P.021 Electdetect: An Artificial Neural Network for the Detection of Artifacts in intracranial EEG Recordings From Patients with Epilepsy
Bibliographic record
Abstract
Background: Intracranial electroencephalography (iEEG) recordings are obtained from the sampling of sub-cortical structures and provide extraordinary insight into the spatiotemporal dynamics of the brain. As these recordings are increasingly obtained at higher channel counts and greater sampling frequencies, preprocessing through visual inspection is becoming untenable. Consequently, artificial neural networks (ANNs) are now being leveraged for this task. Methods: One-hour recordings from six patients diagnosed with drug-resistant epilepsy at Toronto Western Hospital were obtained alongside fiduciary ECG and EOG activity. R-wave peaks and local maxima were identified in the ECG and EOG recordings, respectively, and were time-mapped onto the iEEG recordings to delimit one-second epochs around 1.6 million cardiac and 600 thousand ocular artifacts. Epochs were then split into train-test-evaluation sets and fed into an ANN as one-second spectrograms (0 - 1,000 Hz) over 30-time steps. Results: The ANN model achieved formidable classification results on the evaluation set with an F1, positive predictive value, and sensitivity scores of 0.93. Furthermore, model architecture computed the classification probability at each time-step and enabled insight into the spatiotemporal features driving classification. Conclusions: We expect this research to promote the public sharing of new ANN from multiple institutions and enable novel automated algorithms for artifact detection in iEEG recordings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".