2020
Ríos-Muñoz, Gonzalo; Artés-Rodríguez, Antonio; Fernández-Avilés, Francisco; Arenal, Ángel
Real-Time Ventricular Cancellation in Unipolar Atrial Fibrillation Electrograms Artículo de revista
En: Frontiers in Bioengineering and Biotechnology, vol. 8, no 789, 2020.
Enlaces | BibTeX | Etiquetas: atrial fibrillation, biomedical signal processing, multi-electrode catheter, real-time, unipolar electrograms
@article{AArtes20d,
title = {Real-Time Ventricular Cancellation in Unipolar Atrial Fibrillation Electrograms},
author = {Gonzalo R\'{i}os-Mu\~{n}oz and Antonio Art\'{e}s-Rodr\'{i}guez and Francisco Fern\'{a}ndez-Avil\'{e}s and \'{A}ngel Arenal},
doi = {https://doi.org/10.3389/fbioe.2020.00789},
year = {2020},
date = {2020-07-30},
journal = {Frontiers in Bioengineering and Biotechnology},
volume = {8},
number = {789},
keywords = {atrial fibrillation, biomedical signal processing, multi-electrode catheter, real-time, unipolar electrograms},
pubstate = {published},
tppubtype = {article}
}
2015
Luengo, David; Monzon, Sandra; Trigano, Tom; Vía, Javier; Artés-Rodríguez, Antonio
Blind Analysis of Atrial Fibrillation Electrograms: A Sparsity-Aware Formulation Artículo de revista
En: Integrated Computer-Aided Engineering, vol. 22, no 1, pp. 71–85, 2015.
Resumen | Enlaces | BibTeX | Etiquetas: atrial fibrillation, biomedical signal processing
@article{Luengo2014bb,
title = {Blind Analysis of Atrial Fibrillation Electrograms: A Sparsity-Aware Formulation},
author = {David Luengo and Sandra Monzon and Tom Trigano and Javier V\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://content.iospress.com/articles/integrated-computer-aided-engineering/ica00471
http://www.tsc.uc3m.es/~dluengo/sparseEGM.pdf},
year = {2015},
date = {2015-01-01},
journal = {Integrated Computer-Aided Engineering},
volume = {22},
number = {1},
pages = {71--85},
abstract = {The problem of blind sparse analysis of electrogram (EGM) signals under atrial fibrillation (AF) conditions is considered in this paper. A mathematical model for the observed signals that takes into account the multiple foci typically appearing inside the heart during AF is firstly introduced. Then, a reconstruction model based on a fixed dictionary is developed and several alternatives for choosing the dictionary are discussed. In order to obtain a sparse solution, which takes into account the biological restrictions of the problem at the same time, the paper proposes using a Least Absolute Shrinkage and Selection Operator (LASSO) regularization followed by a post-processing stage that removes low amplitude coefficients violating the refractory period characteristic of cardiac cells. Finally, spectral analysis is performed on the clean activation sequence obtained from the sparse learning stage in order to estimate the number of latent foci and their frequencies. Simulations on synthetic signals and applications on real data are provided to validate the proposed approach.},
keywords = {atrial fibrillation, biomedical signal processing},
pubstate = {published},
tppubtype = {article}
}
2012
Monzon, Sandra; Trigano, Tom; Luengo, David; Artés-Rodríguez, Antonio
Sparse Spectral Analysis of Atrial Fibrillation Electrograms. Proceedings Article
En: 2012 IEEE International Workshop on Machine Learning for Signal Processing, pp. 1–6, IEEE, Santander, 2012, ISSN: 1551-2541.
Resumen | Enlaces | BibTeX | Etiquetas: Algorithm design and analysis, atrial fibrillation, atrial fibrillation electrogram, biomedical signal processing, dominant frequency, Doped fiber amplifiers, electrocardiography, Harmonic analysis, Heart, heart disorder, Indexes, Mathematical model, medical signal processing, multiple foci, multiple uncoordinated activation foci, signal processing technique, sparse spectral analysis, sparsity-aware learning, sparsity-aware learning technique, spectral analysis, spike train
@inproceedings{Monzon2012,
title = {Sparse Spectral Analysis of Atrial Fibrillation Electrograms.},
author = {Sandra Monzon and Tom Trigano and David Luengo and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6349721},
issn = {1551-2541},
year = {2012},
date = {2012-01-01},
booktitle = {2012 IEEE International Workshop on Machine Learning for Signal Processing},
pages = {1--6},
publisher = {IEEE},
address = {Santander},
abstract = {Atrial fibrillation (AF) is a common heart disorder. One of the most prominent hypothesis about its initiation and maintenance considers multiple uncoordinated activation foci inside the atrium. However, the implicit assumption behind all the signal processing techniques used for AF, such as dominant frequency and organization analysis, is the existence of a single regular component in the observed signals. In this paper we take into account the existence of multiple foci, performing a spectral analysis to detect their number and frequencies. In order to obtain a cleaner signal on which the spectral analysis can be performed, we introduce sparsity-aware learning techniques to infer the spike trains corresponding to the activations. The good performance of the proposed algorithm is demonstrated both on synthetic and real data.},
keywords = {Algorithm design and analysis, atrial fibrillation, atrial fibrillation electrogram, biomedical signal processing, dominant frequency, Doped fiber amplifiers, electrocardiography, Harmonic analysis, Heart, heart disorder, Indexes, Mathematical model, medical signal processing, multiple foci, multiple uncoordinated activation foci, signal processing technique, sparse spectral analysis, sparsity-aware learning, sparsity-aware learning technique, spectral analysis, spike train},
pubstate = {published},
tppubtype = {inproceedings}
}