Prediction of epileptic seizures using fNIRS and machine learning
Bibliographic record
Abstract
Research to predict epileptic seizures has been mainly focused on the analysis of electroencephalography (EEG) signals; however, recent research efforts have encouraged the use of a relatively new optical signal modality, called functional Near-Infrared Spectroscopy (fNIRS). In fNIRS, near-infrared light is injected into the scalp and the intensity of the reflected light is registered in optodes. Light absorption in hemoglobin depends on the level of blood oxygenation, which is related to brain activity. In this technique, two parameters are measured at each optode, the relative level of oxygenated hemoglobin (HbO) and the relative level of deoxygenated hemoglobin (HbR). In this work we investigated the feasibility of predicting epileptic seizures, using either fNIRS, EEG, or a combination of both signals. In one set of experiments, different implementations for epileptic seizure prediction are tested by using (1) different combinations of electrical and optical signals (EEG, HbO, HbR, EEG+HbO, EEG+HbR, HbO+HbR, EEG+HbO+HbR) and (2) two different classifiers, ( Support Vector Machine - SVM and Multi-Layer Perceptron - MLP). In the second set of experiments, seizures are predicted within a five-minute window that is moved up to 15 minutes before the start of the epileptic seizure. By computing the Positive Predictive Value (PPV) and the accuracy , it is demonstrated that fNIRS-based epileptic prediction outperforms EEG-based epileptic prediction. By using optical signals and the SVM classifier, a PPV greater than 99% and an accuracy of 100% were obtained. PPV values of 100% are also obtained when seizures are predicted up to 15 minutes in advance. Furthermore, Kernel Discriminant Analysis (KDA) is used to demonstrate that the highest separability among the classes, corresponding to different epileptic signal phases ( pre-ictal , ictal , and inter-ictal ), is achieved when fNIRS recordings are used as features for prediction. Finally, fNIRS-based epileptic seizure prediction is tested with Random Chance classifiers. In this study, we showed that fNIRS signals are an effective tool to predict epileptic seizures, even without the use of EEG signals, which are the current standard for seizure prediction.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".