Application of Signal Classifiers in Auditory Evoked Potentials for the Detection of Pathological Patients

Authors

  • M. G. Baldiviezo Centro de Procesamiento de Señales e Imágenes, Facultad Regional Buenos Aires, Universidad Tecnológica Nacional
  • J. L. Barbería Centro de Procesamiento de Señales e Imágenes, Facultad Regional Buenos Aires, Universidad Tecnológica Nacional
  • C. B. Bontempo Centro de Procesamiento de Señales e Imágenes, Facultad Regional Buenos Aires, Universidad Tecnológica Nacional
  • Y. Corsaro Centro de Procesamiento de Señales e Imágenes, Facultad Regional Buenos Aires, Universidad Tecnológica Nacional
  • F. Fernandez Biancardi Centro de Procesamiento de Señales e Imágenes, Facultad Regional Buenos Aires, Universidad Tecnológica Nacional
  • M. R. Hernando Centro de Procesamiento de Señales e Imágenes, Facultad Regional Buenos Aires, Universidad Tecnológica Nacional
  • L. Licata Caruso Centro de Procesamiento de Señales e Imágenes, Facultad Regional Buenos Aires, Universidad Tecnológica Nacional
  • A. Paglia Centro de Procesamiento de Señales e Imágenes, Facultad Regional Buenos Aires, Universidad Tecnológica Nacional
  • M. R. Rodríguez Centro de Procesamiento de Señales e Imágenes, Facultad Regional Buenos Aires, Universidad Tecnológica Nacional
  • W. E. Legnani Centro de Procesamiento de Señales e Imágenes, Facultad Regional Buenos Aires, Universidad Tecnológica Nacional https://orcid.org/0000-0002-6949-0728

DOI:

https://doi.org/10.5540/tcam.2022.024.01.00063

Keywords:

auditory evoked potentials, permutation entropy, signal classification, fractal dimension, Lyapunov exponent, zero crossing rate, support vector machines, random forest, K-nearest neighbours.

Abstract

The auditory brainstem response (ABR) by evoked potentials is a widespread auditory pathway assessment technique. This is largely applied due to its cost-effectiveness, practicality and ease of use. In contrast, it requires a trained professional to carry out the analysis of the results. This motivates several research efforts to increase the independence of the diagnostician. To this end, the present work shows the ability of three signal classification tools to differentiate ABR studies of normal hearing subjects from those who may have some pathology. As a starting point, the PhysioNet short term auditory evoked potentials databases are used to calculate the features later applied to construct the dataset. The features used are diverse classes of permutation entropy, fractal dimension, the Lyapunov exponent and the zero crossing rate. To ensure more accurate results, a Montecarlo simulation of one thousand trials is employed to train the classifiers and test the results. The goodness of the classification is performed upon the basis of the computation of quality parameters, such as the area under the receiver operating characteristic curve (ROC), the F1 score coefficient and the accuracy. In all the cases the methodology proposed gives high performance results that encourage the line of research of the present work.

References

D. L. Jewett and J. S. WILLISTON, “Auditory-evoked far fields averaged from the scalp of humans,” Brain, vol. 94, no. 4, pp. 681–696, 1971.

J. R. Daube and D. I. Rubin, “Needle electromyography,” Muscle & Nerve: Official Journal of the American Association of Electrodiagnostic Medicine, vol. 39, no. 2, pp. 244–270, 2009.

R. A. Prayson and J. R. Goldblum, Neuropathology. Elsevier Churchill-Livingstone, 2005.

I. Silva and M. Epstein, “Objective estimation of loudness growth in hearingimpaired listeners,” The Journal of the Acoustical Society of America, vol. 131, no. 1, p. 353–362, 2012.

I. Silva and M. Epstein, “Estimating loudness growth from tone-burst evoked responses,” The Journal of the Acoustical Society of America, vol. 127, no. 6, p. 3629–3642, 2010.

M. Baldiviezo, C. Bontempo, J. Barbería, Y. Corsaro, F. Fernandez Biancardi, M. Hermando, A. Paglia, M. Rodriguez, and W. Legnani, “Application of entropic measures in the study of auditory evoked potentials for the detection of pathological patients,” in Proceedings of VIII MACI 2021, Volume 8, pp. 633–636, Asociación Argentina de Matemática Aplicada, Computacional e Industrial, 2021.

B. Boashash, Time-frequency signal analysis and processing: a comprehensive reference. Academic press, 2015.

C. Torrence and G. P. Compo, “A practical guide to wavelet analysis,” Bulletin of the American Meteorological society, vol. 79, no. 1, pp. 61–78, 1998.

S. Mallat, “A wavelet tour of signal processing,” 1999.

G. Lee, R. Gommers, F. Waselewski, K. Wohlfahrt, and A. O’Leary, “Pywavelets A python package for wavelet analysis,” Journal of Open Source Software, vol. 4, no. 36, p. 1237, 2019.

N.-S. Kwak, K.-R. Müller, and S.-W. Lee, “A convolutional neural network for steady state visual evoked potential classification under ambulatory environment,” PloS one, vol. 12, no. 2, p. e0172578, 2017.

D. Mao, H. Innes-Brown, M. A. Petoe, C. M. McKay, and Y. T. Wong, “Spectral features of cortical auditory evoked potentials inform hearing threshold and intensity percepts in acoustic and electric hearing,” Journal of neural engineering, vol. 18, no. 4, p. 046078, 2021.

R. Al Osman and H. Al Osman, “On the use of machine learning for classifying auditory brainstem responses: A scoping review,” IEEE Access, 2021.

J. Yperman, T. Becker, D. Valkenborg, V. Popescu, N. Hellings, B. Van Wijmeersch, and L. M. Peeters, “Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosis,” BMC neurology, vol. 20, no. 1, pp. 1–15, 2020.

M. Ravan, J. P. Reilly, L. J. Trainor, and A. Khodayari-Rostamabad, “A machine learning approach for distinguishing age of infants using auditory evoked potentials,” Clinical neurophysiology, vol. 122, no. 11, pp. 2139–2150, 2011.

J. Sohn, I.-Y. Jung, Y. Ku, and Y. Kim, “Machine-learning-based rehabilitation prognosis prediction in patients with ischemic stroke using brainstem auditory evoked potential,” Diagnostics, vol. 11, no. 4, p. 673, 2021.

R. M. McKearney and R. C. MacKinnon, “Objective auditory brainstem response classification using machine learning,” International journal of audiology, vol. 58, no. 4, pp. 224–230, 2019.

M. Paulraj, K. Subramaniam, S. B. Yaccob, A. H. B. Adom, and C. Hema, “A machine learning approach for distinguishing hearing perception level using auditory evoked potentials,” in 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES), pp. 991–996, IEEE, 2014.

N. Acır, Ö. Özdamar, and C. Güzelis, “Automatic classification of auditory brainstem responses using svm-based feature selection algorithm for threshold detection,” Engineering Applications of Artificial Intelligence, vol. 19, no. 2, pp. 209–218, 2006.

N. H. Shah, A. Milstein, and S. C. Bagley, “Making machine learning models clinically useful,” Jama, vol. 322, no. 14, pp. 1351–1352, 2019.

W. S. Noble, “What is a support vector machine?,” Nature biotechnology, vol. 24, no. 12, pp. 1565–1567, 2006.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.

L. Breiman, “Random forests,” Machine learning, vol. 45, no. 1, pp. 5–32, 2001.

L. E. Peterson, “K-nearest neighbor,” Scholarpedia, vol. 4, no. 2, p. 1883, 2009.

T. Higuchi, “Approach to an irregular time series on the basis of the fractal theory,” Physica D: Nonlinear Phenomena, vol. 31, no. 2, pp. 277–283, 1988.

A. Wolf, J. B. Swift, H. L. Swinney, and J. A. Vastano, “Determining lyapunov exponents from a time series,” Physica D: nonlinear phenomena, vol. 16, no. 3, pp. 285–317, 1985.

B. K. Shivamoggi, Nonlinear dynamics and chaotic phenomena: An introduction, vol. 103. Springer, 2014.

H. Kantz, “A robust method to estimate the maximal lyapunov exponent of a time series,” Physics letters A, vol. 185, no. 1, pp. 77–87, 1994.

C. Bandt and B. Pompe, “Permutation entropy: a natural complexity measure for time series,” Physical review letters, 2002.

J. Amigó, Permutation complexity in dynamical systems: ordinal patterns, permutation entropy and all that. Springer Science & Business Media, 2010.

C. Bandt, “A new kind of permutation entropy used to classify sleep stages from invisible eeg microstructure,” Entropy, vol. 19, no. 5, p. 197, 2017.

K. Keller, T. Mangold, I. Stolz, and J. Werner, “Permutation entropy: New ideas and challenges,” Entropy, vol. 19, no. 3, p. 134, 2017.

F. H. A. de Araujo, L. Bejan, O. A. Rosso, and T. Stosic, “Permutation entropy and statistical complexity analysis of brazilian agricultural commodities,” Entropy, vol. 21, no. 12, p. 1220, 2019.

S. J. Watt and A. Politi, “Permutation entropy revisited,” Chaos, Solitons & Fractals, vol. 120, pp. 95–99, 2019.

D. Cuesta-Frau, “Using the information provided by forbidden ordinal patterns in permutation entropy to reinforce time series discrimination capabilities,” Entropy, 2020.

B. Fadlallah, B. Chen, K. A., and J. Príncipe, “Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information,” Physical Review E, vol. 87, 2013.

A. M. Fraser and H. L. Swinney, “Independent coordinates for strange attractors from mutual information,” Physical review A, vol. 33, no. 2, p. 1134, 1986.

N. Chauhan, T. Isshiki, and D. Li, “Speaker recognition using lpc, mfcc, zcr features with ann and svm classifier for large input database,” in 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), pp. 130–133, IEEE, 2019.

G. Sharma, K. Umapathy, and S. Krishnan, “Trends in audio signal feature extraction methods,” Applied Acoustics, vol. 158, p. 107020, 2020.

Downloads

Published

2023-03-14

How to Cite

Baldiviezo, M. G., Barbería, J. L., Bontempo, C. B., Corsaro, Y., Fernandez Biancardi, F., Hernando, M. R., … Legnani, W. E. (2023). Application of Signal Classifiers in Auditory Evoked Potentials for the Detection of Pathological Patients. Trends in Computational and Applied Mathematics, 24(1), 63–81. https://doi.org/10.5540/tcam.2022.024.01.00063

Issue

Section

Original Article