{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T10:41:38Z","timestamp":1783766498489,"version":"3.55.0"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031720857","type":"print"},{"value":"9783031720864","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72086-4_2","type":"book-chapter","created":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T20:34:45Z","timestamp":1727987685000},"page":"14-24","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Domain Adaption Approach for\u00a0EEG-Based Automated Seizure Classification with Temporal-Spatial-Spectral Attention"],"prefix":"10.1007","author":[{"given":"Xiaoya","family":"Fan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengzhi","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenru","family":"Hao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,4]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","unstructured":"Albaqami, H., Hassan, G.M., Datta, A.: Wavelet-based multi-class seizure type classification system. Appl. Sci. 12(11) (2022). https:\/\/doi.org\/10.3390\/app12115702","DOI":"10.3390\/app12115702"},{"key":"2_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104780","volume":"84","author":"H Albaqami","year":"2023","unstructured":"Albaqami, H., Hassan, G.M., Datta, A.: Mp-seiznet: A multi-path cnn bi-lstm network for seizure-type classification using eeg. Biomedical Signal Processing and Control 84, 104780 (2023)","journal-title":"Biomedical Signal Processing and Control"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Ali, H., Karim, F., Qureshi, J.J., Abuassba, A.O., Bulbul, M.F.: Seizure prediction using bidirectional lstm. In: Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health: International 2019 Cyberspace Congress, CyberDI and CyberLife, Beijing, China, December 16\u201318, 2019, Proceedings, Part I 3. pp. 349\u2013356. Springer (2019)","DOI":"10.1007\/978-981-15-1922-2_25"},{"key":"2_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121727","volume":"238","author":"M Anita","year":"2024","unstructured":"Anita, M., Kowshalya, A.M.: Automatic epileptic seizure detection using msa-dcnn and lstm techniques with eeg signals. Expert Syst. Appl. 238, 121727 (2024)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"2_CR5","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1038\/s41598-019-56958-y","volume":"10","author":"Kyung-Ok Cho","year":"2020","unstructured":"Cho, Kyung-Ok and Jang, Hyun-Jong: Comparison of different input modalities and network structures for deep learning-based seizure detection. Sci Rep 10(1), \u00a0122 (2020)","journal-title":"Sci Rep"},{"key":"2_CR6","first-page":"1","volume":"72","author":"X Cui","year":"2023","unstructured":"Cui, X., Wang, T., Lai, X., Jiang, T., Gao, F., Cao, J.: Cross-subject seizure detection by joint-probability-discrepancy-based domain adaptation. IEEE Transactions on Instrumentation and Measurement 72, 1\u201313 (2023)","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"issue":"9","key":"2_CR7","doi-asserted-by":"publisher","first-page":"3097","DOI":"10.1111\/ejn.14142","volume":"48","author":"X Fan","year":"2018","unstructured":"Fan, X., Gaspard, N., Legros, B., Lucchetti, F., Ercek, R., Nonclercq, A.: Seizure evolution can be characterized as path through synaptic gain space of a neural mass model. Eur. J. Neurosci. 48(9), 3097\u20133112 (2018). https:\/\/doi.org\/10.1111\/ejn.14142","journal-title":"Eur. J. Neurosci."},{"key":"2_CR8","unstructured":"Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International conference on machine learning. pp. 1180\u20131189. PMLR (2015)"},{"key":"2_CR9","volume-title":"Clep: Contrastive learning for epileptic seizure prediction using a spatio-temporal-spectral network","author":"L Guo","year":"2023","unstructured":"Guo, L., Yu, T., Zhao, S., Li, X., Liao, X., Li, Y.: Clep: Contrastive learning for epileptic seizure prediction using a spatio-temporal-spectral network. IEEE Trans. Neural Syst. Rehabil. Eng. (2023)"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Lee, Y., won Hwang, J., Lee, S., Bae, Y., Park, J.: An energy and GPU-computation efficient backbone network for real-time object detection. 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) pp. 752\u2013760 (2019)","DOI":"10.1109\/CVPRW.2019.00103"},{"key":"2_CR12","first-page":"1","volume":"72","author":"D Liang","year":"2023","unstructured":"Liang, D., Liu, A., Gao, Y., Li, C., Qian, R., Chen, X.: Semi-supervised domain-adaptive seizure prediction via feature alignment and consistency regularization. IEEE Transactions on Instrumentation and Measurement 72, 1\u201312 (2023)","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"2_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103555","volume":"75","author":"P Peng","year":"2022","unstructured":"Peng, P., Xie, L., Zhang, K., Zhang, J., Yang, L., Wei, H.: Domain adaptation for epileptic eeg classification using adversarial learning and riemannian manifold. Biomedical Signal Processing and Control 75, 103555 (2022)","journal-title":"Biomedical Signal Processing and Control"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Pratiwi, N.K.C., Wijayanto, I., Fu\u2019adah, Y.N.: Performance Analysis of an Automated Epilepsy Seizure Detection Using EEG Signals Based on 1D-CNN Approach. In: ICEBEHI. pp. 265\u2013277. Springer (2022)","DOI":"10.1007\/978-981-19-1804-9_21"},{"issue":"17","key":"2_CR15","doi-asserted-by":"publisher","first-page":"19186","DOI":"10.1109\/JSEN.2021.3090062","volume":"21","author":"D Priyasad","year":"2021","unstructured":"Priyasad, D., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Interpretable seizure classification using unprocessed EEG with multi-channel attentive feature fusion. IEEE Sens. J. 21(17), 19186\u201319197 (2021)","journal-title":"IEEE Sens. J."},{"key":"2_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107678","volume":"240","author":"JS Ra","year":"2023","unstructured":"Ra, J.S., Li, T., et\u00a0al.: A novel epileptic seizure prediction method based on synchroextracting transform and 1-dimensional convolutional neural network. Computer Methods and Programs in Biomedicine 240, 107678 (2023)","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"2_CR17","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.neunet.2020.01.017","volume":"124","author":"S Raghu","year":"2020","unstructured":"Raghu, S., Sriraam, N., Temel, Y., Rao, S.V., Kubben, P.L.: EEG based multi-class seizure type classification using convolutional neural network and transfer learning. Neural Netw. 124, 202\u2013212 (2020)","journal-title":"Neural Netw."},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Shah, V., Golmohammadi, M., Ziyabari, S., Von\u00a0Weltin, E., Obeid, I., Picone, J.: Optimizing channel selection for seizure detection. In: 2017 IEEE signal processing in medicine and biology symposium (SPMB). pp.\u00a01\u20135. IEEE (2017)","DOI":"10.1109\/SPMB.2017.8257020"},{"key":"2_CR19","doi-asserted-by":"publisher","first-page":"83","DOI":"10.3389\/fninf.2018.00083","volume":"12","author":"V Shah","year":"2018","unstructured":"Shah, V., Von\u00a0Weltin, E., Lopez, S., McHugh, J.R., Veloso, L., Golmohammadi, M., Obeid, I., Picone, J.: The temple university hospital seizure detection corpus. Frontiers in neuroinformatics 12, \u00a083 (2018)","journal-title":"Frontiers in neuroinformatics"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Shankar, A., Dandapat, S., Barma, S.: Seizure type classification using eeg based on gramian angular field transformation and deep learning. In: EMBC. pp. 3340\u20133343. IEEE (2021)","DOI":"10.1109\/EMBC46164.2021.9629791"},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"Shanmugam, S., Dharmar, S.: A CNN-LSTM hybrid network for automatic seizure detection in EEG signals. Neural Comput. Appl. pp. 1\u201313 (2023)","DOI":"10.1007\/s00521-023-08832-2"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Shekokar, K., Dour, S.: Epileptic seizure detection based on lstm model using noisy eeg signals. In: ICECA. pp. 292\u2013296. IEEE (2021)","DOI":"10.1109\/ICECA52323.2021.9675941"},{"key":"2_CR23","unstructured":"Song, Y., Jia, X., Yang, L., Xie, L.: Transformer-based spatial-temporal feature learning for eeg decoding. arXiv preprint arXiv:2106.11170 (2021)"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Strein, M., Holton-Burke, J.P., Smith, L.R., Brophy, G.M.: Prevention, treatment, and monitoring of seizures in the intensive care unit. Journal of Clinical Medicine 8 (2019)","DOI":"10.3390\/jcm8081177"},{"key":"2_CR25","unstructured":"Tang, S., Dunnmon, J., Saab, K.K., et\u00a0al.: Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis. In: ICLR (2021)"},{"key":"2_CR26","unstructured":"Tang, S., Dunnmon, J.A., Liangqiong, Q., Saab, K.K., Baykaner, T., Lee-Messer, C., Rubin, D.L.: Modeling multivariate biosignals with graph neural networks and structured state space models. In: Conference on Health, Inference, and Learning. pp. 50\u201371. PMLR (2023)"},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Verma, A., Janghel, R.R.: Epileptic seizure detection using deep recurrent neural networks in eeg signals. In: Advances in Biomedical Engineering and Technology: Select Proceedings of ICBEST 2018. pp. 189\u2013198. Springer (2021)","DOI":"10.1007\/978-981-15-6329-4_17"},{"key":"2_CR28","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: Efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 11534\u201311542 (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"2_CR29","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.neucom.2021.06.048","volume":"459","author":"X Wang","year":"2021","unstructured":"Wang, X., Wang, X., Liu, W., Chang, Z., K\u00e4rkk\u00e4inen, T., Cong, F.: One dimensional convolutional neural networks for seizure onset detection using long-term scalp and intracranial eeg. Neurocomput. 459, 212\u2013222 (2021)","journal-title":"Neurocomput."},{"issue":"6","key":"2_CR30","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ad0859","volume":"20","author":"Z Wang","year":"2023","unstructured":"Wang, Z., Zhang, W., Li, S., Chen, X., Wu, D.: Unsupervised domain adaptation for cross-patient seizure classification. Journal of Neural Engineering 20(6), 066002 (2023)","journal-title":"Journal of Neural Engineering"},{"key":"2_CR31","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer vision - ECCV 2018","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: CBAM: Convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer vision - ECCV 2018. pp. 3\u201319. Springer International Publishing, Cham (2018)"},{"key":"2_CR32","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1109\/TNSRE.2022.3156931","volume":"30","author":"Xucun Yan","year":"2022","unstructured":"Yan, Xucun and Yang, Dongping and Lin, Zihuai and Vucetic, Branka: Significant low-dimensional spectral-temporal features for seizure detection. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 668\u2013677 (2022)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"2_CR33","doi-asserted-by":"crossref","unstructured":"Zhao, X., Sol\u00e9-Casals, J., Li, B., et\u00a0al.: Classification of epileptic IEEG signals by CNN and data augmentation. In: ICASSP. pp. 926\u2013930. IEEE (2020)","DOI":"10.1109\/ICASSP40776.2020.9052948"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72086-4_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T20:35:10Z","timestamp":1727987710000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72086-4_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031720857","9783031720864"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72086-4_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"4 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}