Browsing by Subject "Singular value decomposition (SVD)"

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Open Access
Assessment of information redundancy in ECG signals
(IEEE, 1997-09) Acar, Burak; Özçakır, Lütfü; Köymen, Hayrettin
In this paper, the morphological information redundancy in standard 12 lead ECG channels is studied. Study is based on decomposing the ECG channels into orthogonal channels by an SVD based algorithm and then reconstructing them. Then 7 of 8 independently recorded ECG channels are decomposed and the missing channel is reconstructed from these orthogonal channels. Thus the unique morphological information content of each ECG channel is assessed through the loss of clinical information in the reconstructed signal. A comparison of the clinical parameters measured from the reconstructed and original ECG is reported.
Open Access
SVD-based on-line exercise ECG signal orthogonalization
(Institute of Electrical and Electronics Engineers, 1999-03) Acar, B.; Köymen, Hayrettin
An orthogonalization method to eliminate unwanted signal components in standard 12-lead exercise electrocardiograms (ECG's) is presented in this work. A singular-value-decomposition-based algorithm is proposed to decompose the signal into two time-orthogonal subspaces; one containing the ECG and the other containing artifacts like baseline wander and electromyogram. The method makes use of redundancy in 12-lead ECG. The same method is also tested for reconstruction of a completely lost channel. The online implementation of the method is given. It is observed that the first two decomposed channels with highest energy are sufficient to reconstruct the ST-segment and J- point. The dimension of the signal space, on the other hand, does not exceed three. Data from 23 patients, with duration ranging from 9 to 21 min, are used.An orthogonalization method to eliminate unwanted signal components in standard 12-lead exercise electrocardiograms (ECG's) is presented in this work. A singular-value-decomposition-based algorithm is proposed to decompose the signal into two time-orthogonal subspaces; one containing the ECG and the other containing artifacts like baseline wander and electromyogram. The method makes use of redundancy in 12-lead ECG. The same method is also tested for reconstruction of a completely lost channel. The online implementation of the method is given. It is observed that the first two decomposed channels with highest energy are sufficient to reconstruct the ST-segment and J-point. The dimension of the signal space, on the other hand, does not exceed three. Data from 23 patients, with duration ranging from 9 to 21 min, are used.
Open Access
Tomographic reconstruction of the ionospheric electron density as a function of space and time
(ELSEVIER, 2009) Erturk, O.; Arıkan, Orhan; Arikan, F.
Electron density distribution is the major determining parameter of the ionosphere. Computerized Ionospheric Tomography (CIT) is a method to reconstruct ionospheric electron density image by computing Total Electron Content (TEC) values from the recorded Global Positioning Satellite System (GPS) signals. Due to the multi-scale variability of the ionosphere and inherent biases and errors in the computation of TEC, CIT constitutes an underdetermined ill-posed inverse problem. In this study, a novel Singular Value Decomposition (SVD) based CIT reconstruction technique is proposed for the imaging of electron density in both space (latitude, longitude, altitude) and time. The underlying model is obtained from International Reference Ionosphere (IRI) and the necessary measurements are obtained from earth based and satellite based GPS recordings. Based on the IRI-2007 model, a basis is formed by SVD for the required location and the time of interest. Selecting the first few basis vectors corresponding to the most significant singular values, the 3-D CIT is formulated as a weighted least squares estimation problem of the basis coefficients. By providing significant regularization to the tomographic inversion problem with limited projections, the proposed technique provides robust and reliable 3-D reconstructions of ionospheric electron density.