Comparison of sequential test strategies based on Monte Carlo simulations in the detection of auditory steady-state responses

Authors

Keywords:

Encephalogram, sequential tests, critical value, false positive, optimization, Monte Carlo

Abstract

It is common to use sequential testing strategies to help reduce the time of automated detection of an auditory steady-state response (ASSR). However, the application of repeated tests leads to an increase of false positive rate. Monte Carlo-based strategies are used to overcome this obstacle. Despite several paper could be found describing such strategies, no comprehensive comparison was found in the literature. The chosen strategies are based on Monte Carlo simulations to calculate critical values and were faithfully replicated for comparison purposes, and then the test application parameters were varied to suggest an optimization. The detection rate and/or the detection speed improved with each implemented strategy, except for the one related to the year 2013, which increased the false positive rate to 15.3%. The other strategies kept the false positive rate under control. The Pareto curves compared the optimizations of the strategies and revealed that the modified 2015 strategy had the performance achieving 5.6% higher than the original parameters. The automated detection of ASSR improved with each implemented strategy, but not all of them kept a controlled false positive rate (2013 and 2015). The 2015 modified strategy had the highest detection rate in the shortest time.

Downloads

Download data is not yet available.

Author Biographies

Victor Hugo de Souza Ragazzi, Federal University of Viçosa

Victor de Souza is an undergraduate student in Electrical Engineering at the Federal University of Viçosa, Brazil. Currently  he is in the third year of research in biomedical engineering, investigating detection methods in electroencephalograms (EEG) and heart rate analysis in tachograms.

Alexandre Gomes Caldeira, Federal University of Minas Gerais

Alexandre Gomes is a M.Sc. graduate student in Electrical Engineering at UFMG. He obtained his B.Sc. in Electrical Engineering from UFV in 2024 and an Electrotechnics Technical degree from CEFET-MG in 2022. His research and interests focus on Signal Processing, Linear and Nonlinear Systems, Modeling, Statistics, and Machine Learning applications to tasks in Neuroscience, Biomedical Engineering, and Robotics.

Patrícia Nogueira Vaz, Federal University of Minas Gerais

Patricia Nogueira is an Electrical Engineer from the Federal University of Viçosa, she earned her D.Sc. in 2023 with a focus on auditory response detection in electroencephalograms at the Federal University of Minas Gerais. Currently, she works as a teacher at the education institute in Minas Gerais.

Felipe Antunes, Institute of Education and Technology of Minas Gerais

Felipe Antunes is an Electrical Engineer from the Federal University of Viçosa, he earned his M.Sc. in 2018 with a focus on modeling and control at the Federal University of São João Del Rei, Brazil. Currently, he works as a professor of basic, technical, and technological education at IFMG.

Leonardo Bonato Felix, Federal University of Viçosa

Leonardo Bonato obtained an electrical Engineering degree at the Federal University of São João del Rei in 2002. MSc (2004) and DSc (2006) from UFMG. PhD (2020) from the University of Southampton, Inglaterra. Currently, he is a professor, researcher, and advisor at the Federal Universities UFV, UFSJ, and UFMG. Head of the Biomedical Engineering Research Laboratory, NIAS.

References

Position statement: priniciples and guidelines for early hearing detection and intervention programs. Pediatrics,” 2007. https://doi.org/10.1542/peds.2007-2333

“Diretrizes de atenção da triagem auditiva neonatal,” 2012

Y. S. Sininger, L. L. Hunter, P. A. Roush, S. Windmill, D. Hayes, and K. M. Uhler, “Protocol for rapid, accurate, electrophysiologic, auditory assessment of infants and toddlers,” J Am Acad Audiol, 2020. 10.3766/jaaa.19046

T. Picton, M. S. John, A. Dimitrijevic, and D. Purcell, “Human auditory steady-state responses,” International Journal of Audiology, 2003. 10.3109/14992020309101316

W. Wagner and P. Plinkert, “The relationship between auditory threshold and evoked otoacoustic emissions,” Eur Arch Oto-Rhino-Laryngology, 1999. 10.1007/s004050050136

S. Norton, M. Gorga, J. Widen, R. Folsom, Y. Sininger, and B. Cone Wesson, “Identification of neonatal hearing impairment: Evaluation of transient evoked otoacoustic emission, distortion product otoacoustic emission, and auditory brain stem response test performance,” Ear Hear, 2000. 10.1097/00003446-200010000-00013

M. Leigh, “Hearing screening: Oae vs automated abr,” www.interacoustics.com, 2022

Y. H. Tarawneh, R. H. Sohrabi, H. A. M. W. Mulders, N. R. Martins, and M. P. D. Jayakody, “Comparison of auditory steady-state responses with conventional audiometry in older adults,” Front. Neurol., 2022. 10.3389/fneur.2022.924096

A. Canale, M. Lacilla, L. A. Calalot, and R. Albera, “Auditory steady-state responses and clinical applications,” Eur Arch Otorhinolaryngol, 2016. 10.1007/s00405-006-0017-y

M. A. Chesnaye, L. S. Bell, M. J. Harte, and M. D. Simpson, “Objective measures for detecting the auditory brainstem response: comparisons of specificity, sensitivity and detection time,” International Journal of Audiology, 2017. 10.1080/14992027.2018.1447697

M. A. Chesnaye, “The convolutional group sequential test: reducing test time for evoked potentials,” IEEE Transactions on Biomedical Engineering, 2019. 10.1109/TBME.2019.2919696

E. Stürzebecher, C. M., and W. K.-D, “Objective response detection in the frequency domain: Comparison of several q-sample tests,” Audiol Neurootol, 1999. 10.1159/000013815

H. Luts, B. Van Dun, J. Alaerts, and J. Wouters, “The influence of the detection paradigm in recording auditory steady-state responses,” Ear and Hearing, 2008. 10.1097/AUD.0b013e318174f051

W. D’haenens, M. B. Vinck, L. Maes, A. Bockstael, H. Keppler, B. Philips, F. Swinnen, and I. Dhooge, “Determination and evalu- ation of clinically efficient stopping criteria for the multiple auditory steady-state response technique,” Clinical Neurophysiology, 2010. 10.1016/j.clinph.2010.03.008

D. Ehrmann-Muller, W. Shehata-Dieler, A. Alzoubi, R. Hagen, and M. Cebulla, “Using assr with narrow-band chirps to evaluate hearing in children and adults,” European Archives of Oto-Rhino-Laryngology, 2020. 10.1007/s00405-020-06053-0

Y. S. Sininger, L. L. Hunter, D. Hayes, P. A. Roush, and K. M. Uhler., “Evaluation of speed and accuracy of next-generation auditory steady state response and auditory brainstem response audiometry in

children with normal hearing and hearing loss,” Ear Hearing, 2018. 10.1097/AUD.0000000000000580

R. Galambos and S. Makeig, “The cerp: Event-related perturbations in steady-state responses,” Brain Dynamics, 1981. https://doi.org/10.1007/978-3-642-74557-7_30

T. Zanoteli, F. Antunes, M. D. Simpson, E. Mazoni, A. Marçal, and F. L. Bonato, “Faster automatic assr detection using sequential tests,” International Journal of Audiology, 2020. 10.1080/14992027.2020.1728402

N. P. Vaz, A. F., E. Mazoni, and F. L. Bonato, “Automated detection of auditory response: nondetection stopping criterion and repeatability studies for multichannel eeg,” Computer Methods In Biomechanics And Biomedical Engineering, 2023. https://doi.org/10.1080/10255842.2023.2232071

M. Cebulla and E. . Stürzebecher, “Automated auditory response detection: Further improvement of the statistical test strategy by using progressive test steps of iteration,” International Journal of Audiology, 2015. 10.3109/14992027.2015.1017659

M. A. Chesnaye, “A group sequential test for abr detection,” International Journal of Audiology, 2019. 10.1080/14992027.2019.1625486

E. Stürzebecher, C. M., and C. Elberling, “Automated auditory response detection: Statistical problems with repeated testing,” International Journal of Audiology, 2005. 10.1080/14992020400029228

M. Cebulla, E. Stürzebecher, and E. C, “Objective detection of auditory steady-state responses: Comparison of one-sample and q-sample tests,” J Am Acad Audiol, 2006. 10.3766/jaaa.17.2.3

B. Moore, “A modification of the rayleigh test for vector data,” Biometrika, 1980. https://doi.org/10.2307/2335330

K. Mardia, “Statistics of directional data,” Academic Press, 1975. https://doi.org/10.1111/j.2517-6161.1975.tb01550.x

E. Stürzebecher and C. M., “Automated auditory response detection: Improvement of the statistical test strategy,” International Journal of Audiology, 2013. 10.3109/14992027.2013.822995

S. M. Kay, Fundamentals of Statistical Signal Processing. Prentice-Hall, 1993

Downloads

Published

2024-08-31

How to Cite

de Souza Ragazzi, V. H. ., Gomes Caldeira, A., Nogueira Vaz, P., Antunes, F., & Bonato Felix, L. (2024). Comparison of sequential test strategies based on Monte Carlo simulations in the detection of auditory steady-state responses. IEEE Latin America Transactions, 22(9), 733–738. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/9006

Issue

Vol. 22 No. 9 (2024): Ordinary Issue

Section

Computing