Abstract-The time-domain signals representing the heart rate variability (HRV) in the presence of an ectopic beat exhibit a sharp transient at the position of the ectopic beat, which corrupts the signal, particularly the power spectral density (PSD) of the HRV. Consequently, there is a need for correction of this type of beat prior to any HRV analysis. This paper deals with the PSD estimation of the HRV by means of the heart timing (HT) signal when ectopic beats are present. These beat occurrence times are modeled from a generalized, continuous time integral pulse frequency modulation model and, from this point of view, a specific method for minimizing the effect of the presence of ectopic beats is presented to work together with the HT signal. By using both, a white noise driven autoregressive model of the HRV signal with artificially introduced ectopic beats and actual heart rate series including ectopic beats, the more usual methods of HRV spectral estimation are compared. Results of the PSD estimation error function of the number of ectopic beats are presented. These results demonstrate that the proposed method has one order of magnitude lower error than usual ectopic beats removal strategies in preserving PSD, thus, this strategy better recovers the original clinical indexes of interest.Index Terms-Ectopic beat, heart rate variability, heart timing signal, integral pulse frequency modulation (IPFM) model, spectral analysis.

Abstract-The heart rate variability (HRV) is an extended tool to analyze the mechanisms controlling the cardiovascular system. In this paper, the integral pulse frequency modulation model (IPFM) is assumed. It generates the beat occurrence times from a modulating signal. This signal is thought to represent the autonomic nervous system action, mostly studied in its frequency components. Different spectral estimation methods try to infer the modulating signal characteristics from the available beat timing on the electrocardiogram signal. These methods estimate the spectrum through the heart period (HP) or the heart rate (HR) signal. We introduce a new time domain HRV signal, the Heart Timing (HT) signal. We demonstrate that this HT signal, in contrast with the HR or HP, makes it possible to recover an unbiased estimation of the modulating signal spectra. In this estimation we avoid the spurious components and the low-pass filtering effect generated when analyzing HR or HP.

We propose to characterize optical power transmission in stepindex plastic optical fibers by estimating fiber diffusion and attenuation as functions of the propagation angle. We assume that power flow is described by Gloge s differential equation and find a global solution that was fitted to experimental far field patterns registered using a CCD camera as a function of fiber length. The diffusion and attenuation functions obtained describe completely the fiber behavior and thus, along with the power flow equation, can be used to predict the optical power distribution for any condition.

We present a method to obtain the frequency response of step index (SI) plastic optical fibers (POFs) based on the power flow equation generalized to incorporate the temporal dimension where the fibre diffusion and attenuation are functions of the propagation angle. To solve this equation we propose a fast implementation of the finite-difference method in matrix form. Our method is validated by comparing model predictions to experimental data. In addition, the model provides the space-time evolution of the angular power distribution when it is transmitted throughout the fibre which gives a detailed picture of the POFs capabilities for information transmission. Model predictions show that angular diffusion has a strong impact on temporal pulse widening with propagation.

Our main goal is to provide a comprehensive explanation of the existing differences in bending losses arising from having step-index multimode plastic optical fibers with different cladding thickness and under different types of conditions, namely, the variable bend radius R, the number of fiber turns, or the fiber diameter. For this purpose, both experimental and numerical result of bending losses are presented for different cladding thicknesses and conditions. For the measurements, two cladding thicknesses have been considered: one finite and another infinite. A fiber in air has a finite cladding thickness, and rays are reflected at the cladding-air interface, whereas a fiber covered by oil is equivalent to having an infinite cladding, since the very similar refractive index of oil prevents reflections from occurring at the cladding-oil interface. For the sake of comparison, numerical simulations based on ray tracing have been performed for finite-cladding step-index multimode waveguides. The numerical results reinforce the experimental data, and both the experimental measurements and the computational simulations turn out to be very useful to explain the behavior of refracting and tunneling rays along bent multimode waveguides and along finite-cladding fibers.

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Several indexes have been reported to improve the accuracy of exercise test electrocardiogram (ECG) analysis in the diagnosis of coronary artery disease (CAD), compared with the classical ST depression criterion. Some of them combine repolarisation measurements with heart rate (HR) information (such as the so-called ST/HR hysteresis); others are obtained from the depolarisation period (such as the Athens QRS score); finally, there are heart rate variability (HRV) indexes that account for the nervous system activity. The aim of this study was to identify the best exercise ECG indexes for CAD diagnosis. First, a method to automatically estimate repolarisation and depolarisation indexes in the presence of noise during a stress test was developed. The method is divided into three stages: first, a preprocessing step, where QRS detection, filtering and baseline beat rejection are applied to the raw ECG, prior to a weighted averaging; secondly, a post-processing step in which potentially noisy averaged beats are identified and discarded based on their noise variance; finally, the measurement step, in which ECG indexes are computed from the averaged beats. Then, a multivariate discriminant analysis was applied to classify patients referred for the exercise test into two groups: ischaemic (positive coronary angiography) and low-risk (Framingham risk index < 5%). HR-corrected repolarisation indexes improved the sensitivity (SE) and specificity (SP) of the classical exercise test (SE = 90%, SP = 79% against SE = 65%, SP = 66%). Depolarisation indexes also achieved an improvement over ST depression measurements (SE = 78%, SP = 81%). HRV indexes obtained the best classification results in our study population (SE = 94%, SP = 92%) by means of the very high-frequency power (VHF) (0.4-1 Hz) at stress peak.