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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F04%3A03099614%21RIV%2F2005%2FMSM%2F212305%2FN
rdf:type
skos:Concept n16:Vysledek
dcterms:description
Není k dispozici Holter signals are ambulatory long-term electrocardiographic (ECG) registers used to detect heart diseases which are difficult to find in normal ECGs. These signals normally include several registers and its duration is up to 48 hours. The principal problem for the cardiologists consists of the manual inspection of the whole holter ECG to find all those beats whose morphology differ from the normal synus rhythm. The later analisys of these arrhythmia beats yields a diagnostic from the pacient's heart condition. Using Hidden Markov Models (HMM) for computer clustering has became a very useful tool for cardiologists avoiding the manual inspection. In this paper we improve the performance of the HMM clustering method introducing a preclustering stage in order to diminish the number of elements to be finally processed and reducing the global computational cost. An experimental comparative study is carried out, utilizing records form the MIT-BIH Arrhythmia database. Finally some results ar. Holter signals are ambulatory long-term electrocardiographic (ECG) registers used to detect heart diseases which are difficult to find in normal ECGs. These signals normally include several registers and its duration is up to 48 hours. The principal problem for the cardiologists consists of the manual inspection of the whole holter ECG to find all those beats whose morphology differ from the normal synus rhythm. The later analisys of these arrhythmia beats yields a diagnostic from the pacient's heart condition. Using Hidden Markov Models (HMM) for computer clustering has became a very useful tool for cardiologists avoiding the manual inspection. In this paper we improve the performance of the HMM clustering method introducing a preclustering stage in order to diminish the number of elements to be finally processed and reducing the global computational cost. An experimental comparative study is carried out, utilizing records form the MIT-BIH Arrhythmia database. Finally some results ar.
dcterms:title
Není k dispozici Pre-clustering of Electrocardiographic Signals using Ergodic Hidden Markov Models Pre-clustering of Electrocardiographic Signals using Ergodic Hidden Markov Models
skos:prefLabel
Není k dispozici Pre-clustering of Electrocardiographic Signals using Ergodic Hidden Markov Models Pre-clustering of Electrocardiographic Signals using Ergodic Hidden Markov Models
skos:notation
RIV/68407700:21230/04:03099614!RIV/2005/MSM/212305/N
n4:strany
939 ; 947
n4:aktivita
n20:Z
n4:aktivity
Z(MSM 210000012)
n4:dodaniDat
n11:2005
n4:domaciTvurceVysledku
n8:2793172
n4:druhVysledku
n10:D
n4:duvernostUdaju
n13:S
n4:entitaPredkladatele
n17:predkladatel
n4:idSjednocenehoVysledku
581227
n4:idVysledku
RIV/68407700:21230/04:03099614
n4:jazykVysledku
n7:eng
n4:klicovaSlova
Hidden Markov Models; Holter Electrocardiogram
n4:klicoveSlovo
n5:Holter%20Electrocardiogram n5:Hidden%20Markov%20Models
n4:kontrolniKodProRIV
[7CA85F064D77]
n4:mistoKonaniAkce
Lisbon
n4:mistoVydani
Berlin
n4:nazevZdroje
Structural, Syntactic, and Statistical Pattern Recognition
n4:obor
n12:JC
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
3
n4:rokUplatneniVysledku
n11:2004
n4:tvurceVysledku
Micó, P. Novák, Daniel Cuesa, D.
n4:typAkce
n14:WRD
n4:zahajeniAkce
2004-08-18+02:00
n4:zamer
n18:MSM%20210000012
s:numberOfPages
9
n19:hasPublisher
Springer-Verlag
n15:isbn
3-540-22570-6
n21:organizacniJednotka
21230