Auditory brainstem response classification: A hybrid model using time and frequency features

References 

1. Starr A, Don M. Brain potentials evoked by acoustic stimuli. In:  Picton TW editors. Handbook of electroencephalography and clinical neurophysiology: human event-related potentials. vol. 3:Amsterdam: Elsevier; 1988;p. 97–150
2. Jewett DL. Volume conducted potentials in response to auditory stimuli as detected by averaging in the cat. Electroencephalography and clinical neurophysiology (Suppl.). vol. 28. Amsterdam: Elsevier; 1970;p. 609–18
3. Chiappa KH, Hill RA. Brain stem auditory evoked potentials: interpretation. In:  Chiappa KH editors. Evoked potentials in clinical medicine. Philadelphia: Lippincott-Raven Publishers; 1997;p. 199–229
4. Chiappa KH. Brain stem auditory evoked potentials: methodology. In:  Chiappa KH editors. Evoked potentials in clinical medicine. Philadelphia: Lippincott-Raven Publishers; 1997;p. 157–198
5. Alpsan D, Ozdamar O. Auditory brainstem evoked potential classification for threshold detection by neural networks. 1. Network design, similarities between human-expert and network classification, feasibility. Automedica. 1992;15:67–82
6. Pratt TL, Olsen WO, Bauch CD. Four-channel ABR recordings: consistency in interpretation. Am J Audiol. 1995;4:47–54
7. Alpsan D, Ozdamar O. Auditory brainstem evoked potential classification for threshold detection by neural networks. 2. Effects of input coding, training set size, and composition and network size on performance. Automedica. 1992;15:83–93
8. Habraken JBA, van Gils MJ, Cluitmans PJM. Identification of peak V in brainstem auditory evoked potentials with neural networks. Comput Biol Med. 1993;23:369–380
   • MEDLINE
   • CrossRef
9. McCullagh PJ, King G, McAllister HG, Houston HG. Classification of brainstem auditory evoked potentials using artificial neural networks. In:  Brender J,  Christensen JP,  Scherrer JR,  McNair P editor. Proceedings of the medical informatics Europe ’96. Amsterdam: IOS Press; 1996;p. 547–550
10. Delgado RE, Ozdamar O. Automated auditory brainstem response interpretation. IEEE Eng Med Biol. 1994;227–237
11. Vannier E, Adam O, Motsch JF. Objective detection of brainstem auditory evoked potentials with a priori information from higher presentation levels. Artif Intell Med. 2002;25:283–301
    • Abstract
    • Full Text
    • Full-Text PDF (309 KB)
    • CrossRef
12. Ozdamar O, Delgado RE, Eilers RE, Urbano RC. Automated electrophysiologic hearing testing using a threshold-seeking algorithm. J Am Acad Audiol. 1994;5:77–88
    • MEDLINE
13. Acır N, Özdamar Ö, Güzelis C. Automatic classification of auditory brainstem responses using SVM-based feature selection algorithm for threshold detection. J Eng Appl Artif Intell. 2006;19:209–218
14. Hinton G. Learning distributed representations of concepts. In: Proceedings of the eighth annual conference of the cognitive science society. New Jersey: Erlbaum; 1986;p. 1–12
15. Quinlan JR. Induction of decision trees. Mach Learning. 1986;1:81–106
16. SPSS Inc., Clementine Data Mining Software, referred to at: http://www.spss.com (Accessed: May 26, 2006).
17. Davey RT, McCullagh PJ, McAllister HG, Houston HG. Modelling of the brainstem evoked response for objective automated interpretation. In: Proceedings of the fourth international IEEE EMBS special topic conference on information technology applications in biomedicine. ITAB; 2003;p. 191–194
18. Turney PD. A theory of cross-validation error. J Exp Theor Artif Intell. 1994;6:361–391
19. Azuaje F. Genomic data sampling and its effect on classification performance assessment. BMC Bioinform. 2003;4:1–14
20. Ho TK. A theory of multiple classifier systems and its application to visual word recognition. Ph.D. Thesis, SUNY at Buffalo; 1992.
21. Ho TK, Hull JJ, Srihari SN. Decision combination in multiple classifier systems. IEEE Trans Pattern Anal Mach Intell. 1994;16:66–75
    • CrossRef
22. Xu L, Krzyzak A, Suen CY. Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans Syst Man Cybernet. 1992;22:418–435
23. Hansen LK, Salamon P. Neural network ensembles. IEEE Trans Pattern Anal Mach Intell. 1990;12:993–1001
    • CrossRef
24. Kuncheva LI. On combining multiple classifiers. In: Proceedings of the seventh international conference IPMU. 1998;p. 1890–1891
25. Nugent CD. An intelligent framework for the classification of the ECG, Ph.D. Thesis, University of Ulster at Jordanstown, Northern Ireland, 1998.
26. Patterson DW. Probabilistic reasoning. Introduction to artificial intelligence & expert systems. New Jersey: Prentice-Hall; 1990;p. 107–25
27. Liu W. The Dempster–Shafer theory of evidence. Propositional, probabilistic and evidential reasoning. vol. 77. Heidelberg: Physica-Verlag; 2001;p. 118–58
28. Dempster AP. A generalization of Bayesian interest. J R Statist Soc. 1968;205–247
29. Shafer G. A mathematical theory of evidence. New Jersey: Princeton University Press; 1976;
30. Guan JW, Bell DA. Approximate reasoning and evidence theory. Inform Sci. 1997;96:207–235
31. Lowrance JD, Garvey TD, Strat TM. A framework for evidential-reasoning systems. In:  Shafer G,  Pearl J editor. Readings in uncertain reasoning. California: Morgan Kaufmann Publishers Inc.; 1986;p. 611–648
32. Lee ES, Zhu Q. Fuzzy and evidence reasoning. vol. 6. Heidelberg: Physica-Verlag; 1995;p. 217
33. Nugent CD, Webb JAC, Black ND. Feature and classifier fusion for 12-lead ECG classification. Med Inform Internet Med. 2000;25:225–235
    • MEDLINE
34. O’Connell B, Myers H. Receiver operating characteristic (ROC) curves. J Clin Nursing. 2002;11:134–136
35. Davey RT, McCullagh PJ, McAllister HG, Houston HG. The use of artificial neural networks for the objective determination of hearing threshold, using the auditory brainstem response. In:  Begg R,  Kamruzzaman J,  Sarker R editor. Neural networks in healthcare: potentials and challenges. Hershey: IDEA Group Inc.; 2006;p. 195–215
36. Mallat S. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal. Mach Intell. 1989;2:674–693