dc.contributor.author |
Luengo García, David
|
dc.contributor.author |
Ríos Muñoz, Gonzalo Ricardo |
dc.contributor.author |
Elvira Arregui, Víctor
|
dc.date.accessioned |
2022-05-16T14:06:44Z |
dc.date.available |
2022-05-16T14:06:44Z |
dc.date.issued |
2015-09-06 |
dc.identifier.bibliographicCitation |
2015 Computing in Cardiology Conference (CinC 2015), September 6-9, 2015, Nice, France (pp. 585-588.) Estados Unidos: IEEE. |
dc.identifier.isbn |
978-1-5090-0684-7 (online) |
dc.identifier.isbn |
978-1-5090-0685-4 (print) |
dc.identifier.issn |
2325-887X-3 (online) |
dc.identifier.issn |
2325-8861-2 (print) |
dc.identifier.uri |
http://hdl.handle.net/10016/34814 |
dc.description |
Proceeding of 2015 Computing in Cardiology Conference (CinC 2015), September 6-9, 2015, Nice, France |
dc.description.abstract |
Multi-channel intracardiac electrocardiograms (electrograms) are sequentially acquired during heart surgery performed on patients with sustained atrial fibrillation (AF) to guide radio frequency catheter ablation. These electrograms are used by cardiologists to determine candidate areas for ablation (e.g., areas corresponding to high dominant frequencies or complex electrograms). In this paper, we introduce a novel hierarchical causality analysis method for the multi-output sequentially acquired electrograms. The causal model obtained provides important information regarding delays among signals as well as the direction and strength of their causal connections. The tool developed may ultimately serve to guide cardiologists towards candidate areas for catheter ablation. Preliminary results on synthetic signals are used to validate the proposed approach. |
dc.description.sponsorship |
This work has been supported by the Spanish government’s projects ALCIT (TEC2012-38800-C03-01), AGES (S2010/BMD-2422), and OTOSiS (TEC2013-41718-R), and COMPREHENSION (TEC2012-38883-C02-01). D. Luengo has also been funded by the BBVA Foundation’s “I Convocatoria de Ayudas Fundación BBVA a Investigadores, Innovadores y Creadores Culturales”. |
dc.format.extent |
4 |
dc.language.iso |
eng |
dc.publisher |
IEEE |
dc.rights |
©2015 IEEE |
dc.subject.other |
Silicon |
dc.subject.other |
Reactive power |
dc.subject.other |
Catheters |
dc.subject.other |
Heart |
dc.subject.other |
Standards |
dc.subject.other |
Sensors |
dc.subject.other |
Rotors |
dc.title |
Causality analysis of atrial fibrillation electrograms |
dc.type |
conferenceObject |
dc.type |
bookPart |
dc.description.status |
Publicado |
dc.subject.eciencia |
Biología y Biomedicina |
dc.identifier.doi |
https://doi.org/10.1109/CIC.2015.7410978 |
dc.rights.accessRights |
openAccess |
dc.relation.projectID |
Gobierno de España. TEC2012-38800-C03-01/ALCIT |
dc.relation.projectID |
Gobierno de España. S2010/BMD-2422/AGES |
dc.relation.projectID |
Gobierno de España. TEC2013-41718-R/OTOSiS |
dc.relation.projectID |
Gobierno de España. TEC2012-38883-C02-01/COMPREHENSION |
dc.type.version |
acceptedVersion |
dc.relation.eventdate |
September 6-9,2015 |
dc.relation.eventplace |
Niza, Francia |
dc.relation.eventtitle |
Computing in Cardiology (CinC 2015), 42nd Annual Conference, 6-9 September, 2015 |
dc.relation.eventtype |
proceeding |
dc.identifier.publicationfirstpage |
585 |
dc.identifier.publicationlastpage |
588 |
dc.identifier.publicationtitle |
2015 Computing in Cardiology Conference (CinC), September 6-9, 2015, Nice, France |
dc.identifier.uxxi |
CC/0000033183 |
dc.contributor.funder |
Ministerio de Economía y Competitividad (España) |
dc.affiliation.dpto |
UC3M. Departamento de Teoría de la Señal y Comunicaciones |
dc.affiliation.grupoinv |
UC3M. Grupo de Investigación: Tratamiento de la Señal y Aprendizaje (GTSA) |