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q-bio.NC · Neurons and Cognition

A contact number rule gave higher mean concordance than a fixed power classifier at 62 SEEG label boundaries in 16 test patients

This study tested clinical seizure onset label ranking at consecutive numbered SEEG contacts. It used public OpenNeuro ds004100, version 1.1.3. One fixed power classifier was trained in 17 patients and tested in 17 separate patients. Sixteen test patients had 62 eligible label boundaries. In run 01, mean patient concordance was 0.597 for the classifier and 0.808 for a contact number rule. The classifier minus number contrast was -0.211, with a 95 percent patient bootstrap interval of [-0.327, -0.089]. The exact two sided sign reversal test gave p = 0.0068359375. It assumes independent test patients and sign symmetry under the null. The training sample and model were held fixed. The classifier tied at 43 boundaries. All opposite label comparisons in the same shafts gave classifier concordance of 0.708. The run 02 contrast was -0.167, with interval [-0.344, 0.002]. Run 02 reused the same patients and labels. All 70 source signal records and 74 metadata checks passed. Fresh signal reconstruction reproduced the analysis. The package preserves the graph, features, model, code, source versions, checks, and run instructions. This informed secondary study concerns one hospital cohort and one fixed model. It does not test treatment benefit.

stereoelectroencephalography · SEEG · seizure onset zone · concordance · contact boundaries · interictal power · nearest neighbors · epilepsy

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References

5
  1. usesA fixed contact number rule gave higher mean label concordance than 6 dB rise timing at 61 boundaries in 26 SEEG patients with two seizures each sci:sha256:5dcaeda8a765ce331dbd08c9c328b5f05f53c48c5849a99e84b1280f2bf8ba2c

    This study uses the fixed rule that favors the smaller contact number and the idea of patient weighted label boundary comparisons. The earlier study used seizure timing on bipolar channels. The present study tests interictal power on single contacts.

  2. usesA fixed SEEG classifier exceeded shaft label controls when each of 34 test patients was excluded from training sci:sha256:03a311212756488675cc58401c7a73678371ea420ad844766380854f12412e6d

    This study reuses the seven log power features, five nearest neighbor model choice, cohort selection, and source package. It adds a fixed training sample of 17 patients and a consecutive contact graph in separate test patients.

  3. usesContact splits gave higher mean pooled AUC for seizure onset labels than shaft splits with a fixed spectral random forest in 34 SEEG patients sci:sha256:56a9020d2fcfaf8dce31031e250ead4147aed4b96d5049afb0ef063a225a356e

    The input contact features and consistent label cohort were inherited from this publication through the fixed classifier package. This study keeps that feature and label selection and makes a new patient split.

  4. usesMedian 80–150 Hz power rank agreement was lower between references than between recordings on matched pairs in 35 SEEG participants sci:sha256:eaad9a72db87113b65f5fda15f4c3043dc3463ac2f155c6b9d8b01404ec3ab9f

    This study uses the saved common average spectra, EDF reader, pinned metadata, and source byte range records inherited from this publication. It retrieved all 70 selected source records again and reproduced all saved spectra.

  5. usesdoi:10.18112/openneuro.ds004100.v1.1.3

    This study uses interictal SEEG and clinical labels from OpenNeuro ds004100 version 1.1.3.

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