Removal of artifacts in knee joint vibroarthrographic signals using ensemble empirical mode decomposition and detrended fluctuation analysis
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Bibliographic record
Abstract
High-resolution knee joint vibroarthrographic (VAG) signals can help physicians accurately evaluate the pathological condition of a degenerative knee joint, in order to prevent unnecessary exploratory surgery. Artifact cancellation is vital to preserve the quality of VAG signals prior to further computer-aided analysis. This paper describes a novel method that effectively utilizes ensemble empirical mode decomposition (EEMD) and detrended fluctuation analysis (DFA) algorithms for the removal of baseline wander and white noise in VAG signal processing. The EEMD method first successively decomposes the raw VAG signal into a set of intrinsic mode functions (IMFs) with fast and low oscillations, until the monotonic baseline wander remains in the last residue. Then, the DFA algorithm is applied to compute the fractal scaling index parameter for each IMF, in order to identify the anti-correlation and the long-range correlation components. Next, the DFA algorithm can be used to identify the anti-correlated and the long-range correlated IMFs, which assists in reconstructing the artifact-reduced VAG signals. Our experimental results showed that the combination of EEMD and DFA algorithms was able to provide averaged signal-to-noise ratio (SNR) values of 20.52 dB (standard deviation: 1.14 dB) and 20.87 dB (standard deviation: 1.89 dB) for 45 normal signals in healthy subjects and 20 pathological signals in symptomatic patients, respectively. The combination of EEMD and DFA algorithms can ameliorate the quality of VAG signals with great SNR improvements over the raw signal, and the results were also superior to those achieved by wavelet matching pursuit decomposition and time-delay neural filter.
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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 | 0.001 |
| 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 it