Label shuffles within contact name groups give expected AUC of 0.621 for slow power in two 30-second SEEG clips from 25 patients
We tested slow power in public SEEG data from 25 patients. Each patient supplied two 30-second interictal clips from OpenNeuro ds004100, version 1.1.3. We used saved features from an earlier study and checked two raw recording prefixes again. The observed mean AUC for seizure-onset labels was 0.649. Uniform label shuffles within contact name groups gave an exact expected mean AUC of 0.621. This null mean was 0.121 above 0.5, with a 95% patient bootstrap interval of 0.054 to 0.185. The observed mean minus the null mean was 0.027, with an interval of -0.017 to 0.072. In 2,000 independent controls drawn under this conditional null, global shuffles rejected 100% and shuffles within groups rejected 5.05% at a 5% level. These models test different label assumptions. The study does not prove which model applies to clinical labels. We provide exact null formulas, a reusable score-table audit tool, exhaustive checks, source records, code, results, and reproduction instructions. The data overlap with the earlier study. The sample and short clips limit general use.
stereoelectroencephalography · SEEG · permutation tests · area under the curve · exchangeability · null models · epilepsy
References
3- usesMatching contacts by name group reduced slow power AUC for seizure-onset labels in 30-second SEEG clips from 24 patients sci:sha256:5c45a9c4687c91d299a386e3d4190ce7597ef78e121f53372be7743edf88e921
We used its public SEEG power features, source metadata, pinned raw prefix retrieval records, and signal feature code. We preserved its complete payload. We applied new exact label-null moments, paired-run tests, and independent conditional controls. This is a follow-up on the same data.
- usesdoi:10.18112/openneuro.ds004100.v1.1.3
The study uses features and source records from OpenNeuro ds004100 v1.1.3; the archived source metadata records this release and its CC0 license.
- doi:10.1093/brain%2Fawab480
Cited by
1 publicationPublic catalog entries on Sciscape.
- usesA fixed SEEG power classifier gave higher AUC than shaft label controls in 34 patients with two recordings each
Its fixed-feature shaft label controls informed the question. We extend those controls to a trained model by changing training and test labels together.