{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T08:01:23Z","timestamp":1764403283305,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031434266"},{"type":"electronic","value":"9783031434273"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-43427-3_33","type":"book-chapter","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T21:01:41Z","timestamp":1694898101000},"page":"547-563","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Knowledge Distillation with\u00a0Graph Neural Networks for\u00a0Epileptic Seizure Detection"],"prefix":"10.1007","author":[{"given":"Qinyue","family":"Zheng","sequence":"first","affiliation":[]},{"given":"Arun","family":"Venkitaraman","sequence":"additional","affiliation":[]},{"given":"Simona","family":"Petravic","sequence":"additional","affiliation":[]},{"given":"Pascal","family":"Frossard","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Ahmedt-Aristizabal, D., Fernando, T., Denman, S., Petersson, L., Aburn, M.J., Fookes, C.: Neural memory networks for seizure type classification. In: 2020 IEEE Engineering in Medicine & Biology Society (EMBC), pp. 569\u2013575. IEEE (2020)","key":"33_CR1","DOI":"10.1109\/EMBC44109.2020.9175641"},{"key":"33_CR2","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/978-3-030-66843-3_8","volume-title":"Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology","author":"U Asif","year":"2020","unstructured":"Asif, U., Roy, S., Tang, J., Harrer, S.: Seizurenet: multi-spectral deep feature learning for seizure type classification. In: Kia, S.M., et al. (eds.) Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology, pp. 77\u201387. Springer International Publishing, Cham (2020)"},{"issue":"5","key":"33_CR3","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1016\/0013-4694(82)90046-3","volume":"54","author":"JL Blom","year":"1982","unstructured":"Blom, J.L., Anneveldt, M.: An electrode cap tested. Electroencephalogr. Clin. Neurophysiol. 54(5), 591\u20134 (1982)","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"unstructured":"Chen, Y., Bian, Y., Xiao, X., Rong, Y., Xu, T., Huang, J.: On self-distilling graph neural network. CoRR abs\/2011.02255 (2020), https:\/\/arxiv.org\/abs\/2011.02255","key":"33_CR4"},{"unstructured":"Covert, I.C., et al.: Temporal graph convolutional networks for automatic seizure detection. In: Machine Learning for Healthcare Conference, pp. 160\u2013180. PMLR (2019)","key":"33_CR5"},{"doi-asserted-by":"publisher","unstructured":"Deng, X., Zhang, Z.: Graph-free knowledge distillation for graph neural networks. In: Zhou, Z. (ed.) Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event \/ Montreal, Canada, 19\u201327 August 2021, pp. 2321\u20132327. ijcai.org (2021). https:\/\/doi.org\/10.24963\/ijcai.2021\/320","key":"33_CR6","DOI":"10.24963\/ijcai.2021\/320"},{"doi-asserted-by":"publisher","unstructured":"Feng, K., Li, C., Yuan, Y., Wang, G.: Freekd: Free-direction knowledge distillation for graph neural networks. In: Zhang, A., Rangwala, H. (eds.) KDD \u201922: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14\u201318, 2022, pp. 357\u2013366. ACM (2022). https:\/\/doi.org\/10.1145\/3534678.3539320","key":"33_CR7","DOI":"10.1145\/3534678.3539320"},{"doi-asserted-by":"publisher","unstructured":"F\u00fcrbass, F., et al.: Automatic multimodal detection for long-term seizure documentation in epilepsy. Clin. Neurophysiol.128(8), 1466\u20131472 (2017). https:\/\/doi.org\/10.1016\/j.clinph.2017.05.013, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1388245717301980","key":"33_CR8","DOI":"10.1016\/j.clinph.2017.05.013"},{"doi-asserted-by":"publisher","unstructured":"Gabeff, V., et al.: Interpreting deep learning models for epileptic seizure detection on EEG signals. Artif. Intell. Medicine 117, 102084 (2021). https:\/\/doi.org\/10.1016\/j.artmed.2021.102084, https:\/\/doi.org\/10.1016\/j.artmed.2021.102084","key":"33_CR9","DOI":"10.1016\/j.artmed.2021.102084 10.1016\/j.artmed.2021.102084"},{"key":"33_CR10","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","volume":"129","author":"J Gou","year":"2021","unstructured":"Gou, J., Yu, B., Maybank, S.J., Tao, D.: Knowledge distillation: a survey. Int. J. Comput. Vision 129, 1789\u20131819 (2021)","journal-title":"Int. J. Comput. Vision"},{"unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)","key":"33_CR11"},{"issue":"9","key":"33_CR12","doi-asserted-by":"publisher","first-page":"1919","DOI":"10.1007\/s11517-020-02208-7","volume":"58","author":"T Ie\u0161mantas","year":"2020","unstructured":"Ie\u0161mantas, T., Alzbutas, R.: Convolutional neural network for detection and classification of seizures in clinical data. Med. Biol. Eng. Comput. 58(9), 1919\u20131932 (2020)","journal-title":"Med. Biol. Eng. Comput."},{"unstructured":"Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144 (2016)","key":"33_CR13"},{"key":"33_CR14","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/0013-4694(58)90053-1","volume":"10","author":"HH Jasper","year":"1958","unstructured":"Jasper, H.H.: The ten-twenty electrode system of the international federation. Electroencephalogr. Clin. Neurophysiol. 10, 370\u2013375 (1958)","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"unstructured":"Joshi, C.K., Liu, F., Xun, X., Lin, J., Foo, C.: On representation knowledge distillation for graph neural networks. CoRR abs\/2111.04964 (2021), https:\/\/arxiv.org\/abs\/2111.04964","key":"33_CR15"},{"unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)","key":"33_CR16"},{"issue":"1","key":"33_CR17","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/S1474-4422(09)70304-7","volume":"9","author":"P Kwan","year":"2010","unstructured":"Kwan, P., Brodie, M.J.: Definition of refractory epilepsy: defining the indefinable? Lancet Neurol. 9(1), 27\u201329 (2010). https:\/\/doi.org\/10.1016\/S1474-4422(09)70304-7","journal-title":"Lancet Neurol."},{"unstructured":"Maddison, C.J., Mnih, A., Teh, Y.W.: The concrete distribution: A continuous relaxation of discrete random variables. arXiv preprint arXiv:1611.00712 (2016)","key":"33_CR18"},{"doi-asserted-by":"publisher","unstructured":"Maganti, R.K., Rutecki, P.: EEG and Epilepsy Monitoring. Continuum (Minneapolis, Minn.) 19(3), 598\u2013622 (2013). https:\/\/doi.org\/10.1212\/01.CON.0000431378.51935.d8","key":"33_CR19","DOI":"10.1212\/01.CON.0000431378.51935.d8"},{"doi-asserted-by":"publisher","unstructured":"Obeid, I., Picone, J.: The Temple University Hospital EEG Data Corpus. Front. Neurosci. 10 (2016). https:\/\/doi.org\/10.3389\/fnins.2016.00196","key":"33_CR20","DOI":"10.3389\/fnins.2016.00196"},{"doi-asserted-by":"crossref","unstructured":"Raghu, S., Sriraam, N., Temel, Y.e.a.: EEG based multi-class seizure type classification using convolutional neural network and transfer learning. Neural Netw. 124, 202\u2013212 (2020)","key":"33_CR21","DOI":"10.1016\/j.neunet.2020.01.017"},{"doi-asserted-by":"publisher","unstructured":"Rahmani, A., Venkitaraman, A., Frossard, P.: A meta-gnn approach to personalized seizure detection and classification. CoRR abs\/2211.02642 (2022). https:\/\/doi.org\/10.48550\/arXiv.2211.02642","key":"33_CR22","DOI":"10.48550\/arXiv.2211.02642"},{"doi-asserted-by":"crossref","unstructured":"Roy, S., Asif, U., Tang, J., Harrer, S.: Seizure type classification using eeg signals and machine learning: Setting a benchmark. In: 2020 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), pp. 1\u20136. IEEE (2020)","key":"33_CR23","DOI":"10.1109\/SPMB50085.2020.9353642"},{"issue":"1","key":"33_CR24","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61\u201380 (2008)","journal-title":"IEEE Trans. Neural Netw."},{"doi-asserted-by":"publisher","unstructured":"Schiratti, J.B., Le Douget, J.E., Le Van Quyen, M., Essid, S., Gramfort, A.: An ensemble learning approach to detect epileptic seizures from long intracranial EEG recordings. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 856\u2013860 (2018). https:\/\/doi.org\/10.1109\/ICASSP.2018.8461489","key":"33_CR25","DOI":"10.1109\/ICASSP.2018.8461489"},{"unstructured":"Shafer, M.P.O.: What Is Epilepsy? (2014). https:\/\/www.epilepsy.com\/learn\/about-epilepsy-basics","key":"33_CR26"},{"issue":"1","key":"33_CR27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40708-020-0102-9","volume":"7","author":"MK Siddiqui","year":"2020","unstructured":"Siddiqui, M.K., Morales-Menendez, R., Huang, X., Hussain, N.: A review of epileptic seizure detection using machine learning classifiers. Brain Inform. 7(1), 1\u201318 (2020)","journal-title":"Brain Inform."},{"doi-asserted-by":"publisher","unstructured":"Sopic, D., Aminifar, A., Atienza, D.: e-Glass: a wearable system for real-time detection of epileptic seizures. In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1\u20135 (2018). https:\/\/doi.org\/10.1109\/ISCAS.2018.8351728","key":"33_CR28","DOI":"10.1109\/ISCAS.2018.8351728"},{"doi-asserted-by":"crossref","unstructured":"Strypsteen, T., Bertrand, A.: End-to-end learnable eeg channel selection for deep neural networks with gumbel-softmax. J. Neural Eng. 18(4), 0460a9 (2021)","key":"33_CR29","DOI":"10.1088\/1741-2552\/ac115d"},{"unstructured":"Tang, S., et al.: Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis. In: Proceedings on the International Conference on Learning Representations (2022)","key":"33_CR30"},{"doi-asserted-by":"crossref","unstructured":"Yang, Y., Qiu, J., Song, M., Tao, D., Wang, X.: Distilling Knowledge from Graph Convolutional Networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7074\u20137083 (2020)","key":"33_CR31","DOI":"10.1109\/CVPR42600.2020.00710"},{"doi-asserted-by":"publisher","unstructured":"Zhang, C., Liu, J., Dang, K., Zhang, W.: Multi-Scale Distillation from Multiple Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence 36(4), 4337\u20134344 (2022). https:\/\/doi.org\/10.1609\/aaai.v36i4.20354","key":"33_CR32","DOI":"10.1609\/aaai.v36i4.20354"},{"doi-asserted-by":"crossref","unstructured":"Zhou, S., et al.: Distilling Holistic Knowledge with Graph Neural Networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10387\u201310396 (2021)","key":"33_CR33","DOI":"10.1109\/ICCV48922.2021.01022"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43427-3_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T21:05:49Z","timestamp":1694898349000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43427-3_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434266","9783031434273"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43427-3_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"We hereby draw the attention of the reviewers that in our experimental work we have made use of only the publicly available dataset: Temple University Hospital EEG Seizure Data Corpus (TUSZ). This dataset to the best of our knowledge has been anonymized and great care has been taken by the providers of the dataset during the acquisition, processing, and reporting of the dataset.Additionally, to the best of our knowledge, we do not envision any ethical issues stemming from the use of our work by any third party. We are aware that in general personalized models could potentially have information that could be deemed as sensitive. But we do not directly foresee this being an issue with our model given that we make use of no personal information or identity of the patients\/data in our work.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical statement"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"829","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"196","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.63","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}