{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T17:36:52Z","timestamp":1770745012878,"version":"3.49.0"},"reference-count":52,"publisher":"World Scientific Pub Co Pte Ltd","issue":"04","funder":[{"DOI":"10.13039\/501100001700","name":"Ministry of Education, Culture, Sports, Science, and Technology of Japan","doi-asserted-by":"crossref","award":["19K07964"],"award-info":[{"award-number":["19K07964"]}],"id":[{"id":"10.13039\/501100001700","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001700","name":"Ministry of Education, Culture, Sports, Science, and Technology of Japan","doi-asserted-by":"crossref","award":["24K10648"],"award-info":[{"award-number":["24K10648"]}],"id":[{"id":"10.13039\/501100001700","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:p>Interictal epileptiform discharges (IEDs) are crucial for epilepsy diagnosis but are often undetectable on scalp EEG (scEEG). This study aims to develop a Support Vector Machine classifier to detect mesial temporal lobe (MTL) IEDs invisible on scEEG using simultaneous intracranial EEG (iEEG) and scEEG recordings and to identify contributing scEEG features. Data from 17 patients with epilepsy were analyzed. IED epochs were extracted where IEDs were present on iEEG but absent on scEEG. Control epochs were selected from periods without IEDs on both Electroencephalographies (EEGs). Feature selection was performed, and a classifier was developed and validated with external data from 35 MTL epilepsy (MTLE) and 33 nonepileptic patients. The classifier used 58 selected features and achieved an accuracy of 0.70 and an area under the receiver operating characteristic curve of 0.78 on holdout validation. External validation revealed significant differences in IED-classified epoch frequencies before and after drug withdrawal in patients with MTLE, and between MTLE and nonepileptic groups. Feature analysis identified high-frequency power suppression, increased ipsilateral connectivity, and enhanced cross-frequency coupling as markers of IEDs that were undetectable in scEEG. This study shows that machine learning can detect MTL IEDs invisible to scEEG, revealing related scEEG changes and aiding EEG analysis.<\/jats:p>","DOI":"10.1142\/s0129065726500024","type":"journal-article","created":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T15:21:02Z","timestamp":1764429662000},"source":"Crossref","is-referenced-by-count":0,"title":["Scalp Electroencephalography Markers of Hidden Interictal Epileptiform Discharges in the Mesial Temporal Lobe"],"prefix":"10.1142","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9866-9172","authenticated-orcid":false,"given":"Takahiro","family":"Yamaguchi","sequence":"first","affiliation":[{"name":"Department of Neurology Neurological Institute, Graduate School of Medical Sciences, Kyushu University Fukuoka 812-0054, Japan"},{"name":"Division of Clinical Chemistry and Laboratory Medicine, Kyushu University Hospital, Fukuoka 812-0054, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4292-4602","authenticated-orcid":false,"given":"Taira","family":"Uehara","sequence":"additional","affiliation":[{"name":"Department of Neurology School of Medicine, International University of Health and Welfare, Narita 286-8686, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6001-1244","authenticated-orcid":false,"given":"Toshiki","family":"Okadome","sequence":"additional","affiliation":[{"name":"Department of Neurology Neurological Institute, Graduate School of Medical Sciences, Kyushu University Fukuoka 812-0054, Japan"},{"name":"Discipline of Physiology Adelaide Medical School, The University of Adelaide Adelaide 5055, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4091-5988","authenticated-orcid":false,"given":"Takahiko","family":"Mukaino","sequence":"additional","affiliation":[{"name":"Department of Neurology Neurological Institute, Graduate School of Medical Sciences, Kyushu University Fukuoka 812-0054, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1116-7194","authenticated-orcid":false,"given":"Takafumi","family":"Shimogawa","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery Neurological Institute, Graduate School of Medical Sciences, Kyushu University Fukuoka 812-0054, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1990-1485","authenticated-orcid":false,"given":"Nobutaka","family":"Mukae","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery Neurological Institute, Graduate School of Medical Sciences, Kyushu University Fukuoka 812-0054, Japan"},{"name":"Department of Neurosurgery Aso Iizuka Hospital Iizuka 820-8505, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5437-943X","authenticated-orcid":false,"given":"Hiroshi","family":"Shigeto","sequence":"additional","affiliation":[{"name":"Division of Medical Technology, Department of Health Sciences Graduate School of Medical Sciences, Kyushu University Fukuoka 812-0054, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9525-4254","authenticated-orcid":false,"given":"Noriko","family":"Isobe","sequence":"additional","affiliation":[{"name":"Department of Neurology Neurological Institute, Graduate School of Medical Sciences, Kyushu University Fukuoka 812-0054, Japan"}]}],"member":"219","published-online":{"date-parts":[[2026,2,5]]},"reference":[{"key":"S0129065726500024BIB001","doi-asserted-by":"publisher","DOI":"10.1111\/j.1468-1331.2008.02260.x"},{"issue":"1","key":"S0129065726500024BIB002","first-page":"S4","volume":"80","author":"Tatum W. O.","year":"2013","journal-title":"Neurology"},{"key":"S0129065726500024BIB003","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065723500016"},{"key":"S0129065726500024BIB004","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065720500744"},{"key":"S0129065726500024BIB005","doi-asserted-by":"publisher","DOI":"10.1111\/j.1528-1167.2005.11404.x"},{"key":"S0129065726500024BIB006","doi-asserted-by":"publisher","DOI":"10.1038\/nm.4330"},{"key":"S0129065726500024BIB007","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awz269"},{"key":"S0129065726500024BIB008","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065725500182"},{"key":"S0129065726500024BIB009","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awu214"},{"key":"S0129065726500024BIB010","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0068038"},{"key":"S0129065726500024BIB011","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065724500175"},{"key":"S0129065726500024BIB012","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065724500400"},{"key":"S0129065726500024BIB013","doi-asserted-by":"publisher","DOI":"10.1001\/jamaneurol.2022.0888"},{"key":"S0129065726500024BIB014","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065718500600"},{"key":"S0129065726500024BIB015","unstructured":"A. Gramfort, MEG and EEG data analysis with MNE-Python."},{"key":"S0129065726500024BIB016","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-2649-2"},{"key":"S0129065726500024BIB017","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0686-2"},{"key":"S0129065726500024BIB018","doi-asserted-by":"publisher","DOI":"10.25080\/Majora-92bf1922-00a"},{"key":"S0129065726500024BIB019","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1005893"},{"key":"S0129065726500024BIB020","unstructured":"F. Pedregosa, F. Pedregosa, G. Varoquaux, G. Varoquaux, N. Org, A. Gramfort, A. Gramfort, V. Michel, V. Michel, L. Fr, B. Thirion, B. Thirion, O. Grisel, O. Grisel, M. Blondel, P. Prettenhofer, P. Prettenhofer, R. Weiss, V. Dubourg, V. Dubourg, J. Vanderplas, A. Passos, A. Tp, and D. Cournapeau, Scikit-learn: Machine Learning in Python. Mach Learn PYTHON."},{"key":"S0129065726500024BIB021","unstructured":"F. A. Fortin, F. A. Fortin, F. M. D. Rainville, F. M. De-Rainville, M. A. Gardner, M. A. Gardner, M. Parizeau, M. Parizeau, C. Gagne, and C. Gagne, DEAP: Evolutionary Algorithms Made Easy."},{"key":"S0129065726500024BIB022","doi-asserted-by":"crossref","unstructured":"T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama, Optuna: A Next-generation Hyperparameter Optimization Framework (2019). Available from: http:\/\/arxiv.org\/abs\/1907.10902.","DOI":"10.1145\/3292500.3330701"},{"key":"S0129065726500024BIB023","unstructured":"L. Gautier, rpy2 (2024). Available from: https:\/\/github.com\/rpy2\/rpy2\/releases\/tag\/RELEASE_ 3_5_17."},{"key":"S0129065726500024BIB024","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v067.i01"},{"key":"S0129065726500024BIB025","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v082.i13"},{"key":"S0129065726500024BIB026","volume-title":"R: A Language and Environment for Statistical Computing","author":"Core Team R.","year":"2024"},{"key":"S0129065726500024BIB027","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2003.810706"},{"key":"S0129065726500024BIB028","doi-asserted-by":"publisher","DOI":"10.1109\/IEMBS.2001.1020545"},{"key":"S0129065726500024BIB029","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2008.02.001"},{"key":"S0129065726500024BIB030","doi-asserted-by":"publisher","DOI":"10.1016\/0013-4694(73)90260-5"},{"key":"S0129065726500024BIB031","doi-asserted-by":"publisher","DOI":"10.1109\/TAU.1967.1161901"},{"key":"S0129065726500024BIB032","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2021.03.007"},{"key":"S0129065726500024BIB033","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2011.08.014"},{"key":"S0129065726500024BIB034","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2007.06.026"},{"key":"S0129065726500024BIB035","doi-asserted-by":"publisher","DOI":"10.1016\/0167-8760(95)00046-1"},{"key":"S0129065726500024BIB036","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.01.055"},{"key":"S0129065726500024BIB037","unstructured":"J. Platt, Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods (1999). Available from: https:\/\/api.semanticscholar.org\/CorpusID:56563878."},{"key":"S0129065726500024BIB038","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2013.11.024"},{"key":"S0129065726500024BIB039","unstructured":"Z. Y. Taha, Optimizing feature selection with genetic algorithms: A review of methods and applications."},{"key":"S0129065726500024BIB040","doi-asserted-by":"publisher","DOI":"10.1109\/4235.996017"},{"key":"S0129065726500024BIB041","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1814092116"},{"key":"S0129065726500024BIB042","doi-asserted-by":"crossref","unstructured":"S. E. Moon, S. Jang and J. S. Lee, Convolutional Neural Network Approach for EEG-based Emotion Recognition using Brain Connectivity and its Spatial Information (2018). Available from: http:\/\/arxiv.org\/abs\/1809.04208.","DOI":"10.1109\/ICASSP.2018.8461315"},{"key":"S0129065726500024BIB043","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-83337-3"},{"key":"S0129065726500024BIB044","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-42971-3"},{"key":"S0129065726500024BIB045","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/bhj157"},{"key":"S0129065726500024BIB046","doi-asserted-by":"publisher","DOI":"10.1016\/j.nbd.2024.106409"},{"key":"S0129065726500024BIB047","doi-asserted-by":"publisher","DOI":"10.1016\/0013-4694(75)90215-1"},{"key":"S0129065726500024BIB048","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac64c4"},{"key":"S0129065726500024BIB049","doi-asserted-by":"publisher","DOI":"10.1111\/epi.12904"},{"key":"S0129065726500024BIB050","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04146-4"},{"key":"S0129065726500024BIB051","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04359-7"},{"key":"S0129065726500024BIB052","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2682102"}],"container-title":["International Journal of Neural Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0129065726500024","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T03:11:24Z","timestamp":1770693084000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S0129065726500024"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,5]]},"references-count":52,"journal-issue":{"issue":"04","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["10.1142\/S0129065726500024"],"URL":"https:\/\/doi.org\/10.1142\/s0129065726500024","relation":{},"ISSN":["0129-0657","1793-6462"],"issn-type":[{"value":"0129-0657","type":"print"},{"value":"1793-6462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,5]]},"article-number":"2650002"}}