Breathing pattern characterization in chronic heart failure patients using the respiratory flow signal

One-Step Fabrication of Paper-Based Inkjet-Printed Graphene for Breath Monitor Sensors

Irregularities in breathing patterns can be detected using breath monitor sensors, and this help clinicians to predict health disorders ranging from sleep disorders to heart failures. Variations in humidity during the inhalation and exhalation of breath have been utilized as a marker to detect breath patterns, and graphene-based devices are the favored sensing media for relative humidity (RH). In general, most graphene-based RH sensors have been used to explore resistance change as a measurement parameter to calibrate against the RH value, and they are prone to noise interference. Here, we fabricated RH sensors using graphene ink as a sensing medium and printed them in the shape of interdigital electrodes on glossy paper using an office inkjet printer. Further, we investigated the capacitance change in the sensor for the RH changes in the range of 10-70%. It exhibited excellent sensitivity with 0.03 pF/% RH, good stability, and high intraday and interday repeatability, with relative standard deviations of 1.2% and 2.2%, respectively. Finally, the sensor was embedded into a face mask and interfaced with a microcontroller, and capacitance change was measured under three different breathing situations: normal breathing, deep breathing, and coughing. The result show that the dominant frequency for normal breath is 0.22 Hz, for deep breath, it is 0.11 Hz, and there was no significant dominant cough frequency due to persistent coughing and inconsistent patterns. Moreover, the sensor exhibited a short response and recovery time (<5 s) during inhalation and exhalation. Thus, the proposed paper-based RH sensor is promising wearable and disposable healthcare technology for clinical and home care health applications.

Mining Knowledge of Respiratory Rate Quantification and Abnormal Pattern Prediction

The described application of granular computing is motivated because cardiovascular disease (CVD) remains a major killer globally. There is increasing evidence that abnormal respiratory patterns might contribute to the development and progression of CVD. Consequently, a method that would support a physician in respiratory pattern evaluation should be developed. Group decision-making, tri-way reasoning, and rough set–based analysis were applied to granular computing. Signal attributes and anthropomorphic parameters were explored to develop prediction models to determine the percentage contribution of periodic-like, intermediate, and normal breathing patterns in the analyzed signals. The proposed methodology was validated employing k-nearest neighbor (k-NN) and UMAP (uniform manifold approximation and projection). The presented approach applied to respiratory pattern evaluation shows that median accuracies in a considerable number of cases exceeded 0.75. Overall, parameters related to signal analysis are indicated as more important than anthropomorphic features. It was also found that obesity characterized by a high WHR (waist-to-hip ratio) and male sex were predisposing factors for the occurrence of periodic-like or intermediate patterns of respiration. It may be among the essential findings derived from this study. Based on classification measures, it may be observed that a physician may use such a methodology as a respiratory pattern evaluation-aided method.

Modulation-demodulation hypothesis of periodic breathing in human respiration

Periodic breathing (PB) is a diseased condition of the cardiorespiratory system, and mathematically it is modelled as an oscillation. Modeling approaches replicate periodic oscillation in the minute ventilation due to a higher than normal gain of the feedback signals from the chemoreceptors coupled with a longer than normal latency in feedback, and do not consider the waxing-waning pattern of the oronasal airflow. In this work, a noted regulation model is extended by integrating respiratory mechanics and respiratory central pattern generator (rCPG) model, using modulation-demodulation¹ hypothesis. This is a top-down modeling approach, and it is assumed that the sensory feedback signal from the chemoreceptors modulates the output of the rCPG model. It is also assumed that the brainstem network is responsible for the demodulation process. The respiratory mechanics is modeled as a multi-input multi-output (MIMO) system, where modulated and demodulated neural signals are applied as input and the minute ventilation and the oronasal airflow are specified as output. The minute ventilation signal drives the regulation model, completing the feedback loop. The proposed model is validated by comparing the model output with the clinical data. Using the modulation-demodulation hypothesis, a respiratory mechanics model is formulated in the form of a linear state-space model, which can be useful for providing assisted ventilation in clinical conditions.

Resonance as the Mechanism of Daytime Periodic Breathing in Patients with Heart Failure

RATIONALE

In patients with chronic heart failure, daytime oscillatory breathing at rest is associated with a high risk of mortality. Experimental evidence, including exaggerated ventilatory responses to CO₂ and prolonged circulation time, implicates the ventilatory control system and suggests feedback instability (loop gain > 1) is responsible. However, daytime oscillatory patterns often appear remarkably irregular versus classic instability (Cheyne-Stokes respiration), suggesting our mechanistic understanding is limited.

OBJECTIVES

We propose that daytime ventilatory oscillations generally result from a chemoreflex resonance, in which spontaneous biological variations in ventilatory drive repeatedly induce temporary and irregular ringing effects. Importantly, the ease with which spontaneous biological variations induce irregular oscillations (resonance "strength") rises profoundly as loop gain rises toward 1. We tested this hypothesis through a comparison of mathematical predictions against actual measurements in patients with heart failure and healthy control subjects.

METHODS

In 25 patients with chronic heart failure and 25 control subjects, we examined spontaneous oscillations in ventilation and separately quantified loop gain using dynamic inspired CO₂ stimulation.

MEASUREMENTS AND MAIN RESULTS

Resonance was detected in 24 of 25 patients with heart failure and 18 of 25 control subjects. With increased loop gain-consequent to increased chemosensitivity and delay-the strength of spontaneous oscillations increased precipitously as predicted (r = 0.88), yielding larger (r = 0.78) and more regular (interpeak interval SD, r = -0.68) oscillations (P < 0.001 for all, both groups combined).

CONCLUSIONS

Our study elucidates the mechanism underlying daytime ventilatory oscillations in heart failure and provides a means to measure and interpret these oscillations to reveal the underlying chemoreflex hypersensitivity and reduced stability that foretells mortality in this population.

ResCSRF: Algorithm to Automatically Extract Cheyne-Stokes Respiration Features From Respiratory Signals

OBJECTIVE

Cheyne-Stokes respiration (CSR) related features are significantly associated with cardiac dysfunction. Scoring of these features is labor intensive and time-consuming. To automate the scoring process, an algorithm (ResCSRF) has been developed to extract these features from nocturnal measurement of respiratory signals.

METHODS

ResCSRF takes four signals (nasal flow, thorax, abdomen, and finger oxygen saturation) as input. It first detects CSR cycles and then calculates the respiratory features (cycle length, lung-to-periphery circulation time, and time to peak flow). It outputs nightly statistics (mean, median, standard deviation, and percentiles) of these features. It was developed and blindly tested on a group of 49 chronic heart failure patients undergoing overnight in-home unattended respiratory polygraphy recordings.

RESULTS

The performance of ResCSRF was evaluated against manual expert scoring (ES) (consensus between two independent sleep scorers). In terms of percentage of CSR per recording, the mean difference [reproducibility coefficient (RPC)] between ResCSRF and ES was 0.5(6.4) and 0.5(8.1) for development and test set, respectively. The nightly statistics of CSR-related features output by ResCSRF showed high correlation with ES on the blind test set with the mean difference of less than 3 s and RPC of less than 7 s.

CONCLUSIONS

These results indicate that ResCSRF is capable of automating the scoring of CSR-related features and could potentially be implemented into a remote monitoring system to regularly monitor patients' cardiac function.

The movement and deposition of PM2.5 in the upper respiratory tract for the patients with heart failure: an elementary CFD study

BACKGROUND

PM2.5 is an important factor to affect the patients with respiratory and cardiovascular diseases. Clinical studies have found that the morbidity and mortality of patients with heart failure (HF) have a close relationship with the movement and deposition state of PM2.5. One reason is that the breathing pattern of patients with HF has obvious difference with healthy people, however the effect caused by these differences on the distribution regularity of PM2.5 in the respiratory tract is still unclear. Hence, a computational fluid dynamics simulation was conducted to clarify the aerodynamic effect of breathing pattern of patients with HF on respiratory system.

METHODS

Ideal upper respiratory tract geometric model was established based on standardized aerosol research laboratory of Alberta and Weibel A dimension. The discrete phase method is used to calculate the movement of the airflow and particles. The flow rate were chosen as the inlet boundary conditions, and the outlets are set at a constant pressure. The rate of particle deposition, distribution location, wall pressure, flow velocity and wall shear stress are obtained, and compared to the normal control.

RESULTS

The results demonstrated that the rate of escaped particles in every bronchial outlet of the patients with HF was more than the normal controls, meanwhile the trapped was less (1024 < 1160). There was higher by 12.9% possibility that the PM2.5 entered the lungs than the normal control.

CONCLUSION

The aerodynamic performances of HF patients are different from normal control. Compared to the normal control, under similar environment, there is higher possibility of PM2.5 moving into lungs, and these particles could affect the function of the respiratory system, resulting in the deterioration of the state of cardiovascular system. In short, it's necessary to pay more attention to the living environment of HF patients, to reduce the content of PM2.5 particles in the air, and reduce the damage of PM2.5 particles caused by breathing patterns.

Time-varying signal analysis to detect high-altitude periodic breathing in climbers ascending to extreme altitude

This work investigates the performance of cardiorespiratory analysis detecting periodic breathing (PB) in chest wall recordings in mountaineers climbing to extreme altitude. The breathing patterns of 34 mountaineers were monitored unobtrusively by inductance plethysmography, ECG and pulse oximetry using a portable recorder during climbs at altitudes between 4497 and 7546 m on Mt. Muztagh Ata. The minute ventilation (VE) and heart rate (HR) signals were studied, to identify visually scored PB, applying time-varying spectral, coherence and entropy analysis. In 411 climbing periods, 30-120 min in duration, high values of mean power (MP(VE)) and slope (MSlope(VE)) of the modulation frequency band of VE, accurately identified PB, with an area under the ROC curve of 88 and 89%, respectively. Prolonged stay at altitude was associated with an increase in PB. During PB episodes, higher peak power of ventilatory (MP(VE)) and cardiac (MP(LF)(HR) ) oscillations and cardiorespiratory coherence (MP(LF)(Coher)), but reduced ventilation entropy (SampEn(VE)), was observed. Therefore, the characterization of cardiorespiratory dynamics by the analysis of VE and HR signals accurately identifies PB and effects of altitude acclimatization, providing promising tools for investigating physiologic effects of environmental exposures and diseases.

Periodic breathing during ascent to extreme altitude quantified by spectral analysis of the respiratory volume signal

High altitude periodic breathing (PB) shares some common pathophysiologic aspects with sleep apnea, Cheyne-Stokes respiration and PB in heart failure patients. Methods that allow quantifying instabilities of respiratory control provide valuable insights in physiologic mechanisms and help to identify therapeutic targets. Under the hypothesis that high altitude PB appears even during physical activity and can be identified in comparison to visual analysis in conditions of low SNR, this study aims to identify PB by characterizing the respiratory pattern through the respiratory volume signal. A number of spectral parameters are extracted from the power spectral density (PSD) of the volume signal, derived from respiratory inductive plethysmography and evaluated through a linear discriminant analysis. A dataset of 34 healthy mountaineers ascending to Mt. Muztagh Ata, China (7,546 m) visually labeled as PB and non periodic breathing (nPB) is analyzed. All climbing periods within all the ascents are considered (total climbing periods: 371 nPB and 40 PB). The best crossvalidated result classifying PB and nPB is obtained with Pm (power of the modulation frequency band) and R (ratio between modulation and respiration power) with an accuracy of 80.3% and area under the receiver operating characteristic curve of 84.5%. Comparing the subjects from 1(st) and 2(nd) ascents (at the same altitudes but the latter more acclimatized) the effect of acclimatization is evaluated. SaO(2) and periodic breathing cycles significantly increased with acclimatization (p-value &#60; 0.05). Higher Pm and higher respiratory frequencies are observed at lower SaO(2), through a significant negative correlation (p-value &#60; 0.01). Higher Pm is observed at climbing periods visually labeled as PB with > 5 periodic breathing cycles through a significant positive correlation (p-value &#60; 0.01). Our data demonstrate that quantification of the respiratory volume signal using spectral analysis is suitable to identify effects of hypobaric hypoxia on control of breathing.

Study of the oscillatory breathing pattern in elderly patients

Some of the most common clinical problems in elderly patients are related to diseases of the cardiac and respiratory systems. Elderly patients often have altered breathing patterns, such as periodic breathing (PB) and Cheyne-Stokes respiration (CSR), which may coincide with chronic heart failure. In this study, we used the envelope of the respiratory flow signal to characterize respiratory patterns in elderly patients. To study different breathing patterns in the same patient, the signals were segmented into windows of 5 min. In oscillatory breathing patterns, frequency and time-frequency parameters that characterize the discriminant band were evaluated to identify periodic and non-periodic breathing (PB and nPB). In order to evaluate the accuracy of this characterization, we used a feature selection process, followed by linear discriminant analysis. 22 elderly patients (7 patients with PB and 15 with nPB pattern) were studied. The following classification problems were analyzed: patients with either PB (with and without apnea) or nPB patterns, and patients with CSR versus PB, CSR versus nPB and PB versus nPB patterns. The results showed 81.8% accuracy in the comparisons of nPB and PB patients, using the power of the modulation peak. For the segmented signal, the power of the modulation peak, the frequency variability and the interquartile ranges provided the best results with 84.8% accuracy, for classifying nPB and PB patients.

Analysis of the respiratory flow cycle morphology in chronic heart failure patients applying principal components analysis

The study of flow cycle morphology provides new information about the breathing pattern. This study proposes the characterization of cycle morphology in chronic heart failure patients (CHF) patients, with periodic (PB) and non-periodic breathing (nPB) patterns, and healthy subjects. Principal component analysis is applied to extract a respiratory cycle model for each time segment defined by a 30-s moving window. To characterize morphology of the model waveform, a number of parameters are extracted whose significance is evaluated in terms of the following three classification problems: CHF patients with either PB or nPB, CHF patients versus healthy subjects, and nPB patients versus healthy subjects. 26 CHF patients (8 with PB and 18 with non-periodic breathing pattern (nPB)) and 35 healthy subjects are studied. The results show that a respiratory cycle compressed in time characterizes PB patients, i.e., shorter inspiratory and expiratory periods, and higher dispersion of the maximum inspiratory and expiratory flow value (accuracy of 87%). The maximal expiratory flow instant occurs earlier in CHF patients than in healthy subjects (accuracy of 87%), with a steeper slope between inspiration and expiration. It is also found that the standard deviation of the expiratory period, evaluated for each subject, is much lower in CHF patients than in healthy subjects. The maximal expiratory flow instant occurs earlier (accuracy of 84%) in nPB patients, when comparing subjects with similar respiratory pattern like nPB patients and healthy subjects.