Neural Network based Classification of ECG signals using LM Algorithm

Nayak, Subramanya G and Puttamadappa, C (2009) Neural Network based Classification of ECG signals using LM Algorithm. World Academy of Science, Engineering and Technology, 60. pp. 579-581. ISSN 2070 – 3724

[img]
Preview
PDF
B._GSN___WASET_PAPER.pdf - Published Version

Download (308kB)

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 differentiation and count of spikes for different thresholds, mean, standard deviation, energy, residuals on curve fitting, LPC coefficients 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: Neural Networks, Electrocardiogram, Linear; Prediction Coefficients.
Subjects: Engineering > MIT Manipal > Electronics and Communication
Depositing User: MIT Library
Date Deposited: 13 Apr 2013 08:45
Last Modified: 13 Apr 2013 08:45
URI: http://eprints.manipal.edu/id/eprint/79689

Actions (login required)

View Item View Item