About: High-Speed Feature Extraction in Holter Electrocardiogram Using Principal Component Analysis     Goto   Sponge   NotDistinct   Permalink

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  • Není k dispozici (cs)
  • Due to their long duration (up to 48 hours) and because of the enormous quantity of beats involved, it results really difficult to perform a manual inspection of HOLTER electrocardiographic signals (ECG). In the other side, we have patients with chronic heart-troubles whose heart rithm needs to be checked everytime using portable ECG recording machines. Within this scenario it would be very interesting the possibility of real-time ANALYZING and clustering ECG records in order to detect arrhythmia beats as soon as possible, allowing the patient to realize and prevent from a critical heart attack. To achieve this goal we have designed and implemented a C++ application that is based on the Principal Component Analysis (PCA) method applied to the beats (from a portable ECG recording machine) to decompose and cluster them in real-time. At the first stage of this article we will define holter ECG records and discuss about their desirable features for processing them ensuring better results y
  • Due to their long duration (up to 48 hours) and because of the enormous quantity of beats involved, it results really difficult to perform a manual inspection of HOLTER electrocardiographic signals (ECG). In the other side, we have patients with chronic heart-troubles whose heart rithm needs to be checked everytime using portable ECG recording machines. Within this scenario it would be very interesting the possibility of real-time ANALYZING and clustering ECG records in order to detect arrhythmia beats as soon as possible, allowing the patient to realize and prevent from a critical heart attack. To achieve this goal we have designed and implemented a C++ application that is based on the Principal Component Analysis (PCA) method applied to the beats (from a portable ECG recording machine) to decompose and cluster them in real-time. At the first stage of this article we will define holter ECG records and discuss about their desirable features for processing them ensuring better results y (en)
Title
  • Není k dispozici (cs)
  • High-Speed Feature Extraction in Holter Electrocardiogram Using Principal Component Analysis
  • High-Speed Feature Extraction in Holter Electrocardiogram Using Principal Component Analysis (en)
skos:prefLabel
  • Není k dispozici (cs)
  • High-Speed Feature Extraction in Holter Electrocardiogram Using Principal Component Analysis
  • High-Speed Feature Extraction in Holter Electrocardiogram Using Principal Component Analysis (en)
skos:notation
  • RIV/68407700:21230/04:03099085!RIV/2005/MSM/212305/N
http://linked.open.../vavai/riv/strany
  • 81 ; 83
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM 210000012)
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
  • 566184
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/04:03099085
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • ECG; Feature Extraction; PCA (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [3F4A74E0DD71]
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
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Novák, Daniel
  • Cuesta-Frau, D.
  • Micó, P.
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • VUTIUM Press
https://schema.org/isbn
  • 80-214-2633-0
http://localhost/t...ganizacniJednotka
  • 21230
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