Classification of electrocardiogram signals with support vector machines and extreme learning machine

Neural Computing and Applications

Volumn 21, Issue 6, 2012, Pages 1331-1339

  Karpagachelvi, S ^a   Arthanari, M ^b   Sivakumar, M ^c  

^a MOTHER TERESA WOMEN S UNIVERSITY   (India)

^b Bharathidasan School of Computer Applications   (India)

^c ANNA UNIVERSITY   (India)

Author keywords

Electrocardiogram (ECG) signals classification; Extreme learning machine; Feature detection; Feature reduction; Generalization capability; Model selection issue; Support vector machine

Indexed keywords

ELECTROCARDIOGRAM SIGNAL; EXTREME LEARNING MACHINE; FEATURE DETECTION; FEATURE REDUCTION; GENERALIZATION CAPABILITY; MODEL SELECTION ISSUE;

DISEASES; ELECTROCARDIOGRAPHY;

SUPPORT VECTOR MACHINES;

EID: 84865648358     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-011-0572-z     Document Type: Article

References (24)

1. Osowski S, Linh TH (2001) ECG beat recognition using fuzzy hybrid neural network. IEEE Trans Biomed Eng 48(11): 1265-1271.
2. Hu YH, Palreddy S, Tompkins W (1997) A patient adaptable ECG beat classifier using a mixture of experts approach. IEEE Trans Biomed Eng 44(9): 891-900.
3. Khadra L, Al-Fahoum AS, Binajjaj S (2005) A quantitative analysis approach for cardiac arrhythmia classification using higher order spectral techniques. IEEE Trans Biomed Eng 52(11): 1840-1845.
4. de Chazal F, Reilly RB (2006) A patient adapting heart beat classifier using ECG morphology and heartbeat interval features. IEEE Trans Biomed Eng 53(12): 2535-2543.
5. Andreao RV, Dorizzi B, Boudy J (2006) ECG signal analysis through hidden Markov models. IEEE Trans Biomed Eng 53(8): 1541-1549.
6. Mitra S, Mitra M, Chaudhuri BB (2006) A rough set-based inference engine for ECG classification. IEEE Trans Instrum Meas 55(6): 2198-2206.
7. Siew C-K, Huang G-B (2005) Extreme learning machine with randomly assigned RBF Kernels. Int'l J Inf Technol 11(1): 16-24.
8. Xiong S-W, Liu H-B, Niu X-X (2005) Fuzzy support vector machines based on FCM clustering. In: Proceedings of 2005 international conference on machine learning and cybernetics, August 2005, vol. 5, pp 2608-2613.
9. Mark R, Moody G (1997) MIT-BIH Arrhythmia Database 1997 [Online]. Available http://ecg. mit. edu/dbinfo. html.
10. Minami K, Nakajima H, Toyoshima T (1999) Real-time discrimination of ventricular tachyarrhytmia with Fourier transform neural network. IEEE Trans Biomed Eng 46: 179-185.
11. Daubechies I (1998) Orthonormal bases of compactly wavelets. Commun Pure Appl Math (41): 909-996.
12. Thakor NV (1993) Multiresolution wavelet analysis of evoked potentials. IEEE Trans Biomed Eng 40(11): 1085-1093.
13. Vapnik VN (1995) The nature of statistical learning theory. Springer, New York, 187 pp.
14. Vapnik VN (1998) Statistical learning theory. Wiley, New York.
15. Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machine. IEEE Trans Geosci Remote Sens 42(8): 1778-1790.
16. Hsu C-W, Lin C-J (2002) A comparison of methods formulticlass support vector machines. IEEE Trans Neural Netw 13(2): 415-425.
17. http://www. physionet. org/physiotools/ecgpuwave/src/.
18. Wei JJ, Chang CJ, Shou NK, Jan GJ (2001) ECG data compression using truncated singular value decomposition. IEEE Trans Biomed Eng 5(4): 290-299.
19. Agresti A (2002) Categorical data analysis, 2nd edn. Wiley, New York.
20. Park H-S, Woo S-M, Kim Y-S, Kang B-J, Ban S-W (2009) ECG pattern classification based on generic feature extraction. In: Proceedings of the 3rd WSEAS international conference on circuits, systems, signal and telecommunications (CISST'2009), pp 21-25.
21. Nazmy TM, El-Messiry H, Al-bokhity B (2009) Adaptive neuro-fuzzy inference system for classification of ECG signals. J Theor Appl Inf Technol.
22. Sabry-Rizk M, Zgallai W, Hardiman P, O'Riordan J (1997) Third-order cumulant signature matching technique for non-invasive fetal heart beat identification. In: IEEE international conference on acoustics, speech, and signal processing, vol. 5, pp 3781-3784.
23. Emanet N (2009) ECG beat classification by using discrete wavelet transform and Random Forest algorithm. In: Fifth international conference on soft computing, computing with words and perceptions in system analysis, decision and control, pp 1-4.
24. Wen C, Yeh MF, Chang KC (2007) ECG beat classification using GreyART network. Signal Process IET 1(1): 19-28.