Comparison of discrete wavelet and Fourier transforms for ECG beat classification

Z. Dokur; T. Ölmez; E. Yazgan

doi:10.1049/el:19991095

Comparison of discrete wavelet and Fourier transforms for ECG beat classification

Z. Dokur^*
, T. Ölmez
, E. Yazgan

^*Bu çalışma için yazışmadan sorumlu yazar

Elektronik ve Hab.Müh.Bölümü

Istanbul Technical University

Araştırma sonucu: Dergiye katkı › Makale › bilirkişi

69 Atıf (Scopus)

Özet

Two feature extraction methods, Fourier and wavelet analyses for ECG beat classification, are comparatively investigated. ECG features are searched by dynamic programming according to the divergence values. 10 types of ECG beat from an MIT-BIH database are classified with a success of 97% using a restricted Coulomb energy neural network trained by genetic algorithms.

Orijinal dil	İngilizce
Sayfa (başlangıç-bitiş)	1502-1504
Sayfa sayısı	3
Dergi	Electronics Letters
Hacim	35
Basın numarası	18
DOI'lar	https://doi.org/10.1049/el:19991095
Yayın durumu	Yayınlandı - 2 Eyl 1999

Belgeye Erişim

10.1049/el:19991095

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Alıntı Yap

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abstract = "Two feature extraction methods, Fourier and wavelet analyses for ECG beat classification, are comparatively investigated. ECG features are searched by dynamic programming according to the divergence values. 10 types of ECG beat from an MIT-BIH database are classified with a success of 97\% using a restricted Coulomb energy neural network trained by genetic algorithms.",

author = "Z. Dokur and T. {\"O}lmez and E. Yazgan",

year = "1999",

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AU - Ölmez, T.

AU - Yazgan, E.

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N2 - Two feature extraction methods, Fourier and wavelet analyses for ECG beat classification, are comparatively investigated. ECG features are searched by dynamic programming according to the divergence values. 10 types of ECG beat from an MIT-BIH database are classified with a success of 97% using a restricted Coulomb energy neural network trained by genetic algorithms.

AB - Two feature extraction methods, Fourier and wavelet analyses for ECG beat classification, are comparatively investigated. ECG features are searched by dynamic programming according to the divergence values. 10 types of ECG beat from an MIT-BIH database are classified with a success of 97% using a restricted Coulomb energy neural network trained by genetic algorithms.

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U2 - 10.1049/el:19991095

DO - 10.1049/el:19991095

M3 - Article

AN - SCOPUS:0033359340

SN - 0013-5194

VL - 35

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JO - Electronics Letters

JF - Electronics Letters

IS - 18

ER -

Comparison of discrete wavelet and Fourier transforms for ECG beat classification

Özet

Belgeye Erişim

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