Energy-Efficient Spiking-CNN-Based Cross-Patient Seizure Detection
Bibliographic record
Abstract
A neuromorphic spiking convolutional neural network (SCNN) is presented for cross-patient seizure detection using multi-modal features from multi-channel electroencephalogram (EEG) data. A mixture of spectral, temporal, and spatial features is employed for building robustness against domain-specific noise/artifacts, hence boosting detection sensitivity and specificity. The feature set is converted to temporally-coded spikes before being fed to the SCNN classifier. Thanks to the asynchronous spike-based multiplier-less operation, the SCNN significantly reduces the classification computational cost without sacrificing accuracy. The developed algorithm was validated on a publicly available dataset and an average sensitivity of 83.02%, a specificity of 86.31%, and a false positive rate of 0.69/hr were achieved for cross-patient seizure detection. Our results show that a 1-bit Integer-Net leads to less than 2% drop in sensitivity compared with a 32-bit real-value resolution CNN model while offering more than 27× improvement in memory efficiency. The SCNN achieves an estimated energy efficiency of$1.28\mu\mathrm{J}$/classification, which translates into a 98.6% improvement compared to a conventional CNN implementation with the same accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".