Signal decomposition by multi-scale PCA and its applications to long-term EEG signal classification
Why this work is in the frame
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Bibliographic record
Abstract
Data coming from a real-world complex system are usually contaminated by certain levels of noise or some irrelevant components, which do not contribute to improve signal classification accuracy. Also in signal de-noising, 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 and PCA for de-noising and decomposing complex biomedical signals in both spatial and temporal domains for signal classification. We also develop a new classification method, called Empirical Classification (EC), based on the characteristics of data we analyzed. These methods were applied to a publicly available EEG database for the purpose of epilepsy diagnosis and epileptic seizure detection. Our study shows that signal decomposition by the multi-scale PCA method coupled with the EC method, leads to a highly promising classification accuracy in classifying epileptic EEG signals. Our methods are also applicable for classifying biomedical images.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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