ECG baseline wander removal with recovery of the isoelectric level

Baseline wander removal is an unavoidable step in ECG signal processing. The in-band nature of this noise makes its removal difficult without affecting the ECG. Many approaches have been proposed in the literature. Among them, cubic spline interpolation is the only one able to recover the isoelectric level in the detrended signal. However, it exhibits poor detrending performance. In this paper we extend our recent approach based on the notion of quadratic variation reduction, to address the problem of recovery of the isoelectric level. This is achieved by constraining the amplitude of few fiducial points to lie on isoelectric segments. Conversely to cubic spline interpolation, which requires a fiducial point for each beat, the proposed approach requires very few points: as few as one single point is sufficient. Simulation results show that the proposed approach largely outperforms cubic spline interpolation, being very effective in removing baseline wander and recovering the isoelectric level, while preserving ECG morphology

Fast and effective estimation of narrowband components for bioelectrical signals

In this paper we propose a novel approach for estimating narrowband components from bioelectrical signals. The approach is based on the notion of modulated quadratic variation, introduced as a measure of variability for narrowband signals. The algorithm is the closed-form solution to a constrained convex optimization problem, where narrowband components are estimated tracking the slow variations around a central frequency in the measured signal. The approach is general and can be applied to any bioelectrical signal, either for diagnostic or denoising purposes. In this paper we assess its performance on ECG and EMG signals. Numerical results show its effectiveness in removing narrowband artifacts, such as power-line interference, while preserving signal morphology. It greatly outperforms conventional notch filtering. Moreover, it is also very fast, as its computational complexity is linear in the size of the vector to process

Statistical assessment of performance of algorithms for detrending RR series

Detrending RR series is a common processing step prior to HRV analysis. Customarily, RR series, which are inherently unevenly sampled, are interpolated and uniformly resampled, thus introducing errors in subsequent HRV analysis. We have recently proposed a novel approach to detrending unevenly sampled series, which is based on the notion of weighted quadratic variation reduction. In this paper, we extensively assess its performance on RR series through a statistical analysis. Numerical results confirm the effectiveness of the approach, which outperforms state-of-the-art methods. Furthermore, it is statistically uniformly better than competing algorithms. A sensitivity analysis shows that it is robust to variations of its controlling parameter. The algorithm is simple and favorable in terms of computational complexity, thus being suitable for long-term HRV analysis. To the best of the authors' knowledge, it is the fastest algorithm for detrending RR series

Translation as a metaphor for salvation : eighteenth century English versions of Dante's Commedia

Any student of the ways in which English artists appropriated Dante for their own purposes ends up asking himself the question: Why was it that Dante so obsessed first the Romantics, and then even more the Victorians, and later many of the Moderns? At least part of the answer is that, throughout this period, Dante has stood in the centre of English literature and culture, not only as a historical figure and a great artist, but also a fictional character - the protagonist of his own poem. He was, therefore, real but also fictional, and further, because of the nature of his fiction, he could be seen either as almost divine or, alternatively, as demonic. He thus became a myth, which could embody many of the values of English culture, and transform them. This process, by which Dante assumes and transforms a set of native values, is well illustrated in the first flourishing of interest in Dante, in the eighteenth century. The primary aim of this paper is to shed some light onto the process by which Dante became a catalyst in the transmission and transformation of certain values in English literature. This process occurred mainly through translations into English from the Commedia. My secondary aim here is to illustrate the function which translation assumes in providing a new emerging literary tradition with a model, which is read and interpreted in various ways.peer-reviewe

Dante, Garibaldi, Mazzini : some English interpretations of Italian historical figures

When, in 1819, Byron wrote his poem The Prophecy of Dante, he made Dante speak as an Englishman of the early 1800s. The myths that shape Dante's verses in Byron's poem proceed directly from English romanticism, and the prophecy Dante utters about a future Italian unification all point to the hopes and views of Byron himself. Byron acts as a ventriloquist, and Dante is his mouthpiece. The points which make of Dante a suitable Byronic hero, and which Byron highlights in his poem, seem to be the following: he is a political figure, indeed a warrior who has been engaged in his life in direct action; on the other hand, he is also, at the present time, an exile, spumed by society. Also, he is a voyager, in fact, the complete traveller since his travels in Italy and in the realms of the afterlife are both seen as having actually taken place. So Byron uses certain facts of Dante's own life and art to give shape to his Byronic Dante.peer-reviewe

Fast Detrending of Unevenly Sampled Series with Application to HRV

Abstract Detrending RR series is a common processing step prior to HRV analysis. In the classical approaches RR series, which are inherently unevenly sampled, are interpolated and uniformly resampled, thus introducing errors in subsequent HRV analysis. In this paper, we propose a novel approach to detrending unevenly sampled series and apply it to RR series. The approach is based on the notion of weighted quadratic variation, which is a suitable measure of variability for unevenly sampled series. Detrending is performed by solving a constrained convex optimization problem that exploits the weighted quadratic variation. Numerical results confirm the effectiveness of the approach. The algorithm is simple and favorable in terms of computational complexity, which is linear in the size of the series to detrend. This makes it suitable for long-term HRV analysis. To the best of the authors' knowledge, it is the fastest algorithm for detrending RR series

mid term surgical results of proximal ulnar and radial fracture dislocations classified and treated with a new comprehensive classification system

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