A deep learning approach for detecting epileptic spike in magnetoencephalography signals
Bibliographic record
Abstract
Epilepsy is one of the most serious neurological disorders that affects people of all ages. In Canada, an average of 15,500 people discover epilepsy symptoms each year [1]. Numerous scholars have conducted extensive research in automated detection of epilepsy spike for presurgical assessment. However, the study of Magnetoencephalography (MEG) spike detection is limited to under 30 patients’ data. In this thesis, we explore a deep learning approach for detecting spike in interictal MEG recordings of up to 300 epileptic patients in an automated fashion. We evaluate the convolutional neural network architecture and long short-term memory method on both 2D images and 3D spatiotemporal MEG recordings. For 2D images, we tested a simple 3 layer Convolutional Neural Network (CNN/ConvNet) model and a transfer learning model, and achieved an accuracy of 83.12% and 82.73%, sensitivity of 91.66% and 78.52%, and specificity of 74.58% and 86.94%. For 3D spatiotemporal data, we tested the 3 dimensional CNN model and Long short-term memory (LSTM) model to get 86.04% and 83.09% in accuracy, 92.37% and 87.18% in sensitivity, and 79.69% and 78.99% in specificity. The methods show an increasing performance with larger datasets, which provide us confidence on the validity of the proposed automation technique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".