Objective auditory brainstem response classification using machine learning

References

• Acır, N., Ö. Özdamar, and C. Güzeliş. 2006. “Automatic Classification of Auditory Brainstem Responses Using SVM-based Feature Selection Algorithm for Threshold Detection.” Engineering Applications of Artificial Intelligence 19 (2): 209–218. doi:10.1016/j.engappai.2005.08.004.
   Web of Science ®Google Scholar
• Alpsan, D. 1991. “Classification Of Auditory Brainstem Responses By Human Experts And Backipropagation Neural Networks.” Paper presented at proceedings of the annual international conference of the IEEE engineering in medicine and biology society volume 13: 1991, pp. 1425–1426.
   Google Scholar
• Berger, J. R., and A. S. Blum. 2007. “Brainstem Auditory Evoked Potentials.” In The Clinical Neurophysiology Primer, edited by A. S. Blum and S. B. Rutkove, 475–484. Totowa, NJ: Humana Press.
   Google Scholar
• Brueggeman, P. M., and S. R. Atcherson. 2012. “Threshold Estimation Using the Auditory Brainstem Response.” In Auditory Electrophysiology: A Clinical Guide, edited by S. R. Atcherson and T. M. Stoody, 203–219. New York, NY: Thieme.
   Google Scholar
• Cebulla, M., E. Stürzebecher, and K. D. Wernecke. 2000. “Objective Detection of Auditory Brainstem Potentials: Comparison of Statistical Tests in the Time and Frequency Domains.” Scandinavian Audiology 29 (1): 44–51. doi:10.1080/010503900424598.
   PubMed Web of Science ®Google Scholar
• Chawla, N. V., K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. 2002. “SMOTE: Synthetic Minority over-sampling Technique.” Journal of Artificial Intelligence Research 16: 321–357. doi:10.1613/jair.953.
   Web of Science ®Google Scholar
• Chinchor, N. 1992. “MUC-4 Evaluation Metrics.” Paper presented at proceedings of the 4th conference on message understanding – MUC4 ’92. Morristown, NJ, USA: Association for Computational Linguistics, 22–29.
   Google Scholar
• Cohen, J. 1960. “A Coefficient of Agreement for Nominal Scales.” Educational and Psychological Measurement 20 (1): 37–46. doi:10.1177/001316446002000104.
   Web of Science ®Google Scholar
• Davey, R., P. McCullagh, G. Lightbody, and G. McAllister. 2007. “Auditory Brainstem Response Classification: A Hybrid Model Using Time and Frequency Features.” Artificial Intelligence in Medicine 40 (1): 1–14. doi:10.1016/j.artmed.2006.07.001.
   PubMed Web of Science ®Google Scholar
• Dobie, R. A. 1993. “Objective Response Detection.” Ear and Hearing 14 (1): 31–35. doi:10.1097/00003446-199302000-00005
   PubMed Web of Science ®Google Scholar
• Elberling, C., and M. Don. 1984. “Quality Estimation of Averaged Auditory Brainstem Responses.” International Journal of Audiology 13 (3): 187–197. doi:10.3109/14992028409043059.
   Google Scholar
• Goldberger, A. L., L. A. Amaral, L. Glass, J. M. Hausdorff, and P. C. Ivanov. 2000. “PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals.” Circulation 101 (23): E215–E220.
   PubMed Web of Science ®Google Scholar
• Haboosheh, R. 2007. “Diagnostic Auditory Brainstem Response Analysis: Evaluation of Signal-to-noise Ratio Criteria Using Signal Detection Theory.” University of British Columbia. Accessed August 8 2018. https://open.library.ubc.ca/cIRcle/collections/ubctheses/831/items/1.0100795#share
   Google Scholar
• Haenssle, H. A., C. Fink, R. Schneiderbauer, F. Toberer, T. Buhl, A. Blum, A. Kalloo, et al. 2018. “Man against Machine: Diagnostic Performance of a Deep Learning Convolutional Neural Network for Dermoscopic Melanoma Recognition in Comparison to 58 Dermatologists.” Annals of Oncology. 29 (8): 1836–1842.
   PubMed Web of Science ®Google Scholar
• He, K., X. Zhang, S. Ren, and J. Sun. 2015. “Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification.” Accessed June 16 2018. http://arxiv.org/abs/1502.01852
   Google Scholar
• Hobson, T. 2016. “Audiology Diagnostic Assessment Protocol.” Queensland, Australia. Accessed June 11, 2018. https://www.childrens.health.qld.gov.au/wp-content/uploads/PDF/healthy-hearing/hh-audiology-protocol.pdf.
   Google Scholar
• Jewett, D. L., M. N. Romano, and J. S. Williston. 1970. “Human Auditory Evoked Potentials: Possible Brain Stem Components Detected on the Scalp.” Science 167 (3924): 1517–1518. doi:10.1126/science.167.3924.1517.
   PubMed Web of Science ®Google Scholar
• Keras. 2018. “Keras Documentation.” Accessed June 18 2018. https://keras.io/
   Google Scholar
• Landis, J. R., and G. G. Koch. 1977. “The Measurement of Observer Agreement for Categorical Data.” Biometrics 33 (1): 159–174. doi:10.2307/2529310
   PubMed Web of Science ®Google Scholar
• LeCun, Y., P. Haffner, L. Bottou, and Y. Bengio. 1999. Object Recognition with Gradient-Based Learning. Berlin, Heidelberg: Springer.
   Google Scholar
• Li, Y., and F. Liu. 2016. Whiteout: Gaussian Adaptive Noise Regularization in Deep Neural Networks. Accessed June 11 2018. http://arxiv.org/abs/1612.01490
   Google Scholar
• Nilsson, N. J. 1971. Problem-solving Methods in Artificial Intelligence. New York, NY: McGraw-Hill.
   Google Scholar
• Rajpurkar, P., A. Y. Hannun, M. Haghpanahi, C. Bourn, and A. Y. Ng. 2017. “Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks.” Stanford ML Group. Accessed June 11 2018. http://arxiv.org/abs/1707.01836
   Google Scholar
• Samuel, A. L. 1959. “Some Studies in Machine Learning Using the Game of Checkers.” IBM Journal of Research and Development 3 (3): 210–229. doi:10.1147/rd.33.0210.
   Web of Science ®Google Scholar
• Silva, I., and M. Epstein. 2010. “Estimating Loudness Growth from Tone-burst Evoked Responses.” The Journal of the Acoustical Society of America 127 (6): 3629–3642. doi:10.1121/1.3397457
   PubMed Web of Science ®Google Scholar
• Silver, D., T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai., A. Guez, M. Lanctot, et al. 2017. “Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm.” Accessed June 11 2018. http://arxiv.org/abs/1712.01815
   Google Scholar
• Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. 2014. “Dropout: A Simple Way to Prevent Neural Networks from Overfitting.” Journal of Machine Learning Research 15: 1929–1958.
   Web of Science ®Google Scholar
• Stone, M. 1974. “Cross-validatory Choice and Assessment of Statistical Predictions (with Discussion).” Journal of the Royal Statistical Society 36 (2): 111–147.
   Google Scholar
• Sutton, G., and G. Lightfoot. 2013. Guidance for Auditory Brainstem Response testing in Babies. Reading: British Society of Audiology. https://www.thebsa.org.uk/wp-content/uploads/2014/08/NHSP_ABRneonate_2014.pdf
   Google Scholar
• Vidler, M., and D. Parker. 2004. “Auditory Brainstem Response Threshold Estimation: Subjective Threshold Estimation by Experienced Clinicians in a Computer Simulation of the Clinical Test.” International Journal of Audiology 43 (7): 417–429. doi:10.1080/14992020400050053.
   PubMed Web of Science ®Google Scholar
• Wolpert, D. H., and W. G. Macready. 1997. “No Free Lunch Theorems for Optimization.” IEEE Transactions on Evolutionary Computation 1 (1): 67–82. doi:10.1109/4235.585893.
   Google Scholar