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Electrocardiogram Beat Classification Using BAT-Optimized Fuzzy KNN Classifier
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Author(s): Atul Kumar Verma (Dr. B. R. Ambedkar National Institute of Technology, India), Indu Saini (Dr. B. R. Ambedkar National Institute of Technology, India)and Barjinder Singh Saini (Dr. B. R. Ambedkar National Institute of Technology, India)
Copyright: 2019
Pages: 10
Source title:
Medical Data Security for Bioengineers
Source Author(s)/Editor(s): Butta Singh (Guru Nanak Dev University, India), Barjinder Singh Saini (Dr. B. R. Ambedkar National Institute of Technology, India), Dilbag Singh (Dr. B. R. Ambedkar National Institute of Technology, India)and Anukul Pandey (Dumka Engineering College, India)
DOI: 10.4018/978-1-5225-7952-6.ch007
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Abstract
In this chapter, the BAT-optimized fuzzy k-nearest neighbor (FKNN-BAT) algorithm is proposed for discrimination of the electrocardiogram (ECG) beats. The five types of beats (i.e., normal [N], right bundle block branch [RBBB], left bundle block branch [LBBB], atrial premature contraction [APC], and premature ventricular contraction [PVC]) are taken from MIT-BIH arrhythmia database for the experimentation. Thereafter, the features are extracted from five type of beats and fed to the proposed BAT-tuned fuzzy KNN classifier. The proposed classifier achieves the overall accuracy of 99.88%.
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