Active noise cancelling in near-infrared spectroscopy
Bibliographic record
Abstract
A modern application of NIRS moves towards implantable methods to overcome the limitation. In implantable NIRS, the sensor is implanted adjacent to the organ of interest. The implant's mechanical structure, shape, and total volume are crucial to ensuring usability and minimizing invasiveness. Since thinner and smaller implant encapsulation reduces the distance between the electronic circuit of the sensor and the tissue, the equivalent capacitance between the tissue and the implantable system (consisting of the sensor and controller) can increase dramatically. The CMV (Common-Mode Voltage) is a voltage on the patient's body due to electromagnetic and electrical coupling. CMV is an essential noise source for recording biological signals; however, implantable NIRS sensors can induce a more significant noise because of the higher capacitance effect. During the preamplifier, the CMV can appear and be transformed to differential voltage, contaminating the original signal and decreasing the signal-to-noise ratio. Electromagnetic Shielding and a high CMRR (Common-Mode Rejection Ratio) amplifier are conventional methods for preventing noise contamination with common-mode voltage. However, these methods are not robust enough to protect the signal of interest in the presence of high-amplitude CMV. We proposed the active CMV reduction technique to eliminate the effect of CMV and improve the SNR of the NIRS signal. It can measure and eradicate induced CMV by injecting a minimal amount of electric current into the patient non-invasively. This paper proposes an ANC (Active noise cancellation) electronic circuit that eliminates CMV.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".