Controlling test specificity for auditory evoked response detection using a frequency domain bootstrap
Controlling test specificity for auditory evoked response detection using a frequency domain bootstrap
Background: Statistical detection methods are routinely used to automate auditory evoked response (AER) detection and assist clinicians with AER measurements. However, many of these methods are built around statistical assumptions that can be violated for AER data, potentially resulting in reduced or unpredictable test performances. This study explores a frequency domain bootstrap (FDB) and some FDB modifications to preserve test performance in serially correlated non-stationary data. Method: The FDB aims to generate many surrogate recordings, all with similar serial correlation as the original recording being analysed. Analysing the surrogates with the detection method then gives a distribution of values that can be used for inference. A potential limitation of the conventional FDB is the assumption of stationary data with a smooth power spectral density (PSD) function, which is addressed through two modifications. Comparisons with existing methods: The FDB was compared to a conventional parametric approach and two modified FDB approaches that aim to account for heteroskedasticity and non-smooth PSD functions. Hotelling's T
2(HT2) test applied to auditory brainstem responses was the test case. Results: When using conventional HT2, false-positive rates deviated significantly from the nominal alpha-levels due to serial correlation. The false-positive rates of the modified FDB were consistently closer to the nominal alpha-levels, especially when data was strongly heteroskedastic or the underlying PSD function was not smooth due to e.g. power lines noise. Conclusion: The FDB and its modifications provide accurate, recording-dependent approximations of null distributions, and an improved control of false-positive rates relative to parametric inference for auditory brainstem response detection.
Chesnaye, Michael
5f337509-3255-4322-b1bf-d4d3836b36ec
Bell, Steven
91de0801-d2b7-44ba-8e8e-523e672aed8a
Harte, J.M.
20107083-3305-4997-8426-0372ff0b9954
Simpson, David
53674880-f381-4cc9-8505-6a97eeac3c2a
1 November 2021
Chesnaye, Michael
5f337509-3255-4322-b1bf-d4d3836b36ec
Bell, Steven
91de0801-d2b7-44ba-8e8e-523e672aed8a
Harte, J.M.
20107083-3305-4997-8426-0372ff0b9954
Simpson, David
53674880-f381-4cc9-8505-6a97eeac3c2a
Chesnaye, Michael, Bell, Steven, Harte, J.M. and Simpson, David
(2021)
Controlling test specificity for auditory evoked response detection using a frequency domain bootstrap.
Journal of Neuroscience Methods, 363, [109352].
(doi:10.1016/j.jneumeth.2021.109352).
Abstract
Background: Statistical detection methods are routinely used to automate auditory evoked response (AER) detection and assist clinicians with AER measurements. However, many of these methods are built around statistical assumptions that can be violated for AER data, potentially resulting in reduced or unpredictable test performances. This study explores a frequency domain bootstrap (FDB) and some FDB modifications to preserve test performance in serially correlated non-stationary data. Method: The FDB aims to generate many surrogate recordings, all with similar serial correlation as the original recording being analysed. Analysing the surrogates with the detection method then gives a distribution of values that can be used for inference. A potential limitation of the conventional FDB is the assumption of stationary data with a smooth power spectral density (PSD) function, which is addressed through two modifications. Comparisons with existing methods: The FDB was compared to a conventional parametric approach and two modified FDB approaches that aim to account for heteroskedasticity and non-smooth PSD functions. Hotelling's T
2(HT2) test applied to auditory brainstem responses was the test case. Results: When using conventional HT2, false-positive rates deviated significantly from the nominal alpha-levels due to serial correlation. The false-positive rates of the modified FDB were consistently closer to the nominal alpha-levels, especially when data was strongly heteroskedastic or the underlying PSD function was not smooth due to e.g. power lines noise. Conclusion: The FDB and its modifications provide accurate, recording-dependent approximations of null distributions, and an improved control of false-positive rates relative to parametric inference for auditory brainstem response detection.
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Accepted/In Press date: 31 August 2021
e-pub ahead of print date: 9 September 2021
Published date: 1 November 2021
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Funding Information:
The authors would like to acknowledge Debbie Cane and Sarah M.K. Madsen for collecting the subject recorded ABR data and the recordings of EEG background noise, respectively. The use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, were used in the completion of this work. This work was supported by the Oticon Foundation , Denmark.
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© 2021 Elsevier B.V.
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Local EPrints ID: 451969
URI: http://eprints.soton.ac.uk/id/eprint/451969
ISSN: 0165-0270
PURE UUID: 931a23c7-3412-42b7-b527-352f69cc1312
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Date deposited: 05 Nov 2021 17:30
Last modified: 06 Jun 2024 04:19
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Author:
Michael Chesnaye
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J.M. Harte
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