Pattern Recognition Application in ECG Arrhythmia Classification

In this paper, we propose a pattern recognition algorithm for arrhythmia recognition. Irregularity in the electrical activity of the heart (arrhythmia) is one of the leading reasons for sudden cardiac death in the world. Developing automatic computer aided techniques to diagnose this condition with high accuracy can play an important role in aiding cardiologists with decisions. In this work, we apply an adaptive segmentation approach, based on the median value of R-R intervals, on the de-noised ECG signals from the publically available MIT-BIH arrhythmia database and split signal into beat segments. The combination of wavelet transform and uniform one dimensional local binary pattern (1-D LBP) is applied to extract sudden variances and distinctive hidden patterns from ECG beats. Uniform 1-D LBP is not sensitive to noise and is computationally effective. ELM classification is adopted to classify beat segments into five types, based on the ANSI/AAMI EC57:1998 standard recommendation. Our preliminary experimental results show the effectiveness of the proposed algorithm in beat classification with 98.99% accuracy compared to the state of

[1]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

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

[3]  M. I. Owis,et al.  A novel technique for cardiac arrhythmia classification using spectral correlation and support vector machines , 2015, Expert systems with applications.

[4]  Yilmaz Kaya,et al.  1D-local binary pattern based feature extraction for classification of epileptic EEG signals , 2014, Appl. Math. Comput..

[5]  Manu Thomas,et al.  Automatic ECG arrhythmia classification using dual tree complex wavelet based features , 2015 .

[6]  Hossein Ebrahimnezhad,et al.  Classification of Arrhythmias Using Linear Predictive Coefficients and Probabilistic Neural Network , 2013 .

[7]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

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

[9]  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.

[10]  B. K. Julsing,et al.  Face Recognition with Local Binary Patterns , 2012 .

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

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

[13]  Vlado Delic,et al.  Efficient ECG Modeling using Polynomial Functions , 2011 .

[14]  Naomie Salim,et al.  Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals , 2016, Comput. Methods Programs Biomed..

[15]  Shamim Nemati,et al.  Semisupervised ECG Ventricular Beat Classification With Novelty Detection Based on Switching Kalman Filters , 2015, IEEE Transactions on Biomedical Engineering.

[16]  Marzuki Khalid,et al.  Illumination Normalization using 2D Wavelet , 2012 .

[17]  Raúl Alcaraz,et al.  Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms , 2015, Entropy.

[18]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

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

[20]  Chandan Chakraborty,et al.  Cardiac decision making using higher order spectra , 2013, Biomed. Signal Process. Control..

[21]  U. Rajendra Acharya,et al.  ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform , 2013, Biomed. Signal Process. Control..