Sleep EMG analysis using sparse signal representation and classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The development of automatic sleep based abnormality detection in patient for sleep related problem is a key field in the recent research. However the sleep signals are obtained as long-time recordings and inhibit complex characteristics, making their analysis computationally challenging. As a result, recognition methods that facilitate efficient dimensionality reduction are developed to suit different applications. In recent years sparse representation schemes provide an effective means for achieving best possible data reduction by comparing the input with pre-formulated dictionaries, especially for huge datasets. Recent research proves the usability of these methods for signal classification. In this paper, a robust technique is provided for sparse representation of small dataset signal types. Here, the signal decomposition is obtained using the l(1)-minimization technique, following which a generalization based on the leave-one-out (LOO) is performed. The dependency of the proposed algorithm is analyzed, using a sparsity measure, in order to verify the dependency between the input data and extracted feature space. Performance measures obtained using long-term sleep data shows an average classification accuracy of 80% and further validates the usefulness of the technique for long term biomedical signal analysis.
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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 | 0.001 |
| 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