BEAT CLASSIFICATION USING HYBRID WAVELET TRANSFORM BASED FEATURES AND SUPERVISED LEARNING APPROACH
DOI:
https://doi.org/10.24297/jac.v13i8.5709Abstract
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.Downloads
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References
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(23) MIT BIH arrhythmia database- https://www.physionet.org/physiobank/database/mitdb/
(2) M.G. Tsipouras, D.I. Fotiadis, Automatic arrhythmia detection based on time and time-frequency analysis of heart rate variability, Computer Methods and Programs in Biomedicine 74 (2004) 95–108.
(3) Eduardo José da S. Luz, William Robson Schwartz, Guillermo Cámara-Cháveza, David Menotti, “ECG-based heartbeat classification for arrhythmia detection: A surveyâ€, Computer Methods and Program in Biomedicine, 2015.
(4) M.G. Tsipouras, D.I. Fotiadis, D. Sideris, An arrhythmia classification system RR-interval signal, Artificial Intelligence in Medicine 33 (2005) 237–250.
(5) Berat Dogan and Mehmet Korürek, “A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domainsâ€, Applied Soft Computing 12 (2012) 3442–3451.
(6) Yakup Kutlu and Damla Kuntalp, “A multi-stage automatic arrhythmia recognition and classification systemâ€, Computers in Biology and Medicine 41 (2011) 37–45.
(7) A. Koski, “Modelling ECG signals with Hidden Markov modelsâ€, Artificial Intelligence in Medicine 8 (1996) 453–471.
(8) Elif Derya Ãœbeyli, “Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponentsâ€, computer methods and programs in biomedicine 93 (2009) 313–321.
(9) Ataollah Ebrahim Zadeh, Ali Khazaee and Vahid Ranaee, “Classification of the electrocardiogram signals using supervised classifiers and efficient featuresâ€, computer methods and programs in biomedicine 99 (2010) 179–194.
(10) M.R. Homaeinezhad , S.A. Atyabi, E. Tavakkoli, H.N. Toosi, A. Ghaffari and R. Ebrahimpour, “ECG arrhythmia recognition via a neuro-SVM–KNN hybrid classifier with virtual QRS image-based geometrical featuresâ€, Expert Systems with Applications 39 (2012) 2047–2058.
(11) Mohit Kumar, Ram Bilas Pachori and U. Rajendra Acharya , “Characterization of coronary artery disease using flexible analytic wavelet transform applied on ECG signalsâ€, Biomedical Signal Processing and Control 31 (2017) 301–308.
(12) Roshan Joy Martis, U. Rajendra Acharyaa and Lim Choo Mina, “ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transformâ€, Biomedical Signal Processing and Control 8 (2013) 437–448.
(13) S.S. Mehta and N.S. Lingayat, “Development of SVM based classification techniques for the delineation of wave components in 12-lead electrocardiogramâ€, Biomedical Signal Processing and Control 3 (2008) 341–349.
(14) S.N. Yu, Y.H. Chen, Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network, Pattern Recognit. Lett. 28 (2007) 1142–1150.
(15) A. Khazaee, A. Ebrahimzadeh, Classification of electrocardiogram signals with support vector machines and genetic algorithms using power spectral features, Biomed. Signal Process. Control 5 (2010) 252–263.
(16) Kadambe S, Srinivasan P. Adaptive wavelets for signal classification and compression. Int J Electron Commun (AEÜ) 2006; 60:45–55.
(17) Malini suvarna. and Venkategowda N., “Performance Measure and Efficiency of Chemical Skin Burn Classification Using KNN Methodâ€, Procedia Computer Science, 70, 48 – 54, 2015.
(18) N. Ghoggali, F. Melgani, Y. Bazi, A multiobjective genetic SVM approach for classification problems with limited training samples, IEEE Transactions on Geoscience and Remote Sensing 47 (2009) 1707–1718.
(19) R. Linder, D. Dew, H. Sudhoff, D. Theegarten, K. Remberger, S.J. Poppl, and M. Wagner, “The ’Subsequent Artificial Neural Network’ (SANN) Approach Might Bring More Classificatory Power to ANN-Based DNA Microarray Analyses,†Bioinformatics, vol. 20, no. 18, pp. 3544-3552, 2004.
(20) G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme Learning Machine: A New Learning Scheme of Feed forward Neural Networks,†Proc. Int’l Joint Conf. Neural Networks (IJCNN ’04), July 2004.
(21) G.-B. Huang and C.-K. Siew, “Extreme Learning Machine: RBF Network Case,†Proc. Eighth Int’l Conf. Control, Automation, Robotics, and Vision (ICARCV ’04), Dec. 2004.
(22) G.-B. Huang and C.-K. Siew, “Extreme Learning Machine with Randomly Assigned RBF Kernels,†Int’l J. Information Technology, vol. 11, no. 1, 2005.
(23) MIT BIH arrhythmia database- https://www.physionet.org/physiobank/database/mitdb/
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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
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