Wavelet based detection of ventricular arrhythmias with neural network classifier | Zendy

Sankara Subramanian Arumugam | Zendy; Gurusamy Gurusamy | Zendy; Selvakumar Gopalasamy | Zendy

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Wavelet based detection of ventricular arrhythmias with neural network classifier

Author(s) -

Sankara Subramanian Arumugam,

Gurusamy Gurusamy,

Selvakumar Gopalasamy

Publication year - 2009

Publication title -

journal of biomedical science and engineering

Language(s) - English

Resource type - Journals

eISSN - 1937-688X

pISSN - 1937-6871

DOI - 10.4236/jbise.2009.26064

Subject(s) - daubechies wavelet , pattern recognition (psychology) , wavelet , artificial intelligence , computer science , discrete wavelet transform , artificial neural network , wavelet transform , classifier (uml) , speech recognition

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

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