Hybrid wavelet-support vector classification of waveforms

Journal of Computational and Applied Mathematics

Volumn 148, Issue 2, 2002, Pages 375-400

  Strauss, Daniel J ^a,b   Steidl, Gabriele ^a  

^a UNIVERSITY OF MANNHEIM   (Germany)

^b SciComp/Computational Intelligence   (Germany)

Author keywords

Adapted filter banks; Frames; Radial basis functions; Reproducing kernel Hilbert spaces; Support vector machines; Waveform recognition; Wavelets

Indexed keywords

ACOUSTIC EMISSIONS; ALGORITHMS; LEARNING SYSTEMS; REGRESSION ANALYSIS; VECTORS;

SUPPORT VECTOR MACHINES (SVM);

WAVEFORM ANALYSIS;

WAVE;

ALGORITHM; ELECTROGRAPHY; EVOKED AUDITORY RESPONSE; MACHINE; SUPPORT VECTOR MACHINE; VALIDATION PROCESS; WAVEFORM; WAVELET;

EID: 0037113763     PISSN: 03770427     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0377-0427(02)00557-5     Document Type: Article

References (57)

1. H. Bölcskei, F. Hlawatsch, H.G. Feichtinger, Frame-theoretic analysis of oversampled filter banks, IEEE Trans. Signal Process. 46 (1998) 3256-3268.
2. C. Burges, A tutorial on support vector machines for pattern recognition, Data Mining Knowledge Discovery 2 (1998) 121-167.
3. C. Burges, Geometry and invariance in kernel based methods, in: B. Schölkopf, C. Burges, A.J. Smola (Eds.), Advances in Kernel Methods-Support Vector Learning, MIT Press, Cambridge, MA, 1999, pp. 89-116.
4. R.J. Coggins, M.A. Jabri, A low-complexity intracardiac electrogram compression algorithm, IEEE Trans. Biomed. Eng. 46 (1999) 83-91.
5. R.R. Coifman, D. Donoho, Translation invariant de-noising, in: A Antoniadis (Ed.), Wavelet in Statistics, Lecture Notes, Springer, New York, 1995, pp. 125-150.
6. N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, 2000.
7. Z. Cvetković, M. Vetterli, Oversampled filter banks, IEEE Trans. Signal Process. 46 (1998) 1245-1255.
8. I. Daubechies, Ten Lectures on Wavelets, SIAM, Philadelphia, PA, 1992.
9. I. Daubechies, W. Sweldens, Factoring wavelet transforms into lifting steps, J. Fourier Anal. Appl. 4 (1998) 245-267.
10. P.H. Delsarte, B. Macq, D.T.M. Slock, Signal-adapted multiresolution transforms for image coding, IEEE Trans. Inform. Theory 38 (1992) 897-903.
11. R. Fletcher, Practical Methods of Optimization, Wiley, New York, 1990.
12. M.S. Floater, A. Iske, Multistep scattered data interpolation using compactly supported radial basis functions, J. Comput. Appl. Math. 73 (1996) 65-78.
13. R.F. Gilum, Sudden cardiac death in the united states, Circulation 79 (1989) 756-776.
14. L. Hermes, J.M. Buhmann, Feature selection for support vector machines, in: Proceedings of the International Conference on Pattern Recognition 2000, Barcelona, Spain, 2000.
15. S. Hoth, Relationship between parameters of evoked otoacoustic emissions and hearing loss, Audiologische Akustik 1 (1995) 20-29.
16. N. Intrator, Q. Huynh, Y. Wong, B.H.K. Lee, Wavelet feature extraction for discrimination tasks, in: Proceedings of the Canadian Workshop on Information Theory, Toronto, 1997, pp. 83-86.
17. T. Joachims, Making large-scale support vector machine learning practical, in: B. Schölkopf, C. Burges, A.J. Simola (Eds.), Advances in Kernel Methods-Support Vector Learning, MIT Press, Cambridge, MA, 1999, pp. 169-184.
18. D.T. Kemp, Stimulated acoustic emissions from within the human auditory system, J. Acoust. Soc. Amer. 64 (1978) 1386-1391.
19. J.T. Kim, Y.H. Lee, T. Isshiki, H. Kunieda, Scalable VLSI architectures for lattice-structure-based discrete wavelet transform, IEEE Trans. Circuits Systems II 45 (1998) 1031-1043.
20. G.S. Kimeldorf, G. Wahba, Some results on tchebycheffian spline functions, J. Anal. Appl. 33 (1971) 82-95.
21. N.G. Kingsbury, Complex wavelets for shift invariant analysis and filtering of signals, J. Appl. Comput. Harmonic Anal. 10 (2001) 243-253.
22. R.J. Myerburg, K.M. Kessler, A. Castellanos, Sudden cardiac death, structure, function, and time-dependence of risk, Circulation 85 (1992) 12-110.
23. M. Oren, C. Papageorgiou, P. Sinha, E. Osuna, Pedestrian detection using wavelet templates, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San juan, Puerto Rico, pp. 193-199.
24. C. Papageoriou, M. Oren, T. Poggio, A general framework for object detection, in: Proceedings of the International Conference on Computer Vision (ICCV'98), Bombay, India, 1998, pp. 555-562.
25. S. Pittner, S.V. Kamarthi, Feature extraction from wavelet coefficients for pattern recognition tasks, IEEE Trans. Pattern Anal. Mach. Intell. 21 (1999) 83-88.
26. T. Poggio, T. Vetter, Recognition and structure from one 2d model view: observations on prototypes, object classes, and symmetries, AI Memo 1347, Massachusetts Institute of Technology, Cambridge, MA, 1992.
27. R. Probst, B.L. Lonsbury-Martin, G.K. Martin, A review of otoacoustic emissions, J. Acoust. Soc. Amer. 89 (1991) 2027-2067.
28. G. Rutledge, G. McLean, Comparison of several wavelet packet feature extraction algorithms, IEEE Trans. Pattern Anal. Mach. Intell. (2000), submitted for publication.
29. N. Saito, R.R. Coifman, Local discrimination bases, in: A.F. Laine, M.A Unser (Eds.), Wavelet Applications in Signal and Image Processing II, Proc. SPIE 2303, July 1994, pp. 2-14.
30. R. Schaback, Creating surfaces from scattered data using radial basis functions in: M. Dæhlen, T. Lyche, L.L. Schumaker (Eds. , Mathematical Methods in Computer Aided Geometric Design III, Vanderbilt University Press, Nashville, TN, 1995, pp. 477-496.
31. R. Schaback, Reconstruction of multivariate functions from scattered data, Monograph, Institute for Numerical and Applied Mathematics, University of Göttingen, 1997.
32. B. Schölkopf, C. Burges, V. Vapnik, Incorporating invariances in support vector learning machines, in: M.I. Jordan, M.J. Kearns, S.A. Solla (Eds.), Artificial Neural Networks, ICANN'96, Springer Lecture Notes in Computer Science, Vol. 1112, Springer, Berlin, Germany, 1996, pp. 47-52.
33. B. Schölkopf, S. Mika, C. Burges, P. Knirsch, K.-R. Müller, G. Rätsch, A.J. Smola, Input space vs. feature space in kernel-based methods, IEEE Trans. Neural Networks 10 (1999) 1000-1017.
34. B. Schölkopf, P.Y. Simard, A.J. Smola, V. Vapnik, Prior knowledge in support vector kernels, in: M.I. Jordan, M.J. Kearns, S.A. Solla (Eds.), Advances in Neural Information Processing Systems, Vol. 10, MIT Press, Cambridge, MA, 1998 pp. 640-646.
35. B. Schölkopf, K. Sung, C. Burges, F. Girosi, P. Niyogi, T. Poggio, V. Vapnik, Comparing support vector machines with Gaussian kernels to radial basic function classifiers, IEEE Trans. Signal Process. 45 (1997) 2758-2765.
36. M. Shensa, The discrete wavelet transform: Wedding the á trous and mallat algorithms, IEEE Trans. Signal Process. 40 (1992) 2464-2482.
37. M.M. Shieh, L. Clem, L. Malden, P.A.E. Fain, Improved supraventricular and ventricular tachycardia discrimination using electrogram morphology in an implantable cardioverter defibrillator, G. Ital. Cardiol. 28 (suppl. 1) (1998) 245-251.
38. I. Steinwart, On the influence of the kernel on the generalization ability of support vector machines, Technical Report 01-01, 2001, Institute of Mathematics, University of Jena, 2001.
39. S.A. Stevenson, J.M. Jenkins, L.A. Dicarlo, Analysis of the intraventricular electrogram for differentiation of distinct monomorphic ventricular arrhythmias, PACE 20 (1997) 2730-2738.
40. G. Strang, T. Nguyen, Wavelets and Filter Banks, Wellesley-Cambridge Press, Wellesley, MA, 1996.
41. D. Strauss, J. Jung, A. Rieder, Y. Manoli, Classification of endocardial electrograms using adapted wavelet packets and neural networks, Ann. Biomed. Eng., in press.
42. D. Strauss, T. Sinnwell, A. Rieder, Y. Manoli, J. Jung, A promising approach to morphological endocardial signal discriminations: adapted multiresolution signal decompositions, Appl. Signal Process. 6 (1999) 182-193.
43. The Antiarrhythmics Versus Implantable Defibrillators (AVID) Investigators A comparison of antiarrythmic drug therapy with implantable defibrillators in patients resuscitated from near fatal ventricular arrhythmias, N. Engl. J. Med. 337 (1997) 1576-1583.
44. R.D. Throne, J.M. Jenkins, S.A. Winston, C.J. Finelli, L.A. Dicarlo, Discrimination of retrograde from antegrade atrial activation using intracardiac electrogram waveform analysis, PACE 12 (1989) 1622-1630.
45. G. Tognola, F. Grandori, P. Ravazzani, Wavelet analysis of click-evoked otoacoustic emissions, IEEE Trans. Biomed. Eng. 45 (1998) 686-697.
46. L.J. Trejo, M.J. Shensa, Feature extraction of event-related potentials using wavelets: An application to human performance monitoring, Brain Language 66 (1999) 89-107.
47. P.P. Vaidyanathan, Multirate Systems and Filter Banks, Prentice-Hall, Englewood Cliffs, NJ, 1993.
48. P.P. Vaidyanathan, P.Q. Hoang, Lattice structures for optimal design and robust implementation of two-channel perfect reconstruction qmf filter banks, IEEE Trans. Acoust. Speech. Signal Process. 36 (1988) 81-94.
49. V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, 1995.
50. M. Vetterli, J. Kovačević, Wavelets and Subband Coding, Prentice-Hall, Englewood Cliffs, NJ, 1995.
51. G. Wahba, Support vector machines, reproducing kernel hilbert spaces and the randomized GACV, in: B. Schölkopf, C. Burges, A.J. Smola (Eds.), Advanced in Kernel Methods-Support Vector Learning, MIT Press, Cambridge, MA, 1999, pp. 293-306.
52. Z. Wang, Z. He, J.D.Z. Chen, Filter banks and neural networks-based feature extraction and automatic classification of electrogastrogam, Ann. Biomed. Eng. 27 (1999) 88-95.
53. M. Weber, D. Böcker, D. Bansch, J. Brunn, M. Castrucci, R. Gradaus, G. Breithardt, M. Block, Efficacy and safety of the initial use of stability and onset criteria in implantable cardioverter defibrillator, J. Cardiovasc. Electrophysiol. 10 (1999) 145-153.
54. H. Wendland, Piecewise polynomial, positive definite functions and compactly supported functions radial basis functions of minimal degree, Adv. in Comput. Math. 4 (1995) 389-396.
55. J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, V. Vapnik, Feature selection for SVMs, in: A.J. Smola, P.L. Bartlett, B. Schölkopf, D. Schuurmans (Eds.), Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA, Vol. 13, 2000.
56. H. Zou, A.H. Tewfik, Parameterization of compactly supported wavelets orthonormal wavelets IEEE Trans. Signal Process. 41 (1993) 1428-1431;
57. S.H. Wang, A.H. Tewfik, H. Zou, Correction to Parameterization of compactly supported wavelets orthonormal wavelets IEEE Trans. Signal Process 42 (1994) 208-109.