The definition of training and testing sets is carried out with a careful approach, in which a patient leave-one-patient-out (LOPO) strategy is advised to obtain test and train subsets. In each split, all example data generated from the same input patient would be used for testing, and the remaining patient’s data would compose the training subset. In the particular case of having 6 patients, the LOPO strategy is followed to prevent overfitting, due to the similarities between the PCLs of a single patient. To assess the model performance through LOPO strategy and tuning parameters at the same time, a cross-validation approach was used in order to find the parameters’ which presents the best configuration of the model.