Selection of effective features for ECG beat recognition based on nonlinear correlations

Artificial Intelligence in Medicine

Volumn 54, Issue 1, 2012, Pages 43-52

  Chen, Ying Hsiang ^a   Yu, Sung Nien ^a  

^a NATIONAL CHUNG CHENG UNIVERSITY   (Taiwan)

Author keywords

Computer aided diagnosis; Electrocardiogram; Higher order statistics; Mutual information; Nonlinear correlation; Wavelet transform

Indexed keywords

BEAT CLASSIFICATION; ECG BEAT RECOGNITION; ELECTROCARDIOGRAM; FEATURE DIMENSIONS; FEEDFORWARD BACKPROPAGATION; HIGHER ORDER STATISTICS; MUTUAL INFORMATIONS; NON-LINEAR CORRELATIONS; NONLINEAR FEATURES; REDUCTION PROCESS; REDUNDANT FEATURES; SUB-BANDS;

COMPUTER AIDED DIAGNOSIS; CORRELATION METHODS; DISCRETE WAVELET TRANSFORMS; ELECTROCARDIOGRAPHY; NEURAL NETWORKS; REDUNDANCY; WAVELET TRANSFORMS;

FEATURE EXTRACTION;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; CLASSIFIER; ELECTROCARDIOGRAM; FILTER; HEART BEAT; LINEAR CORRELATION BASED FILTER; LINEAR SYSTEM; NONLINEAR CORRELATION BASED FILTER; NONLINEAR SYSTEM; PRIORITY JOURNAL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DIAGNOSIS, COMPUTER-ASSISTED; ELECTROCARDIOGRAPHY; HEART RATE; HUMANS; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED; WAVELET ANALYSIS;

EID: 83555179186     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2011.09.004     Document Type: Article

References (30)

1. Jekova I., Bortolan G., Christov I. Assessment and comparison of different methods for heartbeat classification. Med Eng Phys 2008, 30:248-257.
2. Chen S.W. Complexity-measure-based sequential hypothesis testing for real-time detection of lethal cardiac arrhythmias. EURASIP J Adv Signal Process 2007, 1-8.
3. Hosseini H.G., Luo D., Reynolds K.J. The comparison of different feed forward neural network architectures for ECG signal diagnosis. Med Eng Phys 2006, 28:372-378.
4. Christov I., Gomez-Herrero G., Krasteva V., Jekova I., Gotchev A., Egiazarian K. Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification. Med Eng Phys 2006, 28:876-887.
5. Tsipouras M.G., Fotiadis D.I., Sideris D. An arrhythmia classification system based on the RR-interval signal. Artif Intell Med 2005, 33:237-250.
6. Tsipouras M.G., Voglis C., Fotiadis D.I. A framework for fuzzy expert system-application to cardiovascular diseases. IEEE Trans Biomed Eng 2007, 54:2089-2105.
7. Exarchos T.P., Tsipouras M.G., Exarchos C.P., Papaloukas C., Fotiadis D.I., Michalis L.K. A methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision tree. Artif Intell Med 2007, 40:187-200.
8. Asl B.M., Setarehdan S.K., Mohebbi M. Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. Artif Intell Med 2008, 44:51-64.
9. Khadra L., Al-Fahoum A.S., Binajjaj S. A quantitative analysis approach for cardiac arrhythmia classification using higher order spectral techniques. IEEE Trans Biomed Eng 2005, 52:1840-1845.
10. Yu S.-N., Chen Y.-H. Noise-tolerant electrocardiogram beat classification based on higher order statistics of subband components. Artif Intell Med 2009, 46:165-178.
11. Yu S.-N., Chen Y.-H. Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network. Pattern Recognit Lett 2007, 28:1142-1150.
12. Ubeyli E.D. ECG beats classification using multiclass support vector machines with error correcting output codes. Digit Signal Prog 2007, 17:675-684.
13. Prasad G.K., Sahambi J.S. Classification of EGG arrhythmias using multi-resolution analysis and neural networks. Proceedings of the IEEE region 10 conference on convergent technologies for the Asia-Pacific (IEEE TENCON 2003) 2003, 227-231. IEEE Bangalore Section, Bangalore, India. H.P. Khincha (Ed.).
14. Ince T., Kiranyaz S., Gabbouj M. A generic and robust system for automated patient-specific classification of ECG signals. IEEE Trans Biomed Eng 2009, 56:1415-1426.
15. Osowski S., Linh T.H. ECG beat recognition using fuzzy hybrid neural network. IEEE Trans Biomed Eng 2001, 48:1265-1271.
16. Osowski S., Hoai L.T., Markiewicz T. Support vector machine-based expert system for reliable heartbeat recognition. IEEE Trans Biomed Eng 2004, 51:582-589.
17. Jiang W., Kong S.G. Block-based neural networks for personalized ECG signal classification. IEEE Trans Neural Netw 2007, 18:1750-1761.
18. Lagerholm M., Peterson C., Braccini G., Edenbrandt L., Sornmo L. Clustering ECG complexes using Hermite functions and self-organizing maps. IEEE Trans Biomed Eng 2000, 47:838-848.
19. Polat K., Gunes S. Detection of ECG arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine. Appl Math Comput 2007, 186:898-906.
20. Guler I., Ubeyli E.D. A modified mixture of experts network structure for ECG beats classification with diverse features. Eng Appl Artif Intell 2005, 18:845-856.
21. Engin M. ECG beat classification using neuro-fuzzy network. Pattern Recognit Lett 2004, 25:1715-1722.
22. Melgani F., Bazi Y. Classification of electrocardiogram signals with support vector machines and particle swarm optimization. IEEE Trans Inf Technol Biomed 2008, 12:667-677.
23. Liu H. Evolving feature selection. IEEE Intell Syst 2005, 20:64-76.
24. Yu L., Liu H. Feature selection for high-dimensional data: afast correlation-based filter solution. Proceedings of the 12th international conference on machine leaning (ICML2003) 2003, 856-863. AAAI Press, Menlo Park, CA. T. Fawcett, N. Mishra (Eds.).
25. Robnik-Sikonja M., Kononenko I. Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn 2003, 53:23-69.
26. Li Y., Wu Z.-F., Liu J.-M., Tang Y.-Y. Efficient feature selection for high-dimensional data using two-level filter. Proceeding of 2004 international conference on machine learning and cybernetics (ICMLC 2004) 2004, 1711-1716. IEEE, Piscataway, NJ. IEEE SMCT Committee (Ed.).
27. Mark R., Moody G.B. MIT-BIH arrhythmia database directory 1988, MIT, Cambridge, MA.
28. Martens S.M.M., Mischi M., Oei S.G., Bergmans J.W.M. An improved adaptive power line interference canceller for electrocardiography. IEEE Trans Biomed Eng 2006, 53:2220-2231.
29. Duda R.O., Hart P.E., Stork D.G. Pattern classification 2001, John Wiley & Sons, Inc., New York.
30. Haykin S. Neural networks: a comprehensive foundation 1994, Prentice-Hall, Englewood Cliffs, NJ.