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The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients’ readiness, there is still around 15–20% of predictive failure rate. This work is a proof of concept focused on adding new value to the prediction of the weaning outcome. Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) methods are evaluated as new complementary estimates to assess weaning readiness. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to estimate the CPC, including Time-Frequency Coherence, Dynamic Mutual Information and Orthogonal Subspace Projections. The cohort study includes 22 patients in pressure support ventilation, ready to undergo the SBT, analysed in the 24 h previous to the SBT. Of these, 13 had a successful weaning and 9 failed the SBT or needed reintubation –being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. Results revealed that HRV parameters can vary considerably depending on the time at which they are measured. This fact could be attributed to circadian rhythms, having a strong influence on HRV values. On the contrary, significant statistical differences are found in the proposed CPC parameters when comparing the values of the two groups, and throughout the whole recordings. In addition, differences are greater at night, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced respiratory sinus arrhythmia. Therefore, results sugge, Circuits and Systems

Objective: Respiratory sinus arrhythmia (RSA) refers to heart rate oscillations synchronous with respiration, and it is one of the major representations of cardiorespiratory coupling. Its strength has been suggested as a biomarker to monitor different conditions, and diseases. Some approaches have been proposed to quantify the RSA, but it is unclear which one performs best in specific scenarios. The main objective of this study is to compare seven state-of-the-art methods for RSA quantification using data generated with a model proposed to simulate, and control the RSA. These methods are also compared, and evaluated on a real-life application, for their ability to capture changes in cardiorespiratory coupling during sleep. Methods: A simulation model is used to create a dataset of heart rate variability, and respiratory signals with controlled RSA, which is used to compare the RSA estimation approaches. To compare the methods objectively in real-life applications, regression models trained on the simulated data are used to map the estimates to the same measurement scale. Results, and conclusion: RSA estimates based on cross entropy, time-frequency coherence, and subspace projections showed the best performance on simulated data. In addition, these estimates captured the expected trends in the changes in cardiorespiratory coupling during sleep similarly. Significance: An objective comparison of methods for RSA quantification is presented to guide future analyses. Also, the proposed simulation model can be used to compare existing, and newly proposed RSA estimates. It is freely accessible online., Circuits and Systems

The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients’ readiness, there is still around 15–20% of predictive failure rate. This work is a proof of concept focused on adding new value to the prediction of the weaning outcome. Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) methods are evaluated as new complementary estimates to assess weaning readiness. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to estimate the CPC, including Time-Frequency Coherence, Dynamic Mutual Information and Orthogonal Subspace Projections. The cohort study includes 22 patients in pressure support ventilation, ready to undergo the SBT, analysed in the 24 h previous to the SBT. Of these, 13 had a successful weaning and 9 failed the SBT or needed reintubation –being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. Results revealed that HRV parameters can vary considerably depending on the time at which they are measured. This fact could be attributed to circadian rhythms, having a strong influence on HRV values. On the contrary, significant statistical differences are found in the proposed CPC parameters when comparing the values of the two groups, and throughout the whole recordings. In addition, differences are greater at night, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced respiratory sinus arrhythmia. Therefore, results sugge, Signal Processing Systems

Objective: Respiratory sinus arrhythmia (RSA) refers to heart rate oscillations synchronous with respiration, and it is one of the major representations of cardiorespiratory coupling. Its strength has been suggested as a biomarker to monitor different conditions, and diseases. Some approaches have been proposed to quantify the RSA, but it is unclear which one performs best in specific scenarios. The main objective of this study is to compare seven state-of-the-art methods for RSA quantification using data generated with a model proposed to simulate, and control the RSA. These methods are also compared, and evaluated on a real-life application, for their ability to capture changes in cardiorespiratory coupling during sleep. Methods: A simulation model is used to create a dataset of heart rate variability, and respiratory signals with controlled RSA, which is used to compare the RSA estimation approaches. To compare the methods objectively in real-life applications, regression models trained on the simulated data are used to map the estimates to the same measurement scale. Results, and conclusion: RSA estimates based on cross entropy, time-frequency coherence, and subspace projections showed the best performance on simulated data. In addition, these estimates captured the expected trends in the changes in cardiorespiratory coupling during sleep similarly. Significance: An objective comparison of methods for RSA quantification is presented to guide future analyses. Also, the proposed simulation model can be used to compare existing, and newly proposed RSA estimates. It is freely accessible online., Signal Processing Systems

Respiratory sinus arrhythmia (RSA) is one of the forms of cardiorespiratory coupling. It has been suggested as a potential biomarker for diverse illnesses and conditions. In general, methods for non-invasive quantification of the RSA combine information from heart rate variability (HRV) and respiratory signals. Abnormal beats, which commonly occur in different populations, alter the reliability of the HRV and thus hinder the quantification of the RSA. To overcome this problem, several methods for detection and correction of irregular beats have been reported in literature. However, the effect of each of these methods on the quantification of the RSA is not well understood yet. For this reason, an approach that avoids this step might be useful. This paper presents an alternative based on robust regression models. For comparison purposes, an algorithm to detect and correct for irregular beats, in combination with a state-of-the-art RSA estimate, are tested. A similar performance is achieved with both approaches. These results show that the proposed robust methodology is able to capture the strength of the RSA, even when irregular beats are present, avoiding the irregularities correction step., Circuits and Systems

Respiratory sinus arrhythmia (RSA) is one of the forms of cardiorespiratory coupling. It has been suggested as a potential biomarker for diverse illnesses and conditions. In general, methods for non-invasive quantification of the RSA combine information from heart rate variability (HRV) and respiratory signals. Abnormal beats, which commonly occur in different populations, alter the reliability of the HRV and thus hinder the quantification of the RSA. To overcome this problem, several methods for detection and correction of irregular beats have been reported in literature. However, the effect of each of these methods on the quantification of the RSA is not well understood yet. For this reason, an approach that avoids this step might be useful. This paper presents an alternative based on robust regression models. For comparison purposes, an algorithm to detect and correct for irregular beats, in combination with a state-of-the-art RSA estimate, are tested. A similar performance is achieved with both approaches. These results show that the proposed robust methodology is able to capture the strength of the RSA, even when irregular beats are present, avoiding the irregularities correction step., Signal Processing Systems

The evaluation of the flow properties of biomass powders is essential for the design of handling systems within a thermochemical valorization context. The Discrete Element Method (DEM) is a valuable tool for simulating the bulk behavior of granular materials that has rarely been used for biomass feedstocks. This work focuses on the numerical investigation of the flow of raw and torrefied biomass particles in a loose and dynamic conditioning using a rotating drum. The relevance of DEM parameters calibrated using bulk experiments (angle-of-repose, bulk density, retainment ratio) is tested by comparison with experimental data obtained using a rotating drum system. The calibrated DEM material model considers the elongated, submillimetric and cohesive nature of the biomass powder. Several flowability descriptors (Upper Angle of Stability, size of avalanches, fraction of revolution to trigger events and irregularity of the free surface) are evaluated using both experimental data and DEM simulations. DEM results reproduced well the experimental trends and distinguished between the different cohesive extent of the samples. DEM is therefore a relevant technique for assessing flowability of biomass powders in a non-consolidated dynamic flow. This paves the way for investigating the effects of particle characteristics on bulk flow, which are briefly discussed., Accepted Author Manuscript, Transport Engineering and Logistics