Electrocardiogram Artifacts Caused by Deep Brain Stimulation
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) is increasingly used to treat a variety of neurological conditions (e.g. movement disorders and chronic pain). This prospective study was designed to detect electrocardiogram (ECG) artifacts induced by deep brain stimulation and to investigate which factors (patient disease, electrode position within the brain or type of stimulation) produced these artifacts. METHODS: Twelve patients (four women, eight men) with deep brain stimulators were enrolled in the study. Patients were selected to represent the common indications for DBS (Parkinson's disease, tremor, dystonia), the common electrode locations (pallidum, thalamus, subthalamic nucleus) and the two types of stimulation (monopolar, bipolar). Patients had one ECG with the DBS turned 'on' and another with the DBS turned 'off'. The ECGs were then randomized and read by a cardiologist blinded to the status of the patient and DBS and artifacts were noted to be either present or absent. RESULTS: The six patients using monopolar stimulation all had artifacts on their electrocardiograms. These artifacts were severe enough to interfere with ECG interpretation. There were no artifacts detected in the six patients using bipolar stimulation. Electrode location and patient disease appeared to have no effect on ECG artifact. CONCLUSIONS: Deep brain stimulation can cause ECG artifacts when monopolar settings are used. These artifacts are not present with bipolar settings or when the DBS is turned 'off'. Knowledge of these potential ECG artifacts and how to avoid them is essential to facilitate accurate ECG interpretation.
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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 | 0.001 | 0.005 |
| 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) | 0.002 | 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".