Bibliographic record
Abstract
Wireless electroencephalogram (EEG) systems have become an increasingly important tool for the diagnosis and management of epilepsy. One major consideration in using such a wireless EEG-based system is the stringent battery energy constraint at the sensor side. Different solutions to reduce the power consumption are therefore highly desired. Conventionally, the entire EEG signals acquired by the sensor nodes are continuously streamed to an external data server (where seizure detection is carried out). Such approach incurs a high power consumption, which substantially limits the battery life of the sensor node. In this study, we examine the use of data reduction techniques, including compressive sensing-based EEG compression and various low-complexity feature extraction techniques, for reducing the amount of data that has to be transmitted and thereby reducing the required power consumption at the sensor side. The performance of such techniques is evaluated in terms of power consumption and seizure detection efficacy. Results show that by extracting and transmitting only the nonlinear autocorrelation features of the EEG signals to the server, the battery life of the system is increased by 14 times relative to the conventional approach of transmitting the entire EEG signals, while the same seizure detection performance is maintained (94.1% sensitivity and 99.9% specificity).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".