Synthesis of Power Line Notch Filter in Wearable Biomedical Devices for Wireless Body Area Network
Bibliographic record
Abstract
With the rising prevalence of wearable biomedical devices for recording diverse biosignals, the potential for inaccurate interpretations due to noise susceptibility, including false alarms and misdiagnoses, is increasingly recognized.This study presents the systematic design and evaluation of a current-mode-based active notch filter aimed at eliminating the pervasive power-line interference (PLI) that corrupts biosignals.The filter is constructed using a modern and versatile current-mode analog building block (ABB), specifically the voltage differencing gain amplifier (VDGA).The design of this second-order filter incorporates a single VDGA as an active component and two capacitors as passive components, eliminating the need for resistors.Employing this filter, the influence of the 50Hz PLI on biological signals is effectively suppressed.The filter exhibits desirable properties of the current-mode circuit, including reduced power consumption, an expanded dynamic range, and enhanced accuracy.Key parameters of the filter, such as the pole frequency and the quality factor, can be electronically adjusted and orthogonally tuned via the bias currents of the VDGA.To validate its functionality, the proposed filter design was simulated using the PSPICE simulator with the MAX435 IC's macro-model.The simulation results revealed a notch depth and total harmonic distortion (THD) of -52.9 dB and -55 dB, respectively.The performance of this filter was subsequently compared with existing analog notch filters detailed in the literature.Given its efficacy and simplicity, the proposed filter is deemed suitable for deployment in high-performance biomedical detection equipment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".