NDLI: Fractionated electrograms and rotors detection in chronic atrial fibrillation using model-based clustering

Content Provider	IEEE Xplore Digital Library
Author	Orozco-Duque, A. Duque, S.I. Ugarte, J.P. Tobon, C. Novak, D. Kremen, V. Castellanos-Dominguez, G. Saiz, J. Bustamante, J.
Copyright Year	2014
Description	Author affiliation: GI2B, Inst. Tecnol. Metropolitano, Medellin, Colombia (Tobon, C.) \|\| Centro de Bioingenieria, Univ. Pontificia Bolivariana, Medellin, Colombia (Orozco-Duque, A.; Duque, S.I.; Ugarte, J.P.; Bustamante, J.) \|\| Dept. of Cybern., Czech Tech. Univ. in Prague, Prague, Czech Republic (Novak, D.) \|\| Czech Inst. of Inf., Robot., & Cybern., Czech Tech. Univ. in Prague, Prague, Czech Republic (Kremen, V.) \|\| Signal Process. & Recognition Group, Univ. Nac. de Colombia, Manizales, Colombia (Castellanos-Dominguez, G.) \|\| I3BH, Univ. Politec. de Valencia, Valencia, Spain (Saiz, J.)
Abstract	The identification of atrial fibrillation (AF) substrates is needed to improve ablation therapy guided by electrograms, although mechanisms that sustain AF are not fully understood. Detection of complex fractionated atrial electrograms (CFAE) is used for this purpose. Nonetheless, efficacy of this method is inadequate in the case of chronic AF. Recent hypothesis proposes the rotors as fibrillatory substrate. Novel approaches seek to relate CFAE with rotor; nevertheless, such methods are not able to identify the associated substrate. Furthermore, the patterns that characterize CFAE generated by rotors remain unknown. Thus, tracking of rotors is an unsolved issue. In this paper, we propose a non-supervised method to find patterns associated with fibrillatory substrates in chronic AF. We extracted two features based on local activation wave detection and one feature based on non-linear dynamics. Gaussian mixture model-based clustering was used to discriminate CFAE patterns. Resulting clusters are visualized in an electroanatomic map. We assessed the proposed method in a real database labeled according to the level of fractionation and in a simulated episode of chronic AF in which a rotor was detected. Our results indicate that the method proposed can separate different levels of fractionation in CFAE, and provide evidence that clustering can be used to locate the vortex of the rotors. Provided approach can support ablation therapy procedures by means of CFAE patterns discrimination.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	1579
Ending Page	1582
File Size	3311159
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6943905
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Rotors Fractionation Feature extraction Databases Entropy Substrates Medical services
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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