Efficient Combination Scheme for Arrhythmia Detection Using ECG Signals |
Author(s): |
REVATHI , PRATHYUSHA INSTITUTE OF TECHNOLOGY AND MANAGEMENT; L.Vanitha, prathyusha institute of technology and management; G.Revathi, prathyusha institute of technology and management |
Keywords: |
Arrhythmia, Cardiac arrest, ECG, principal component analysis (PCA), k- nearest neighbor (KNN), probabilistic neural network (PNN) |
Abstract |
Arrhythmia is a health problem and it is a drastic cause for many kinds of heart diseases. Some arrhythmia may even cause to death. The main objective of this work is to combine the decision of each classifier in an efficient way to improve the classification accuracy of the classifiers to predict the disease. As different classifiers provide different opinion to the target system, the combined decision will provide more robust and accurate result. The HRV parameters obtained from the ECG of normal and the arrhythmia affected patients is used for classification. Combination of classifiers like k-Nearest Neighbor (k-NN), Principal Component Analysis (PCA), Probabilistic neural network, Decision tree and Adaptive boost classifier is used in this work. The choice or judgment of the efficient classifiers is selected and combined using majority voting system for detection of final classification. |
Other Details |
Paper ID: IJSRDV2I12019 Published in: Volume : 2, Issue : 12 Publication Date: 01/03/2015 Page(s): 19-22 |
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