Wavelength-dependent properties of motion artifacts in action potentials acquired with dual wavelength cardiac optical mapping impact the performance of ratiometry
Bibliographic record
Abstract
Cardiac optical mapping in whole heart preparations is a research tool that contributes to the understanding of normal and abnormal cardiac electrical activity. The value of the technique is challenged by the presence of motion artifacts in the action potentials retrieved. Motion artifacts appear as a distortion of the action potential and affect the evaluation of electrophysiological parameters of interest such as action potential duration. Dual wavelength optical mapping offers the possibility of correcting motion artifacts by taking advantage of the ratiometric properties of the potentiometric dye used to record the electrical activity. In dual wavelength optical mapping, action potentials are recorded at two wavelengths and a ratio signal is calculated between the signals, removing the artifacts common to both wavelengths. Ratiometry relies on the assumption that motion artifacts in the two channels are similar in direction and shape. However, in practice ratiometry does not completely remove motion artifacts, a fact that has not yet been explained. Differences in signal amplitude between channels have been reported by earlier studies; however, these differences can be dealt with by scaling the signals appropriately. Early data acquired for this study suggest more complex differences exist between motion artifacts acquired in both channels. These differences affect the performance of ratiometry and may explain the inability of the technique to completely remove motion artifacts. This paper presents early examples of how the differences in shape, direction and amplitude between motion artifacts in dual wavelength recordings affect the results of the calculated ratio signal.
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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 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".