Clustering MIT-BIH arrhythmias with Ant Colony Optimization using time domain and PCA compressed wavelet coefficients

In this paper, Ant Colony Optimization (ACO) based clustering analysis of ECG arrhythmias taken from the MIT-BIH Arrhythmia Database is proposed. Both time domain and discrete wavelet transform (DWT) based frequency domain features are used in the analysis. Since the number of wavelet coefficients are huge amount as compared to the time domain parameters, Principal Component Analysis (PCA) based compression is applied on them in order to decrease their number to the number of time domain features. Then, the reduced numbers of frequency parameters are combined with the time domain features, in order to get the total feature sets. Different types of feature sets are tried and the classification results are compared. These are: time domain feature set, frequency domain feature set and the mixture of them. A neural network algorithm is developed in parallel to verify and measure the ACO classifier's success. Moreover, linear discriminant analysis (LDA) is used to show the effect of clustering on the system's results. The method is tested with MIT-BIH database to classify normal beats and five different critical and having vital importance arrhythmia types. Chosen six classes are normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), right bundle branch block (RBBB), ventricular fusion (F) and fusion (f). Comparison results indicate that the mixture feature set gave a better success for the classification.

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

[2]  R. J. Kuo,et al.  Application of ant K-means on clustering analysis , 2005 .

[3]  Willis J. Tompkins,et al.  Design of Microcomputer-Based Medical Instrumentation , 1981 .

[4]  Stanislaw Osowski,et al.  ECG beat recognition using fuzzy hybrid neural network , 2001, IEEE Trans. Biomed. Eng..

[5]  Pei-Jarn Chen,et al.  Multiple Cardiac Arrhythmia Recognition Using Adaptive Wavelet Network , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[6]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[7]  B.G. Celler,et al.  Low computational cost classifiers for ECG diagnosis using neural networks , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[8]  Lin Ling,et al.  ECG analysis based on PCA and SOM , 2003, International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003.

[9]  Liang-Yu Shyu,et al.  Using wavelet transform and fuzzy neural network for VPC detection from the holter ECG , 2004, IEEE Transactions on Biomedical Engineering.

[10]  Cheng-Fa Tsai,et al.  ACODF: a novel data clustering approach for data mining in large databases , 2004 .

[11]  A. Josko.  Discrete Wavelet Transform In Automatic ECG Signal Analysis , 2007, 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007.

[12]  J. E. Jackson.  A User's Guide to Principal Components , 1991 .

[13]  Rosaria Silipo,et al.  Artificial neural networks for automatic ECG analysis , 1998, IEEE Trans. Signal Process..

[14]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

[15]  P. Rautaharju,et al.  Selection of a reduced set of parameters for classification of ventricular conduction defects by cluster analysis , 1993, Proceedings of Computers in Cardiology Conference.

[16]  J. Friedman.  Regularized Discriminant Analysis , 1989 .

[17]  Carsten Peterson,et al.  Clustering ECG complexes using Hermite functions and self-organizing maps , 2000, IEEE Trans. Biomed. Eng..

[18]  T. Olmez,et al.  Classification of ECG waveforms using a novel neural network , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[19]  G. Lyons,et al.  Bayesian ANN classifier for ECG arrhythmia diagnostic system: a comparison study , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[20]  B. Bakshi.  Multiscale PCA with application to multivariate statistical process monitoring , 1998 .

[21]  M. Bakri,et al.  Automatic detection of QRS complexes in ECG signals collected from patients after cardiac surgery , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[22]  H. K. Verma,et al.  ECG Modeling and QRS Detection using Principal Component Analysis , 2006 .

[23]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[24]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[25]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[26]  C. Li,et al.  Detection of ECG characteristic points using wavelet transforms. , 1995, IEEE transactions on bio-medical engineering.

[27]  Z. Benyo,et al.  Quick ECG Analysis for On-Line Holter Monitoring Systems , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[28]  Jenn Taur Shey,et al.  Measurement 12-lead ECG character points using line segment clustering technique , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[29]  Sung-Nien Yu,et al.  Integration of independent component analysis and neural networks for ECG beat classification , 2008, Expert Syst. Appl..

[30]  D. Fotiadis,et al.  Maternal and fetal heart rate extraction from abdominal recordings using multi-scale principal components analysis , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[31]  P. Laguna,et al.  Evaluation of a wavelet-based ECG waveform detector on the QT database , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[32]  Mehmet Korürek,et al.  A new arrhythmia clustering technique based on Ant Colony Optimization , 2008, J. Biomed. Informatics.

[33]  B. Karlik,et al.  A recognition of ECG arrhytihemias using artificial neural networks , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[34]  Hartmut Dickhaus,et al.  Classification of QRS morphology in Holter monitoring , 1999, Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N.

[35]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[36]  A. Ahmadian,et al.  ECG Feature Extraction Based on Multiresolution Wavelet Transform , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[37]  B. A. Harvey,et al.  Neural network-based EKG pattern recognition , 2002 .

[38]  I.R. Legarreta.  Component selection for Principal Component Analysis-based extraction of atrial fibrillation , 2006, 2006 Computers in Cardiology.

[39]  Michael G. Strintzis,et al.  ECG analysis using nonlinear PCA neural networks for ischemia detection , 1998, IEEE Trans. Signal Process..