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Wavelet based detection of ventricular arrhythmias with neural network classifier
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Journal of Biomedical Science and Engineering·Volume 02 (2009)·Pages 439–444·Published 26 October 2009·DOI10.4236/jbise.2009.26064
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Abstract
This paper presents an algorithm based on the wavelet decomposition, for feature extraction from the Electrocardiogram (ECG) signal and recognition of three types of Ventricular Arrhythmias using neural networks. A set of Discrete Wavelet Transform (DWT) coefficients, which contain the maximum information about the arrhythmias, is selected from the wavelet decomposition. These coefficients are fed to the feed forward neural network which classifies the arrhythmias. The algorithm is applied on the ECG registrations from the MIT-BIH arrhythmia and malignant ventricular arrhythmia databases. We applied Daubechies 4 wavelet in our algorithm. The wavelet decomposition enabled us to perform the task efficiently and produced reliable results.
KeywordsDaubechies 4 WaveletECGFeed Forward Neural NetworkVentricular ArrhythmiasWavelet De-composition
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