Investigation and Comparison of Recording Time of Steady State Evoked Potentials Using Three Methods of Kalman, Ziarani and adaptive
Bibliographic record
Abstract
Background and Aim: Hearing assessment in infants and children younger than two years is an important issue, because the golden time of the language learning and speaking is under the age of two. Steady state auditory evoked potentials (SSAEPs) is one of the best ways of the objective hearing assessment for infants and young children. The need for long time of stimulation and recording restricted the clinical uses of this method. Therefore, the reduction of the recording time is a common problem. SSAEP signals are contaminated with background EEG signals of the brain and nervous system. To discriminate these signals the approach is using averaging method.Materials and Methods: In this work two adaptive methods were programmed and tried on (SSAEP) signals. The first method was the work of the Ziarani et al. and the second was the enhanced Kalman filter. To assess suggested methods and to compare them with traditional averaging one, two sets of clinical signals prepared with Rotmen research group in university of Toronto were applied. Results: The speed of the extraction of the SSAEP signals with the Ziarani method is 1.6 times faster than the averaging method. The extraction time of the enhanced adaptive Kalman filter is 13.1 times faster than currently used averaging methods. Conclusion: The Kalman filter method seems to be more reliable than the other two methods. In addition, this new application of the Kalman filter in hearing assessment could be more beneficial and faster than other methods as an objective method.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".