On the use of single-channels for sensing multisource activity in biomedical signals
Bibliographic record
Abstract
This paper presents a framework which allows for the extraction of information from just single-channel measurements of biomedical signals. Such a method is vital as an intelligent preprocessing stage to the transmission of such information, for example, in the remote sensing of biomedical signals. The framework consists of two fundamental aspects; the first is a dynamical embedding step which provides a representation of multidimensional data from just a single channel recording; this is then followed by a variant of standard independent component analysis known as constrained independent component analysis. This latter method allows for the extraction of one of many sources underlying the measurement space, through the provision of a basic reference signal. The reference signal, or constraint, can be changed to extract different sources from the measured data - if they exist. Although the method can be applied to a whole range of biomedical signals, it is demonstrated here in the context of the extraction of seizure information from a single channel of pre-recorded (multichannel) epileptiform EEG. It can be seen that seizure information can be extracted from a single channel EEG recording and the sensitivity of the location of the measurement channel used (in relation to the focus of the epileptiform activity) is reduced considerably.
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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 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".