Impact of Skin–Electrode Interface on Electrocardiogram Measurements Using Conductive Textile Electrodes
Bibliographic record
Abstract
Physicians' understanding of biosignals as measured with medical instruments becomes the foundation of their decisions and diagnoses of patients, as they rely strongly on what the instruments show. Thus, it is critical and very important to ensure that the instruments' recordings exactly reflect what is happening in the patient's body so that the acquired signal is the real one or at least as close to the real in-body signal as possible. This is such an important issue that sometimes physicians use invasive measurements to obtain the real biosignal. Generating an in-body signal from what a measurement device shows is called “signal purification” or “reconstruction” and can be done only when we have adequate information about the interface between the body and the monitoring device. In this paper, first, we present a device that we developed for electrocardiogram (ECG) acquisition and transfer to PC. To evaluate the performance of the device, we use it to measure ECG and apply conductive textile as our ECG electrode. Then, we evaluate ECG signals captured by different electrodes, specifically traditional gel Ag/AgCl and dry golden plate electrodes, and compare the results, allowing us to investigate if ECG measured with the device is proper for applications where no skin preparation is allowed, such as ECG-assisted blood pressure monitoring devices. Next, we propose a method to reconstruct the ECG signal from the signal acquired by our device, with respect to the interface characteristics and their relation to the ECG. The interface in this paper is skin-electrode interface for conductive textiles. In the last stage of this paper, we explore the effects of pressure on skin-electrode interface impedance and its parametrical variation.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".