Ventilatory Control Research Articles

Analysis and research on inherent angle ventilation control of residential kitchen range hoods

The emission of oil fume particles during kitchen cooking processes constitutes a significant factor that impacts indoor air quality and human health. Range hoods, as indispensable ventilation devices in kitchens, play a crucial role in reducing indoor particle concentrations. This study employs a combined approach of orthogonal experiments and computational fluid dynamics simulations to investigate the exhaust characteristics of inherent-angle-measurement-based range hoods. It analyzes the diffusion patterns of cooking-generated particulate matter under various cooking scenarios, considering factors such as the opening and closing of windows, cooking source velocities, and different airflow rates of the range hood. Based on different airflow organization patterns, a supplementary ventilation system is designed. The rationality and effectiveness of the supplementary ventilation system are analyzed to establish an effective control scheme for reducing particulate matter in various kitchen environments. The simulated research results indicate that particles disperse along the inclined surface of the side-draft range hood, resulting in higher pollutant concentrations in the breathing zone compared to the cooking zone. Increasing the exhaust airflow rate of the range hood enhances particle capture efficiency by 9.8 %. The induced airflow generated by opening windows improves particle capture efficiency by 12.9 %. However, when the velocity of the window-induced airflow exceeds 1.5 m/s, it hampers the promotion of particle capture efficiency. In the case of windows being unconditionally open, implementing a ceiling-mounted supplementary air supply system effectively improves the kitchen's particle concentration, achieving a maximum particle capture efficiency of 97.66 %. When windows can be opened, setting a baffle on the range hood, the simulation results revealed that with a baffle angle of 120° and a length of 0.3 m, the particle concentration capture efficiency in the breathing zone increased by 34.59 %. This achieved the standard limit requirement of 50 µg/m3, thus meeting the indoor air quality requirements.

Modernization of an automated office ventilation control system using artificial intelligence

Modernization of an automated office ventilation control system using artificial intelligence

Improved Control of Propeller Ventilation Based on POA-XGBoost and Ship Dynamics/Control Model

Under adverse sea conditions, propeller ventilation caused by in-and-out water can decrease the reliability of the ship power grid and the lifespan of the propulsion shaft system. Predicting the development of propeller ventilation severity while identifying it can contribute to improving propeller ventilation control. In this study, the eXtreme Gradient Boosting (XGBoost) algorithm combined with a ship dynamics/control model is proposed as a propeller ventilation identification and prediction method. Meanwhile, the Pelican optimization algorithm (POA), particle swarm optimization (PSO), and genetic algorithm (GA) are applied to determine the optimal hyperparameters of the XGBoost algorithm. The results indicate that the method can effectively identify the current propeller ventilation state and predict whether a full ventilation state will occur after experiencing a partial propeller ventilation state. The comparison results indicate that the POA has a better optimization effect on the XGBoost algorithm for propeller ventilation identification and prediction. The method proposed in this study provides crucial technical support for the effective switching of propulsion control strategies for ship electric propulsion systems under adverse sea conditions.

Computer vision to advance the sensing and control of built environment towards occupant-centric sustainable development: A critical review

As urban development progresses, the built environment control has faced more critical challenges in improving energy efficiency, air quality, and environmental comfort. Occupant information (e.g., occupant status and behavior) sensing is a key but challenging aspect of built environmental control. Computer vision (CV) technology provides a new way for multi-dimensional information acquisition. However, a critical review is lacking in the cross-research area of CV and built environment control, particularly considering the technological advancements following the COVID-19 pandemic. This article reviews the latest advancements in the built environment from international sources, with a focus on the research frontier in four branches: ventilation and indoor air quality control, COVID-19 control, thermal environment control, and lighting control. Through critical comparisons and analyses, it demonstrates that CV technology can effectively sense highly dynamic built environments, which greatly enhances the data dimension, resolution and accuracy compared to existing sensing technologies. Reported data shows that CV technology achieved an average detection accuracy of about 95% for occupant-related information and 86% for comfort-related information. Effective methods to improve the accuracy include incorporating data fusion by using other sensors, upgrading algorithms, and improving the model training. Particularly, the COVID-19 pandemic has driven the development of mask detection and social distancing detection using CV. The challenges, future trends and potential applications are discussed. This study emphasizes the need for cross-field integration of CV and built environment to facilitate the sharing of cutting-edge techniques and knowledge, which will stimulate more innovations in the future.

Clinical Analysis of Repeat Balloon Eustachian Tube Dilatation

Background and Objectives Obstructive Eustachian tube dysfunction (OETD) is a complex condition in which middle ear ventilation control is impaired. OETD is often not improved by medication nor by nasal washing or nasal spray. On the other hand, balloon Eustachian tube dilatation (BETD) is an effective surgery in OETD although repeating BETD is controversial as it involves additional procedure such as ventilation tube, middle ear steroid irrigation, and trans-tympanic Eustachian tube probing in some cases. This study aims to analyze the clinical differences between patients who received single BETD and repeat BETD, and to evaluate the efficacy of repeat BETD.Subjects and Method For the study, 110 single groups, and 16 repeat groups were selected from patients who underwent BETD from Nov 2019 to Aug 2022. Age, gender, underlying diseases, history of rhinitis and smoking, and Eustachian Tube Dysfunction Questionnaire-7 (ETDQ-7) were analyzed for the selected groups.Results There were no significant differences in gender, underlying disease, or history of rhinitis between the two groups. However, age was significantly younger in the repeat group (45.91 vs. 33.38, <i>p</i>=0.006), while smoking (5.64 vs. 1.66, <i>p</i>=0.01) was significantly higher in the single group. The rate of improvement in the ETDQ-7 was 81.3% in the single group and 69.2% in both initial and repeated surgeries in the repeat group. The complete remission rate was 60% in the single group, and 38.5% in both initial and repeated surgeries of the repeat group.Conclusion For patients who are unsatisfied with the first surgery, it is meaningful enough to recommend repeat BETD, for which additional procedures are performed simultaneously.

The effect of inspiratory rise time on mechanical power calculations in pressure control ventilation: dynamic approach

BackgroundMechanical power may serve as a valuable parameter for predicting ventilation-induced injury in mechanically ventilated patients. Over time, several equations have been developed to calculate power in both volume control ventilation (VCV) and pressure control ventilation (PCV). Among these equations, the linear model mechanical power equation (MPLM) closely approximates the reference method when applied in PCV. The dynamic mechanical power equation (MPdyn) computes power by utilizing the ventilatory work of breathing parameter (WOBv), which is automatically measured by the mechanical ventilator. In our study, conducted in patients with Covid-19 Acute Respiratory Distress Syndrome (C-ARDS), we calculated mechanical power using both the MPLM and MPdyn equations, employing different inspiratory rise times (Tslope) at intervals of 5%, ranging from 5 to 20% and compared the obtained results.ResultsIn our analysis, we used univariate linear regression at both I:E ratios of 1:2 and 1:1, considering all Tslope values. These analyses revealed that the MPdyn and MPLM equations exhibited strong correlations, with R2 values exceeding 0.96. Furthermore, our Bland–Altman analysis, which compared the power values derived from the MPdyn and MPLM equations for patient averages and all measurements, revealed a mean difference of −0.42 ± 0.41 J/min (equivalent to 2.6% ± 2.3%, p < 0.0001) and −0.39 ± 0.57 J/min (equivalent to 3.6% ± 3.5%, p < 0.0001), respectively. While there was a statistically significant difference between the equations in both absolute value and relative proportion, this difference was not considered clinically relevant. Additionally, we observed that each 5% increase in Tslope time corresponded to a decrease in mechanical power values by approximately 1 J/min.ConclusionsThe differences between mechanical power values calculated using the MPdyn and MPLM equations at various Tslope durations were determined to lack clinical significance. Consequently, for practical and continuous mechanical power estimation in Pressure-Controlled Ventilation (PCV) mode, the MPdyn equation presents itself as a viable option. It is important to note that as Tslope times increased, the calculated mechanical power exhibited a clinically relevant decrease.

VENTILATION CONTROL OF THE NEW SAFE CONFINEMENT OF THE CHORNOBYL NUCLEAR POWER PLANT BASED ON NEURO-FUZZY NETWORKS

The accident at the Chornobyl Nuclear Power Plant (ChNPP) in Ukraine in 1986 became one of the largest technological disasters in human history. During the accident cleanup, a special protective structure called the Shelter Object was built to isolate the destroyed reactor from the environment. However, the planned operational lifespan of the Shelter Object was only 30 years. Therefore, with the assistance of the international community, a new protective structure called the New Safe Confinement (NSC) was constructed and put into operation in 2019. The NSC is a large and complex system that relies on a significant number of various tools and subsystems to function. Due to temperature fluctuations and the influence of wind, hydraulic processes occur within the NSC, which can lead to the release of radioactive aerosols into the environment. The personnel of the NSC prevents these leaks, including through ventilation management. Considering the long planned operational term of the NSC, the development and improvement of information technologies for its process automation is a relevant task. The purpose of this paper is to develop a method for managing the ventilation system of the NSC based on neuro-fuzzy networks. An investigation of the current state of ventilation control in the NSC has been conducted, and automation tools for the process have been proposed. Using an adaptive neuro-fuzzy inference system (ANFIS) and statistical data on the NSC's operation, neuro-fuzzy models have been formed, which allows to calculate the expenses of the ventilation system using the Takagi-Sugeno method. The verification of the proposed approaches on a test data sample demonstrated sufficiently high accuracy of the calculations, confirming the potential practical utility in decision-making regarding NSC’s ventilation management. The results of this paper can be useful in the development of digital twins of the NSC for process management and personnel training.

Obstructive sleep apnea and central/peripheral chemosensitivity: an essential part of pathophysiological mechanisms

Unstable ventilatory control is one of the key pathophysiological mechanisms of obstructive sleep apnea (OSA), and the activity of chemoreceptors is an important part of ventilatory control. Chemosensitivity has a significant impact on the severity and prognosis of OSA, and the incidence of comorbidities. The focus on reducing chemosensitivity can be seen as an emerging theme to promote individualized and precise treatment of OSA. Further exploration of chemosensitivity in OSA will be an emerging direction and a major challenge for current and future research in the field of sleep.

Optimizing hybrid ventilation and daylight-linked dimming control for carbon reduction and thermal comfort in a subtropical high-rise office building

The dominant contributors to electricity consumption in existing buildings are air-conditioning and lighting systems. This study aims to optimize hybrid ventilation and daylight-linked dimming control in a subtropical high-rise office building. The objective is to reduce carbon emissions while ensuring thermal and visual comfort. These two strategies can be retrofitted in a typical curtain wall system with vertical windows without encountering any technical limitations. EnergyPlus is used to analyze the impact of these strategies on enhancing thermal comfort and illuminance while achieving electricity savings in the high-rise office building. Based on the typical meteorological year of Hong Kong, using natural ventilation in perimeter zones can reduce the energy use intensity (EUI) by 2.6 to 3.41 % for 30.09 to 34.97 % of total operating hours, depending on window-to-wall ratios (WWRs). Furthermore, daylight-linked dimming control in perimeter zones can reduce EUI by 18.91 % to 21.9 % in the HVAC portion and 8.09 % to 12.25 % in the lighting portion during 91.39 % to 98.28 % of total operating hours. Using daylighting with a high WWR helps reduce 6.31 to 9.21 % in total greenhouse gas emissions. The study also develops a logistic regression model using three climatic variables to predict the natural ventilation status. Overall, the use of daylighting greatly improves the percentage of acceptable predicted mean vote (PMV), with the greatest improvements observed in perimeter zones facing east and west on the middle floors. However, the combined application of both strategies results in a tempered improvement. The novelty of this study lies in providing guidance on suitable WWRs for implementing both strategies to achieve carbon reduction. Moreover, the significance lies in providing a quantitative assessment of how enhanced ventilation and lighting controls contribute to lower carbon emissions while improving thermal comfort.

Characterization Technique for a Doppler Radar Occupancy Sensor

Occupancy sensors are electronic devices used to detect the presence of people in monitored areas, and the output of these sensors can be used to optimize lighting control, heating and ventilation control, and real-estate utilization. Testing methods already exist for certain types of occupancy sensors (e.g., passive infrared) to evaluate their relative performance, allowing manufacturers to report coverage patterns for different types of motion. However, the existing published techniques are mostly tailored for passive-infrared sensors and therefore limited to evaluation of large motions, such as walking and hand movement. Here we define a characterization technique for a Doppler radar occupancy sensor based on detecting a small motion representing human breathing, using a well-defined readily reproducible target. The presented technique specifically provides a robust testing method for a single-channel continuous wave Doppler-radar based occupancy sensor, which has variation in sensitivity within each wavelength of range. By comparison with test data taken from a human subject, we demonstrate that the mobile target provides a reproducible alternative for a human target that better accounts for the impact of sensor placement. This characterization technique enables generation of coverage patterns for breathing motion for single-channel continuous wave Doppler radar-based occupancy sensors.

Mixed air temperature reset by data-driven model for optimal economizer control

An economizer system means that outside air is introduced to reduce energy consumption when the outdoor air is sufficient to be used for cooling, and adjusts the outdoor, ventilation, and exhaust damper to maintain the mixed air temperature set value during heating, ventilation and air conditioning control. The set values for economizer control include high and low limit that determine whether to operate economizer, and mixed air temperature. At this time, the mixed air temperature is set to a fixed value. Such a fixed set value may cause problems such as energy waste because it does not consider changes in indoor and outdoor environments. Therefore, in this study, an optimal economizer control was developed that produces the maximum economizer effect by resetting the mixed air temperature set value considering the indoor and outdoor conditions. For this, a data-driven prediction model was developed, and based on the prediction model, an optimal economizer control was developed that resetting the mixed air temperature set value. And the developed control was evaluated by TRNSYS-Matlab co-simulator, and as a result, the mixed air temperature set value was changed in real time, and energy consumption was reduced by about 20% compared to the dry-bulb temperature control.

Real-Time Control of the Air Volume in Ventilation Facilities by Limiting CO$_{2}$ Concentration With Cluster Algorithms

Ventilation improves indoor air quality and reduces airborne infections. It is particularly important at present because of the COVID-19 pandemic. Commercially available ventilation facilities can only be instantly turned on/off or at a set time with adjustable air volumes (high, middle, and low). However, maintaining the indoor carbon dioxide concentration while reducing the energy consumption of these facilities is challenging. Hence, this study developed clustering algorithms to determine the carbon dioxide concentration limit thus enabling real-time air volume adjustment. These limit values were set using the existing energy recovery ventilation (ERV) controller. In the experiment, dual estimation was adopted, and the constructing building energy models from data were sampled at a low rate to compare that the ventilation facilities are only turned on/off. In addition, switching control is closely related to fuzzy control; that is, fuzzy control can be considered a smooth version of switching control. The experimental results indicated that the limits of 600 and 700 ppm were suitable to effectively control the real-time air volume based on the ERV operation. An ERV-based carbon dioxide concentration limit reduced the energy consumption of ventilation facilities by 11% implications of this study.

Building Ventilation Optimization Through Occupant-Centered Computer Vision Analysis

Abstract Buildings consume about 40 % of all energy. Ventilation plays a significant role in both the energy consumption of buildings and the comfort of occupants. To achieve energy efficiency and comfort, smarter ventilation control algorithms can be employed, such as those with feedback based on CO2 levels. Furthermore, by knowing the current number of people in a space, ventilation can theoretically be adjusted to maintain a constant CO2 level without wasting energy when people are not present. An additional benefit of such control could arise due to occupants’ habits. For example, if a person senses elevated CO2 levels, even if the ventilation system has started operating more intense, they might choose to open a window, potentially compromising energy efficiency. Therefore, if the control algorithm were to maintain a constant CO2 level, occupants may be less likely to open windows. In our work, we explore a model in combination with a custom monitoring system based on computer vision to implement such control. The monitoring system combines outside and inside CO2 sensors with precise people counting based on computer vision to provide data to the model. The model relies on the mass balance equation for CO2 and considers the historical data of the number of occupants and their activities to estimate the overall CO2 generation in indoor spaces. The results suggest that the model can effectively forecast CO2 dynamics with an absolute deviation of 40 ppm. However, it was observed that the analysis of the actual air exchange level could be compromised by several factors.

Efficiency testing of active noise control in the duct outlet and effect of the error microphone location

In a duct, the performance of the active control system depends among other things on the position of the loudspeaker. In ventilation systems, they are usually inserted in the ductwork along the duct wall or in the duct if its diameter is large enough not to generate too much pressure drop. In this paper, we describe an active noise control system placed not in the duct but at its outlet, instead of the ventilation grille. The control loudspeaker being no longer in the duct, it must remain close enough to the outlet to "colocalize" the two sources of sound waves (ventilation noise and control) and keep a global noise control while leaving a sufficiently large opening to allow a similar ventilation flow obtained with a standard ventilation mouth. The error microphone is placed outside the duct and the influence of its position is also analyzed on a ventilation system prototype. The obtained attenuation is good and extends over a wider range of frequencies than when the loudspeaker is placed at the periphery of the duct. Moreover, its position at the outlet of the duct is better for using the speaker as a source of sound diffusion.

Application of floating recirculation coefficient in automatic mine ventilation control design

Application of floating recirculation coefficient in automatic mine ventilation control design

Protocol and Statistical Analysis Plan for the Mode of Ventilation During Critical Illness (MODE) Trial

For every critically ill adult receiving invasive mechanical ventilation, clinicians must select a mode of ventilation. The mode of ventilation determines whether the ventilator directly controls the tidal volume or the inspiratory pressure. Newer hybrid modes allow clinicians to set a target tidal volume; the ventilator controls and adjusts the inspiratory pressure. A strategy of low tidal volumes and low plateau pressure improves outcomes, but the optimal mode to achieve these targets is not known. Can a cluster-randomized trial design be used to assess whether the mode of mandatory ventilation affects the number of days alive and free of invasive mechanical ventilation among critically ill adults? The Mode of Ventilation During Critical Illness (MODE) trial is a cluster-randomized, multiple-crossover pilot trial being conducted in the medical ICU at an academic center. The MODE trial compares the use of volume control, pressure control, and adaptive pressure control. The study ICU is assigned to a single-ventilator mode (volume control vs pressure control vs adaptive pressure control) for continuous mandatory ventilation during each 1-month study block. The assigned mode switches every month in a randomly generated sequence. The primary outcome is ventilator-free days to study day 28, defined as the number of days alive and free of invasive mechanical ventilation from the final receipt of mechanical ventilation to 28 days after enrollment. Enrollment began November 1, 2022, and will end on July 31, 2023. This manuscript describes the protocol and statistical analysis plan for the MODE trial of ventilator modes comparing volume control, pressure control, and adaptive pressure control. Prespecifying the full statistical analysis plan prior to completion of enrollment increases rigor, reproducibility, and transparency of the trial results. The trial was registered with clinicaltrials.gov on October 3, 2022, before initiation of patient enrollment on November 1, 2022 (ClinicalTrials.gov identifier: NCT05563779).

Research on Intelligent Control of Regional Air Volume Based on Machine Learning

To address the challenge of intelligently controlling air volume in regions affected by the frequent fluctuations in underground ventilation networks, a remote intelligent air regulation method based on machine learning was presented. This method encompasses three core components: local fan frequency conversion regulation, associated branch air resistance regulation, and a comprehensive integration of both. Leveraging foundational mine ventilation theory, the principles behind branch sensitivity air regulation were dissected. By applying these principles, the key performance indicators crucial for the regulation of air volume within the ventilation system were identified. Subsequently, an intelligent model for regional air volume control was constructed. To validate the approach, an experimental platform for intelligent air volume control was established, guided by geometric, dynamic, and kinematic similarity criteria. Then, the experimental methodologies for simulating various ventilation scenarios were discussed, the data acquisition techniques were introduced, and the obtained results were analyzed. Employing machine learning techniques, we utilized five distinct algorithms to predict the operational parameters of targeted air volume ventilation equipment. It enabled precise and efficient control of air volume within the region. The results indicated that the least squares support vector machine (LS-SVM) stood out by delivering high-precision predictions of target air volume ventilation equipment parameters, all while maintaining a relatively short calculation time. This swift generation of feedback data and corresponding air volume control strategies will contribute to the precise management of air volume in the area. This work served as a valuable theoretical and practical guide for intelligent mining ventilation control.

A novel deep-learning framework for short-term prediction of cooling load in public buildings

Optimal control of heating, ventilation, and air conditioning (HVAC) systems, along with demand-side management, are both cost-effective methods in the process of energy conservation and carbon reduction. The successful implementation of these initiatives largely hinges on accurate cooling load predictions. Due to the complex nonlinear and dynamic time-varying nature of demand loads, however, it is a formidable challenge to accurately predict the cooling load. To address these issues, a novel deep learning-based prediction framework, aTCN-LSTM, is proposed. First, a gate-controlled multi-head temporal convolutional network is designed to capture the inherent nonlinear and local temporal features from the time series of cooling loads. Second, a sparse probabilistic self-attention mechanism is integrated with a bidirectional long short-term memory (BiLSTM) network to extract the long-term dependencies within the cooling load sequences. Finally, through integration with the proposed two components, the framework is developed and validated through a 14-month real cooling load forecasting problem for a 51-story hotel building in Guangzhou, China. Experiments and comparison studies demonstrate the effectiveness and superiority of the proposed method. The mean absolute percentage error of the proposed method's 1-step, 6-step, and 12-step prediction results is reduced by 27.48%, 14.05%, and 13.38%, respectively, compared with the state-of-the-art baseline model. Consequently, it stands poised to serve as an effective guide for HVAC chiller scheduling and demand management initiatives.

Health care support systems for informal settlement rental housing during the COVID-19 season: landlord’s perspective

PurposeThis paper aims to examine landlords’ health support systems to tenants to control COVID-19 in selected informal settlement rental housing (ISRH) in Ghana, dwelling on landlords’ views.Design/methodology/approachThe paper used the concurrent imbedded mixed-methods approach and grounded the findings in the socio-ecological theory. The authors collected both qualitative and quantitative data from 242 landlords in 13 informal settlements across Ghana using quotas. The authors undertook semi-structured face-to-face and telephone interviews. The authors conducted content and thematic qualitative data analysis and used simple descriptive statistical data analysis.FindingsThe paper discovered that tenants had limited knowledge on the transmission of the pandemic, forcing landlords to regulate their building services usage, ventilation and thermal control, entertainment, common areas and rent advancement for tenants to control the pandemic. Also, tenants found it difficult to comply with the rules on ventilation for fear of criminal attacks, while high social connection and interaction among renters and inadequate enforcement caused the non-adherence by renters to social gathering. Again, landlords had difficulty in contract-tracing visitors suspected to be infected with the virus.Originality/valueThe use of concurrent and imbedded mixed methods to investigate landlords’ viewpoints on their support in health needs of their tenants to regulate COVID-19. The prescriptions from the study provide practical applications to formulate a mix of housing and health policies to formalize the support of landlords to their tenants in ISRH in Ghana.

Abstract 18921: Obstructive Sleep Apnea in the Presence of Heart Failure With Preserved Ejection Fraction is Associated With Low Arousal Threshold and Increased Sleep Fragmentation

Introduction: Heart failure with preserved ejection fraction (HFpEF) is often accompanied by obstructive sleep apnea (OSA). The effects of HFpEF on OSA pathophysiology remain relatively unexplored. Pulmonary congestion and sympathetic nervous activity (SNA) may drive ventilatory control instability (high loop gain [LG]). Elevated SNA also theoretically predisposes sleep to easier interruption by breathing events of OSA, reflected by low arousal threshold (ArTH). Hypothesis: In comparison to patients with OSA alone, patients with both OSA and HFpEF have higher LG and lower ArTH. Methods: We reviewed records to include patients with 1) transthoracic echocardiography with EF&gt;40% and left ventricular diastolic dysfunction by decreased age-adjusted e’ and/or left atrial volume index &gt;28 mL/m 2 ; 2) OSA by apnea-hypopnea index (AHI3A) ≥15 events/hour; and 3) raw polysomnography (PSG) data available for advanced signal analysis. LG and ArTH were estimated by Phenotyping Using Polysomnography (PUP), a MATLAB algorithm that quantifies pathophysiologic mechanisms of OSA using PSG data. Values in HFpEF+OSA patients were compared to values from research volunteers with OSA alone. Results: The HFpEF+OSA group included 21 patients. The OSA alone group included 43 research volunteers. LG was lower for HFpEF+OSA (0.63±18) compared to OSA alone (0.69±18), but the difference was not statistically significant. ArTH was significantly lower for HFpEF+OSA (117.0±15.9 %eupnea) compared to OSA alone (152.8±54.8 %eupnea, p = &lt;0.001) and remained significant after adjustment for age, sex, and body mass index. ArTH was correlated with sleep efficiency (R = 0.43, p = &lt;0.001) and negatively correlated with wake after sleep onset (R = -0.35, p =0.005). Conclusions: HFpEF is associated with low arousal threshold, which may contribute to sleep fragmentation. Our data did not confirm the hypothesis of greater breathing instability in patients with HFpEF and OSA compared to OSA alone.