Multivariable Regression Model to Estimate Tidal Volume for Different Respiratory Patterns.

Respiratory patterns present great variability, both in healthy subjects and in patients with different diseases and forms of nasal, oral, superficial or deep breathing. The analysis of this variability depends, among others, on the device used to record the signals that describe these patterns. In this study, we propose multivariable regression models to estimate tidal volume (VT) considering different breathing patterns. Twenty-three healthy volunteers underwent continuous multisensor recordings considering different modes of breathing. Respiratory flow and volume signals were recorded with a pneumotachograph and thoracic and abdominal respiratory inductive plethysmographic bands. Several respiratory parameters were extracted from the volume signals, such as inspiratory and expiratory areas (Areains, Areaexp), maximum volume relative to the cycle start and end (VTins, VTexp), inspiratory and expiratory time (Tins, Texp), cycle duration (Ttot), and normalized parameters of clinical interest. The parameters with the greatest individual predictive power were combined using multivariable models to estimate VT. Their performance were quantified in terms of determination coefficient (R2), relative error (ER) and interquartile range (IQR). Using only three parameters, the results obtained for the thoracic band (VTexp, Ttot, Areaexp) were better than those obtained from the abdominal band (VTexp, Tins, Areains) with R2 = 0.94 (IQR: 0.07); ER = 6.99 (IQR: 6.12) vs R2 = 0.91 (IQR: 0.09), ER = 8.70 (IQR: 4.62). Overall performance increased to R2 = 0.97 (IQR: 0.02) and ER = 4.60 (IQR: 3.68) when parameters from the different bands were combined, further improving when was applied to segments with different inspiration-expiration patterns. In particular, the nose-nose ER = 1.39 (IQR: 0.73), nose-mouth ER = 2.11 (IQR: 1.23) and mouth-mouth ER = 2.29 (IQR: 1.44) patterns showed the best results compared to those obtained for basal, shallow and deep breathing.Clinical relevance- Respiratory pattern variability can be described using multivariable regression model for tidal volume.

Respiratory Pattern Analysis for Different Breathing Types and Recording Sensors in Healthy Subjects.

Accurate monitoring of respiratory activity can lead to early identification and treatment of possible respiratory failure. However, spontaneous breathing can vary considerably. To quantify this variability, this study aimed at comparing the breathing pattern characteristics obtained from several recording sensors during different breathing types. Respiratory activity was recorded with a pneumotachograph and two inductive plethysmographic bands, thoracic and abdominal, in 23 healthy volunteers (age 21.5±1.2 years, 13 females). The subjects were asked to breathe at their natural rate, in successive stages: first freely, then through their nose, nose and mouth, mouth alone, and finally deep and shallow. Both band signals were compared to the pneumotach-derived (gold standard) volume signal. The time series of inspiratory and expiratory duration, total cycle duration and tidal volume were estimated from each of these signals, and also from the sum of the thoracic and abdominal bands. This composite signal showed the highest correlation with the volume signal for almost all subjects, and also had a significantly higher correlation with those obtained from the gold standard volume, compared to either band. In general, breathing parameters increased from basal to nose-mouth breathing, had a minimum in shallow breathing and a maximum in deep breathing. Women exhibited a significantly longer exhalation phase than men during deep breathing, in the combined bands and the gold standard volume. In conclusion, variations in respiratory cycle morphology in different breathing types can be well captured by the simple addition of abdominal and thoracic band signals.Clinical Relevance- Breathing pattern variability can be identified by the combination of abdominal and thoracic bands.

Analysis of Heart Rate Variability during the Performance of the Wim Hof Method in Healthy Subjects.

Cardiorespiratory interaction is related to the heart rate variability (HRV) synchronized with respiration. These metrics help to comprehend the autonomic nervous system (ANS) functionality in cardiovascular mechanisms. In this work, we aim to study the HRV in healthy subjects aged 18-24 years during the breathing techniques based on deep breaths followed by apnoeas, developed by Wim Hof (WHM). The attributes of all participates have been treated as a group and therefore, separated by gender. A total of 11 intervals have been distinguished: starting of basal respiration (SRI = 1), controlled deep breaths (CDB = 3), long expiratory apnoea (LEA = 3), short inspiratory apnoea (SIA = 3) and ending with basal respiration again (FRI = 1). To strengthen the HRV knowledge extraction from these scenarios, time and frequency analysis is conducted. In general, breathing and apnoea intervals presented significant statistically differences (p < 0.05), heart rate (HR) mean between SRI and FRI (p < 0.001), RR variability of LEA intervals (p < 0.01), root mean square of RR intervals during CDB (p < 0.05), maximum high frequency (HF) peak amplitude between SRI and FRI (p = 0.016), and low frequency (LF) area for LEA intervals (p < 0.001). When performing the frequency analysis, it has been observed that the sympathetic nervous system (SNS) has a higher contribution in the apnoea intervals. In conclusion, the WHM method implementation seems to involve a decrease in the HR. Specific breathing techniques could help to control the body in different conditions.Clinical Relevance- The WHM seems to imply a decrease on HR. Furthermore, after the implementation of the WHM, women presented higher HRV.

Baroreflex activity through the analysis of the cardio-respiratory variability influence over blood pressure in cardiomyopathy patients.

A large portion of the elderly population are affected by cardiovascular diseases. Early prognosis of cardiomyopathies remains a challenge. The aim of this study was to classify cardiomyopathy patients by their etiology based on significant indexes extracted from the characterization of the baroreflex mechanism in function of the influence of the cardio-respiratory activity over the blood pressure. Forty-one cardiomyopathy patients (CMP) classified as ischemic (ICM-24 patients) and dilated (DCM-17 patients) were considered. In addition, thirty-nine control (CON) subjects were used as reference. The beat-to-beat (BBI) time series, from the electrocardiographic (ECG) signal, the systolic (SBP), and diastolic (DBP) time series, from the blood pressure signal (BP), and the respiratory time (TT), from the respiratory flow (RF) signal, were extracted. The three-dimensional representation of the cardiorespiratory and vascular activities was characterized geometrically, by fitting a polygon that contains 95% of data, and by statistical descriptive indices. DCM patients presented specific patterns in the respiratory response to decreasing blood pressure activity. ICM patients presented more stable cardiorespiratory activity in comparison with DCM patients. In general, CMP shown limited ability to regulate changes in blood pressure. In addition, patients also shown a limited ability of their cardiac and respiratory systems response to regulate incremental changes of the vascular variability and a lower heart rate variability. The best classifiers were used to build support vector machine models. The optimal model to classify ICM versus DCM patients achieved 92.7% accuracy, 94.1% sensitivity, and 91.7% specificity. When comparing CMP patients and CON subjects, the best model achieved 86.2% accuracy, 82.9% sensitivity, and 89.7% specificity. When comparing ICM patients and CON subjects, the best model achieved 88.9% accuracy, 87.5% sensitivity, and 89.7% specificity. When comparing DCM patients and CON subjects, the best model achieved 87.5% accuracy, 76.5% sensitivity, and 92.3% specificity. In conclusion, this study introduced a new method for the classification of patients by their etiology based on new indices from the analysis of the baroreflex mechanism.

How does obstructive sleep apnea alter cerebral hemodynamics?

STUDY OBJECTIVES: We aimed to characterize the cerebral hemodynamic response to obstructive sleep apnea/hypopnea events, and evaluate their association to polysomnographic parameters. The characterization of the cerebral hemodynamics in obstructive sleep apnea (OSA) may add complementary information to further the understanding of the severity of the syndrome beyond the conventional polysomnography. METHODS: Severe OSA patients were studied during night sleep while monitored by polysomnography. Transcranial, bed-side diffuse correlation spectroscopy (DCS) and frequency-domain near-infrared diffuse correlation spectroscopy (NIRS-DOS) were used to follow microvascular cerebral hemodynamics in the frontal lobes of the cerebral cortex. Changes in cerebral blood flow (CBF), total hemoglobin concentration (THC), and cerebral blood oxygen saturation (StO2) were analyzed. RESULTS: We considered 3283 obstructive apnea/hypopnea events from sixteen OSA patients (Age (median, interquartile range) 57 (52-64.5); females 25%; AHI (apnea-hypopnea index) 84.4 (76.1-93.7)). A biphasic response (maximum/minimum followed by a minimum/maximum) was observed for each cerebral hemodynamic variable (CBF, THC, StO2), heart rate and peripheral arterial oxygen saturation (SpO2). Changes of the StO2 followed the dynamics of the SpO2, and were out of phase from the THC and CBF. Longer events were associated with larger CBF changes, faster responses and slower recoveries. Moreover, the extrema of the response to obstructive hypopneas were lower compared to apneas (p < .001). CONCLUSIONS: Obstructive apneas/hypopneas cause profound, periodic changes in cerebral hemodynamics, including periods of hyper- and hypo-perfusion and intermittent cerebral hypoxia. The duration of the events is a strong determinant of the cerebral hemodynamic response, which is more pronounced in apnea than hypopnea events.

Analysis of the Cardiorespiratory Pattern of Patients Undergoing Weaning Using Artificial Intelligence.

The optimal extubating moment is still a challenge in clinical practice. Respiratory pattern variability analysis in patients assisted through mechanical ventilation to identify this optimal moment could contribute to this process. This work proposes the analysis of this variability using several time series obtained from the respiratory flow and electrocardiogram signals, applying techniques based on artificial intelligence. 154 patients undergoing the extubating process were classified in three groups: successful group, patients who failed during weaning process, and patients who after extubating failed before 48 hours and need to reintubated. Power Spectral Density and time-frequency domain analysis were applied, computing Discrete Wavelet Transform. A new Q index was proposed to determine the most relevant parameters and the best decomposition level to discriminate between groups. Forward selection and bidirectional techniques were implemented to reduce dimensionality. Linear Discriminant Analysis and Neural Networks methods were implemented to classify these patients. The best results in terms of accuracy were, 84.61 ± 3.1% for successful versus failure groups, 86.90 ± 1.0% for successful versus reintubated groups, and 91.62 ± 4.9% comparing the failure and reintubated groups. Parameters related to Q index and Neural Networks classification presented the best performance in the classification of these patients.

Coherence Analysis between the Surface Diaphragm EMG Envelope Signal and the Respiratory Signal derived from the ECG in Patients assisted by Mechanical Ventilation.

Prolonged use of mechanical ventilation (MV) can lead to greater complications for a patient. In clinical practice, it is important to identify patients who could fail in the extubation process. However, accurately predicting the outcome of this process remains a challenge. The diaphragm muscle is one of the most active elements in the breathing process. On the other hand, there are several techniques to derive respiratory information from the ECG signal. Signals derived from diaphragmatic activity and from the ECG, such as the envelope of the surface diaphragm electromyographic signal (sEMGi) and the respiratory signal derived from the electrocardiogram (ECG) could contribute to analyze the respiratory response in patients assisted by MV. This work proposes the analysis of the coherence between sEMGi and EDR signals to determine possible differences in the respiratory pattern between successful and failed patients undergoing weaning. 40 patients with MV, candidates for weaning trial process and underwent a spontaneous breathing test were analyzed, classified into: a successful group (SG: 19 patients) that maintained spontaneous breathing after the test, and a failed group (FG: 21 patients) that required reconnection to the MV. The cross correlation, power spectral density and magnitude squared coherence (MSC) of the sEMGi and the EDR signals were estimated. According to the results, the MSC parameters such as area under the curve and mean coherence value presented statistically significance differences between the two groups of patients (p = 0.024). Our results suggest that both sEMGi and EDR signals could provide information about the behavior of the respiratory system in these patients. Clinical Relevance- This study analyzes the correlation and the coherence between the envelope of the surface electromyographic signal and the respiratory signal derived from the ECG to characterize the respiratory pattern of successful and failed patients on weaning process.

Design of a Classifier to Determine the Optimal Moment of Weaning of Patients undergoing to the T-tube Test.

Weaning from mechanical ventilation in the intensive care unit is a complex and relevant clinical problem. Prolonged mechanical ventilation leads to a variety of medical complications that increase hospital stay and costs, in addition to contributing the morbidity and mortality, affecting long-term quality of life. This work presents a methodology to establish the optimal moment of extubation of a patient connected to a mechanical ventilator, submitted to the T-Tube test. 133 patients are analyzed, classified into two groups: successful group (94 patients) and failed group (39 patients). The behaviour of the respiratory function is characterized through the mean, standard deviation, kurtosis, skewness, interquartile range and coefficient of interval of the respiratory flow time series. To classify these patients, neural networks (NN) and support vector machines (SVM) classifier are used, considering time intervals of the 450s, 600s and 900s. According to the results, the best classification is obtained using the SVM. Clinical Relevance-The paper determines the optimal moment for weaning a patient connected to a mechanical ventilator using machine learning techniques.

Cardiorespiratory Phase Synchronization in Elderly Patients with Periodic and non-Periodic Breathing Patterns.

Cardiorespiratory Phase Synchronization (CRPS) is the manifestation of the non-linear coupling between the cardiac and the respiratory systems, different to the Respiratory Sinus Arrythmia (RSA). This takes place when the heartbeats occur at the same relative phase of the breathing, during a succession of respiratory cycles. In this study, we investigated the CRPS in 45 elderly patients admitted to the semi-critical unit of a hospital. The patients were classified according to their respiratory state as non-Periodic Breathing (nPB), Periodic Breathing (PB) and Cheyne-Stokes Respiration (CSR). The phase synchrogram between the electrocardiographic and respiratory signals was computed using the Hilbert transform technique. A continuous measure of the CRPS was obtained from the synchrogram, and was characterized by the average duration of synchronized epochs (A vgDurSync), the percentage of synchronized time (%Sync), the number of synchronized epochs (NumSync), and the frequency ratio between the cardiac and respiratory oscillators (FreqRat). These measures were studied using two different thresholds (0.1 and 0.05) for the amplitude of the synchronization and a minimum duration threshold of 10s. According to the results, the AvgDurSync and %Sync had a decreasing trend in patients with breathing periodicity. In addition, CSR patients presented the lowest values A vgDurSync and %Sync. Therefore, the CRPS method could be a useful tool for characterizing periodic respiratory patterns in elderly patients, which might be related to chronic heart failure. Clinical Relevance- This study analyzes the synchronization between cardiac and respiratory systems in elderly patients with a possible progressive decompensation in the cardiac function.

Recurrence Plot-based Classification of Ischemic and Dilated Cardiomyopathy Patients.

A large portion of the elderly population are affected by cardiovascular diseases. The early prognosis of cardiomyopathies is still a challenge. The aim of this study was to classify cardiomyopathy patients by their etiology in function of significant indexes extracted from the characterization of the recurrence plot of the systems involved. Thirty-nine cardiomyopathy patients (CMP) classified as ischemic (ICM - 24 patients) and dilated (DCM-15 patients) were considered. In addition, thirty-nine control subjects (CON) were used as reference. The beat-to-beat (BBI) time series, from the electrocardiographic signal, the systolic (SBP), and diastolic (DBP) time series, from the blood pressure signal, and the respiratory time (FLW) from the respiratory flow signal, were extracted. The recurrence plot from each signal considered were calculated and characterized by a total of 12 indexes. The best classifiers were used to build support vector machine models. The optimal model to classify ICM versus DCM patients achieved 92.3% accuracy, 95.8% sensitivity, and 86.6% specificity. When comparing CMP patients and CON subjects, the best model achieved 85.8% accuracy, 92.3% sensitivity, and 80.1% specificity. Our results suggest a more deterministic behavior in DCM patients. Clinical Relevance - This study explores the recurrence plot for the classification of ICM and DCM patients.

Classification of ischemic and dilated cardiomyopathy patients based on the analysis of the pulse transit time.

Cardiomyopathies diseases affects a great number of the elderly population. An adequate identification of the etiology of a cardiomyopathy patient is still a challenge. The aim of this study was to classify patients by their etiology in function of indexes extracted from the characterization of the pulse transit time (PTT). This time series represents the time taken by the pulse pressure to propagate through the length of the arterial tree and corresponding to the time between R peak of ECG and the mid-point of the diastolic to systolic slope in the blood pressure signal. For each patient, the PTT time series was extracted. Thirty cardiomyopathy patients (CMP) classified as ischemic (ICM - 15 patients) and dilated (DCM - 15 patients) were analyzed. Forty-three healthy subjects (CON) were used as a reference. The PTT time series was characterized through statistical descriptive indices and the joint symbolic dynamics method. The best indices were used to build support vector machine models. The optimal model to classify ICM versus DCM patients achieved 89.6% accuracy, 78.5% sensitivity, and 100% specificity. When comparing CMP patients and CON subjects, the best model achieved 91.3% accuracy, 91.3% sensitivity, and 88.3% specificity. Our results suggests a significantly lower pulse transit time in ischemic patients.Clinical relevance- This study analyzed the suitability of the pulse transit time for the classification of ICM and DCM patients.

A new protocol to compare successful versus failed patients using the electromyographic diaphragm signal in extubation process.

In clinical practice, when a patient is undergoing mechanical ventilation, it is important to identify the optimal moment for extubation, minimizing the risk of failure. However, this prediction remains a challenge in the clinical process. In this work, we propose a new protocol to study the extubation process, including the electromyographic diaphragm signal (diaEMG) recorded through 5-channels with surface electrodes around the diaphragm muscle. First channel corresponds to the electrode on the right. A total of 40 patients in process of withdrawal of mechanical ventilation, undergoing spontaneous breathing tests (SBT), were studied. According to the outcome of the SBT, the patients were classified into two groups: successful (SG: 19 patients) and failure (FG: 21 patients) groups. Parameters extracted from the envelope of each channel of diaEMG in time and frequency domain were studied. After analyzing all channels, the second presented maximum differences when comparing the two groups of patients, with parameters related to root mean square (p = 0.005), moving average (p = 0.001), and upward slope (p = 0.017). The third channel also presented maximum differences in parameters as the time between maximum peak (p = 0.004), and the skewness (p = 0.027). These results suggest that diaphragm EMG signal could contribute to increase the knowledge of the behaviour of respiratory system in these patients and improve the extubation process.Clinical Relevance-This establishes the characterization of success and failure patients in the extubation process.

Comparison of ECG-Derived Respiration Estimation Methods on Healthy Subjects in function of Recording Site and Subject Position and Gender.

Respiration rate can be assessed by analyzing respiratory changes of the electrocardiogram (ECG). Several methods can be applied to derive the respiratory signal from the ECG (EDR signal). In this study, four EDR estimation methods based on QRS features were analyzed. A database with 44 healthy subjects (16 females) in supine and sitting positions was analyzed. Respiratory flow and ECG recordings on leads I, II, III and a Chest lead was studied. A QR slope-based method, an RS slope-based method, an QRS angle-based method and an QRS area-based method were applied. Their performance was evaluated by the correlation coefficient with the reference respiratory volume signal. Significantly higher correlation coefficients in the range r = 0.77 - 0.86 were obtained with the Chest lead for all methods. The EDR estimation method based on the QRS angle provided the highest similarity with the volume signal for all recording leads and subject positions. We found no statistically significant differences according to gender or subject position.Clinical Relevance- This work analyzes the EDR signal from four electrocardiographic leads to obtain the respiratory signal and contributes to a simplified analysis of respiratory activity.

Cardiorespiratory and Vascular Variability Analysis to Classify Patients with Ischemic and Dilated Cardiomyopathy.

Heart diseases are the leading cause of death in developed countries. Ascertaining the etiology of cardiomyopathies is still a challenge. The objective of this study was to classify cardiomyopathy patients through cardio, respiratory and vascular variability analysis, considering the vascular activity as the input and output of the baroreflex response. Forty-one cardiomyopathy patients (CMP) classified as ischemic (ICM, 24 patients) and dilated (DCM, 17 patients) were analyzed. Thirty-nine elderly control subjects (CON) were used as reference. From the electrocardiographic, respiratory flow, and blood pressure signals, following temporal series were extracted: beat-to-beat intervals (BBI), total respiratory cycle time series (TT), and end- systolic (SBP) and diastolic (DBP) blood pressure amplitudes, respectively. Three-dimensional representation of the cardiorespiratory and vascular activities was characterized geometrically, by fitting a polygon that contains 95% of data, and by statistical descriptive indices. The best classifiers were used to build support vector machine models. The optimal model to classify ICM versus DCM patients achieved 92.7% accuracy, 94.1% sensitivity, and 91.7% specificity. When comparing CMP patients and CON subjects, the best model achieved 86.2% accuracy, 82.9% sensitivity, and 89.7% specificity. These results suggest a limited ability of cardiac and respiratory systems response to regulate the vascular variability in these patients.

Risk Stratification in Idiopathic Dilated Cardiomyopathy Patients Using Cardiovascular Coupling Analysis.

Cardiovascular diseases are one of the most common causes of death; however, the early detection of patients at high risk of sudden cardiac death (SCD) remains an issue. The aim of this study was to analyze the cardio-vascular couplings based on heart rate variability (HRV) and blood pressure variability (BPV) analyses in order to introduce new indices for noninvasive risk stratification in idiopathic dilated cardiomyopathy patients (IDC). High-resolution electrocardiogram (ECG) and continuous noninvasive blood pressure (BP) signals were recorded in 91 IDC patients and 49 healthy subjects (CON). The patients were stratified by their SCD risk as high risk (IDCHR) when after two years the subject either died or suffered life-threatening complications, and as low risk (IDCLR) when the subject remained stable during this period. Values were extracted from ECG and BP signals, the beat-to-beat interval, and systolic and diastolic blood pressure, and analyzed using the segmented Poincaré plot analysis (SPPA), the high-resolution joint symbolic dynamics (HRJSD) and the normalized short time partial directed coherence methods. Support vector machine (SVM) models were built to classify these patients according to SCD risk. IDCHR patients presented lowered HRV and increased BPV compared to both IDCLR patients and the control subjects, suggesting a decrease in their vagal activity and a compensation of sympathetic activity. Both, the cardio -systolic and -diastolic coupling strength was stronger in high-risk patients when comparing with low-risk patients. The cardio-systolic coupling analysis revealed that the systolic influence on heart rate gets weaker as the risk increases. The SVM IDCLR vs. IDCHR model achieved 98.9% accuracy with an area under the curve (AUC) of 0.96. The IDC and the CON groups obtained 93.6% and 0.94 accuracy and AUC, respectively. To simulate a circumstance in which the original status of the subject is unknown, a cascade model was built fusing the aforementioned models, and achieved 94.4% accuracy. In conclusion, this study introduced a novel method for SCD risk stratification for IDC patients based on new indices from coupling analysis and non-linear HRV and BPV. We have uncovered some of the complex interactions within the autonomic regulation in this type of patient.

Cardiovascular Coupling-Based Classification of Ischemic and Dilated Cardiomyopathy Patients.

Cardiovascular diseases are one of the most common causes of death in elderly patients. The etiology of cardiomyopathies is difficult to discern clinically. The objective of this study was to classify cardiomyopathy patients using coupling analysis, through their cardiovascular behavior and the baroreflex response. A total of thirty-eight cardiomyopathy patients (CMP) classified as ischemic (ICM, 25 patients) and dilated (DCM, 13 patients) were analyzed. Thirty elderly control subjects (CON) were used as reference. Their electrocardiographic (ECG) and blood pressure (BP) signals were studied. To characterize the cardiovascular activity, the following temporal series were extracted: beat-to-beat intervals (from the ECG signal), and end- systolic and diastolic blood pressure amplitudes (from the BP signal). Non-linear characterization techniques like high resolution joint symbolic dynamics, segmented Poincaré plot analysis, normalized shorttime partial directed coherence, and dual sequence method were used to characterize these times series. The best indices were used to build support vector machine models for classification. The optimal model for ICM versus DCM patients achieved 84.2% accuracy, 76.9% sensitivity, and 88% specificity. When CMP patients and CON subjects were compared, the best model achieved 95.5% accuracy, 97.3% sensitivity, and 93.3% specificity. These results suggest a disfunction in the baroreflex mechanism in cardiomyopathies patients.

Linear Mixed Effects Modelling of Oxygen Desaturation after Sleep Apneas and Hypopneas: A Pilot Study.

Obstructive Sleep Apnea severity is commonly determined after a sleep polysomnographic study by the Apnea-Hypopnea Index (AHI). This index does not contain information about the duration of events, and weights apneas and hypopneas alike. Significant differences in disease severity have been reported in patients with the same AHI. The aim of this work was to study the effect of obstructive event type and duration on the subsequent oxygen desaturation (SaO2) by mixed-effects models. These models allow continuous and categorical independent variables and can model within-subject variability through random effects. The desaturation depth dSaO2, desaturation duration dtSaO2 and desaturation area dSaO2A were analyzed in the 2022 apneas and hypopneas of eight severe patients. A mixed-effects model was defined to account for the influence of event duration (AD), event type, and their interaction on SaO2 parameters. A two-step backward model reduction process was applied for random and fixed effects optimization. The optimum model obtained for dtSaO2 suggests an almost subject-independent proportion increase with AD, which did not significantly change in apneas as compared to hypopneas. The optimum model for dSaO2 reveals a significantly higher increase as a function of AD in apneas than hypopneas. Dependence of on event type and duration was different in every subject, and a subject-specific model could be obtained. The optimum model for SaO2A combines the effects of the other two. In conclusion, the proposed mixed-effects models for SaO2 parameters allow to study the effect of respiratory event duration and type, and to include repeated events within each subject. This simple model can be easily extended to include the contribution of other important factors such as patient severity, sleep stage, sleeping position, or the presence of arousals.

Respiratory Sinus Arrhythmia Quantified with Linear and Non-Linear Techniques to Classify Dilated and Ischemic Cardiomyopathy.

In congestive heart failure (CHF), dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two highly related pathologies that are not fully characterized. The aim of this study is to assess respiratory sinus arrhythmia (RSA) index of the parasympathetic system, in order to discriminate between both pathologies, DCM and ICM. For this, ECG-signals of 49 subjects (12 DCM patients, 21 ICM patients, 6 ICM patients with diabetes mellitus (DM) type II and 10 control subjects) from the database HERIS II and of 173 subjects (50 DCM, 50 ICM, 15 DCM with DM type II, 15 ICM with DM type II and 47 control subjects) from the database MUSIC2 were analyzed. The RSA was quantified using linear and non-linear analysis methods (fractal dimension and entropy). The results showed a significant difference between ICM and DCM subjects (p=0.013) with a sensitivity of 83% and specificity of 90%. Decreasing RSA values were present in CHF patients, especially in ICM patients, in comparison with healthy subjects. Alterations in the parasympathetic system due to DM were also identified.

Cardiorespiratory Phase Synchronization increases during certain mental stimuli in healthy subjects.

Several neurological and mechanical non-linear mechanisms relate the respiratory and cardiovascular systems to one another. Besides the well-known modulation of heart rate by respiration, another form of non-linear interaction between both systems is Cardiorespiratory Phase Synchronization (CRPS). In this study we investigated CRPS on a group of 27 healthy individuals subject to a stimulation protocol with five different mental states: a basal state, a videogame, a comedy video, a suspense video and a reading state. Acontinuous measure of CRPS was calculated from the phase synchrogram between respiratory and electrocardiographic signals. Periods of CRPS were characterized by their average duration (AvDurSync) and by the percentage of synchronized time (%Sync) within each mental state. These measures were studied considering two thresholds: a minimum amplitude and a minimum duration for synchronization. Each subject exhibited a particular pattern of phase locking ratios along the different mental states. We observed that, in all states, %Sync decreased and AvDurSync increased in proportion to the minimum duration threshold. Both measures were inversely proportional to the minimum amplitude threshold.uring the videogame, subjects showed a significantly higher %Sync as compared to any other mental stimulus, irrespective of the minimum duration threshold. Mental stimulation can be an alternative approach to enhance cardiorespiratory coupling when subjects have difficulties to perform aerobic exercise, such as in patients with Chronic Obstructive Pulmonary Disease or Chronic Heart failure.

Characterization of the microvascular cerebral blood flow response to obstructive apneic events during night sleep.

Obstructive apnea causes periodic changes in cerebral and systemic hemodynamics, which may contribute to the increased risk of cerebrovascular disease of patients with obstructive sleep apnea (OSA) syndrome. The improved understanding of the consequences of an apneic event on the brain perfusion may improve our knowledge of these consequences and then allow for the development of preventive strategies. Our aim was to characterize the typical microvascular, cortical cerebral blood flow (CBF) changes in an OSA population during an apneic event. Sixteen patients (age 58 ± 8 years , 75% male) with a high risk of severe OSA were measured with a polysomnography device and with diffuse correlation spectroscopy (DCS) during one night of sleep with 1365 obstructive apneic events detected. All patients were later confirmed to suffer from severe OSA syndrome with a mean of 83 ± 15 apneas and hypopneas per hour. DCS has been shown to be able to characterize the microvascular CBF response to each event with a sufficient contrast-to-noise ratio to reveal its dynamics. It has also revealed that an apnea causes a peak increase of microvascular CBF ( 30 ± 17 % ) at the end of the event followed by a drop ( - 20 ± 12 % ) similar to what was observed in macrovascular CBF velocity of the middle cerebral artery. This study paves the way for the utilization of DCS for further studies on these populations.