Presents a new TU complex detection and characterization algorithm that consists of two stages; the first is a mathematical modeling of the electrocardiographic segment after QRS complex; the second uses classic threshold comparison techniques, over the signal and its first and second derivatives, to determine the significant points of each wave. Later, both T and U waves are morphologically classified. Amongst the principal innovations of this algorithm is the inclusion of U-wave characterization and a mathematical modeling stage, that avoids many of the problems of classic techniques when there is a low signal-to-noise ratio or when wave morphology is atypical. The results of the algorithm validation with the recently appeared QT database are also shown. For T waves these results are better when compared to other existing algorithms. U-wave results cannot be contrasted with other algorithms as, to the authors' knowledge, none are available. Examples showing the causes of principal discrepancies between the authors' algorithm and the QT database annotations are also given, and some ways of attempting to improve and benefit from the proposed algorithm are suggested.

N this article we describe the applica- I tion of a new artificial neural network model aimed at the morphological classi- fication of heartbeats detected on a multi- channel ECG signal. We emphasize the special characteristics of the algorithm as an adaptive classifier with the capacity to dynamically self-organize its response to the characteristics of the ECG input sig- nal. We also present evaluation results based on traces from the MIT-BIH ar- rhythmia database. Importance of Classifying the QRS Complex Analysis of the ECG signal is of great importance in the detection of cardiac anomalies. One of the most important signal intervals in this respect is the QRS complex, which is associated with elec- trical ventricular activation. Besides other information contained in this signal segment, its principal application is to be found in the important modifications that its morphology undergoes in beats of ventricular origin. Consequently, the morphological classification of QRS complexes (henceforth QRSs) consti- tutes an important step in the detection of ventricular arrhythmias, the solution of which, apart from constituting another pattern-recognition problem, has certain characteristics that should be described. First, the number and variety of the possi- ble morphological classes is practically infinite. The appearance of heartbeats varies considerably, not only among pa- tients, but also as the consequences of movement, modifications in the electri- cal characteristics of the body, etc. Thus, it is not possible to construct a training set that is representative of all the possi- ble morphologies. On the other hand, the ECG signal fre- quently presents noise components of various origins; among these one could point out the biological ones, such as, for example, the movement of patients' mus- cular masses, which generates high-fre-

Sutil+ is an intelligent monitoring system that is under development. The aim is to endow the system with more intelligent behaviour and to make the presentation of information to the user as flexible as possible, in space, time, and form. We present two telemonitoring solutions that enable access to information resulting from the monitoring of patients in coronary care units, independent of the location of the system user. We concentrate on aspects of data acquisition and storage and, above all, interaction with the user.