Classification of ECG Signals using ANN with Resilient Back Propagation Algorithm

Nayak, Subramanya G and Nayak, Dayananda (2012) Classification of ECG Signals using ANN with Resilient Back Propagation Algorithm. International Journal of Computer Applications, 54 (6). pp. 20-23. ISSN 0975 – 8887

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Abstract

Electrocardiogram is one important physiological signal, which is used in assessing cardiac health. The extraction of features used for identification of the state of ECG is discussed in this paper. Using MAT LAB programs/tools, different statistical features are extracted from both normal and arrhythmia spectra. These features include arithmetic mean, median, variance, residuals on curve fitting etc. The values of the feature vector reveal information regarding cardiac health state. Then a classical multilayer feed forward neural network with back propagation algorithm is employed to serve as a classifier of the feature vector, giving 100% successful results for the specific data set considered.

Item Type: Article
Uncontrolled Keywords: Electrocardiogram, Back propagation algorithm, Neural Network
Subjects: Engineering > MIT Manipal > Electronics and Communication
Depositing User: MIT Library
Date Deposited: 11 Apr 2013 05:57
Last Modified: 11 Apr 2013 05:57
URI: http://eprints.manipal.edu/id/eprint/79640

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