Paced Deep Breathing Research Articles

Model-based assessment of cardiopulmonary autonomic regulation in paced deep breathing.

Autonomic dysfunction can lead to many physical and psychological diseases. The assessment of autonomic regulation plays an important role in the prevention, diagnosis, and treatment of these diseases. A physiopathological mathematical model for cardiopulmonary autonomic regulation, namely Respiratory-Autonomic-Sinus (RSA) regulation Model, is proposed in this study. A series of differential equations are used to simulate the whole process of RSA phenomenon. Based on this model, with respiration signal and ECG signal simultaneously acquired in paced deep breathing scenario, we manage to obtain the cardiopulmonary autonomic regulation parameters (CARP), including the sensitivity of respiratory-sympathetic nerves and respiratory-parasympathetic nerves, the time delay of sympathetic, the sensitivity of norepinephrine and acetylcholine receptor, as well as cardiac remodeling factor by optimization algorithm. An experimental study has been conducted in healthy subjects, along with subjects with hypertension and coronary heart disease. CARP obtained in the experiment have shown their clinical significance.

One-Minute Deep Breathing Assessment and its Relationship to 24-Hour HRV Measurements

Heart rate variability (HRV), the change in the time intervals between adjacent heartbeats, is an emergent property of interdependent regulatory systems that operates on different time scales to adapt to environmental and psychological challenges. Low age-adjusted HRV has been confirmed as a strong, independent predictor of future health problems in both healthy people and patients with a wide range of diseases that correlate with all-cause mortality. Twenty-four–hour HRV recordings are considered the gold standard and have greater predictive power on health risk than short-term recordings. However, it is not always practical or cost effective to obtain 24-hour HRV recordings, and short-term recordings have been widely used in research and clinical applications for many years. This article will report on the first in a series of research investigations on short-term HRV assessments. The first study examined the correlations between a 10-minute resting state, a 1-minute paced deep breathing protocol, response to handgrip, and 24-hour HRV measures in 28 healthy individuals. Based on the results of the initial study, the primary study examined the correlations between the 1-minute paced deep breathing assessment and 24-hour measures in a general population of 805 individuals. Overall, the findings from the studies suggested that the 1-minute paced deep breathing assessments were highly correlated with 24-hour measures of vagally mediated HRV and very-low-frequency power. The findings from this study suggest that the 1-minute paced deep breathing protocol is an ideal short-term assessment that can be used in a health risk screening context. When low values are observed, it is recommended that a 24-hour assessment be conducted.

Safety and Tolerability of an Innovative Virtual Reality-Based Deep Breathing Exercise in Concussion Rehabilitation: A Pilot Study

ABSTRACT Objective To examine the safety and tolerability of a virtual reality-based deep breathing exercise for children and adolescents who are slow to recover from concussion. Methods: Fifteen participants (ages 11 to 22; mean = 16.9 years) were recruited from a specialty concussion clinic within a tertiary care medical center. Participants completed a 5-min paced deep breathing exercise administered via a virtual reality headset. Results: Nearly all participants (93.3%) reported the experience was either positive or extremely positive. No participants reported significant discomfort or discontinued the exercise. Three participants reported a mild increase in headache, dizziness, or nausea. Participants reported significant decreases in stress (r =.57), tension (r =.73), fatigue (r =.73), and confusion (r =.67), with large effect sizes, following the deep breathing exercise. Conclusion: A brief, virtual reality-based deep breathing exercise is worthy of additional study as a rehabilitation component for children and adolescents with prolonged concussion recoveries.

Feasibility of Using a Single Heart Rate-Based Measure for Real-time Feedback in a Voluntary Deep Breathing App for Children: Data Collection and Algorithm Development.

BackgroundDeep diaphragmatic breathing, also called belly breathing, is a popular behavioral intervention that helps children cope with anxiety, stress, and their experience of pain. Combining physiological monitoring with accessible mobile technology can motivate children to comply with this intervention through biofeedback and gaming. These innovative technologies have the potential to improve patient experience and compliance with strategies that reduce anxiety, change the experience of pain, and enhance self-regulation during distressing medical procedures.ObjectiveThe aim of this paper was to describe a simple biofeedback method for quantifying breathing compliance in a mobile smartphone app.MethodsA smartphone app was developed that combined pulse oximetry with an animated protocol for paced deep breathing. We collected photoplethysmogram data during spontaneous and subsequently paced deep breathing in children. Two measures, synchronized respiratory sinus arrhythmia (RSAsync) and the corresponding relative synchronized inspiration/expiration heart rate ratio (HR-I:Esync), were extracted from the photoplethysmogram.ResultsData collected from 80 children aged 5-17 years showed a positive RSAsync effect in all participants during paced deep breathing, with a median (IQR; range) HR-I:Esync ratio of 1.26 (1.16-1.35; 1.01-1.60) during paced deep breathing compared to 0.98 (0.96-1.02; 0.82-1.18) during spontaneous breathing (median difference 0.25, 95% CI 0.23-0.30; P<.001). The measured HR-I:Esync values appeared to be independent of age.ConclusionsAn HR-I:Esync level of 1.1 was identified as an age-independent threshold for programming the breathing pattern for optimal compliance in biofeedback.

Effect of a neck compression collar on cardiorespiratory and cerebrovascular function in postural orthostatic tachycardia syndrome (POTS).

Postural orthostatic tachycardia syndrome (POTS) is accompanied by reduced brain blood flow, autonomic dysfunction, and orthostatic intolerance. We hypothesized that wearing a neck compression collar would attenuate orthostatic symptoms, increase brain blood flow, and influence autonomic reflexes. Ten participants with POTS (9 women, age: 36 ± 10) underwent two trials of supine rest, paced deep breathing (6 breaths/min), Valsalva maneuver (40 mmHg for 15 s), and 70° upright tilt. For one trial, participants wore a neck compression device (Q30 Innovations). Blood pressure, heart rate (HR), brain blood flow velocity, stroke volume, respiratory rate, and end-tidal gases were continuously measured. The Vanderbilt Orthostatic Symptom Score was compiled at the end of tilt. The use of the collar reduced the orthostatic symptom score of participants with POTS during upright tilt (26.9 ± 12.5 to 18.7 ± 13.1, P = 0.04). Collar compression in the supine condition reduced the low-frequency domain of HR variability (60 ± 18 to 51 ± 23 normalized units, P = 0.04) and increased the change in HR (15 ± 5 to 17 ± 6 bpm, P = 0.02) and E:I ratio (1.2 ± 0.1 to 1.3 ± 0.1, P = 0.01) during paced deep breathing. Throughout tilt, wearing the collar reduced respiratory rate (baseline: 13 ± 3 to 12 ± 4 breath/min; tilt: 18 ± 5 to 15 ± 5 breath/min; main effect of collar P = 0.048), end-tidal oxygen (baseline: 115 ± 5 to 112 ± 5 mmHg; tilt: 122 ± 10 to 118 ± 11 mmHg; main effect of collar P = 0.026). In participants with POTS, wearing the Q-collar reduced orthostatic symptoms, increased the HR response to deep breathing, and decreased resting ventilation.NEW & NOTEWORTHY We found that using a neck compression collar alleviated orthostatic symptoms in upright posture in participants with postural orthostatic tachycardia syndrome (POTS). This could be due to compression of the baroreceptors and subsequent changes in autonomic function. Indeed, we observed increased heart rate responsiveness to paced deep breathing and reductions of respiratory rate and end-tidal O2 (suggesting reduced ventilation). Further, wearing the collar reduced mean blood velocity in the brain during Valsalva perhaps due to higher brain blood volume.

A Hyperdimensional Computing Framework for Analysis of Cardiorespiratory Synchronization During Paced Deep Breathing

Autonomic function during deep breathing (DB) is normally scored based on the assumption that the heart rate is synchronized with the breathing. We have observed individuals with subtle arrhythmias during DB, where an autonomic function cannot be evaluated. This paper presents a novel method for analyzing cardiorespiratory synchronization: feature-based analysis of the similarity between heart rate and respiration using the principles of hyperdimensional computing. Heart rate and respiration signals were modeled using Fourier series analysis. Three feature variables were derived and mapped to binary vectors in a high-dimensional space. Using both synthesized data and recordings from patients/healthy subjects, the similarity between the feature vectors was assessed using Hamming distance (high-dimensional space), Euclidean distance (original space), and with a coherence-based index. Methods were evaluated via the classification of the similarity indices into three groups. The distance-based methods achieved good separation of signals into classes with different degrees of cardiorespiratory synchronization, also providing identification of patients with low cardiorespiratory synchronization but high values of conventional DB scores. Moreover, binary high-dimensional vectors allowed an additional analysis of the obtained Hamming distance. Feature-based similarity analysis using hyperdimensional computing is capable of identifying signals with low cardiorespiratory synchronization during DB due to arrhythmias. Vector-based similarity analysis could be applied to other types of feature variables than based on spectral analysis. The proposed methods for robustly assessing cardiorespiratory synchronization during DB facilitate the identification of individuals where the evaluation of the autonomic function is problematic or even impossible, thus increasing the correctness of the conventional DB scores.

Exploring a 1-Minute Paced Deep-Breathing Measurement of Heart Rate Variability as Part of a Workers\u2019 Health Assessment

Low heart rate variability (HRV) is related to health problems that are known reasons for sick-leave or early retirement. A 1-minute-protocol could allow large scale HRV measurement for screening of health problems and, potentially, sustained employability. Our objectives were to explore the association of HRV with measures of health. Cross-sectional design with 877 Dutch employees assessed during a Workers’ Health Assessment. Personal and job characteristics, workability, psychological and mental problems, and lifestyle were measured with questionnaires. Biometry was measured (BMI, waist circumference, blood pressure, glucose, cholesterol). HRV was assessed with a 1-minute paced deep-breathing protocol and expressed as mean heart rate range (MHRR). A low MHRR indicates a higher health risk. Groups were classified age adjusted for HRV and compared. Spearman correlations between raw MHRR and the other measures were calculated. Significant univariable correlations (p < 0.05) were entered in a linear regression model to explore the multivariable association with MHRR. Age, years of employment, BMI and waist circumference differed significantly between HRV groups. Significant correlations were found between MHRR and age, workability, BMI, waist circumference, cholesterol, systolic and diastolic blood-pressure and reported physical activity and alcohol consumption. In the multivariable analyses 21.1% of variance was explained: a low HRV correlates with aging, higher BMI and higher levels of reported physically activity. HRV was significantly associated with age, measures of obesity (BMI, waist circumference), and with reported physical activity, which provides a first glance of the utility of a 1-minute paced deep-breathing HRV protocol as part of a comprehensive preventive Workers’ Health Assessment.

One-Minute Deep Breathing Assessment and its Relationship to 24-h Heart Rate Variability Measurements

Background: Heart rate variability (HRV), the change in the time intervals between successive pairs of heartbeats, is influenced by interdependent regulatory systems operating over different time scales to adapt to psychological challenges and environmental demands. Low ageadjusted HRV is predictive of upcoming health challenges in healthy people as well as a wide range of diseases in patients and correlates with allcause mortality. 24h HRV recordings are considered the “gold standard” and have greater predictive power on health risk than shortterm recordings. However, it is not typically costeffective or practical to acquire 24h HRV recordings. This has led to the growing use of shortterm recordings in research and clinical assessments. Objective: The first study examined the correlations between a 10min restingstate period, a 1min paced deep breathing protocol, response to handgrip, and 24h HRV measures in 28 healthy individuals. Based on the results of the initial study, the primary study examined the correlations between the 1min paced deep breathing assessment and 24h measures in a general population of 805 individuals. Results: The highest correlations for the HRV variables were with the vagally mediated sources of HRV. The 1min paced deep breathing was positively correlated with 24h highfrequency power (r = 0.60, P &lt; 0.01), root mean square of successive difference (r = 0.62, P &lt; 0.01), lowfrequency (LF) power (r = 0.64, P &lt; 0.01), veryLF power (r = 0.57, P &lt; 0.01) total power (r = 0.42, P &lt; 0.01), standard deviation of normaltonormal interval (SDNN) index (r = 0.59, P &lt; 0.01), and SDNN (r = 0.41, P &lt; 0.01). Conclusions: The findings from this study suggest that the 1min paced deep breathing protocol is an ideal shortterm assessment that can be used in a health risk screening context. When low values are observed, it is recommended that a 24h assessment be conducted.

Normal scores of deep breathing tests: beware of dysrhythmia in transthyretin amyloidosis

Background: The heart rate (HR) response to paced deep breathing (DB) is a common test of cardiac autonomic function, where high heart rate variability (HRV) is considered to reflect normal autonomic function. We evaluated the DB test in patients with hereditary transthyretin amyloid (ATTRm) amyloidosis, where autonomic dysregulation and atrial arrhythmias are common.Methods: Paced DB was performed during one minute (six breaths/min) in 165 recordings in adult ATTRm amyloidosis patients with the TTR Val30Met mutation, 42 hypertrophic cardiomyopathy (HCM) patients and 211 healthy subjects. HRV was scored by traditional DB indices and by a novel regularity index, estimating the fraction of the HRV that was coherent with the breathing pattern.Results: Twenty per cent of ATTRm amyloidosis patients presented with age-adjusted HRV scores within normal limits but poor regularity due to subtle atrial arrhythmias and cardiac conduction disturbances. Forty-seven per cent of ATTRm amyloidosis patients presented with HRV scores below normal limits, whereas HCM patients presented with higher HRV than ATTRm amyloidosis patients.Conclusions: Reduced HRV is common in ATTRm amyloidosis patients during DB, however, autonomic function cannot be evaluated in patients presenting with the combination of “normal” scores and low regularity, since their HR responses often reflects dysrhythmias.

Reproducibility of hypocapnic cerebrovascular reactivity measurements using BOLD fMRI in combination with a paced deep breathing task

It has recently been proposed that hypocapnic cerebrovascular reactivity (CVR) can be assessed by measuring the blood oxygenation level dependent (BOLD) response to paced deep breathing (PDB) tasks inducing mild hypocapnia and vasoconstriction. In this work, we aim to assess the test–retest reproducibility and inter-subject variability of BOLD CVR measurements obtained using a PDB task and different methods to analyse the associated BOLD signal. The respiratory protocol consisted of alternating 40s of PDB with normal free breathing; expired CO2 pressure levels (PETCO2) were continuously monitored. CVR was quantified using either a timecourse curve analysis (TCA) approach, where the magnitude of response peaks is emphasized, or general linear modelling (GLM) including optimisation of the BOLD response latencies. The GLM fit was carried out using two types of response regressors: one that was computed as the convolution of PETCO2 traces with a gamma function and another that consisted of the convolution of PDB paradigm blocks with a physiological model of the respiratory response. Haemodynamic response latencies were optimised either on a voxel basis or for the whole imaging region. We found that the GLM method based on PDB task or PETCO2 traces and voxelwise optimisation of response latencies provided the most reproducible measures of CVR. For the average grey matter CVR, the inter-subject coefficient of variation (CVinter) / intra-subject coefficient of variation (CVintra) / intra-class correlation coefficient (ICC) were 20%/8%/0.8 and 27%/8%/0.9, using the task and PETCO2 timecourses, respectively. In terms of the spatial reproducibility, the group mean (±standard deviation) of the spatial ICC (ICCspatial) was 1.04±0.23 and 1.02±0.26, for the task and PETCO2 timecourses, respectively. These results indicate generally good reproducibility of the hypocapnic CVR maps obtained using the proposed PDB task and analysis methodology. This suggests that such protocol may therefore offer a promising alternative to conventional vasoactive challenges, which avoids their discomfort and difficulty.

Removing respiratory artefacts from transthoracic bioimpedance spectroscopy measurements

Transthoracic impedance spectroscopy (TIS) measurements from wearable textile electrodes provide a tool to remotely and non-invasively monitor patient health. However, breathing and cardiac processes inevitably affect TIS measurements, since they are sensitive to changes in geometry and air or fluid volumes in the thorax. This study aimed at investigating the effect of respiration on Cole parameters extracted from TIS measurements and developing a method to suppress artifacts. TIS data were collected from 10 participants at 16 frequencies (range: 10 kHz − 1 MHz) using a textile electrode system (Philips Technologie Gmbh). Simultaneously, breathing volumes and frequency were logged using an electronic spirometer augmented with data from a breathing belt. The effect of respiration on TIS measurements was studied at paced (10 and 16 bpm) deep and shallow breathing. These measurements were repeated for each subject in three different postures (lying down, reclining and sitting). Cole parameter estimation was improved by assessing the tidal expiration point thus removing breathing artifacts. This leads to lower intra-subject variability between sessions and a need for less measurements points to accurately assess the spectra. Future work should explore algorithmic artifacts compensation models using breathing and posture or patient contextual information to improve ambulatory transthoracic impedance measurements.