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  • The paper focuses on the field of artificial intelligence techniques and their use in biomedical data processing. It concentrates on the clustering techniques inspired by various ant colonies and other nature concepts. The paper evaluates the use of the following nature inspired methods: Ant Colony inspired Clustering, Ant Colony inspired method for Decision Tree generation, Radial Basis Function Neural Networks with different learning algorithms and compare them to classical approaches, such as k-means and hierarchical clustering. The methods have been evaluated using the annotated MIT-BIH database. Use of the Dynamic Time Warping measure improved Sensitivity about 0.7 \% and Specificity about 0.9 \% when compared to classical feature extraction. The best-performing method is the agglomerative hierarchical clustering (Se=94.3, Sp=74.1), however it is practically unusable as it is memory and computational demanding.
  • The paper focuses on the field of artificial intelligence techniques and their use in biomedical data processing. It concentrates on the clustering techniques inspired by various ant colonies and other nature concepts. The paper evaluates the use of the following nature inspired methods: Ant Colony inspired Clustering, Ant Colony inspired method for Decision Tree generation, Radial Basis Function Neural Networks with different learning algorithms and compare them to classical approaches, such as k-means and hierarchical clustering. The methods have been evaluated using the annotated MIT-BIH database. Use of the Dynamic Time Warping measure improved Sensitivity about 0.7 \% and Specificity about 0.9 \% when compared to classical feature extraction. The best-performing method is the agglomerative hierarchical clustering (Se=94.3, Sp=74.1), however it is practically unusable as it is memory and computational demanding. (en)
Title
  • Nature Inspired Clustering Methods in The Electrocardiogram Interpretation Process in Cardiology
  • Nature Inspired Clustering Methods in The Electrocardiogram Interpretation Process in Cardiology (en)
skos:prefLabel
  • Nature Inspired Clustering Methods in The Electrocardiogram Interpretation Process in Cardiology
  • Nature Inspired Clustering Methods in The Electrocardiogram Interpretation Process in Cardiology (en)
skos:notation
  • RIV/68407700:21230/10:00170436!RIV11-MSM-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM6840770012)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 273882
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/10:00170436
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Ant Colony Optimizaiton; Ant Algorithms; ECG Interpretation; Decision Trees; Artificial Intelligence (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [05E9F2B7CEAA]
http://linked.open...v/mistoKonaniAkce
  • Brno
http://linked.open...i/riv/mistoVydani
  • Brno
http://linked.open...i/riv/nazevZdroje
  • Analysis of Biomedical Signals and Images, BIOSIGNAL 2010, Proceedings
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Burša, Miroslav
  • Lhotská, Lenka
  • Trávníček, Zdeněk
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
issn
  • 1211-412X
number of pages
http://purl.org/ne...btex#hasPublisher
  • Vysoké učení technické v Brně
https://schema.org/isbn
  • 978-80-214-4106-4
http://localhost/t...ganizacniJednotka
  • 21230
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