{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T22:23:00Z","timestamp":1775254980789,"version":"3.50.1"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:00:00Z","timestamp":1772150400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T00:00:00Z","timestamp":1775174400000},"content-version":"vor","delay-in-days":35,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"NIH\/NINDS","award":["R01NS117904 (ELECTRO-BOOST: Electroencephalography for cerebral trauma recovery and oxygenation)"],"award-info":[{"award-number":["R01NS117904 (ELECTRO-BOOST: Electroencephalography for cerebral trauma recovery and oxygenation)"]}]},{"name":"NIH\/NINDS","award":["R01NS117904 (ELECTRO-BOOST: Electroencephalography for cerebral trauma recovery and oxygenation)"],"award-info":[{"award-number":["R01NS117904 (ELECTRO-BOOST: Electroencephalography for cerebral trauma recovery and oxygenation)"]}]},{"name":"NIH\/NINDS","award":["R01NS117904 (ELECTRO-BOOST: Electroencephalography for cerebral trauma recovery and oxygenation)"],"award-info":[{"award-number":["R01NS117904 (ELECTRO-BOOST: Electroencephalography for cerebral trauma recovery and oxygenation)"]}]},{"name":"NIH\/NINDS","award":["R01NS117904 (ELECTRO-BOOST: Electroencephalography for cerebral trauma recovery and oxygenation)"],"award-info":[{"award-number":["R01NS117904 (ELECTRO-BOOST: Electroencephalography for cerebral trauma recovery and oxygenation)"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-41358-w","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T02:17:22Z","timestamp":1772158642000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A transparent AI assurance and benchmarking framework for EEG seizure detection on TUSZ seeded with a reproducible gradient-boosting ensemble"],"prefix":"10.1038","volume":"16","author":[{"given":"Morteza","family":"Zabihi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emily J.","family":"Gilmore","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kan","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric S.","family":"Rosenthal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,27]]},"reference":[{"key":"41358_CR1","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.knosys.2013.02.014","volume":"45","author":"UR Acharya","year":"2013","unstructured":"Acharya, U. R., Sree, V., Swapna, S., Martis, G., Suri, J. S. & R. J. & Automated EEG analysis of epilepsy: A review. Knowl. Based Syst. 45, 147\u2013165 (2013).","journal-title":"Knowl. Based Syst."},{"key":"41358_CR2","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1109\/RBME.2020.3008792","volume":"14","author":"K Rasheed","year":"2021","unstructured":"Rasheed, K. et al. Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review. IEEE Rev. Biomed. Eng. 14, 139\u2013155 (2021).","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"41358_CR3","doi-asserted-by":"publisher","unstructured":"Shah, V. et al. The Temple University Hospital Seizure Detection Corpus. Front Neuroinform 12,https:\/\/doi.org\/10.3389\/fninf.2018.00083 (2018).","DOI":"10.3389\/fninf.2018.00083"},{"key":"41358_CR4","doi-asserted-by":"publisher","unstructured":"Khosla, A., Khandnor, P. & Chand, T. EEG -based automatic multi\u2010class classification of epileptic seizure types using recurrence plots. Expert Syst 39,https:\/\/doi.org\/10.1111\/exsy.12923 (2022).","DOI":"10.1111\/exsy.12923"},{"key":"41358_CR5","doi-asserted-by":"publisher","first-page":"32820","DOI":"10.1038\/s41598-025-18479-9","volume":"15","author":"H Kashefi Amiri","year":"2025","unstructured":"Kashefi Amiri, H., Zarei, M. & Daliri, M. R. Epileptic seizure detection from electroencephalogram signals based on 1D CNN-LSTM deep learning model using discrete wavelet transform. Sci. Rep. 15, 32820 (2025).","journal-title":"Sci. Rep."},{"key":"41358_CR6","doi-asserted-by":"publisher","first-page":"107484","DOI":"10.1016\/j.bspc.2024.107484","volume":"103","author":"S Wong","year":"2025","unstructured":"Wong, S. et al. Channel-annotated deep learning for enhanced interpretability in EEG-based seizure detection. Biomed. Signal. Process. Control. 103, 107484 (2025).","journal-title":"Biomed. Signal. Process. Control"},{"key":"41358_CR7","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V. & Gulin, A. CatBoost: unbiased boosting with categorical features. (2017)."},{"key":"41358_CR8","first-page":"636","volume":"349","author":"C Dwork","year":"2015","unstructured":"Dwork, C. et al. The reusable holdout: Preserving validity in adaptive data analysis. Sci. (1979). 349, 636\u2013638 (2015).","journal-title":"Sci. (1979)"},{"key":"41358_CR9","unstructured":"Lundberg, S. & Lee, S. I. A Unified Approach to Interpreting Model Predictions. (2017)."},{"key":"41358_CR10","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1001\/jama.2023.26930","volume":"331","author":"NH Shah","year":"2024","unstructured":"Shah, N. H. et al. A Nationwide Network of Health AI Assurance Laboratories. JAMA 331, 245 (2024).","journal-title":"JAMA"},{"key":"41358_CR11","doi-asserted-by":"publisher","first-page":"2370","DOI":"10.3390\/biomedicines11092370","volume":"11","author":"Y Statsenko","year":"2023","unstructured":"Statsenko, Y. et al. Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach. Biomedicines 11, 2370 (2023).","journal-title":"Biomedicines"},{"key":"41358_CR12","doi-asserted-by":"publisher","first-page":"306","DOI":"10.3390\/brainsci14040306","volume":"14","author":"SE S\u00e1nchez-Hern\u00e1ndez","year":"2024","unstructured":"S\u00e1nchez-Hern\u00e1ndez, S. E., Torres-Ramos, S. & Rom\u00e1n-God\u00ednez, I. Salido-Ruiz, R. A. Evaluation of the Relation between Ictal EEG Features and XAI Explanations. Brain Sci. 14, 306 (2024).","journal-title":"Brain Sci."},{"key":"41358_CR13","doi-asserted-by":"publisher","first-page":"10755","DOI":"10.1038\/s41598-025-88270-3","volume":"15","author":"M Liu","year":"2025","unstructured":"Liu, M. et al. Combining meta and ensemble learning to classify EEG for seizure detection. Sci. Rep. 15, 10755 (2025).","journal-title":"Sci. Rep."},{"key":"41358_CR14","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1111\/epi.18113","volume":"66","author":"J Dan","year":"2025","unstructured":"Dan, J. et al. <scp>SzCORE<\/scp>: Seizure Community Open-Source Research Evaluation framework for the validation of <\u2009scp>electroencephalography \u2010based automated seizure detection algorithms. Epilepsia 66, 14\u201324 (2025).","journal-title":"Epilepsia"},{"key":"41358_CR15","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1111\/epi.16812","volume":"62","author":"J Koren","year":"2021","unstructured":"Koren, J., Hafner, S., Feigl, M. & Baumgartner, C. Systematic analysis and comparison of commercial seizure-detection software. Epilepsia 62, 426\u2013438 (2021).","journal-title":"Epilepsia"},{"key":"41358_CR16","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.clinph.2025.04.003","volume":"174","author":"J Koren","year":"2025","unstructured":"Koren, J. et al. Systematic comparison of Commercial seizure detection Software: Update equals Upgrade? Clin. Neurophysiol. 174, 178\u2013188 (2025).","journal-title":"Clin. Neurophysiol."},{"key":"41358_CR17","doi-asserted-by":"crossref","unstructured":"Ganguly, T. M. et al. Seizure Detection in Continuous Inpatient EEG. Neurology 98, e2224\u2013e2232 (2022).","DOI":"10.1212\/WNL.0000000000200267"},{"key":"41358_CR18","doi-asserted-by":"publisher","unstructured":"Zabihi, M., Chaman Zar, A., Grover, P. & Rosenthal, S. E. HyperEnsemble Learning from Multimodal Biosignals to Robustly Predict Functional Outcome after Cardiac Arrest. in (2023). https:\/\/doi.org\/10.22489\/CinC.2023.142","DOI":"10.22489\/CinC.2023.142"},{"key":"41358_CR19","doi-asserted-by":"publisher","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T. & Koyama, M. Optuna. in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2623\u20132631 (ACM, New York, NY, USA, 2019). https:\/\/doi.org\/10.1145\/3292500.3330701","DOI":"10.1145\/3292500.3330701"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-41358-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-41358-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-41358-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T21:01:46Z","timestamp":1775250106000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-41358-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,27]]},"references-count":19,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["41358"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-41358-w","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,27]]},"assertion":[{"value":"4 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"11283"}}