Methodology for determine the moment of disconnection of patients of the mechanical ventilation using neural network

The process of weaning from mechanical ventilation is one of the challenges in intensive care units. In this paper 66 patients under extubation process (T-tube test) were studied: 33 patients with successful trials and 33 patients who failed to maintain spontaneous breathing and were reconnected. Each patient was characterized using 7 time series from respiratory signals, and for each serie was extracted 4 statistics data. Two types of Neural Networks were applied for discriminate between patients from the two groups: radial basis function and multilayer perceptron, getting better results with the second type of network.Postprint (published version

Classifier design for patients on weaning process

The mechanical ventilation (MV) is a therapeutic strategy to mechanically assist or replace spontaneous breathing. With the objective of developing a software support for doctors was performed a study of respiratory signals using the discrete wavelet transform to determine the descriptors to indicate whether the patient can be disconnected from the mechanical ventilator. To reduce the dimensionality of the system was performed a principal component analysis (PCA), establishing three variables optimal, which are the inputs to the classifiers that were analyzed in the article: K-Nearest Neighbor and fuzzy logic.La ventilación mecánica (VM) es una estrategia terapéutica que consiste en asistir o reemplazar mecánicamente la ventilación pulmonar espontánea. Con el objetivo de desarrollar un software de apoyo para los médicos, se realizó un estudio de las señales respiratorias, utilizando la transformada de wavelet discreta, para determinar los descriptores que indiquen si el paciente puede ser desconectado del ventilador mecánico. Para reducir la dimensionalidad del sistema se realizó un análisis de componentes principales (PCA), determinando tres variables óptimas, las cuales son las entradas a los clasificadores que se analizaron en el artículo: K-Nearest Neighbor y lógica difusa

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.Peer ReviewedPostprint (published version

Hyperoxemia and excess oxygen use in early acute respiratory distress syndrome : Insights from the LUNG SAFE study

Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: Concerns exist regarding the prevalence and impact of unnecessary oxygen use in patients with acute respiratory distress syndrome (ARDS). We examined this issue in patients with ARDS enrolled in the Large observational study to UNderstand the Global impact of Severe Acute respiratory FailurE (LUNG SAFE) study. Methods: In this secondary analysis of the LUNG SAFE study, we wished to determine the prevalence and the outcomes associated with hyperoxemia on day 1, sustained hyperoxemia, and excessive oxygen use in patients with early ARDS. Patients who fulfilled criteria of ARDS on day 1 and day 2 of acute hypoxemic respiratory failure were categorized based on the presence of hyperoxemia (PaO2 > 100 mmHg) on day 1, sustained (i.e., present on day 1 and day 2) hyperoxemia, or excessive oxygen use (FIO2 ≥ 0.60 during hyperoxemia). Results: Of 2005 patients that met the inclusion criteria, 131 (6.5%) were hypoxemic (PaO2 < 55 mmHg), 607 (30%) had hyperoxemia on day 1, and 250 (12%) had sustained hyperoxemia. Excess FIO2 use occurred in 400 (66%) out of 607 patients with hyperoxemia. Excess FIO2 use decreased from day 1 to day 2 of ARDS, with most hyperoxemic patients on day 2 receiving relatively low FIO2. Multivariate analyses found no independent relationship between day 1 hyperoxemia, sustained hyperoxemia, or excess FIO2 use and adverse clinical outcomes. Mortality was 42% in patients with excess FIO2 use, compared to 39% in a propensity-matched sample of normoxemic (PaO2 55-100 mmHg) patients (P = 0.47). Conclusions: Hyperoxemia and excess oxygen use are both prevalent in early ARDS but are most often non-sustained. No relationship was found between hyperoxemia or excessive oxygen use and patient outcome in this cohort. Trial registration: LUNG-SAFE is registered with ClinicalTrials.gov, NCT02010073publishersversionPeer reviewe

Preprocessing MRS information for classification of human brain tumours

Peer ReviewedPostprint (published version

Diseño de un clasificador para pacientes en proceso de extubación

La ventilación mecánica (VM) es una estrategia terapéutica que consiste en asistir o reemplazar mecánicamente la ventilación pulmonar espontánea. Con el objetivo de desarrollar un software de apoyo para los médicos, se realizó un estudio de las señales respiratorias, utilizando la transformada de wavelet discreta, para determinar los descriptores que indiquen si el paciente puede ser desconectado del ventilador mecánico. Para reducir la dimensionalidad del sistema se realizó un análisis de componentes principales (PCA), determinando tres variables óptimas, las cuales son las entradas a los clasificadores que se analizaron en el artículo: K-Nearest Neighbor y lógica difusa

Preprocessing MRS information for classification of human brain tumours

Peer Reviewe

Methodology for determine the moment of disconnection of patients of the mechanical ventilation using discrete wavelet transform

The process of weaning from mechanical ventilation is one of the challenges in intensive care units. 66 patients under extubation process (T-tube test) were studied: 33 patients with successful trials and 33 patients who failed to maintain spontaneous breathing and were reconnected. Each patient was characterized using 7 time series from respiratory signals, and for each serie was evaluated the discrete wavelet transform. It trains a neural network for discriminating between patients from the two groups

Oftel consultative document 'Numbering conventions and specified numbering scheme' Commentary on responses

SIGLEAvailable from British Library Document Supply Centre- DSC:GPC/04953 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Methodology for determine the moment of disconnection of patients of the mechanical ventilation using discrete wavelet transform

The process of weaning from mechanical ventilation is one of the challenges in intensive care units. 66 patients under extubation process (T-tube test) were studied: 33 patients with successful trials and 33 patients who failed to maintain spontaneous breathing and were reconnected. Each patient was characterized using 7 time series from respiratory signals, and for each serie was evaluated the discrete wavelet transform. It trains a neural network for discriminating between patients from the two groups