Effect of respiration, talking and small body movements on blood pressure measurement

B3X: a novel efficient algorithm for accurate automated auscultatory blood pressure estimation

Objective.The auscultatory technique is still considered the most accurate method for non-invasive blood pressure (NIBP) measurement, although its reliability depends on operator's skills. Various methods for automated Korotkoff sounds analysis have been proposed for reliable estimation of systolic (SBP) and diastolic (DBP) blood pressures. To this aim, very complex methodologies have been presented, including some based on artificial intelligence (AI). This study proposes a relatively simple methodology, named B3X, to estimate SBP and DBP by processing Korotkoff sounds recordings acquired during an auscultatory NIBP measurement.Approach.The beat-by-beat change in morphology of adjacent Korotkoff sounds is evaluated via their cross-correlation. The time series of the beat-by-beat cross-correlation and its first derivative are analyzed to locate the timings of SBP and DBP values. Extensive tests were performed on a public database of 350 annotated measurements, and the performance was evaluated according to the BHS, AAMI/ANSI, and International Organization for Standardization (ISO) quality standards.Main results.The proposed approach achieved 'A' scores for SBP and DBP in the BHS grading system, and passed the quality tests of AAMI/ANSI and ISO standards. The B3X algorithm outperformed two well-established algorithms for oscillometric NIBP measurement in both SBP and DBP estimation. It also outperformed four AI-based algorithms in DBP estimation, while providing comparable performance for SBP, at the cost of a much lower computational burden. The full code of the B3X algorithm is provided in a public repository.Significance.The very good performances ensured by the proposed B3X algorithm, at a low computational cost and without the need for parameter training, support its direct implementation into clinical blood pressure (BP) monitoring devices. The results of this study pave the way for solving/overcoming the trade-off between the accuracy of the auscultatory technique and the objectivity of oscillatory measurements, by bringing an automated auscultatory BP measurement method in clinical practice.

Impact of oscillometric measurement artefacts in ambulatory blood pressure monitoring on estimates of average blood pressure and of its variability: a pilot study

OBJECTIVE

Ambulatory blood pressure monitoring (ABPM) plays an important role in the diagnosis of hypertension. However, methodological factors and the measurement conditions affect the results and may lead to incorrect classification of the patient. We performed a pilot study to evaluate the impact of oscillometric measurement artefacts on ABPM-derived variables.

METHODS

Four classes of artefacts have been detected: motor activity artefacts, cuff errors, cardiovascular arousals, and arrhythmias. The data consisted of uncorrected measurements (all data), corrected measurements (all artefact free data), and artefact affected data.

RESULTS

A total of 30 individuals (9 female/21 male), aged between 36 and 86 years, mean: 65.5 (standard deviation: 9.5) were included in the study. The average blood pressure (BP) was higher in artefacts-affected measurements compared the artefact-free measurements both for systolic (4.6 mmHg) and diastolic (1.3 mmHg) measurements. Further, artefact-affected systolic BP (SBP) was 6.4 mmHg higher than artefact-free measurements during daytime. Nocturnal measurements showed no artefact-depended differences. Individual comparisons yielded that 23% of the participants crossed the threshold for BP classification for either 24-h, daytime or nocturnal hypertension when comparing uncorrected and artefact-free measurements. Dipping classification changed within 24% of participants. BP variability was 21 and 12% higher for SPB and DBP, respectively, during daytime. These differences were even higher (27% for SBP and 21% for DPB) during night-time.

CONCLUSION

The study reveals that measurement artefacts are frequently present during cuff-based ABPM and do relevantly affect measurement outcome. Exclusion of measurement artefacts is a promising approach to improving cuff-based ABPM accuracy.

Sources of automatic office blood pressure measurement error: a systematic review

OBJECTIVE

Accurate and reliable blood pressure (BP) measurement is important for the prevention and treatment of hypertension. The oscillometric-based automatic office blood pressure measurement (AOBPM) is widely used in hospitals and clinics, but measurement errors are common in BP measurements. There is a lack of systematic review of the sources of measurement errors.

APPROACH

A systematic review of all existing research on sources of AOBPM errors. A search strategy was designed in six online databases, and all the literature published before October 2021 was selected. Those studies that used the AOBPM device to measure BP from the upper arm of subjects were included.

MAIN RESULTS

A total of 1365 studies were screened, and 224 studies were included in this final review. They investigated 22 common error sources with clinical AOBPM. Regarding the causes of BP errors, this review divided them into the following categories: the activities before measurement, patient's factors, measurement environment, measurement procedure, and device settings. 13 sources caused increased systolic and diastolic BP (SBP and DBP), 2 sources caused the decrease in SBP and DBP, only 1 source had no significant effect on BPs, and the other errors had a non-uniform effect (either increase or decrease in BPs). The error ranges for SBP and DBP were -14 to 33 mmHg and -6 to 19 mmHg, respectively.

SIGNIFICANCE

The measurement accuracy of AOBPM is susceptible to the influence of measurement factors. Interpreting BP readings need to be treated with caution in clinical measurements. This review made comprehensive evidence for the need for standardized BP measurements and provided guidance for clinical practitioners when measuring BP with AOBPM devices.

Effect of countermeasure bodygear on cardiac-vascular-respiratory coupling during 6-degree head-down tilt: An earth-based microgravity study

Prolonged exposure to microgravity causes physiological deconditioning in humans. Herein, a novel designed countermeasure gravitational load modulation bodygear has been developed to deal with the ill effects of the microgravity environment. The bodygear is designed to provide the wearer an axial loading from the shoulder to the feet that simulate Earth's gravity. The present study aims to evaluate the effect of bodygear on cardiac, vascular and respiratory systems during head-down tilt (HDT) microgravity analogue. In this, 30 healthy male subjects have volunteered and their average age, height and weight were 24.56 ± 3.87 yr, 168.4 ± 9.17 cm and 65.9 ± 10.51 kg respectively. The physiological signals such as electrocardiogram (ECG), blood pressure (BP) and respiration were recorded non-invasively using Biopac MP100. The signals were sampled at 1,000 Hz and processed using MATLAB 2018b. The signals were analysed in linear well as non-linear domains. The ECG and BP were used to derive R-R interval (RRI) and systolic blood pressure (SBP). The respiration time series (RSP) was derived by extracting R-peaks from the ECG signal and using these peaks to find the respiration amplitude. The non-linear domain analysis was used for the detection and quantification of information flow among the recorded signals. Repeated measure analysis of variance with Bonferroni post-hoc paired t-test was used for statistical analysis with the p < 0.05. The experimental results show that the 6-degree HDT activates the parasympathetic system and decreased the RRI effect on SBP (p = 0.005). Interestingly with the bodygear usage, the sympathetic system activated, mean RRI decreased (p = 0.018) and blood pressure increased (p = 0.031) as compared to baseline. Further, it was also observed that the effect of RRI on SBP (p = 0.029) and SBP on RRI (p = 0.012) was increased with bodygear as compared to HDT without bodygear. The conditional entropy technique aided in analyzing the effect of bodygear on information flow variation in the cardiovascular system of the human body.

Detection of Atrial Fibrillation Using a Home Blood Pressure Monitor

PURPOSE

Atrial fibrillation (AF) is the most common arrhythmia and is associated with an increased risk of complications. A screening test has the potential to prevent AF-related complications. This study investigated the diagnostic accuracy of an automated device for home blood pressure (BP) monitoring, which implements an algorithm for AF detection.

PATIENTS AND METHODS

A modified, automated oscillometric device for home BP monitoring (Omron BP785N (HEM-7321-Z), Omron Healthcare) with an AF detector was used to measure the BP in patients. During each BP measurements, the electrocardiogram (ECG) was recorded simultaneously. Simultaneous BP measurements and ECG recordings were obtained from 99 subjects.

RESULTS

Twenty out of 20 patients with atrial fibrillation were correctly recognized by the device and the device correctly identified 67 patients with sinus rhythm as "Not-AF". On the other hand, 12 patients with basic rhythm: sinus rhythm were incorrectly referred to as "atrial fibrillation". In summary, the device has a diagnostic accuracy of 87.88% with a sensitivity of 100% and a specificity of 84.8%. On the other hand, in 23 patients, the raw data of the device showed that a body movement occurred during the measurement of the blood pressure. If these subjects were excluded of the analysis, then the diagnostic accuracy of the device would be even better, namely 90.79%. The sensitivity would be 100% and the specificity 89.5%.

CONCLUSION

These data suggest that an automated device for home blood pressure has an excellent diagnostic accuracy for detecting an AF and could be used as a reliable screening test for early diagnosis of atrial fibrillation. Body movements have an impact of the accuracy and specificity of a blood pressure monitor.

An evidence-based appraisal of complementary and alternative medicine strategies for the management of hypertension

: Hypertension is a major risk factor for cardiovascular disease and all-cause mortality. Numerous antihypertensive medications and lifestyle changes have proven effective for the reduction of blood pressure (BP). Over the past few decades, the emergence of complementary and alternative medicine (CAM)-based strategies to lower BP have broadened the therapeutic armamentarium for hypertension. CAM is defined as a group of heterogeneous medical treatments that are used to enhance the effect of standard therapy, or, conversely, are implemented as an alternative to standard practice. The available body of evidence does substantiate the BP-lowering effects of certain CAM-based therapies in individuals with and without established hypertension. Collectively, alternative strategies for BP reduction have undergone less rigorous testing than traditional BP-lowering strategies and the lack of robust clinical data has greatly hampered the broad-scale adoption of CAM therapies into clinical practice. Despite these limitations, CAM-based therapies for the reduction of BP require consideration as they could offer substantial public health benefits given the high prevalence of hypertension in the general population. This article reviews some of the most promising CAM-based therapies for the reduction of BP and cardiovascular outcomes based on the current literature.

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.

Deep learning-based automatic blood pressure measurement: evaluation of the effect of deep breathing, talking and arm movement

Objectives: It is clinically important to evaluate the performance of a newly developed blood pressure (BP) measurement method under different measurement conditions. This study aims to evaluate the performance of using deep learning-based method to measure BPs and BP change under non-resting conditions.Materials and methods: Forty healthy subjects were studied. Systolic and diastolic BPs (SBPs and DBPs) were measured under four conditions using deep learning and manual auscultatory method. The agreement between BPs determined by the two methods were analysed under different conditions. The performance of using deep learning-based method to measure BP changes was finally evaluated.Results: There were no significant BPs differences between two methods under all measurement conditions (all p > .1). SBP and DBP measured by deep learning method changed significantly in comparison with the resting condition: decreased by 2.3 and 4.2 mmHg with deeper breathing (both p < .05), increased by 3.6 and 6.4 mmHg with talking, and increased by 5.9 and 5.8 mmHg with arm movement (all p < .05). There were no significant differences in BP changes measured by two methods (all p > .4, except for SBP change with deeper breathing).Conclusion: This study demonstrated that the deep learning method could achieve accurate BP measurement under both resting and non-resting conditions.Key messagesAccurate and reliable blood pressure measurement is clinically important. We evaluated the performance of our developed deep learning-based blood pressure measurement method under resting and non-resting measurement conditions.The deep learning-based method could achieve accurate BP measurement under both resting and non-resting measurement conditions.

Comparison of manual and automated auscultatory blood pressure during graded exercise among people with type 2 diabetes

Manual measurement of blood pressure (BP) during exercise testing is the recommended standard. Automated measurement of BP is an alternative method used during clinical exercise testing, but there is little data comparing manual and automated BP in this setting. The aim of this study was to determine the concordance between manual and automated BP during a standard clinical treadmill exercise test. 416 participants (66 ± 5 years; 54% male) completed a Bruce treadmill exercise test at baseline or follow-up within a clinical trial of participants with type 2 diabetes mellitus. Manual and automated BP were measured simultaneously at each exercise test stage. Manual BP was measured by a technician blinded to automated BP values (Tango+, Suntech). Concordance between manual and automated BP was assessed using mean differences and intraclass correlations (ICC). Concordance between manual and automated BP across all exercise stages was excellent for systolic BP (overall mean difference: 3 ± 11 mm Hg, P = .598; ICC = 0.964 [95% CI 0.942-0.977] and pulse pressure (overall mean difference: 2 ± 14 mm Hg, P = .595; ICC = 0.934 [95% CI 0.899-0.956]). Concordance between manual and automated diastolic BP across all exercise stages was moderate-to-good (overall mean difference: 1 ± 9 mm Hg, P = .905; ICC = 0.784 [95% CI 0.672-0.858]). Automated BP using the Tango + device is concordant with manual BP during early stages of a standard clinical exercise test. Thus, this automated method may be a suitable alternative to manual measurement of BP during clinical exercise testing.

Variation of the Korotkoff Stethoscope Sounds During Blood Pressure Measurement: Analysis Using a Convolutional Neural Network

Korotkoff sounds are known to change their characteristics during blood pressure (BP) measurement, resulting in some uncertainties for systolic and diastolic pressure (SBP and DBP) determinations. The aim of this study was to assess the variation of Korotkoff sounds during BP measurement by examining all stethoscope sounds associated with each heartbeat from above systole to below diastole during linear cuff deflation. Three repeat BP measurements were taken from 140 healthy subjects (age 21 to 73 years; 62 female and 78 male) by a trained observer, giving 420 measurements. During the BP measurements, the cuff pressure and stethoscope signals were simultaneously recorded digitally to a computer for subsequent analysis. Heartbeats were identified from the oscillometric cuff pressure pulses. The presence of each beat was used to create a time window (1 s, 2000 samples) centered on the oscillometric pulse peak for extracting beat-by-beat stethoscope sounds. A time-frequency two-dimensional matrix was obtained for the stethoscope sounds associated with each beat, and all beats between the manually determined SBPs and DBPs were labeled as "Korotkoff." A convolutional neural network was then used to analyze consistency in sound patterns that were associated with Korotkoff sounds. A 10-fold cross-validation strategy was applied to the stethoscope sounds from all 140 subjects, with the data from ten groups of 14 subjects being analyzed separately, allowing consistency to be evaluated between groups. Next, within-subject variation of the Korotkoff sounds analyzed from the three repeats was quantified, separately for each stethoscope sound beat. There was consistency between folds with no significant differences between groups of 14 subjects (P = 0.09 to P = 0.62). Our results showed that 80.7% beats at SBP and 69.5% at DBP were analyzed as Korotkoff sounds, with significant differences between adjacent beats at systole (13.1%, P = 0.001) and diastole (17.4%, P < 0.001). Results reached stability for SBP (97.8%, at sixth beat below SBP) and DBP (98.1%, at sixth beat above DBP) with no significant differences between adjacent beats (SBP P = 0.74; DBP P = 0.88). There were no significant differences at high-cuff pressures, but at low pressures close to diastole there was a small difference (3.3%, P = 0.02). In addition, greater within subject variability was observed at SBP (21.4%) and DBP (28.9%), with a significant difference between both (P < 0.02). In conclusion, this study has demonstrated that Korotkoff sounds can be consistently identified during the period below SBP and above DBP, but that at systole and diastole there can be substantial variations that are associated with high variation in the three repeat measurements in each subject.

IDENTIFYING DEEP BREATH EFFECT ON CARDIOVASCULAR SIGNALS USING CONDITIONAL ENTROPY: AN INFORMATION DOMAIN APPROACH

This quantitative study identifies the coupling changes occurring among cardiac (RR), vascular (SBP) and respiratory (RESP) signals during deep breathing. The deep breathing measures the dysfunction of the parasympathetic autonomic nervous system. The traditional methods based on cross-correlation and coherence analysis lack to measure nonlinear structures and unpredictability of physiological subsystems. Therefore, information domain coupling method based on conditional entropy is proposed to detect the coupling changes. Thirty healthy volunteers were examined for 5[Formula: see text]min at normal breathing and 5[Formula: see text]min during deep breathing (6[Formula: see text]cycles/min). The reduction in respiration rate detects a significant increase in information flow from RESP to RR, RESP to SBP and SBP to RR. The increased interaction from RESP to RR and RESP to SBP at reduced respiration rate indicates the enhancement of respiratory sinus arrhythmia that results in the activation of the parasympathetic nervous system. Also, the balanced cardiovascular interaction observed on normal breathing from RR to SBP disappears, but interaction occurring in baroreflex direction (SBP to RR) increases that helps in the reduction of blood pressure during deep breathing. This detected direction of information flow helps in identifying the coupling changes occurring during parasympathetic nerve activity.

Effect of respiratory pattern on automated clinical blood pressure measurement: an observational study with normotensive subjects

BACKGROUND

It has been reported that deep breathing could reduce blood pressures (BP) in general. It is also known that BP is decreased during inhalation and increased during exhalation. Therefore, the measured BPs could be potentially different during deep breathing with different lengths of inhalation and exhalation. This study aimed to quantitatively investigate the effect of different respiratory patterns on BPs.

METHODS

Forty healthy subjects (20 males and 20 females, aged from 18 to 60 years) were recruited. Systolic and diastolic BPs (SBP and DBP) were measured using a clinically validated automated BP device. There were two repeated measurement sessions for each subject. Within each session, eight BP measurements were performed, including 4 measurements during deep breathing with different respiratory patterns (Pattern 1: 4.5 s vs 4.5 s; Patter 2: 6 s vs 2 s; Pattern 3: 2 s vs 6 s; and Pattern 4: 1.5 s vs 1.5 s, respectively for the durations of inhalation and exhalation) and additional 4 measurements from 1 min after the four different respiratory patterns. At the beginning and end of the two repeated measurement sessions, there were two baseline BP measurements under resting condition.

RESULTS

The key experimental results showed that overall automated SBP significantly decreased by 3.7 ± 5.7 mmHg, 3.9 ± 5.2 mmHg, 1.7 ± 5.9 mmHg and 3.3 ± 5.3 mmHg during deep breathing, respectively for Patterns 1, 2, 3 and 4 (all p < 0.001 except p < 0.05 for Pattern 3). Similarly, the automated DBPs during deep breathing in pattern 1, 2 and 4 decreased by 3.7 ± 5.0 mmHg, 3.7 ± 4.9 mmHg and 4.6 ± 3.9 mmHg respectively (all p < 0.001, except in Pattern 3 with a decrease of 1.0 ± 4.3 mmHg, p = 0.14). Correspondingly, after deep breathing, automated BPs recovered back to normal with no significant difference in comparison with baseline BP (all p > 0.05, except for SBP in Pattern 4).

CONCLUSIONS

In summary, this study has quantitatively demonstrated that the measured automated BPs decreased by different amounts with all the four deep breathing patterns, which recovered back quickly after these single short-term interventions, providing evidence of short-term BP decrease with deep breathing and that BP measurements should be performed under normal breathing condition.

Significantly Reduced Blood Pressure Measurement Variability for Both Normotensive and Hypertensive Subjects: Effect of Polynomial Curve Fitting of Oscillometric Pulses

This study aimed to compare within-subject blood pressure (BP) variabilities from different measurement techniques. Cuff pressures from three repeated BP measurements were obtained from 30 normotensive and 30 hypertensive subjects. Automatic BPs were determined from the pulses with normalised peak amplitude larger than a threshold (0.5 for SBP, 0.7 for DBP, and 1.0 for MAP). They were also determined from cuff pressures associated with the above thresholds on a fitted curve polynomial curve of the oscillometric pulse peaks. Finally, the standard deviation (SD) of three repeats and its coefficient of variability (CV) were compared between the two automatic techniques. For the normotensive group, polynomial curve fitting significantly reduced SD of repeats from 3.6 to 2.5 mmHg for SBP and from 3.7 to 2.1 mmHg for MAP and reduced CV from 3.0% to 2.2% for SBP and from 4.3% to 2.4% for MAP (all P < 0.01). For the hypertensive group, SD of repeats decreased from 6.5 to 5.5 mmHg for SBP and from 6.7 to 4.2 mmHg for MAP, and CV decreased from 4.2% to 3.6% for SBP and from 5.8% to 3.8% for MAP (all P < 0.05). In conclusion, polynomial curve fitting of oscillometric pulses had the ability to reduce automatic BP measurement variability.

Sources of inaccuracy in the measurement of adult patients' resting blood pressure in clinical settings: a systematic review

BACKGROUND

To interpret blood pressure (BP) data appropriately, healthcare providers need to be knowledgeable of the factors that can potentially impact the accuracy of BP measurement and contribute to variability between measurements.

METHODS

A systematic review of studies quantifying BP measurement inaccuracy. Medline and CINAHL databases were searched for empirical articles and systematic reviews published up to June 2015. Empirical articles were included if they reported a study that was relevant to the measurement of adult patients' resting BP at the upper arm in a clinical setting (e.g. ward or office); identified a specific source of inaccuracy; and quantified its effect. Reference lists and reviews were searched for additional articles.

RESULTS

A total of 328 empirical studies were included. They investigated 29 potential sources of inaccuracy, categorized as relating to the patient, device, procedure or observer. Significant directional effects were found for 27; however, for some, the effects were inconsistent in direction. Compared with true resting BP, significant effects of individual sources ranged from -23.6 to +33 mmHg SBP and -14 to +23 mmHg DBP.

CONCLUSION

A single BP value outside the expected range should be interpreted with caution and not taken as a definitive indicator of clinical deterioration. Where a measurement is abnormally high or low, further measurements should be taken and averaged. Wherever possible, BP values should be recorded graphically within ranges. This may reduce the impact of sources of inaccuracy and reduce the scope for misinterpretations based on small, likely erroneous or misleading, changes.

Comparison of stethoscope bell and diaphragm, and of stethoscope tube length, for clinical blood pressure measurement

Objective

This study investigated the effect of stethoscope side and tube length on auscultatory blood pressure (BP) measurement.

Methods

Thirty-two healthy participants were studied. For each participant, four measurements with different combinations of stethoscope characteristics (bell or diaphragm side, standard or short tube length) were each recorded at two repeat sessions, and eight Korotkoff sound recordings were played twice on separate days to one experienced listener to determine the systolic and diastolic BPs (SBP and DBP). Analysis of variance was carried out to study the measurement repeatability between the two repeat sessions and between the two BP determinations on separate days, as well as the effects of stethoscope side and tube length.

Results

There was no significant paired difference between the repeat sessions and between the repeat determinations for both SBP and DBP (all P-values>0.10, except the repeat session for SBP using short tube and diaphragm). The key result was that there was a small but significantly higher DBP on using the bell in comparison with the diaphragm (0.66 mmHg, P=0.007), and a significantly higher SBP on using the short tube in comparison with the standard length (0.77 mmHg, P=0.008).

Conclusion

This study shows that stethoscope characteristics have only a small, although statistically significant, influence on clinical BP measurement. Although this helps understand the measurement technique and resolves questions in the published literature, the influence is not clinically significant.

Respiratory modulation of oscillometric cuff pressure pulses and Korotkoff sounds during clinical blood pressure measurement in healthy adults

Background

Accurate blood pressure (BP) measurement depends on the reliability of oscillometric cuff pressure pulses (OscP) and Korotkoff sounds (KorS) for automated oscillometric and manual techniques. It has been widely accepted that respiration is one of the main factors affecting BP measurement. However, little is known about how respiration affects the signals from which BP measurement is obtained. The aim was to quantify the modulation effect of respiration on oscillometric pulses and KorS during clinical BP measurement.

Methods

Systolic and diastolic BPs were measured manually from 40 healthy subjects (from 23 to 65 years old) under normal and regular deep breathing. The following signals were digitally recorded during linear cuff deflation: chest motion from a magnetometer to obtain reference respiration, cuff pressure from an electronic pressure sensor to derive OscP, and KorS from a digital stethoscope. The effects of respiration on both OscP and KorS were determined from changes in their amplitude associated with respiration between systole and diastole. These changes were normalized to the mean signal amplitude of OscP and KorS to derive the respiratory modulation depth. Reference respiration frequency, and the frequencies derived from the amplitude modulation of OscP and KorS were also calculated and compared.

Results

Respiratory modulation depth was 14 and 40 % for OscP and KorS respectively under normal breathing condition, with significant increases (both p < 0.05) to 16 and 49 % with deeper breathing. There was no statistically significant difference between the reference respiration frequency and those derived from the oscillometric and Korotkoff signals (both p > 0.05) during deep breathing, and for the oscillometric signal during normal breathing (p > 0.05).

Conclusions

Our study confirmed and quantified the respiratory modulation effect on the oscillometric pulses and KorS during clinical BP measurement, with increased modulation depth under regular deeper breathing.

Prevalence of pseudoresistant hypertension due to inaccurate blood pressure measurement

The prevalence of pseudoresistant hypertension (HTN) due to inaccurate BP measurement remains unknown. Triage BP measurements and measurements obtained at the same clinic visit by trained physicians were compared in consecutive adult patients referred for uncontrolled resistant HTN (RHTN). Triage BP measurements were taken by the clinic staff during normal intake procedures. BP measurements were obtained by trained physicians using the BpTRU (VSM Med Tech Ltd. Coquitlam, Canada) device. The prevalence of uncontrolled RHTN and differences in BP measurements were compared. Of 130 patients with uncontrolled RHTN, 33.1% (n = 43) were falsely identified as having uncontrolled RHTN based on triage BP measurements. The median (inter-quartile range) of differences in systolic BP between pseudoresistant and true resistant groups were 23 (17-33) mm Hg and 13 (6-21) mm Hg, respectively (P = .0001). The median (inter-quartile range) of differences in diastolic BP between the two groups were 12 (7-18) mm Hg and 8 (4-11) mm Hg, respectively (P = .001). Triage BP technique overestimated the prevalence of uncontrolled RHTN in approximately 33% of the patients emphasizing the importance of obtaining accurate BP measurements.

Does the position or contact pressure of the stethoscope make any difference to clinical blood pressure measurements: an observational study

This study aimed to investigate the effect of stethoscope position and contact pressure on auscultatory blood pressure (BP) measurement. Thirty healthy subjects were studied. Two identical stethoscopes (one under the cuff, the other outside the cuff) were used to simultaneously and digitally record 2 channels of Korotkoff sounds during linear cuff pressure deflation. For each subject, 3 measurements with different contact pressures (0, 50, and 100 mm Hg) on the stethoscope outside the cuff were each recorded at 3 repeat sessions. The Korotkoff sounds were replayed twice on separate days to each of 2 experienced listeners to determine systolic and diastolic BPs (SBP and DBP). Variance analysis was performed to study the measurement repeatability and the effect of stethoscope position and contact pressure on BPs. There was no significant BP difference between the 3 repeat sessions, between the 2 determinations from each listener, between the 2 listeners and between the 3 stethoscope contact pressures (all P > 0.06). There was no significant SBP difference between the 2 stethoscope positions at the 2 lower stethoscope pressures (P = 0.23 and 0.45), but there was a small (0.4 mm Hg, clinically unimportant) significant difference (P = 0.005) at the highest stethoscope pressure. The key result was that, DBP from the stethoscope under the cuff was significantly lower than that from outside the cuff by 2.8 mm Hg (P < 0.001, 95% confidence interval -3.5 to -2.1 mm Hg). Since it is known that the traditional Korotkoff sound method, with the stethoscope outside the cuff, tends to give a higher DBP than the true intra-arterial pressure, this study could suggest that the stethoscope position under the cuff, and closer to the arterial occlusion, might yield measurements closer to the actual invasive DBP.

Prediction of BP reactivity to talking using hybrid soft computing approaches

High blood pressure (BP) is associated with an increased risk of cardiovascular diseases. Therefore, optimal precision in measurement of BP is appropriate in clinical and research studies. In this work, anthropometric characteristics including age, height, weight, body mass index (BMI), and arm circumference (AC) were used as independent predictor variables for the prediction of BP reactivity to talking. Principal component analysis (PCA) was fused with artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS), and least square-support vector machine (LS-SVM) model to remove the multicollinearity effect among anthropometric predictor variables. The statistical tests in terms of coefficient of determination (R (2)), root mean square error (RMSE), and mean absolute percentage error (MAPE) revealed that PCA based LS-SVM (PCA-LS-SVM) model produced a more efficient prediction of BP reactivity as compared to other models. This assessment presents the importance and advantages posed by PCA fused prediction models for prediction of biological variables.

Effect of respiration on Korotkoff sounds and oscillometric cuff pressure pulses during blood pressure measurement

Blood pressure (BP) measurement accuracy depends on consistent changes in Korotkoff sounds (KorS) for manual measurement and oscillometric pulses for automated measurement, yet little is known about the direct effect of respiration on these physiological signals. The aim of this research was to quantitatively assess the modulation effect of respiration on Korotkoff sounds and oscillometric pulses. Systolic and diastolic blood pressures were measured manually from 30 healthy subjects (age 41 ± 12 years). Three static cuff pressure conditions were studied for two respiratory rates. Cuff pressure [with oscillometric pulses (OscP)], ECG, chest motion respiration [respiration signal (Resp), from magnetometer] and Korotkoff sounds (KorS, from digital stethoscope) were recorded twice for 20 s. The physiological data were evenly resampled. Respiratory frequency was calculated from Resp (f_R), OscP (f_O) and KorS (f_K) from peak spectral frequency. There was no statistically significant difference between f_R and f_O or f_K. Respiratory modulation was observed in all subjects. OscP amplitude modulation changed significantly between the two respiratory rates (p < 0.05) and between the three cuff pressures (p < 0.0001), and decreased significantly with decreasing cuff pressure (p < 0.05). The phase shift between Resp and modulation of OscP was statistically significant with respiratory rates (p < 0.05), but not with cuff pressures. It is accepted that BP in individuals is variable and that this relates to respiration; we now show that this respiration modulates oscillometric pulse and Korotkoff sound amplitudes from which BP is measured.

Finger microvascular responses to deep inspiratory gasp assessed and quantified using wavelet analysis

The physiological changes following a deep inspiratory gasp (DIG) manoeuvre have been described in the literature. However, the lack of a reliable signal processing technique to visualize and quantify these physiological changes has so far limited the applicability of the test to the clinical setting. The main aim of this study was to assess the feasibility of using wavelet analysis to quantify the pulse arrival time (PAT) and its changes during the DIG manoeuvre. Vascular responses were extracted from cardiac (electrocardiogram, ECG) and peripheral pulse (photoplethysmography, PPG) waveforms. Wavelet analysis characterized their cardiovascular responses in healthy adult subjects in the time-frequency space, and for the ECG-PPG inter-relationship in terms of the PAT. PAT showed a characteristic biphasic response to gasp, with an increase of 59 ± 59 ms (p = 0.001) compared to the maximum value reached during quiet breathing, and a decrease of -38 ± 55 ms (p < 0.01) compared to the minimum value during quiet breathing. The response measures were repeatable. This pilot study has successfully shown the feasibility of using wavelet analysis to characterize the cardiovascular waveforms and quantify their changes with DIG.