Fast detection of wave V in ABRs using a smart single sweep analysis system

Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

Volumn 26 I, Issue , 2004, Pages 458-461

  Strauss, Daniel J ^a,c   Delb, Wolfgang ^b   Plinkert, Peter K ^b   Schmidt, Helmut ^c  

^a Key Numerics   (Germany)

^b SAARLAND UNIVERSITY   (Germany)

^c INM LEIBNIZ INSTITUTE FOR NEW MATERIALS   (Germany)

Author keywords

Adapted Filter Banks; Auditory Brainstem Responses; Hearing Screening; Kernel Machines; Single Sweep Analysis; Smart Systems; Wavelets

Indexed keywords

DECISION MAKING; FEATURE EXTRACTION; LEARNING SYSTEMS; MEASUREMENT THEORY; OPTIMIZATION; PROBLEM SOLVING; SENSITIVITY ANALYSIS; SIGNAL TO NOISE RATIO; WAVELET TRANSFORMS;

ADAPTED FILTER BANKS; AUDITORY BRAINSTEM RESPONSES; HEARING SCREENING; KERNEL MACHINES; SINGLE SWEEP ANALYSIS; SMART SYSTEMS;

SCREENING;

EID: 11044223888     PISSN: 05891019     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. C. Yoshinaga-Itano, "Benefits of early intervention for children with hearing loss," Otolaryngol. Clin. North Am., vol. 32, pp. 1089-1102, 1999.
2. C. Yoshinaga-Itano and J. S. Gravel, "The evidence fr universal newborn hearing screening," American Journal of Audiology, vol. 10, pp. 62-4, 2001.
3. B. R. Vohr and A. B. Maxon, "Screening infants or hearing impairment," J. Pediatr., vol. 128, pp. 710-713, 1996.
4. W. Delb, D. Merkel, K. Pilorget, J. Schnitt, and P. K. Plinkert, "Effectiveness of a teoae based screening program, can a patient tracking system effectively be organized using modern information technology and central data management ?," Eur. Arch. Otolaryngol., 2003, DOI: 10.1007/S00405-003-0662-3.
5. D. L. Steward, A. Mehl, Thomson V. Hall, J. W. III, and J. Carrol, M. Hamlett, "Infant hearing impairment and universal hearing screening," J. Perinatol., vol. 20, pp. 128-131, 2000.
6. D. J. Strauss, W. Delb, P. K. Plinkert, and J. Jung, "Hybrid wavelet-kernel based classifiers and novelty detectors in biosignal processing," in Proceedings of the 25th Silver Anniversary International Conference of the IEEE Engineering in Medicine and Biology Society, Cancun, Mexico, 2003, pp. 2765-2769.
7. D. J. Strauss and G. Steidl, "Hybrid wavelet-support vector classification of waveforms," Journal of Computational and Applied Mathematics, vol. 148, pp. 375-400, 2002.
8. V. Vapnik, The Nature of Statistical Learning Theory, Springer, NY, 1995.
9. D. M. J. Tax and R. P. W. Duin, "Data domain description using support vectors," in Proceedings of European Symposium on Artificial Neural Networks '99, Brugge, 1999.
10. R. R. Coifman and M. V. Wickerhauser, "Entropy based algorithms for best basis selection," IEEE. Trans. on Information Theory, vol. 32, pp. 712-718, 1992.
11. M. V. Wickerhauser, Adapted Wavelet Analysis form Theory to Software, A. K. Peters, Ltd., Wellesley, MA, 1994.
12. M. T. Cover and J. A. Thomas, Elements of Information Theory, Wiley, NY, 1991.
13. G. Wahba, "Support vector machines, reproducing kernel hilbert spaces and the randomized GACV," in Advanced in Kernel Methods - Support Vector Learning, B. Schölkopf, C. Burges, and A. J. Smola, Eds., Cambridge, MA, 1999, pp. 293-306.
14. Asa Ben-Hur, David Horn, Hava T. Siegelman, and Vladimir Vapnik, "Support vector clustering," Journal of Machine Learning Research, pp. 125-137, 2001.
15. J. W. Hall, Handbook of Auditory Evoked Responses, Allyn and Bacon, Needham Heights, MA, 1992.
16. D. J. Strauss, W. Delb, and P. K. Plinkert, "Analysis and detection of binaural interaction in auditory brainstem responses by time-scale representations," Computers in Biology and Medicine, 2004, In press.
17. S. Kullback and R. A. Leibler, "On information and sufficiency," Ann. Math. Statist., vol. 22, pp. 79-86, 1951.