Automated Extraction of Auditory Brainstem Response Latencies and Amplitudes by Means of Non-linear Curve Registration

Overview

Journal Comput Methods Programs Biomed

Specialty Medical Informatics

Date 2020 Jun 22

PMID 32563894

Citations 5

Authors

Katrin Krumbholz

Alexander James Hardy

Jessica de Boer

Affiliations

Soon will be listed here.

Abstract

Background And Objectives: Animal results have suggested that auditory brainstem responses (ABRs) to transient sounds presented at supra-threshold levels may be useful for measuring hearing damage that is hidden to current audiometric tests. Evaluating such ABRs requires extracting the latencies and amplitudes of relevant deflections, or "waves". Currently, this is mostly done by human observers manually picking the waves' peaks and troughs in each individual response - a process that is both time-consuming and requiring of expert experience. Here, we propose a highly automated procedure for extracting individual ABR wave latencies and amplitudes based on the well-established methodology of non-linear curve registration.

Methods: First, the to-be-analysed individual ABRs are temporally aligned - either with one another or, if available, with a pre-existing template - by locally compressing or stretching their time axes with smooth and invertible time warping functions. Then, the individual latencies and amplitudes of relevant ABR waves are obtained by picking the latencies of the waves' peaks and troughs on the common (aligned) time axis and combining these with the individual aligned responses and inverse time warping functions.

Results: Using an example ABR data set with a wide range of response latencies and signal-to-noise ratios (SNRs), we test different choices for fitting the time warping functions. We cross-validate the warping results using independent response replicates and compare automatically and manually extracted latencies and amplitudes for ABR waves I and V. Using a Bayesian approach, we show that, for the best registration condition, automatic and manual data were statistically similar.

Conclusions: Non-linear curve registration can be used to temporally align individual ABRs and extract their wave latencies and amplitudes in a way that closely matches results from manual picking.

Citing Articles

Development and Evaluation of Automated Tools for Auditory-Brainstem and Middle-Auditory Evoked Potentials Waves Detection and Annotation.

Manta O, Sarafidis M, Vasileiou N, Schlee W, Consoulas C, Kikidis D Brain Sci. 2022; 12(12).

PMID: 36552135 PMC: 9775187. DOI: 10.3390/brainsci12121675.

Could Tailored Chirp Stimuli Benefit Measurement of the Supra-threshold Auditory Brainstem Wave-I Response?.

de Boer J, Hardy A, Krumbholz K J Assoc Res Otolaryngol. 2022; 23(6):787-802.

PMID: 35984541 PMC: 9789297. DOI: 10.1007/s10162-022-00848-0.

Optimizing non-invasive functional markers for cochlear deafferentation based on electrocochleography and auditory brainstem responses.

Harris K, Bao J J Acoust Soc Am. 2022; 151(4):2802.

PMID: 35461487 PMC: 9034896. DOI: 10.1121/10.0010317.

Arc-Length Re-Parametrization and Signal Registration to Determine a Characteristic Average and Statistical Response Corridors of Biomechanical Data.

Hartlen D, Cronin D Front Bioeng Biotechnol. 2022; 10:843148.

PMID: 35402420 PMC: 8987728. DOI: 10.3389/fbioe.2022.843148.

Automatic Recognition of Auditory Brainstem Response Characteristic Waveform Based on Bidirectional Long Short-Term Memory.

Chen C, Zhan L, Pan X, Wang Z, Guo X, Qin H Front Med (Lausanne). 2021; 7:613708.

PMID: 33505982 PMC: 7829202. DOI: 10.3389/fmed.2020.613708.

References

Pool K, Finitzo T . Evaluation of a computer-automated program for clinical assessment of the auditory brain stem response. Ear Hear. 1989; 10(5):304-10. DOI: 10.1097/00003446-198910000-00006. View

Don M, Ponton C, Eggermont J, Masuda A . Gender differences in cochlear response time: an explanation for gender amplitude differences in the unmasked auditory brain-stem response. J Acoust Soc Am. 1993; 94(4):2135-48. DOI: 10.1121/1.407485. View

Mason S . Effects of high-pass filtering on the detection of the auditory brainstem response. Br J Audiol. 1984; 18(3):155-61. DOI: 10.3109/03005368409078942. View

JEWETT D, Romano M, Williston J . Human auditory evoked potentials: possible brain stem components detected on the scalp. Science. 1970; 167(3924):1517-8. DOI: 10.1126/science.167.3924.1517. View

Vannier E, Adam O, Motsch J . Objective detection of brainstem auditory evoked potentials with a priori information from higher presentation levels. Artif Intell Med. 2002; 25(3):283-301. DOI: 10.1016/s0933-3657(02)00029-5. View

Dau T, Wegner O, Mellert V, Kollmeier B . Auditory brainstem responses with optimized chirp signals compensating basilar-membrane dispersion. J Acoust Soc Am. 2000; 107(3):1530-40. DOI: 10.1121/1.428438. View

Liberman M, Kujawa S . Cochlear synaptopathy in acquired sensorineural hearing loss: Manifestations and mechanisms. Hear Res. 2017; 349:138-147. PMC: 5438769. DOI: 10.1016/j.heares.2017.01.003. View

Russo N, Nicol T, Musacchia G, Kraus N . Brainstem responses to speech syllables. Clin Neurophysiol. 2004; 115(9):2021-30. PMC: 2529166. DOI: 10.1016/j.clinph.2004.04.003. View

Johannesen P, Buzo B, Lopez-Poveda E . Evidence for age-related cochlear synaptopathy in humans unconnected to speech-in-noise intelligibility deficits. Hear Res. 2019; 374:35-48. DOI: 10.1016/j.heares.2019.01.017. View

10.

Eggermont J . Auditory brainstem response. Handb Clin Neurol. 2019; 160:451-464. DOI: 10.1016/B978-0-444-64032-1.00030-8. View

11.

Oliveira F, R S Tavares J . Medical image registration: a review. Comput Methods Biomech Biomed Engin. 2012; 17(2):73-93. DOI: 10.1080/10255842.2012.670855. View

12.

Kujawa S, Liberman M . Synaptopathy in the noise-exposed and aging cochlea: Primary neural degeneration in acquired sensorineural hearing loss. Hear Res. 2015; 330(Pt B):191-9. PMC: 4567542. DOI: 10.1016/j.heares.2015.02.009. View

13.

Bharadwaj H, Mai A, Simpson J, Choi I, Heinz M, Shinn-Cunningham B . Non-Invasive Assays of Cochlear Synaptopathy - Candidates and Considerations. Neuroscience. 2019; 407:53-66. PMC: 6513698. DOI: 10.1016/j.neuroscience.2019.02.031. View

14.

Bradley A, Wilson W . Automated analysis of the auditory brainstem response using derivative estimation wavelets. Audiol Neurootol. 2004; 10(1):6-21. DOI: 10.1159/000081544. View

15.

Elberling C, Wahlgreen O . Estimation of auditory brainstem response, ABR, by means of Bayesian inference. Scand Audiol. 1985; 14(2):89-96. DOI: 10.3109/01050398509045928. View

16.

Gabriel S, Durrant J, Dickter A, KEPHART J . Computer identification of waves in the auditory brain stem evoked potentials. Electroencephalogr Clin Neurophysiol. 1980; 49(3-4):421-3. DOI: 10.1016/0013-4694(80)90240-0. View

17.

Picton T, Hunt M, Mowrey R, Rodriguez R, Maru J . Evaluation of brain-stem auditory evoked potentials using dynamic time warping. Electroencephalogr Clin Neurophysiol. 1988; 71(3):212-25. DOI: 10.1016/0168-5597(88)90006-8. View

18.

Schimmel H . The (+) reference: accuracy of estimated mean components in average response studies. Science. 1967; 157(3784):92-4. DOI: 10.1126/science.157.3784.92. View

19.

Boston J . Automated interpretation of brainstem auditory evoked potentials: a prototype system. IEEE Trans Biomed Eng. 1989; 36(5):528-32. DOI: 10.1109/10.24254. View

20.

Guest H, Munro K, Plack C . Tinnitus with a normal audiogram: Role of high-frequency sensitivity and reanalysis of brainstem-response measures to avoid audiometric over-matching. Hear Res. 2017; 356:116-117. PMC: 5714058. DOI: 10.1016/j.heares.2017.10.002. View