Analysis and classification of the breathing pattern in patients on weaning trial process

La estimación del momento óptimo de retirar la ventilación asistida de un paciente en cuidado intensivo sigue siendo fundamental en la práctica clínica. En este trabajo se estudia el patrón respiratorio a partir de la señal de flujo respiratorio de pacientes en proceso de extubación teniendo en cuenta las siguientes etapas: caracterización de la señal a partir de la identificación de los ciclos respiratorios, análisis del patrón respiratorio a partir del modelado matemático de las series, y clasificación del mismo con el objetivo de identificar patrones de pacientes con posible éxito en el proceso. Se analizaron 153 pacientes clasificados en los grupos éxito, fracaso y reintubados, de acuerdo con el resultado de la prueba de extubación de tubo en T. Se seleccionaron las series temporales de tiempo de espiración, tiempo de inspiración, duración del ciclo respiratorio e índice de respiración superficial dado que presentaron diferencias significativas en los parámetros de valor medio, orden del modelo, primer coeficiente y error final de predicción. Con ellas se obtuvo una exactitud de clasificación del 86% (sensibilidad 0,86 – especificidad 0,84) utilizando un clasificador tipo discrimante lineal. Se analizaron otros clasificadores como regresión logística y máquinas de soporte vectorial.Estimating the optimal time to remove the ventilatory support from a patient in intensive care remains essential in clinical practice. In this work we study the breathing pattern from the respiratory flow signal in the process of weaning considering the following stages: characterization of the signal from the identification of respiratory cycles, respiratory pattern analysis from mathematical modeling of the resulting series, and classification in order to identify patterns of patients with possible success in the process. We analyzed 153 patients classified into three groups: success, failure and reintubated, according to results of T-tube test. The time series for breathing duration, inspiratory time, expiratory time, and shallow breathing index that resulted in significant differences in the mean, model order, first coefficient and final error of prediction were selected. With them we obtained a classification accuracy of 86% (sensitivity 0.84 - specificity 0.86) using a linear classifier discriminate type. Other classifications were analyzed, such as logistic regression and support vector machines

Scale-invariant magnetoresistance in a cuprate superconductor

The anomalous metallic state in high-temperature superconducting cuprates is masked by the onset of superconductivity near a quantum critical point. Use of high magnetic fields to suppress superconductivity has enabled a detailed study of the ground state in these systems. Yet, the direct effect of strong magnetic fields on the metallic behavior at low temperatures is poorly understood, especially near critical doping, $x=0.19$. Here we report a high-field magnetoresistance study of thin films of \LSCO cuprates in close vicinity to critical doping, $0.161\leq x\leq0.190$. We find that the metallic state exposed by suppressing superconductivity is characterized by a magnetoresistance that is linear in magnetic field up to the highest measured fields of $80$T. The slope of the linear-in-field resistivity is temperature-independent at very high fields. It mirrors the magnitude and doping evolution of the linear-in-temperature resistivity that has been ascribed to Planckian dissipation near a quantum critical point. This establishes true scale-invariant conductivity as the signature of the strange metal state in the high-temperature superconducting cuprates.Comment: 10 pages, 3 figure

La Curva en S como Herramienta para la Medición de los Ciclos de Vida de Productos

The aim of this article was to carry out a study of the life cycles of three products of Colombian companies; based on a logistic model of population growth as a life cycles measurement tool. We found that the products life cycles have a similar behavior to the population growth, according to an S curve. The inflection points of the curves were obtained by a nonlinear regression. These points might be used as a tool for strategic decision making in products, in terms of identifying key instants for launching technological innovations, investments and execute marketing strategies