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The Convolutional Group Sequential Test: reducing test time for evoked potentials

The Convolutional Group Sequential Test: reducing test time for evoked potentials
The Convolutional Group Sequential Test: reducing test time for evoked potentials
When using a statistical test for automatically detecting evoked potentials, then the number of stimuli presented to the subject (the sample size for the statistical test) should be specified at the outset. For evoked response detection, this may be inefficient, i.e. because the signal-to-noise ratio (SNR) of the response is not known in advance, the user would usually err on the cautious side, and use a relatively high number of stimuli to ensure adequate statistical power. A more efficient approach is to apply the statistical test repeatedly to the accumulating data over time, as this allows the test to be stopped early for the high SNR responses (thus reducing test time), or later for the low SNR responses. The caveat is that the critical decision boundaries for rejecting the null hypothesis need to be adjusted if the intended type-I error rate is to be obtained. This study presents an intuitive and flexible method for controlling the type-I error rate for sequentially applied statistical tests. The method is built around the discrete convolution of truncated probability density functions, which allows the null distribution for the test statistic to be constructed at each stage of the sequential analysis. Because the null distribution remains tractable, the procedure for finding the stage-wise critical decision boundaries is greatly simplified. The method also permits data-driven adaptations (using data from previous stages) to both the sample size and the statistical test, which offers new opportunities to speed up testing for evoked response detection.
sequential testing, evoked potentials, objective detection methods, data-driven adaptations
0018-9294
Chesnaye, Michael Alexander
5f337509-3255-4322-b1bf-d4d3836b36ec
Bell, Steven
91de0801-d2b7-44ba-8e8e-523e672aed8a
Harte, James Michael
1ed3b723-9209-4f46-911d-2f2f345e0a32
Simpson, David
53674880-f381-4cc9-8505-6a97eeac3c2a
Chesnaye, Michael Alexander
5f337509-3255-4322-b1bf-d4d3836b36ec
Bell, Steven
91de0801-d2b7-44ba-8e8e-523e672aed8a
Harte, James Michael
1ed3b723-9209-4f46-911d-2f2f345e0a32
Simpson, David
53674880-f381-4cc9-8505-6a97eeac3c2a

Chesnaye, Michael Alexander, Bell, Steven, Harte, James Michael and Simpson, David (2019) The Convolutional Group Sequential Test: reducing test time for evoked potentials. IEEE Transactions on Biomedical Engineering. (doi:10.1109/TBME.2019.2919696).

Record type: Article

Abstract

When using a statistical test for automatically detecting evoked potentials, then the number of stimuli presented to the subject (the sample size for the statistical test) should be specified at the outset. For evoked response detection, this may be inefficient, i.e. because the signal-to-noise ratio (SNR) of the response is not known in advance, the user would usually err on the cautious side, and use a relatively high number of stimuli to ensure adequate statistical power. A more efficient approach is to apply the statistical test repeatedly to the accumulating data over time, as this allows the test to be stopped early for the high SNR responses (thus reducing test time), or later for the low SNR responses. The caveat is that the critical decision boundaries for rejecting the null hypothesis need to be adjusted if the intended type-I error rate is to be obtained. This study presents an intuitive and flexible method for controlling the type-I error rate for sequentially applied statistical tests. The method is built around the discrete convolution of truncated probability density functions, which allows the null distribution for the test statistic to be constructed at each stage of the sequential analysis. Because the null distribution remains tractable, the procedure for finding the stage-wise critical decision boundaries is greatly simplified. The method also permits data-driven adaptations (using data from previous stages) to both the sample size and the statistical test, which offers new opportunities to speed up testing for evoked response detection.

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ChesnayeEtAl_TheConvolutionalGroupSequentialTest - Accepted Manuscript
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More information

Accepted/In Press date: 24 April 2019
e-pub ahead of print date: 29 May 2019
Keywords: sequential testing, evoked potentials, objective detection methods, data-driven adaptations

Identifiers

Local EPrints ID: 431636
URI: https://eprints.soton.ac.uk/id/eprint/431636
ISSN: 0018-9294
PURE UUID: e4f014e1-6656-4e32-b2db-98e15fe30ee3

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Date deposited: 11 Jun 2019 16:30
Last modified: 11 Jun 2019 16:30

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Contributors

Author: Michael Alexander Chesnaye
Author: Steven Bell
Author: James Michael Harte
Author: David Simpson

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