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.

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.

Patients classification on weaning trials using neural networks and wavelet transform.

The determination of the optimal time of the patients in weaning trial process from mechanical ventilation, between patients capable of maintaining spontaneous breathing and patients that fail to maintain spontaneous breathing, is a very important task in intensive care unit. Wavelet Transform (WT) and Neural Networks (NN) techniques were applied in order to develop a classifier for the study of patients on weaning trial process. The respiratory pattern of each patient was characterized through different time series.

Brain tumour classification using Gaussian decomposition and neural networks.

The development, implementation and use of computer-based medical decision support systems (MDSS) based on pattern recognition techniques holds the promise of substantially improving the quality of medical practice in diagnostic and prognostic tasks. In this study, the core of a decision support system for brain tumour classification from magnetic resonance spectroscopy (MRS) data is presented. It combines data pre-processing using Gaussian decomposition, dimensionality reduction using moving window with variance analysis, and classification using artificial neural networks (ANN).

Diagnosis of brain tumours from magnetic resonance spectroscopy using wavelets and Neural Networks.

The diagnosis of human brain tumours from noninvasive signal measurements is a sensitive task that requires specialized expertise. In this task, radiology experts are likely to benefit from the support of computer-based systems built around robust classification processes. In this brief paper, a method that combines data pre-processing using wavelets with classification using Artificial Neural Networks is shown to yield high diagnostic classification accuracy for a broad range of brain tumour pathologies.

Data mining of patients on weaning trials from mechanical ventilation using cluster analysis and neural networks.

The process of weaning from mechanical ventilation is one of the challenges in intensive care. 149 patients under extubation process (T-tube test) were studied: 88 patients with successful trials (group S), 38 patients who failed to maintain spontaneous breathing and were reconnected (group F), and 23 patients with successful test but that had to be reintubated before 48 hours (group R). Each patient was characterized using 8 time series and 6 statistics extracted from respiratory and cardiac signals.