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Record W2947233004 · doi:10.1097/aud.0000000000000739

Test-Retest Variability in the Characteristics of Envelope Following Responses Evoked by Speech Stimuli

2019· article· en· W2947233004 on OpenAlexafffund
Vijayalakshmi Easwar, Susan Scollie, Steven J. Aiken, David W. Purcell

Bibliographic record

VenueEar and Hearing · 2019
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsAudiologyRepeatabilityFormantCoefficient of variationAnalysis of varianceCorrelationStandard deviationMathematicsStatisticsVowelCorrelation coefficientPsychologyMedicineSpeech recognitionComputer science

Abstract

fetched live from OpenAlex

OBJECTIVES: The objective of the present study was to evaluate the between-session test-retest variability in the characteristics of envelope following responses (EFRs) evoked by modified natural speech stimuli in young normal hearing adults. DESIGN: EFRs from 22 adults were recorded in two sessions, 1 to 12 days apart. EFRs were evoked by the token /susa∫ i/ (2.05 sec) presented at 65 dB SPL and recorded from the vertex referenced to the neck. The token /susa∫ i/, spoken by a male with an average fundamental frequency [f0] of 98.53 Hz, was of interest because of its potential utility as an objective hearing aid outcome measure. Each vowel was modified to elicit two EFRs simultaneously by lowering the f0 in the first formant while maintaining the original f0 in the higher formants. Fricatives were amplitude-modulated at 93.02 Hz and elicited one EFR each. EFRs evoked by vowels and fricatives were estimated using Fourier analyzer and discrete Fourier transform, respectively. Detection of EFRs was determined by an F-test. Test-retest variability in EFR amplitude and phase coherence were quantified using correlation, repeated-measures analysis of variance, and the repeatability coefficient. The repeatability coefficient, computed as twice the standard deviation (SD) of test-retest differences, represents the ±95% limits of test-retest variation around the mean difference. Test-retest variability of EFR amplitude and phase coherence were compared using the coefficient of variation, a normalized metric, which represents the ratio of the SD of repeat measurements to its mean. Consistency in EFR detection outcomes was assessed using the test of proportions. RESULTS: EFR amplitude and phase coherence did not vary significantly between sessions, and were significantly correlated across repeat measurements. The repeatability coefficient for EFR amplitude ranged from 38.5 nV to 45.6 nV for all stimuli, except for /∫/ (71.6 nV). For any given stimulus, the test-retest differences in EFR amplitude of individual participants were not correlated with their test-retest differences in noise amplitude. However, across stimuli, higher repeatability coefficients of EFR amplitude tended to occur when the group mean noise amplitude and the repeatability coefficient of noise amplitude were higher. The test-retest variability of phase coherence was comparable to that of EFR amplitude in terms of the coefficient of variation, and the repeatability coefficient varied from 0.1 to 0.2, with the highest value of 0.2 for /∫/. Mismatches in EFR detection outcomes occurred in 11 of 176 measurements. For each stimulus, the tests of proportions revealed a significantly higher proportion of matched detection outcomes compared to mismatches. CONCLUSIONS: Speech-evoked EFRs demonstrated reasonable repeatability across sessions. Of the eight stimuli, the shortest stimulus /∫/ demonstrated the largest variability in EFR amplitude and phase coherence. The test-retest variability in EFR amplitude could not be explained by test-retest differences in noise amplitude for any of the stimuli. This lack of explanation argues for other sources of variability, one possibility being the modulation of cortical contributions imposed on brainstem-generated EFRs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.295
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations26
Published2019
Admission routes2
Has abstractyes

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