Classification of Arrhythmias with LDA and ANN using Orthogonal Rotations for Feature Reduction
This paper presents a new approach for feature reduction by using orthogonal rotations. Wavelet coefficients for beat segments are taken as features which are reduced by factor analysis method using orthogonal rotations. LDA (Linear Discriminant Analysis) and ANN (Artificial Neural Network) classifiers are used for classification. The signals are taken from MIT-BIH arrhythmia database to classify into Normal, PVC, Paced, LBBB and RBBB. The performance of classification output has been compared by the performance parameters. Both the classifiers have given best overall accuracy for equimax rotation. 96% accuracy is achieved with LDA classifier,99.2% accuracy is achieved using ANN.
Keywords: ECG, Linear Discriminant Analysis, Artificial Neural Network, Holdout Method, Orthogonal Rotations
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ABOUT THE AUTHORS
Manpreet Kaur
She is working as Associate Professor in Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering and Technology, Longowal. She has completed her B.Tech. from NIT Jalandhar and M.Tech. from Punjab University Chandigarh. Her area of interest is biomedical engineering.
A. S. Arora
He is Professor in Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering and Technology, Longowal. He has completed his B.Tech., M.Tech and Ph.D. from IIT Roorkee.
Manpreet Kaur
She is working as Associate Professor in Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering and Technology, Longowal. She has completed her B.Tech. from NIT Jalandhar and M.Tech. from Punjab University Chandigarh. Her area of interest is biomedical engineering.
A. S. Arora
He is Professor in Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering and Technology, Longowal. He has completed his B.Tech., M.Tech and Ph.D. from IIT Roorkee.