Signal classification via multi-scale PCA and empirical classification methods
Bibliographic record
Abstract
Data coming from a real–world complex system are usually contaminated by noises or some irrelevant components, which do not contribute to improve signal classification accuracy. Also in the process of signal feature enhancement, the performance of any statistical method used to recover the original signals may be impacted by the noise. In this paper, we propose the multi–scale principal component analysis (PCA) method, which combines discrete wavelet transform with PCA for feature enhancement and signal decomposition in both spatial and temporal domains. We developed a new classification method, called empirical classification (EC), to classify the power spectra of the feature extracted signals after the multi–scale PCA procedure. These methods were applied to a publicly available EEG database for the purpose of signal classification. An overall accuracy of 99% for the classification of 500 real EEG recordings under different considered classification problems is obtained. Our results show that signal decomposition by multi–scale PCA coupled with the EC method, leads to a highly promising accuracy in classifying epileptic EEG signals.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".