Wavelet based detection of ventricular arrhythmias with neural network classifier

Sankara Subramanian Arumugam, Gurusamy Gurusamy, Selvakumar Gopalasamy
DOI: 10.4236/jbise.2009.26064                

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.

Keywords

Daubechies 4 Wavelet; ECG; Feed Forward Neural Network; Ventricular Arrhythmias; Wavelet De-composition

Conflicts of Interest

The authors declare no conflicts of interest.

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