In the presence of acute respiratory failure, mechanical ventilation emerges as a temporary alternative to maintain adequate gas exchange in the body such as that which occurs in natural respiration. This technique is widely used in intensive care units. Our objective was to carry out an analysis and interpretation of cardiorespiratory signals in patients assisted by mechanical ventilation, using non-linear analysis techniques of dynamic systems, data mining and machine learning techniques to establish indices that allow determining the appropriate moment of disconnection. in patients during the weaning process. We use three categories: Failure, success and reintubated. We introduced a new variant of Moving Window with Variance Analysis, with which good results are obtained. We have found that by using all the time series available in the database, we have obtained an accuracy of 96% when using simple symbolic dynamics to differentiate between successful weaning and reintubated cases. and 86% when comparing success and failure, which contrasts with the results observed in the state of the art.