José Luis Rojo-Álvarez

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This paper presents a new approach to auto-regressive and moving average (ARMA) modeling based on the support vector method (SVM) for identification applications. A statistical analysis of the characteristics of the proposed method is carried out. An analytical relationship between residuals and SVM-ARMA coefficients allows the linking of the fundamentals(More)
Heart rate variability (HRV) markers have been widely used to characterize the autonomous regulation state of the heart from 24-h Holter monitoring, but long-term evolution of HRV indexes is mostly unknown. A dataset of 7-day Holter recordings of 22 patients with congestive heart failure was studied. A rhythmometric procedure was designed to characterize(More)
Early detection of ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) is crucial for the success of the defibrillation therapy. A wide variety of detection algorithms have been proposed based on temporal, spectral, or complexity parameters extracted from the ECG. However, these algorithms are mostly constructed by considering each(More)
The problem of signal interpolation has been intensively studied in the information theory literature, in conditions such as unlimited band, nonuniform sampling, and presence of noise. During the last decade, support vector machines (SVM) have been widely used for approximation problems, including function and signal interpolation. However, the signal(More)
Heart rate turbulence (HRT) is a transient acceleration and subsequent deceleration of the heart rate after a premature ventricular complex (PVC), and it has been shown to be a strong risk stratification criterion in patients with cardiac disease. In order to reduce the noise level of the HRT signal, conventional measurements of HRT use a patient-averaged(More)
We present a new approach for the reconstruction of ischemic regions from only a few non-contact intracardiac recordings. Hence, it is desirable to exploit the spatio-temporal correlations contained in the data. To this end, we incorporate a time-dependent monodomain model of the cardiac electric activity into the inversion scheme. In order to take into(More)
Multi-temporal classification of remote sensing images is a challenging problem, in which efficient combination of different sources of information (e.g. temporal, contextual, or multi-sensor) can improve the results. In this paper, we present a general framework based on kernel methods for the integration of heterogeneous sources of information. Using the(More)
Indoor location systems based on IEEE 802.11b (WiFi) mobile devices often rely on the received signal strength indicator to estimate the user position. Two key characteristics of these systems have not yet been fully analyzed, namely, the temporal and spatial sampling process required to adequately describe the distribution of the electromagnetic field in(More)