Cardiac Arrhythmia Discrimination Using Evolutionary Computation
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @InProceedings{Martin-Garcia:2014:CinC,
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author = "Juan Francisco Martin-Garcia and
Inmaculada Mora-Jimenez and Arcadio Garcia-Alberola and
Jose Luis Rojo-Alvarez",
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title = "Cardiac Arrhythmia Discrimination Using Evolutionary
Computation",
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booktitle = "Computing in Cardiology Conference (CinC 2014)",
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year = "2014",
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month = sep,
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pages = "121--124",
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ISSN = "2325-8861",
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URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7042994",
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size = "4 pages",
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abstract = "The use of Implantable Cardioverter Defibrillators
(ICD) for cardiac arrhythmia treatment implies a search
for efficiency in terms of discrimination quality and
computational complexity, given that improved
efficiency will automatically turn into more effective
therapy and longer battery lifetime. In this work, we
applied evolutionary computation to create classifiers
capable of discriminating between ventricular and
supraventricular tachycardia (VT/SVT) in episodes
registered by ICDs. Evolutionary computation comprises
several paradigms emulating natural mechanisms for
solving a problem, all of them characterised by a
population of individuals (possible solutions) which
evolve generation after generation to provide fitter
solutions. Genetic programming was the paradigm chosen
here because its solutions, coded as decision trees,
can be both computationally simple and clinically
interpretable. For the experiments, we considered
electrograms (EGM) from episodes registered by ICDs in
spontaneous/induced tachycardia, previously classified
as VT/SVT by clinical experts from several Spanish
healthcare centres. Training data were 38 real-valued
samples, arranged as the concatenation of two beat
segments: a sinus rhythm template immediately previous
to the arrhythmic episode (basal reference), and the
arrhythmic episode template. Several low complexity
trees provided low error rates and allowed
physiological interpretation. The best tree yielded an
error rate of 1.8percent, with both sensitivity and
specificity above 98percent. This solution compares two
samples from the end of the arrhythmic pulse with
another two samples from the sinus rhythm, pointing out
to a relevant discrimination role of the lasting EGM.",
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keywords = "genetic algorithms, genetic programming",
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notes = "Also known as \cite{7042994}",
- }
Genetic Programming entries for
Juan Francisco Martin-Garcia
Inmaculada Mora-Jimenez
Arcadio Garcia-Alberola
Jose Luis Rojo-Alvarez
Citations