ECG waveform classification using the neural network and wavelet transform

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...

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Bibliographische Detailangaben

Hauptverfasser:	Dokur, Z., Olmez, T., Yazgan, E.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Autocorrelation Correlation methods Database systems Discrete Fourier transforms Dynamic programming Electrocardiography Feature extraction Fourier transforms Genetic algorithms Neural networks Spatial databases Vectors Waveform analysis Wavelet analysis Wavelet transforms
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