Pacemaker interference by magnetic fields at power line frequencies
Bibliographic record
Abstract
Human exposure to external 50/60-Hz electric and magnetic fields induces electric fields within the body. These induced fields can cause interference with implanted pacemakers. In the case of exposure to magnetic fields, the pacemaker leads are subject to induced electromotive forces, with current return paths being provided by the conducting body tissues. Modern computing resources used in conjunction with millimeter-scale human body conductivity models make numerical modeling a viable technique for examining any such interference. In this paper, an existing well-verified scalar-potential finite-difference frequency-domain code is modified to handle thin conducting wires embedded in the body. The effects of each wire can be included numerically by a simple modification to the existing code. Results are computed for two pacemaker lead insertion paths, terminating at either atrial or ventricular electrodes in the heart. Computations are performed for three orthogonal 60-Hz magnetic field orientations. Comparison with simplified estimates from Faraday's law applied directly to extracorporeal loops representing unipolar leads underscores problems associated with this simplified approach. Numerically estimated electromagnetic interference (EMI) levels under the worst case scenarios are about 40 microT for atrial electrodes, and 140 microT for ventricular electrodes. These methods could also be applied to studying EMI with other implanted devices such as cardiac defibrillators.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".