Automatic ECG arrhythmia classification using dual tree complex wavelet based features

Abstract Early detection of cardiac diseases using computer aided diagnosis system reduces the high mortality rate among heart patients. The detection of cardiac arrhythmias is a challenging task since the small variations in electrocardiogram (ECG) signals cannot be distinguished precisely by human eye. In this paper, dual tree complex wavelet transform (DTCWT) based feature extraction technique for automatic classification of cardiac arrhythmias is proposed. The feature set comprises of complex wavelet coefficients extracted from the fourth and fifth scale DTCWT decomposition of a QRS complex signal in conjunction with four other features (AC power, kurtosis, skewness and timing information) extracted from the QRS complex signal. This feature set is classified using multi-layer back propagation neural network. The performance of the proposed feature set is compared with statistical features extracted from the sub-bands obtained after decomposition of the QRS complex signal using discrete wavelet transform (DWT) and with four other features (AC power, kurtosis, skewness and timing information) extracted from the QRS complex signal. The experimental results indicate that the DWT and DTCWT based feature extraction technique classifies ECG beats with an overall sensitivity of 91.23% and 94.64%, respectively when tested over five types of ECG beats of MIT-BIH Arrhythmia database.

[1]  F. A. Afsar,et al.  Robust Electrocardiogram Beat Classification using Discrete Wavelet Transform , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.

[2]  Sung-Nien Yu,et al.  Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network , 2007, Pattern Recognit. Lett..

[3]  Manu Thomas,et al.  Classification of cardiac arrhythmias based on dual tree complex wavelet transform , 2014, 2014 International Conference on Communication and Signal Processing.

[4]  D.S. Benitez,et al.  A new QRS detection algorithm based on the Hilbert transform , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[5]  N.S. Nehe,et al.  New feature extraction methods using DWT and LPC for isolated word recognition , 2008, TENCON 2008 - 2008 IEEE Region 10 Conference.

[6]  Elif Derya Übeyli,et al.  Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: A pilot study , 2008, Digit. Signal Process..

[7]  Shubha Kadambe,et al.  Adaptive wavelets for signal classification and compression , 2006 .

[8]  Guangyi Chen,et al.  Automatic EEG seizure detection using dual-tree complex wavelet-Fourier features , 2014, Expert Syst. Appl..

[9]  Philip de Chazal,et al.  Automatic classification of heartbeats using ECG morphology and heartbeat interval features , 2004, IEEE Transactions on Biomedical Engineering.

[10]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[11]  Navnath S. Nehe,et al.  DWT and LPC based feature extraction methods for isolated word recognition , 2012, EURASIP Journal on Audio, Speech, and Music Processing.

[12]  Chandan Chakraborty,et al.  Application of principal component analysis to ECG signals for automated diagnosis of cardiac health , 2012, Expert Syst. Appl..

[13]  Richard Baraniuk,et al.  The Dual-tree Complex Wavelet Transform , 2007 .

[14]  N. Kingsbury.  Complex Wavelets for Shift Invariant Analysis and Filtering of Signals , 2001 .

[15]  Ing Yann Soon,et al.  An auditory model for robust speech recognition , 2008, 2008 International Conference on Audio, Language and Image Processing.

[16]  Nahit Emanet,et al.  ECG beat classification by using discrete wavelet transform and Random Forest algorithm , 2009, 2009 Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control.

[17]  Ataollah Ebrahimzadeh,et al.  An Efficient Technique for Classification of Electrocardiogram Signals , 2009 .

[18]  W.J. Tompkins,et al.  A patient-adaptable ECG beat classifier using a mixture of experts approach , 1997, IEEE Transactions on Biomedical Engineering.

[19]  Gregory T. A. Kovacs,et al.  Robust Neural-Network-Based Classification of Premature Ventricular Contractions Using Wavelet Transform and Timing Interval Features , 2006, IEEE Transactions on Biomedical Engineering.

[20]  H. Gholam Hosseini,et al.  A multi-stage neural network classifier for ECG events , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  Osama S. Faragallah,et al.  Efficient video watermarking based on singular value decomposition in the discrete wavelet transform domain , 2013 .

[22]  Elif Derya íbeyli.  Statistics over features of ECG signals , 2009 .

[23]  Mehmet Korürek,et al.  ECG beat classification using particle swarm optimization and radial basis function neural network , 2010, Expert Syst. Appl..