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ECG waveform classification using the neural network and wavelet transform

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

8 Atıf (Scopus)

Özet

Two feature extraction methods: Fourier analysis and wavelet analysis for ECG waveform classification are comparatively investigated. Ten different ECG waveforms from MIT/BIH database are classified using a neural network trained by genetic algorithms (NeTGA). One set of feature vectors is formed by using DFT coefficients, and the second set is formed by using wavelet transform (WT) coefficients and their autocorrelation values. Elements of the feature vectors are searched by using dynamic programming (DP) according to the divergence values. Wavelet feature set is found to result in better classification accuracy with less number of nodes. It is observed that with the feature set formed by wavelet analysis, NeTGA gives 99.4% classification performance with 26 nodes after a short training time.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
YayınlayanIEEE
Sayfalar273
Sayfa sayısı1
ISBN (Basılı)0780356756
Yayın durumuYayınlandı - 1999
EtkinlikProceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS) - Atlanta, GA, USA
Süre: 13 Eki 199916 Eki 1999

Yayın serisi

AdıAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Hacim1
ISSN (Basılı)0589-1019

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???event.eventtypes.event.conference???Proceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS)
ŞehirAtlanta, GA, USA
Periyot13/10/9916/10/99

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