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.

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 (V) considering different breathing patterns.

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.

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.