Learning sparse dictionary for long-term biomedical signal classification and clustering
Bibliographic record
Abstract
Long-term observational biomedical signals are often used for many medical diagnoses including sleep disorder and epilepsy. Effective management and usage of this type of data through classification or clustering problem is the key to real-world applications. This work focuses on learning a se of selected sparse basis functions, called a double-sparse dictionary, directly from specific data, in order to produce a collection of discriminative features with low variability. Our approach is to combine wavelet transform with sparse principal component analysis (SPCA), namely wavelet sparse PCA (WSPCA), and apply it to a signal segment matrix. The application of this proposed method is demonstrated by classification and clustering problems of long-term EEG signals, and the results are compared to other PCA-based sparse methods. The nearly perfect classification accuracy (i.e., 99.7%) is obtained by using WSPCA for the data we consider. Although PCA leads to the best performance among all methods we considered, WSPCA does not lose classification accuracy significantly and it is more suitable for long-term signal classification due to the time-domain signal dimension reduction by wavelets.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".