Toward EEG-Free Seizure Detection: Evidence of EEG-ECG Synchronization
Bibliographic record
Abstract
Epilepsy is a chronic neurological disorder that affects millions of people worldwide. If not detected promptly, it can lead to serious or even fatal consequences. Traditionally, epileptic seizure detection relies on brain signals such as electroencephalograms (EEGs). While effective, EEG acquisition typically requires non-invasive scalp-mounted electrodes, which limit patient mobility and necessitate a controlled clinical environment. In contrast, electrocardiograms (ECGs) present a promising alternative due to their ease of acquisition, portability, and compatibility with wearable devices. Leveraging ECG signals-either independently or in combination with brain signals offers significant potential for real-time, continuous monitoring, which is critical for timely intervention. This paper investigates the synchrony between the ECG-based model and the EEG-based model, aiming to study the feasibility of using ECG as an alternative method for seizure detection. Using Bland Altman analysis, the results indicate that although EEG provides higher discriminative power, ECG yields promising and comparable performance, highlighting its potential for wearable seizure monitoring. The results are statistically significant according to the t-test.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".