The automatic classification of cardiac conditions—specifically Arrhythmia (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR)—from electrocardiogram (ECG) signals remains a critical challenge in clinical diagnostics. This paper presents the design of a Recurrent Neural Network (RNN) classifier using ECG recordings from PhysioNet-derived datasets. The proposed methodology relies on a multi-domain hand-crafted feature extraction strategy to capture the complex dynamics of cardiac signals. To ensure temporal consistency, ECG signals were segmented into 10 -second windows, from which a comprehensive feature set was computed across four key domains: time-domain statistics, Hjorth parameters, frequency-domain spectral descriptors, and nonlinear measures derived from Poincaré plots. A statistical feature selection pipeline—combining the Kruskal–Wallis test, Benjamini–Hochberg False Discovery Rate (FDR) correction, and effect size analysis—was applied to retain the most discriminative and non-redundant predictors. To identify the most effective temporal modeling strategy, the classifier incorporates a Bayesian hyperparameter optimization framework that automatically selects the optimal recurrent architecture among Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Units (GRU) topologies. This process simultaneously tunes model complexity and regularization parameters to mitigate overfitting. In addition, class imbalance is addressed through a cost-sensitive learning approach using weighted loss functions. The resulting classifier achieves strong discriminatory performance across the three rhythm classes, demonstrating that a Bayesian-optimized RNN architecture combined with domain-specific features provides a solution for cardiac rhythm analysis.

Acute respiratory distress syndrome often necessitates prolonged periods of mechanical ventilation for patient management. Therefore, it is crucial to make appropriate decisions regarding extubation to prevent potential harm to patients and avoid the associated risks of reintubation and extubation cycles. One atypical form of acute respiratory distress syndrome is associated with COVID-19, impacting patients admitted to the intensive care unit. This study presents the design of two classifiers: the first employs machine learning techniques, while the second utilizes a convolutional neural network. Their purpose is to assess whether a patient can safely be disconnected from a mechanical ventilator following a spontaneous breathing test. The machine learning algorithm uses descriptors derived from the variability of time-frequency representations computed with the non-uniform fast Fourier transform. These representations are applied to time series data, which consist of markers extracted from the electrocardiographic and respiratory flow signals sourced from the Weandb database. The input image for the convolutional neural network is formed by combining the spectrum of the RR signal and the spectrum of two parameters recorded from the respiratory flow signal, calculated using non-uniform fast Fourier transform. Three pre-trained network architectures are analyzed: Googlenet, Alexnet and Resnet-18. The best model is obtained with a CNN with the Resnet-18 architecture, presenting an accuracy of 90.1 +/- 4.3%.

Surface electromyographic (sEMG) signals from the diaphragm has become a valuable tool for monitoring muscle activity during the weaning process from mechanical ventilation. However, EMG signals are inherently nonlinear and susceptible to noise contamination, which poses challenges for traditional signal processing methods. In this study, we propose the use of entropy metrics to evaluate the dynamic complexity and irregularity of surface electromyographic (EMG) signals of patients assisted with mechanical ventilation. Shannon entropy and spectral entropy were computed to analyze EMG signals from two surface diaphragm channels recorded in mechanically ventilated patients during extubation preparation. According to clinical criteria, the patients were classified into the successful group (GE) - 19 patients with successful extubation after 48 hours, and the failure group (GF) - 21 patients who required reconnection to the ventilator within 48 hours. sEMG signals were recorded using 5-channel surface electrodes placed around the diaphragm muscle. Shannon and spectral entropies were calculated using a 0.5-minute window with an overlap of 80%. The results presented a greater complexity of the EMG signal in the SG group. This group shows higher peaks in Shannon entropy and elevated spectral entropy values compared to the FG group. Channels 2 and 3 presented the largest statistically significant differences.Clinical Relevance- Analyzing diaphragm EMG signals using entropy metrics could improve patient outcomes by optimizing the timing of extubation. These metrics would serve as a key indicator of readiness for extubation, providing an objective basis for more informed clinical decision-making.

The spectral analysis of non-uniformly sampled biomedical signals presents significant challenges due to technical and physiological constraints that limit uniform sampling. This study compares two approaches for spectral estimation: the Non-Uniform Fourier Transform (NUFT) and the Fast Fourier Transform (FFT) applied to interpolated data. The methodology evaluates the accuracy and stability of the spectral features derived from both methods using respiratory flow signals. The respiratory pattern is characterized through the following time series: expiratory time (TE), inspiratory time (TI), breathing duration (TTot), and tidal volume (VT), and frequency-tidal volume ratio (f/VT) where f is respiratory rate. The interpolated methods: linear, spline, pchip, and makima are analyzed. The Results show that NUFT preserves spectral integrity more effectively by avoiding artifacts introduced by interpolation. These findings support the use of NUFT in biomedical signal processing, particularly in the development of robust machine learning models for clinical decision-making.Clinical Relevance—Reliable spectral features are essential for classification systems used in clinical settings. This study emphasizes the importance of preprocessing in preserving those features and demonstrates how NUFT can support early disease detection and personalized patient monitoring by improving the spectral analysis of irregularly sampled physiological signals.

Acute respiratory distress syndrome (ARDS) is a severe pulmonary condition that often requires mechanical ventilation (MV) to ensure adequate gas exchange and minimize ventilator-induced lung injury. This study compares statistical descriptors derived from respiratory time series with nonlinear variability metrics obtained from Poincaré plots to predict the outcomes of the weaning process in mechanically ventilated patients. The database, which includes respiratory flow recordings from 243 patients who underwent a standardized 30-minute spontaneous breathing test (SBT), was used to validate the classification models. Patients were categorized into three clinical outcome groups: successful weaning (n = 132), failed weaning (n = 88), and reintubation within 48 hours after completion of the trial (n = 23). Features were selected using nonparametric statistical tests and correlation analysis, eliminating redundancy and retaining discriminative variables. Two machine learning (ML) classifiers, random forest and feedforward neural network, were designed to identify patients belonging to each of the three clinical outcome groups. Model performance was assessed using stratified hold-out cross-validation repeated over 150 iterations, with hyperparameters optimized using Bayesian methods. The random forest classifier using Poincaré descriptors achieved a mean accuracy of 90.4% and higher F1-scores across all groups, including those who required reintubation. These findings suggest that Poincaré-based variability metrics, in combination with ensemble learning, may enhance the accurate prediction of MV weaning outcomes in ARDS patients.

Chronic heart failure (CHF) is a significant public health concern due to its increasing prevalence, high number of hospital admissions, and associated mortality. Its prevalence is progressively increasing due to the aging of the population and the decrease in mortality from acute myocardial infarction, among other medical advancements. Consequently, the incidence of CHF predominantly affects older age groups, doubling its prevalence every decade, becoming one of the main causes of mortality in patients older than 65 years. The main objective of this study is to apply machine learning based techniques to determine the best models to classify patients with chronic heart failure through their respiratory pattern. These patterns have been characterized from time series such as inspiratory and expiratory times, breathing duration, and tidal volume obtained from the respiratory flow signal. Based on the behavior of the respiratory pattern, CHF patients were classified into patients with non-periodic breathing, with periodic breathing, and with Cheyene-Stokes respiration (CSR). Time-frequency and statistical techniques have been implemented to analyze these features, and then various classification methods have been applied to define the optimal model with the best accuracy rates. These models could help to better understand the evolution of this disease and in early diagnosis.

The issue of failed weaning is a critical concern in the intensive care unit (ICU) setting. This scenario occurs when a patient experiences difficulty maintaining spontaneous breathing and ensuring a patent airway within the first 48 hours after the withdrawal of mechanical ventilation. Approximately 20 of ICU patients experience this phenomenon, which has severe repercussions on their health. It also has a substantial impact on clinical evolution and mortality, which can increase by 25 to 50. To address this issue, we propose a medical support system that uses a convolutional neural network (CNN) to assess a patients suitability for disconnection from a mechanical ventilator after a spontaneous breathing test (SBT). During SBT, respiratory flow and electrocardiographic activity were recorded and after processed using time-frequency analysis (TFA) techniques. Two CNN architectures were evaluated in this study: one based on ResNet50, with parameters tuned using a Bayesian optimization algorithm, and another CNN designed from scratch, with its structure also adapted using a Bayesian optimization algorithm. The WEANDB database was used to train and evaluate both models. The results showed remarkable performance, with an average accuracy 98 when using CNN from scratch. This model has significant implications for the ICU because it provides a reliable tool to enhance patient care by assisting clinicians in making timely and accurate decisions regarding weaning. This can potentially reduce the adverse outcomes associated with failed weaning events.

Spontaneous breathing trials (SBTs) represent a pivotal phase in the weaning process of mechanically ventilated patients. The objective of these trials is to assess patients readiness to resume independent breathing, thereby facilitating timely weaning and reducing the duration of mechanical ventilation (MV). Nevertheless, accurately predicting the success or failure of SBT remains a significant challenge in clinical practice. This study proposes a healthcare system that employs machine learning techniques to predict the outcome of SBT. The model is trained on respiratory flow and electrocardiogram (ECG) signals, employing the non-uniform discrete Fourier transform (NUDFT) for frequency domain analysis. The SBT prediction model has the potential to significantly enhance clinical decision-making by enabling the early identification of patients at risk for SBT failure, achieving an accuracy of 84.4.

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.

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 (V T ) 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 (Area ins , Area exp ), maximum volume relative to the cycle start and end (VT ins , VT exp ), inspiratory and expiratory time (T ins , T exp ), cycle duration (T tot ), and normalized parameters of clinical interest. The parameters with the greatest individual predictive power were combined using multivariable models to estimate V T . Their performance were quantified in terms of determination coefficient (R 2 ), relative error (E R ) and interquartile range (IQR). Using only three parameters, the results obtained for the thoracic band (VT exp , T tot , Area exp ) were better than those obtained from the abdominal band (VT exp , T ins , Area ins ) with R 2 = 0.94 (IQR: 0.07); E R = 6.99 (IQR: 6.12) vs R 2 = 0.91 (IQR: 0.09), E R = 8.70 (IQR: 4.62). Overall performance increased to R 2 = 0.97 (IQR: 0.02) and E R = 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 E R = 1.39 (IQR: 0.73), nose-mouth E R = 2.11 (IQR: 1.23) and mouth-mouth E R = 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.

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.

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.

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.

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.

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.

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.

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.

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.

27 Obstructive apnea causes periodic changes in cerebral and systemic hemodynamics, which 28 may contribute to the increased risk of cerebrovascular disease of patients with obstructive 29 sleep apnea (OSA) syndrome. The improved understanding of the consequences of an apneic 30 event on the brain perfusion may improve our knowledge of these consequences and then 31 allow for the development of preventive strategies. Our aim was to characterize the typical 32 microvascular, cortical cerebral blood flow (CBF) changes in an OSA population during an 33 apneic event. 34 Sixteen patients (age 58 ± 8 years, 75% male) with a high risk of severe OSA were 35 measured with a polysomnography device and with di use correlation spectroscopy (DCS) 36 during one night of sleep with 1365 obstructive apneic events detected. All patients were 37 later confirmed to su er from severe OSA syndrome with a mean of 83 ± 15 apneas and 38 hypopneas per hour. 39 DCS has been shown to be able to characterize the microvascular CBF ::::::::: response ::: to ::::: each 40 ::::: event :::::: with :: a :::::::::: su cient :::::::::::::::::: contrast-to-noise :::::: ratio ::: to ::::::: reveal :::: its ::::::::::: dynamics. :: It has also revealed 41 that an apnea causes a peak increase of microvascular CBF (30 ± 17 %) at the end of the 42 event followed by a drop (-20 ± 12 %) similar to what was observed in macrovascular CBF 43 velocity of the middle cerebral artery. This study paves the way for the utilization of DCS 44 for further studies on these populations. 45

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.