Implementing Effective Noise Reduction Techniques in Implantable NIRS Sensors
Bibliographic record
Abstract
Near-Infrared Spectroscopy (NIRS) is a noninvasive optical method widely used for evaluating tissue hemodynamics and various physiological characteristics. Despite its advantages, NIRS faces limitations in light sampling depth and spatial resolution, which has led to the development of implantable NIRS sensors. However, these implantable sensors are prone to Common-Mode Voltage (CMV) interference due to their increased sensor-to-tissue capacitance, which can compromise the signal-to-noise ratio and accuracy of measurements.In this paper, we present a novel active CMV reduction technique that enhances the signal-to-noise ratio of NIRS signals. We propose an electrical model of a patient's body and NIRS sensor to characterize the CMV interference and the active CMV cancellation (ACC) electronic circuit. The ACC circuit measures CMV through a common-mode amplifier, which then inverts and introduces the amplified signal to the patient's body via an additional surface electrode. This technique effectively attenuates the CMV (50 and 60 Hz) by 80 to 90 dB, significantly improving the signal quality without causing system instability.The method has been validated through both analytical simulations and experimental measurements, demonstrating the circuit's ability to suppress CMV within a bandwidth of 0.1 to 100 Hz. Experimental verification of the active noise cancellation method was conducted by recording data from the fingertip and palm, showing effective suppression of the CMV. The proposed method has substantial clinical relevance as it enhances the reliability and accuracy of implantable NIRS sensors, enabling more precise monitoring of internal organs and improved patient care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".