and diastolic BP (DBP) (R=0.044, p=0.37). However, on average the watch was able to provide correct directional changes in SBP but not DBP with exercise.

CONCLUSIONS

Despite being an industry first, the Casio BP-100 watch employed a method that gives a great chance of accuracy: a two point, individualized calibration method - more detailed than calibration methods in more modern devices. The watch, on average across a cohort, provided some information on BP directional change but was uncorrelated with cuff-based BP measurement. If the utility of beat-by-beat BP estimation is to be utilized, limitations of this method need to be addressed.

Are Korotkoff Sounds Reliable Markers for Accurate Estimation of Systolic and Diastolic Pressure Using Brachial Cuff Sphygmomanometry?

It is well known that non-invasive blood pressure measurements significantly underestimate true systolic blood pressure (SBP), and overestimate diastolic blood pressure (DBP). The aetiology for these errors has not yet been fully established. This study aimed to investigate the accuracy of Korotkoff sounds for detection of SBP and DBP points as used in brachial cuff sphygmomanometry. Brachial cuff pressure and Korotkoff sounds were obtained in 11 patients (6 males: 69.0 ± 6.2 years, 5 females: 71.8 ± 5.5 years) undergoing diagnostic coronary angiography. K2 Korotkoff sounds were obtained by high-pass filtering (>20 Hz) the microphone-recorded signal to eliminate low frequency components. Analysis of the timing of K2 Korotkoff sounds relative to cuff pressure and intra-arterial pressure shows that the onset of K2 Korotkoff sounds reliably detect the start of blood flow under the brachial cuff and their termination, marks the cuff pressure closely coincident with DBP. We have made the critical observation that blood flow under the cuff does not begin when cuff pressure falls just below SBP as is conventionally assumed, and that the delay in the opening of the artery following occlusion, and the consequent delay in the generation of K2 Korotkoff sounds, may lead to significant errors in the determination of SBP of up to 24 mmHg. Our data suggest a potential role of arterial stiffness as a major component of the errors recorded, with underestimation of SBP much more significant for subjects with stiff arteries than for subjects with more compliant arteries.

A Novel Automated Blood Pressure Estimation Algorithm Using Sequences of Korotkoff Sounds

The use of automated non-invasive blood pressure (NIBP) measurement devices is growing, as they can be used without expertise, and BP measurement can be performed by patients at home. Non-invasive cuff-based monitoring is the dominant method for BP measurement. While the oscillometric technique is most common, a few automated NIBP measurement methods have been developed based on the auscultatory technique. Amongst artificial intelligence (AI) techniques, deep learning has received increasing attention in different fields due to its strength in data classification, and feature extraction problems. This paper proposes a novel automated AI-based technique for NIBP estimation from auscultatory waveforms (AWs) based on converting the NIBP estimation problem to a sequence-to-sequence classification problem. To do this, a sequence of segments was first formed by segmenting the AWs, and their corresponding decomposed detail, and approximation parts obtained by wavelet packet decomposition method, and extracting features from each segment. Then, a label was assigned to each segment, i.e. (i) between systolic, and diastolic segments, and (ii) otherwise, and a bidirectional long short term memory recurrent neural network (BiLSTM-RNN) was devised to solve the resulting sequence-to-sequence classification problem. Adopting a 5-fold cross-validation scheme, and using a data base of 350 NIBP recordings gave an average mean absolute error of 1.7±3.7 mmHg for systolic BP (SBP), and 3.4 ±5.0 mmHg for diastolic BP (DBP) relative to reference values. Based on the results achieved, and comparisons made with the existing literature, it is concluded that the proposed automated BP estimation algorithm based on deep learning methods, and auscultatory waveform brings plausible benefits to the field of BP estimation.

Artificial Intelligence Based Blood Pressure Estimation From Auscultatory and Oscillometric Waveforms: A Methodological Review

Cardiovascular disease is the number one cause of death globally, with elevated blood pressure (BP) being the single largest risk factor. Hence, BP is an important physiological parameter used as an indicator of cardiovascular health. The use of automated non-invasive blood pressure (NIBP) measurement devices is growing, as measurements can be taken by patients at home. While the oscillometric technique is most common, some automated NIBP measurement methods have been developed based on the auscultatory technique. By utilizing (relatively) large BP data annotated by experts, models can be trained using machine learning and statistical concepts to develop novel NIBP estimation algorithms. Amongst artificial intelligence (AI) techniques, deep learning has received increasing attention in different fields due to its strength in data classification and feature extraction problems. This paper reviews AI-based BP estimation methods with a focus on recent advances in deep learning-based approaches within the field. Various architectures and methodologies proposed todate are discussed to clarify their strengths and weaknesses. Based on the literature reviewed, deep learning brings plausible benefits to the field of BP estimation. We also discuss some limitations which can hinder the widespread adoption of deep learning in the field and suggest frameworks to overcome these challenges.

Blood Pressure Estimation Using Time Domain Features of Auscultatory Waveforms and GMM-HMM Classification Approach

This paper presents a novel method to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) from time domain features extracted from auscultatory waveforms (AWs) and using a Gaussian Mixture Models and Hidden Markov Model (GMM-HMM) classification approach. The three time domain features selected include the cuff pressure (CP), the energy of the Korotkoff pulses (KE), and the slope of the KE (SKE). The proposed GMM-HMM can effectively discover the latent structure in AW sequences and automatically learn such structures. The SBP and DBP points are then detected as the cuff pressures at which AW sequence changes its structure. We conclude that the proposed GMM-HMM estimation method is a very promising method improving the accuracy of automated non-invasive measurement of blood pressure.

Blood Pressure Estimation Using Time Domain Features of Auscultatory Waveforms and Deep Learning

This paper presents a novel method to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) from time domain features extracted on auscultatory waveforms (AWs) using a long short term memory (LSTM) recurrent neural network (RNN). The proposed LSTM-RNN can effectively discover the latent structure in AW sequences and automatically learn such structures. The SBP and DBP points are then detected as the cuff pressures at which AW sequence changes its structure. Our LSTM-RNN is a powerful technique for sequence learning and can be used in blood pressure estimation as an alternative way for replacing traditional approaches.

Novel methods of testing and calibration of oscillometric blood pressure monitors

We present a robust method for testing and calibrating the performance of oscillometric non-invasive blood pressure (NIBP) monitors, using an industry standard NIBP simulator to determine the characteristic ratios used, and to explore differences between different devices. Assuming that classical auscultatory sphygmomanometry provides the best approximation to intra-arterial pressure, the results obtained from oscillometric measurements for a range of characteristic ratios are compared against a modified auscultatory method to determine an optimum characteristic ratio, Rs for systolic blood pressure (SBP), which was found to be 0.565. We demonstrate that whilst three Chinese manufactured NIBP monitors we tested used the conventional maximum amplitude algorithm (MAA) with characteristic ratios Rs = 0.4624±0.0303 (Mean±SD) and Rd = 0.6275±0.0222, another three devices manufactured in Germany and Japan either do not implement this standard protocol or used different characteristic ratios. Using a reference database of 304 records from 102 patients, containing both the Korotkoff sounds and the oscillometric waveforms, we showed that none of the devices tested used the optimal value of 0.565 for the characteristic ratio Rs, and as a result, three of the devices tested would underestimate systolic pressure by an average of 4.8mmHg, and three would overestimate the systolic pressure by an average of 6.2 mmHg.