José Luis Rodríguez-Sotelo

TL;DR: This work proposes a new automatic sleep classification method based on unsupervised feature classification algorithms recently developed, and on EEG entropy measures, which reached up to an average of 80% correctly classified stages for each patient separately while keeping the computational cost low.

TL;DR: An unsupervised method based on relevance analysis is described that uses a least-squares optimization of the input feature matrix in a single iteration to address the task of data mining in physiological records using a feature selection scheme.

TL;DR: This paper addresses the specific problem of detecting atrial premature beats, that had been demonstrated to be a marker for stroke risk or cardiac arrhythmias, and consists of a stage to estimate characteristics such as morphology of P wave and QRS complex as well as indices of prematurity and a non-supervised stage to separate heartbeat feature vectors into classes.

TL;DR: In this paper, a metodologia for el diagnostico de lesion de rodilla, patologia comun y de multiples causas is presented. Butte et al. present a sistema propuesto emplea senales electromiograficas de superficie (EMGS) and senales de goniometria, evaluadas with metodos de analisis de senales in el dominio del tiempo-frecuencia como el espectrograma and the transformada wavelet.

TL;DR: A method that improves the feature selection stage for non-supervised analysis of Holter ECG signals is presented, and is focused to classify cardiac arrhythmias into 5 groups, according to the standard of the AAMI.