BEAT CLASSIFICATION USING HYBRID WAVELET TRANSFORM BASED FEATURES AND SUPERVISED LEARNING APPROACH

  • M. Sasireka Assistant Professor(Selection Grade), Kongu Engineering College, Erode-638052.Tamilnadu.
  • A. Senthilkumar Professor and Head, Dr. Mahalingam College of Engineering & Technology Pollachi-642003.Tamilnadu.

Abstract

This paper describes an automatic heartbeat recognition based on QRS detection, feature extraction and classification. In this paper five different type of ECG beats of MIT BIH arrhythmia database are automatically classified. The proposed method involves QRS complex detection based on the differences and approximation derivation, inversion and threshold method. The computation of combined Discrete Wavelet Transform (DWT) and Dual Tree Complex Wavelet Transform (DTCWT) of hybrid features coefficients are obtained from the QRS segmented beat from ECG signal which are then used as a feature vector. Then the feature vectors are given to Extreme Learning Machine (ELM) and k- Nearest Neighbor (kNN) classifier for automatic classification of heartbeat. The performance of the proposed system is measured by sensitivity, specificity and accuracy measures.

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Author Biographies

M. Sasireka, Assistant Professor(Selection Grade), Kongu Engineering College, Erode-638052.Tamilnadu.
Department of EIE,
A. Senthilkumar, Professor and Head, Dr. Mahalingam College of Engineering & Technology Pollachi-642003.Tamilnadu.
Department of EEE,

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Published
2017-02-18
How to Cite
Sasireka, M., & Senthilkumar, A. (2017). BEAT CLASSIFICATION USING HYBRID WAVELET TRANSFORM BASED FEATURES AND SUPERVISED LEARNING APPROACH. JOURNAL OF ADVANCES IN CHEMISTRY, 13(8), 6397-6405. https://doi.org/10.24297/jac.v13i8.5709
Section
Articles