A Comparison of Alternating Polarity and Forward Masking Artifact-Reduction Methods to Resolve the Electrically Evoked Compound Action Potential
Bibliographic record
Abstract
OBJECTIVE: Cochlear implant manufacturers utilize different artifact-reduction methods to measure electrically evoked compound action potentials (ECAPs) in the clinical software. Two commercially available artifact-reduction techniques include forward masking (FwdMsk) and alternating polarity (AltPol). AltPol assumes that responses to the opposing polarities are equal, which is likely problematic. On the other hand, FwdMsk can yield inaccurate waveforms if the masker does not effectively render all neurons into a refractory state. The goal of this study was to compare ECAP thresholds, amplitudes, and slopes of the amplitude growth functions (AGFs) using FwdMsk and AltPol to determine whether the two methods yield similar results. DESIGN: ECAP AGFs were obtained from three electrode regions (basal, middle, and apical) across 24 ears in 20 Cochlear Ltd. recipients using both FwdMsk and AltPol methods. AltPol waveforms could not be resolved for recipients of devices with the older-generation chip (CI24R(CS); N = 6). RESULTS: Results comparing FwdMsk and AltPol in the CI24RE- and CI512-generation devices showed significant differences in threshold, AGF slope, and amplitude between methods. FwdMsk resulted in lower visual-detection thresholds (p < 0.001), shallower slopes (p = 0.004), and larger amplitudes (p = 0.03) compared with AltPol. CONCLUSIONS: Results from this study are consistent with recent findings showing differences in ECAP amplitude and latency between polarities for human CI recipients. When averaged, these differences likely result in a reduced ECAP response with AltPol. The next step will be to separate the effects of artifact-reduction method and stimulus polarity to determine the relative effects of each.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".