{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T11:39:09Z","timestamp":1777635549664,"version":"3.51.4"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100007446","name":"King Khalid University","doi-asserted-by":"publisher","award":["RGP2\/166\/44"],"award-info":[{"award-number":["RGP2\/166\/44"]}],"id":[{"id":"10.13039\/501100007446","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s11227-024-06558-z","type":"journal-article","created":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T10:01:40Z","timestamp":1728554500000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Automated identification and localization of interictal epileptiform discharges: leveraging morphological analysis, five-criterion fulfillment, and machine learning approach"],"prefix":"10.1007","volume":"81","author":[{"given":"Omar","family":"Trigui","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sawsan","family":"Daoud","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed","family":"Ghorbel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mariem","family":"Dammak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chokri","family":"Mhiri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"Ben Hamida","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,10]]},"reference":[{"key":"6558_CR1","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1097\/00004691-200302000-00005","volume":"20","author":"SR Benbadis","year":"2003","unstructured":"Benbadis SR, Tatum WO (2003) Overintepretation of EEGs and misdiagnosis of epilepsy. J Clin Neurophysiol 20:42\u201344","journal-title":"J Clin Neurophysiol"},{"key":"6558_CR2","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.yebeh.2007.05.013","volume":"11","author":"SR Benbadis","year":"2007","unstructured":"Benbadis SR (2007) Errors in EEGs and the misdiagnosis of epilepsy: importance, causes, consequences, and proposed remedies. Epilepsy Behav 11:257\u2013262","journal-title":"Epilepsy Behav"},{"key":"6558_CR3","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1159\/000115641","volume":"59","author":"SR Benbadis","year":"2008","unstructured":"Benbadis SR, Lin K (2008) Errors in EEG interpretation and misdiagnosis of epilepsy. Which EEG patterns are overread? Eur Neurol 59:267\u201371","journal-title":"Eur Neurol"},{"issue":"6","key":"6558_CR4","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1016\/j.clinph.2013.10.021","volume":"125","author":"N Gaspard","year":"2014","unstructured":"Gaspard N, Alkawadri R, Farooque P, Goncharova II, Zaveri HP (2014) Automatic detection of prominent interictal spikes in intracranial EEG: Validation of an algorithm and relationsip to the seizure onset zone. Clin Neurophysiol 125(6):1095\u20131103","journal-title":"Clin Neurophysiol"},{"key":"6558_CR5","first-page":"21","volume":"52","author":"S Noachtar","year":"1999","unstructured":"Noachtar S, Binnie C, Ebersole J, Magui\u00e8re F, Sakamoto A, Westmoreland B (1999) A glossary of terms most commonly used by clinical electroencephalographers and proposal for the report for the EEG findings. Electroencephal Clin Neurophysiol Suppl 52:21\u201341","journal-title":"Electroencephal Clin Neurophysiol Suppl"},{"issue":"12","key":"6558_CR6","doi-asserted-by":"publisher","first-page":"1260","DOI":"10.1109\/10.250582","volume":"40","author":"AA Dingle","year":"1993","unstructured":"Dingle AA, Jones RD, Carroll GJ, Fright WR (1993) A multistage system to detect epileptiform activity in the EEG. IEEE Trans Biomed Eng 40(12):1260\u20131268. https:\/\/doi.org\/10.1109\/10.250582","journal-title":"IEEE Trans Biomed Eng"},{"key":"6558_CR7","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/s40708-016-0044-4","volume":"3","author":"EI Zacharaki","year":"2016","unstructured":"Zacharaki EI, Mporas I, Garganis K et al (2016) Spike pattern recognition by supervised classification in low dimensional embedding space. Brain Inf 3:73\u201383. https:\/\/doi.org\/10.1007\/s40708-016-0044-4","journal-title":"Brain Inf"},{"issue":"2","key":"6558_CR8","doi-asserted-by":"publisher","first-page":"P143","DOI":"10.1016\/j.yebeh.2012.11.048","volume":"26","author":"V Chavakula","year":"2013","unstructured":"Chavakula V, Fern\u00e1ndez IS, Peters JM, Popli G, Bosl W, Rakhade S, Rotenberg A, Loddenkemper T (2013) Automated quantification of spikes. Epilepsy Behav 26(2):P143-152","journal-title":"Epilepsy Behav"},{"issue":"4","key":"6558_CR9","doi-asserted-by":"publisher","first-page":"0460b3","DOI":"10.1088\/1741-2552\/ac0d60","volume":"18","author":"B Wei","year":"2021","unstructured":"Wei B, Zhao X, Shi L, Xu L, Liu T, Zhang J (2021) A deep learning framework with multi-perspective fusion for interictal epileptiform discharges detection in scalp electroencephalogram. J Neural Eng 18(4):0460b3","journal-title":"J Neural Eng"},{"key":"6558_CR10","doi-asserted-by":"publisher","unstructured":"Louren\u00e7o C, Tjepkema-Cloostermans MC, Teixeira LF, van Putten MJAM (2020) Deep learning for interictal epileptiform discharge detection from scalp EEG recordings. In: Henriques J., Neves N., de Carvalho P. (eds) XV Mediterranean Conference on Medical and Biological Engineering and Computing \u2013 MEDICON 2019. MEDICON 2019. IFMBE Proceedings, vol 76. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-31635-8_237.","DOI":"10.1007\/978-3-030-31635-8_237"},{"issue":"11","key":"6558_CR11","doi-asserted-by":"publisher","first-page":"2050030","DOI":"10.1142\/S0129065720500306","volume":"30","author":"J Thomas","year":"2020","unstructured":"Thomas J, Jin J, Thangavel P, Bagheri E, Yuvaraj R, Dauwels J, Rathakrishnan R, Halford JJ, Cash SS, Westover B (2020) Automated detection of interictal epileptiform discharges from scalp electroencephalograms by convolutional neural networks. Int J Neural Syst 30(11):2050030","journal-title":"Int J Neural Syst"},{"issue":"7","key":"6558_CR12","doi-asserted-by":"publisher","first-page":"1433","DOI":"10.1016\/j.clinph.2021.02.403","volume":"132","author":"C da Silva Louren\u00e7o","year":"2021","unstructured":"da Silva Louren\u00e7o C, Tjepkema-Cloostermans MC, van Putten MJ (2021) Machine learning for detection of interictal epileptiform discharges. Clin Neurophysiol 132(7):1433\u20131443","journal-title":"Clin Neurophysiol"},{"key":"6558_CR13","doi-asserted-by":"publisher","first-page":"12087","DOI":"10.1038\/s41598-019-48456-y","volume":"9","author":"KJ Laboy-Ju\u00e1rez","year":"2019","unstructured":"Laboy-Ju\u00e1rez KJ, Ahn S, Feldman DE (2019) A normalized template matching method for improving spike detection in extracellular voltage recordings. Sci Rep 9:12087. https:\/\/doi.org\/10.1038\/s41598-019-48456-y","journal-title":"Sci Rep"},{"key":"6558_CR14","doi-asserted-by":"publisher","first-page":"e2139","DOI":"10.1212\/WNL.0000000000009439","volume":"94","author":"MA Kural","year":"2020","unstructured":"Kural MA, Duez L, Hansen VS, Larsson PG, Rampp S, Schulz R, Tankisi H, Wennberg R, Bibby BM, Scherg M, Beniczky S (2020) Criteria for defining interictal epileptiform discharges in EEG A clinical validation study. Neurology 94:e2139\u2013e2147. https:\/\/doi.org\/10.1212\/WNL.0000000000009439","journal-title":"Neurology"},{"key":"6558_CR15","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.cnp.2017.07.002","volume":"2","author":"N Kane","year":"2017","unstructured":"Kane N, Acharya J, Beniczky S, Caboclo L, Finnigan S, Kaplan PW, Shibasaki H, Pressler R, van Putten MJAM (2017) A revised glossary of terms most commonly used by clinical electroencephalographers and updated proposal for the report format of the EEG findings. Revision 2017. Clin Neurophysiol Pract 2:170\u201385. https:\/\/doi.org\/10.1016\/j.cnp.2017.07.002","journal-title":"Clin Neurophysiol Pract"},{"issue":"9","key":"6558_CR16","doi-asserted-by":"publisher","first-page":"2250","DOI":"10.1016\/j.clinph.2020.06.026","volume":"131","author":"MA Kural","year":"2020","unstructured":"Kural MA, Tankisi H, Duez L, Hansen VS, Udupi A, Wennberg R, Rampp S, Larsson PG, Schulz R, Beniczky S (2020) Optimized set of criteria for defining interictal epileptiform EEG discharges. Clin Neurophysiol 131(9):2250\u20132254. https:\/\/doi.org\/10.1016\/j.clinph.2020.06.026","journal-title":"Clin Neurophysiol"},{"key":"6558_CR17","doi-asserted-by":"publisher","first-page":"941","DOI":"10.3389\/fneur.2020.00941","volume":"11","author":"Y Jabran","year":"2020","unstructured":"Jabran Y, Mahmoudzadeh M, Martinez N, Heberl\u00e9 C, Wallois F, Bourel-Ponchel E (2020) Temporal and spatial dynamics of different interictal epileptic discharges: a time-frequency EEG approach in pediatric focal refractory epilepsy. Front Neurol 11:941","journal-title":"Front Neurol"},{"key":"6558_CR18","doi-asserted-by":"crossref","unstructured":"Kane N, Acharya J, Beniczky S, Caboclo L, Finnigan S, Kaplan PW, Shibasaki H, Pressler R, Putten van MJAM (2017) A revised glossary of terms most commonly used by clinical electroencephalographers and updated proposal for the report format of the EEG findings. Revision 2017. Clinical Neurophysiology Practice 2 170\u2013185.","DOI":"10.1016\/j.cnp.2017.07.002"},{"issue":"2","key":"6558_CR19","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1097\/WNP.0b013e3181d64b1e","volume":"27","author":"MF Selvitelli","year":"2010","unstructured":"Selvitelli MF, Walker LM, Schomer DL, Chang BS (2010) The relationship of interictal epileptiform discharges to clinical epilepsy severity: a study of routine EEGs and review of the literature. J Clin Neurophysiol 27(2):87\u201392","journal-title":"J Clin Neurophysiol"},{"issue":"4","key":"6558_CR20","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1684\/j.1950-6945.2005.tb00139.x","volume":"7","author":"J Janszky","year":"2005","unstructured":"Janszky J, Hoppe M, Clemens Z, Janszky I, Gyimesi C, Schulz R, Ebner A (2005) Spike frequency is dependent on epilepsy duration and seizure frequency in temporal lobe epilepsy. Epileptic Disord 7(4):355\u20139","journal-title":"Epileptic Disord"},{"key":"6558_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.yebeh.2020.107730","volume":"116","author":"M Asadollahi","year":"2021","unstructured":"Asadollahi M, Noorbakhsh M, Salehifar V, Simani L (2021) The significance of interictal spike frequency in temporal lobe epilepsy. Epilepsy Behav 116:107730","journal-title":"Epilepsy Behav"},{"key":"6558_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102653","volume":"68","author":"A Bisht","year":"2021","unstructured":"Bisht A, Singh P (2021) Detection of muscle artifact epochs using entropy based M-DDTW technique in EEG signals. Biomed Signal Process Control 68:102653","journal-title":"Biomed Signal Process Control"},{"key":"6558_CR23","doi-asserted-by":"publisher","first-page":"33911","DOI":"10.1109\/ACCESS.2021.3061692","volume":"9","author":"ST Aung","year":"2021","unstructured":"Aung ST, Wongsawat Y (2021) Analysis of EEG signals contaminated with motion artifacts using multiscale modified-distribution entropy. IEEE Access 9:33911\u201333921","journal-title":"IEEE Access"},{"key":"6558_CR24","doi-asserted-by":"publisher","DOI":"10.3389\/fmolb.2023.1146606","author":"Ling Zhang","year":"2023","unstructured":"Zhang Ling, Wang Xiaolu, Jiang Jun, Xiao Naian, Guo Jiayang, Zhuang Kailong, Li Ling, Houqiang Yu, Tong Wu, Zheng Ming, Chen Duo (2023) Automatic interictal epileptiform discharge (IED) detection based on convolutional neural network (CNN). Front Mol Biosci. https:\/\/doi.org\/10.3389\/fmolb.2023.1146606","journal-title":"Front Mol Biosci"},{"key":"6558_CR25","first-page":"962","volume":"17","author":"Y Hao","year":"2018","unstructured":"Hao Y, Khoo HM, von Ellenrieder N, Zazubovits N, Gotmana J (2018) DeepIED: an epileptic discharge detector for EEG-fMRI based on deep learning Neuroimage. Clinical 17:962\u2013975","journal-title":"Clinical"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06558-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06558-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06558-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T10:05:24Z","timestamp":1728554724000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06558-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,10]]},"references-count":25,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["6558"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06558-z","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,10]]},"assertion":[{"value":"5 September 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2024","order":2,"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 the following financial interests\/personal relationships which may be considered as potential competing interests: Ahmed Ben Hamida reports that financial support was provided by King Khaled Universiy.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"1"}}