NDLI: Comparison of several classifiers to evaluate endocardial electrograms fractionation in human

Content Provider	IEEE Xplore Digital Library
Author	Kremen, V. Kordik, P. Lhotska, L.
Copyright Year	2009
Description	Author affiliation: Department of Cybernetics, Czech Technical University in Prague, Czech Republic (Kremen, V.; Kordik, P.) \|\| Department of Computer Science and Engineering, Czech Technical University in Prague, Czech Republic (Lhotska, L.)
Abstract	Complex fractionated atrial electrograms (CFAEs) may represent the electrophysiological substrate for atrial fibrillation (AF). Progress in signal processing algorithms to identify CFAEs sites is crucial for the development of AF ablation strategies. A novel algorithm for automated description of atrial electrograms (A-EGMs) fractionation based on wavelet transform and several statistical pattern recognition methods was proposed and new methodology of A-EGM processing was designed and tested. The algorithms for A-EGM classification were developed using normal density based classifiers, linear and high degree polynomial classifiers, nearest mean scaled classifiers, nonlinear classifiers, neural networks and j48. All classifiers were compared and tested using a representative set of 1.5 s A-EGMs (n = 68) ranked by 3 independent experts 100% coincidentialy into 4 classes of fractionation: 1 — organized atrial activity; 2 — mild; 3 — intermediate; 4 — high degree of fractionation. Feature extraction and well performing classification algorithms tested here showed maximal error of 15% and mean classifiacation error across all implemented classifiers 9%, and the best mean classification error 5.9% (nearest mean classifier), and classification error of highly fractionated A-EGMs of ~ 9%.
Starting Page	2502
Ending Page	2505
File Size	935317
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5335161
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fractionation Humans Signal processing algorithms Classification algorithms Testing Atrial fibrillation Wavelet transforms Pattern recognition Process design Algorithm design and analysis
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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