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

Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation

One of the most effective vital signs of health conditions is blood pressure. It has such an impact that changes your state from completely relaxed to extremely unpleasant, which makes the task of blood pressure monitoring a main procedure that almost everyone undergoes whenever there is something wrong or suspicious with his/her health condition. The most popular and accurate ways to measure blood pressure are cuff-based, inconvenient, and pricey, but on the bright side, many experimental studies prove that changes in the color intensities of the RGB channels represent variation in the blood that flows beneath the skin, which is strongly related to blood pressure; hence, we present a novel approach to blood pressure estimation based on the analysis of human face video using hybrid deep learning models. We deeply analyzed proposed approaches and methods to develop combinations of state-of-the-art models that were validated by their testing results on the Vision for Vitals (V4V) dataset compared to the performance of other available proposed models. Additionally, we came up with a new metric to evaluate the performance of our models using Pearson's correlation coefficient between the predicted blood pressure of the subjects and their respiratory rate at each minute, which is provided by our own dataset that includes 60 videos of operators working on personal computers for almost 20 min in each video. Our method provides a cuff-less, fast, and comfortable way to estimate blood pressure with no need for any equipment except the camera of your smartphone.

Device-guided slow breathing with direct biofeedback of pulse wave velocity - acute effects on pulse arrival time and self-measured blood pressure

BACKGROUND

There isevidence that device-guided slow breathing using biofeedback acutely reduces blood pressure (BP) and pulse wave velocity [i.e. increased pulse arrival time (PAT)].

OBJECTIVES

The objectives of the study presented here were to test whether the results of changes observed in PAT in earlier studies are reproducible over 1 week and how changes in pulse wave velocity/PAT translate into absolute self-measured BP changes.

METHODS

Patients with a systolic BP 130-160 mmHg or treated essential hypertension (21 females/23 males) were trained to perform unattended device-guided slow breathing exercises for 10 min daily over 5 days. Furthermore, they were skilled to perform self-measurement of BP before and after the breathing exercise using a validated upper-arm device.

RESULTS

Office BP at screening [median (1, 3. Q)] was 137 (132, 142)/83 (79, 87) mmHg. We observed a significant ( P  < 0.05) increase in PAT of 5 ms (SD 12.5 ms) on average after 10 min of guided breathing and an additional 1 ms ( P  < 0.05, SD 8 ms) during the following 5 min of spontaneous breathing compared to baseline. PAT before the exercise remained constant over 5 days paralleled by constant self-measured BP before the exercise. Device-guided breathing was associated with a significant reduction of self-measured SBP of 5 mmHg ( P  < 0.01, SD 8 mmHg). Data furthermore demonstrated that these changes were highly reproducible over 1 week.

CONCLUSIONS

Device-guided slow breathing and biofeedback lead to reproducible and favorable changes (increase) in PAT and SBP (decrease).

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.

Continuous cuffless monitoring of arterial blood pressure via graphene bioimpedance tattoos

Continuous monitoring of arterial blood pressure (BP) in non-clinical (ambulatory) settings is essential for understanding numerous health conditions, including cardiovascular diseases. Besides their importance in medical diagnosis, ambulatory BP monitoring platforms can advance disease correlation with individual behaviour, daily habits and lifestyle, potentially enabling analysis of root causes, prognosis and disease prevention. Although conventional ambulatory BP devices exist, they are uncomfortable, bulky and intrusive. Here we introduce a wearable continuous BP monitoring platform that is based on electrical bioimpedance and leverages atomically thin, self-adhesive, lightweight and unobtrusive graphene electronic tattoos as human bioelectronic interfaces. The graphene electronic tattoos are used to monitor arterial BP for >300 min, a period tenfold longer than reported in previous studies. The BP is recorded continuously and non-invasively, with an accuracy of 0.2 ± 4.5 mm Hg for diastolic pressures and 0.2 ± 5.8 mm Hg for systolic pressures, a performance equivalent to Grade A classification.

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.

Slow-Paced Breathing: Influence of Inhalation/Exhalation Ratio and of Respiratory Pauses on Cardiac Vagal Activity

Slow-paced breathing has been shown to enhance the self-regulation abilities of athletes via its influence on cardiac vagal activity. However, the role of certain respiratory parameters (i.e., inhalation/exhalation ratio and presence of a respiratory pause between respiratory phases) still needs to be clarified. The aim of this experiment was to investigate the influence of these respiratory parameters on the effects of slow-paced breathing on cardiac vagal activity. A total of 64 athletes (27 female; Mage = 22, age range = 18–30 years old) participated in a within-subject experimental design. Participants performed six breathing conditions within one session, with a 5 min washout period between each condition. Each condition lasted 5 min, with 30 respiratory cycles, and each respiratory cycle lasted 10 s (six cycles per minute), with inhalation/exhalation ratios of 0.8, 1.0, 1.2; and with or without respiratory pauses (0.4 s) between respiratory phases. Results indicated that the root mean square of successive differences (RMSSD), a marker of cardiac vagal activity, was higher when exhalation was longer than inhalation. The presence of a brief (0.4 s) post-inhalation and post-exhalation respiratory pause did not further influence RMSSD. Athletes practicing slow-paced breathing are recommended to use an inhalation/exhalation ratio in which the exhalation phase is longer than the inhalation phase.

Non-invasive quantification of the effect of device-guided slow breathing with direct feedback to the patient to reduce blood pressure

OBJECTIVE

Slow breathing is a relaxation exercise recommended for lowering blood pressure (BP). Biofeedback may improve patient adherence and enhance BP lowering effects. Since the pulse arrival time (PAT) is inversely proportional to BP, it can be used to estimate BP changes.

APPROACH

In this pilot study, 30 patients (age 62.9 (SD 7.7) years, 11 F/19 M, Sys. BP 133.0 (SD 17.1) mmHg, Dia. BP 83.8 (SD 10.6) mmHg) performed a device-guided slow breathing exercise. PAT was measured by ECG and plethysmography and immediately presented to the patient, and respiratory sinus arrhythmia (RSA) was calculated retrospectively to measure the adherence to the instructed respiratory rate.

MAIN RESULTS

Respiratory rate was 13.6 (SD 1.9) bpm at baseline and 5.4 (SD 1.0) bpm during guided breathing. PAT continuously and progressively increased from 231.5 (SD 20.3) to 237.3 (SD 18.5) ms (p [Formula: see text] 0.001). The median deviation of RSA from the guided respiratory rate was 0.06 (IQR 0.19) bpm. In three patients, a deviation of > 0.20 bpm was detected, and two of them showed no increase in PAT. In total, 25 patients responded with increase in PAT.

SIGNIFICANCE

In this pilot study we have shown that biofeedback of PAT and RSA are feasible and can further improve motivation and adherence. Furthermore, we have shown that the exercise increased PAT, which indicates a reduction in BP. Due to its ease of use, this method is ideal for home use and self-monitoring.

Respiratory regulation & interactions with neuro-cognitive circuitry

It is increasingly being recognized that active control of breathing - a key aspect of ancient Vedic meditative practices, can relieve stress and anxiety and improve cognition. However, the underlying mechanisms of respiratory modulation of neurophysiology are just beginning to be elucidated. Research shows that brainstem circuits involved in the motor control of respiration receive input from and can directly modulate activity in subcortical circuits, affecting emotion and arousal. Meanwhile, brain regions involved in the sensory aspects of respiration, such as the olfactory bulb, are like-wise linked with wide-spread brain oscillations; and perturbing olfactory bulb activity can significantly affect both mood and cognition. Thus, via both motor and sensory pathways, there are clear mechanisms by which brain activity is entrained to the respiratory cycle. Here, we review evidence gathered across multiple species demonstrating the links between respiration, entrainment of brain activity and functional relevance for affecting mood and cognition. We also discuss further linkages with cardiac rhythms, and the potential translational implications for biorhythm monitoring and regulation in neuropsychiatric disorders.

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.

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.