Enhanced transcutaneous electrical nerve stimulation achieved by a localized virtual bipole: a computational study of human tibial nerve stimulation
Bibliographic record
Abstract
Abstract Objective . Electrical neuromodulation is a clinically effective therapeutic instrument, currently expanding into newer indications and larger patient populations. Neuromodulation technologies are also moving towards less invasive approaches to nerve stimulation. In this study, we investigated an enhanced transcutaneous electrical nerve stimulation (eTENS) system that electrically couples a conductive nerve cuff with a conventional TENS electrode. The objectives were to better understand how eTENS achieves lower nerve activation thresholds, and to test the feasibility of applying eTENS in a human model of peripheral nerve stimulation. Approach. A finite element model (FEM) of the human lower leg was constructed to simulate electrical stimulation of the tibial nerve, comparing TENS and eTENS. Key variables included surface electrode diameter, nerve cuff properties (conductivity, length, thickness), and cuff location. Enhanced neural excitability was predicted by relative excitability (RE > 1), derived using either the activating function (AF) or the nerve activation threshold (MRG model). Main results. Simulations revealed that a localized ‘virtual bipole’ was created on the target nerve, where the isopotential surface of the cuff resulted in large potential differences with the surrounding tissue. The cathodic part (nerve depolarization) of the bipole enhanced neural excitability, predicted by RE values of up to 2.2 (MRG) and 5.5 (AF) when compared to TENS. The MRG model confirmed that action potentials were initiated at the cathodic edge of the nerve cuff. Factors contributing to eTENS were larger surface electrodes, longer cuffs, cuff conductivity (>1×10 3 S m −1 ), and cuff position relative to the cathodic surface electrode. Significance. This study provides a theoretical basis for designing and testing eTENS applied to various neural targets and data suggesting function of eTENS in large models of nerve stimulation. Although eTENS carries key advantages over existing technologies, further work is needed to translate this approach into effective clinical applications.
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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 | 0.001 |
| 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 | 0.001 | 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".