International Journal of Bioinformatics and Biomedical Engineering
Articles Information
International Journal of Bioinformatics and Biomedical Engineering, Vol.1, No.3, Nov. 2015, Pub. Date: Nov. 12, 2015
Classification of ECG Signals Using Legendre Moments
Pages: 284-291 Views: 825 Downloads: 599
Authors
[01] Irshad Khalil, Department of Computer Science, University of Malakand, Lower Dir, Khyber Pakhtunkhwa, Pakistan.
[02] Adnan Khalil, Department of Computer Science, University of Malakand, Lower Dir, Khyber Pakhtunkhwa, Pakistan.
[03] Sami Ur Rehman, Department of Computer Science, University of Malakand, Lower Dir, Khyber Pakhtunkhwa, Pakistan.
[04] Hammad Khalil, Department of Mathematics, University of Malakand, Khyber Pakhtunkhwa, Pakistan.
[05] Rahmat Ali Khan, Dean Faculty of Science, Department of Mathematics, University of Malakand, Lower Dir, Khyber Pakhtunkhwa, Pakistan.
[06] Fakhre Alam, Department of Computer Science, University of Malakand, Lower Dir, Khyber Pakhtunkhwa, Pakistan.
Abstract
During the last few decades, a huge amount of work is devoted to the study of classification techniques of Electrocardiogram (ECG) signals. In this paper, we present a robust algorithm based on shifted Legendre polynomials. These polynomials are successfully implemented to extract features from ECG signals. The ECG signals are first decomposed into its sub band using Legendre polynomials in specific time interval. The objective of the study is to classify two different types of ECG signals. We test two different classifier, KNN and simple logistic. A comparative study is presented on the behaviour and results of these classifiers. Experimental results are obtained for both classifiers and compared with some other results available in the open literature. The experimental results lead us to conclusion that the proposed method provides more accurate results as compare to other classification methods.
Keywords
ECG Signals, Simple Logistic Classifier, Legendre Polynomials, KNN Classifier
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