Effect of number of electrodes, electrode displacement, and RMS measurement noise on the localization accuracy of ECG inverse problem
Bibliographic record
Abstract
The effect of the number of electrodes, electrode displacement and RMS measurement noise was evaluated using an anatomically-detailed computer model of the thorax as a volume conductor. The body surface potential distributions due to cardiac dipole sources were calculated by applying five different electrode montages: the eight electrodes representing the independent leads of the standard 12-lead electrocardiogram (ECG), a modified 24-lead configuration, a Lux 32-lead full-body configuration, a Montreal 64-lead configuration, and a Brussels 120-lead configuration. Inverse solutions were computed using the lead field concept in the presence of both erroneous locations of the electrodes and of RMS measurement noise added to the torso surface potentials. The results indicate that increasing the number of leads enhances the localization accuracy of the inverse problem. With 32 or more electrodes, the localization accuracy remained stabilized despite the added RMS measurement noise. Similarly, increasing the number of displaced electrodes to 32 improved the localization accuracy as compared to cases with fewer electrodes.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".