{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T21:26:50Z","timestamp":1773869210994,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:00:00Z","timestamp":1689552000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:00:00Z","timestamp":1689552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100020771","name":"Natural Science Foundation for Young Scientists of Shanxi Province","doi-asserted-by":"publisher","award":["1000,000"],"award-info":[{"award-number":["1000,000"]}],"id":[{"id":"10.13039\/501100020771","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-16280-2","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T09:01:48Z","timestamp":1689584508000},"page":"17941-17960","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An EEG abnormality detection algorithm based on graphic attention network"],"prefix":"10.1007","volume":"83","author":[{"given":"Junwei","family":"Duan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ningyuan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ningdi","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyu","family":"Guan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,17]]},"reference":[{"issue":"04","key":"16280_CR1","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1142\/S0219519409003152","volume":"9","author":"UR Acharya","year":"2009","unstructured":"Acharya UR, Chua CK, Lim TC et al (2009) Automatic identification of epileptic EEG signals using nonlinear parameters[J]. J Mech Med Biol 9(04):539\u2013553","journal-title":"J Mech Med Biol"},{"key":"16280_CR2","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.bspc.2017.07.022","volume":"39","author":"E Alickovic","year":"2018","unstructured":"Alickovic E, Kevric J, Subasi A (2018) Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction[J]. Biomed Signal Process Control 39:94\u2013102","journal-title":"Biomed Signal Process Control"},{"key":"16280_CR3","doi-asserted-by":"publisher","unstructured":"Bengio Y, Simard P, Frasconi P (1994)\u00a0Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 2002:5. https:\/\/doi.org\/10.1109\/72.279181","DOI":"10.1109\/72.279181"},{"key":"16280_CR4","doi-asserted-by":"crossref","unstructured":"Bhattacharya A, Baweja T, Karri S (2021) Epileptic seizure prediction using deep transformer model.[J]. Int J Neural Syst, 2150058","DOI":"10.1142\/S0129065721500581"},{"key":"16280_CR5","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.future.2020.03.019","volume":"109","author":"X Chen","year":"2020","unstructured":"Chen X, He J, Wu X et al (2020) Sleep staging by bidirectional long short-term memory convolution neural network[J]. Futur Gener Comput Syst 109:188\u2013196","journal-title":"Futur Gener Comput Syst"},{"issue":"3","key":"16280_CR6","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1007\/s11571-020-09626-1","volume":"15","author":"Z Gao","year":"2021","unstructured":"Gao Z, Dang W, Wang X et al (2021) Complex networks and deep learning for EEG signal analysis[J]. Cogn Neurodyn 15(3):369\u2013388","journal-title":"Cogn Neurodyn"},{"key":"16280_CR7","unstructured":"Gong YZ (2020) Analysis of quality of life and its influencing factors in epilepsy patients [D]. Lanzhou University"},{"key":"16280_CR8","unstructured":"Hui X (2020) Research on the correlation and complexity of nonlinear time series [D] Beijing Jiaotong University"},{"key":"16280_CR9","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1016\/j.patcog.2017.12.002","volume":"76","author":"Z Jiao","year":"2018","unstructured":"Jiao Z, Gao X, Wang Y et al (2018) Deep convolutional neural networks for mental load classification based on EEG data[J]. Pattern Recogn 76:582\u2013595","journal-title":"Pattern Recogn"},{"issue":"1","key":"16280_CR10","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.cmpb.2003.09.003","volume":"75","author":"I Kalatzis","year":"2004","unstructured":"Kalatzis I, Piliouras N, Ventouras E, Papageorgiou CC, Rabavilas AD, Cavouras D (2004) Design and implementation of an SVM-based computer classification system for discriminating depressive patients from healthy controls using the p600 component of ERP signals[J]. Comput Methods Prog Biomed 75(1):11\u201322","journal-title":"Comput Methods Prog Biomed"},{"issue":"1","key":"16280_CR11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41572-020-00234-1","volume":"7","author":"DS Knopman","year":"2021","unstructured":"Knopman DS, Amieva H, Petersen RC et al (2021) Alzheimer disease[J]. Nat Rev Dis Prim 7(1):1\u201321","journal-title":"Nat Rev Dis Prim"},{"key":"16280_CR12","doi-asserted-by":"publisher","first-page":"2525","DOI":"10.1016\/j.proeng.2012.06.298","volume":"38","author":"JS Kumar","year":"2012","unstructured":"Kumar JS, Bhuvaneswari P (2012) Analysis of electroencephalography (EEG) signals and its categorization a study[J]. Procedia Eng 38:2525\u20132536","journal-title":"Procedia Eng"},{"issue":"1","key":"16280_CR13","doi-asserted-by":"publisher","first-page":"183","DOI":"10.3390\/s20010183","volume":"20","author":"S Kwon","year":"2019","unstructured":"Kwon S (2019) A CNN-assisted enhanced audio signal processing for speech emotion recognition[J]. Sensors 20(1):183","journal-title":"Sensors"},{"issue":"2","key":"16280_CR14","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.clinph.2012.07.007","volume":"124","author":"SS Lodder","year":"2013","unstructured":"Lodder SS, van Putten MJAM (2013) Quantification of the adult EEG background pattern[J]. Clin Neurophysiol 124(2):228\u2013237","journal-title":"Clin Neurophysiol"},{"key":"16280_CR15","unstructured":"Lun X, Jia S, Hou Y, et al. (2020) GCNs-net: a graph convolutional neural network approach for decoding time-resolved eeg motor imagery signals[J]. arXiv preprint arXiv:2006.08924"},{"key":"16280_CR16","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.neucom.2020.06.009","volume":"410","author":"E Maiorana","year":"2020","unstructured":"Maiorana E (2020) Deep learning for EEG-based biometric recognition[J]. Neurocomputing 410:374\u2013386","journal-title":"Neurocomputing"},{"issue":"1","key":"16280_CR17","doi-asserted-by":"publisher","first-page":"24","DOI":"10.3390\/ijgi9010024","volume":"9","author":"A Milosavljevi\u0107","year":"2020","unstructured":"Milosavljevi\u0107 A (2020) Identification of salt deposits on seismic images using deep learning method for semantic segmentation[J]. ISPRS Int J Geo Inf 9(1):24","journal-title":"ISPRS Int J Geo Inf"},{"key":"16280_CR18","unstructured":"MuMing P (2019) Three development directions of brain science research [J]. Bull Chin Acad Sci 34(07)"},{"key":"16280_CR19","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/978-3-030-31760-7_9","volume-title":"Deep learning for person re-identification in surveillance videos[M]\/\/deep learning: algorithms and applications","author":"SJ Narayanan","year":"2020","unstructured":"Narayanan SJ, Perumal B, Saman S et al (2020) Deep learning for person re-identification in surveillance videos[M]\/\/deep learning: algorithms and applications. Springer, Cham, pp 263\u2013297"},{"issue":"1","key":"16280_CR20","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1049\/csy2.12009","volume":"3","author":"E Nsugbe","year":"2021","unstructured":"Nsugbe E, Samuel OW, Asogbon MG et al (2021) Phantom motion intent decoding for transhumeral prosthesis control with fused neuromuscular and brain wave signals[J]. IET Cyber-Syst Robot 3(1):77\u201388","journal-title":"IET Cyber-Syst Robot"},{"key":"16280_CR21","doi-asserted-by":"publisher","unstructured":"Raghu S, Sriraam N, Temel Y et al (2020) EEG based multi-class seizure type classification using convolutional neural network and transfer learning[J]. Neural Netw 124:202\u2013212. https:\/\/doi.org\/10.1016\/j.neunet.2020.01.017","DOI":"10.1016\/j.neunet.2020.01.017"},{"key":"16280_CR22","volume-title":"EEG signal processing[M]","author":"S Sanei","year":"2013","unstructured":"Sanei S (2013) EEG signal processing[M]. Springer, Netherlands"},{"key":"16280_CR23","unstructured":"Ali Shoeb JG (2010) Application of machine learning to epileptic seizure detection[C]. International Conference on Machine Learning, DBLP"},{"key":"16280_CR24","doi-asserted-by":"publisher","first-page":"113788","DOI":"10.1016\/j.eswa.2020.113788","volume":"163","author":"A Shoeibi","year":"2021","unstructured":"Shoeibi A, Ghassemi N, Alizadehsani R et al (2021) A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals[J]. Expert Syst Appl 163:113788","journal-title":"Expert Syst Appl"},{"issue":"1","key":"16280_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40708-021-00123-7","volume":"8","author":"AAE Shoka","year":"2021","unstructured":"Shoka AAE, Alkinani MH, El-Sherbeny AS et al (2021) Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals[J]. Brain Inf 8(1):1\u201316","journal-title":"Brain Inf"},{"key":"16280_CR26","doi-asserted-by":"publisher","unstructured":"Singh K , Malhotra J (2022) Two-layer LSTM network-based prediction of epileptic seizures using EEG spectral features[J].Complex & Intelligent Systems 8(3):2405\u20132418. https:\/\/doi.org\/10.1007\/s40747-021-00627-z","DOI":"10.1007\/s40747-021-00627-z"},{"key":"16280_CR27","doi-asserted-by":"crossref","unstructured":"Spivak D I (2022) Polynomial functors and Shannon entropy[J]. arXiv preprint arXiv:2201.12878","DOI":"10.4204\/EPTCS.380.19"},{"key":"16280_CR28","doi-asserted-by":"publisher","first-page":"107390","DOI":"10.1016\/j.patcog.2020.107390","volume":"105","author":"M Xu","year":"2020","unstructured":"Xu M, Yao J, Zhang Z et al (2020) Learning EEG topographical representation for classification via convolutional neural network[J]. Pattern Recogn 105:107390","journal-title":"Pattern Recogn"},{"key":"16280_CR29","doi-asserted-by":"publisher","unstructured":"Yao X, Cheng Q, Zhang GQ (2019) Automated classification of seizures against Nonseizures: A Deep Learning Approach[J]. https:\/\/doi.org\/10.48550\/arXiv.1906.02745","DOI":"10.48550\/arXiv.1906.02745"},{"issue":"7","key":"16280_CR30","doi-asserted-by":"publisher","first-page":"3033","DOI":"10.1109\/TCYB.2019.2905157","volume":"50","author":"D Zhang","year":"2019","unstructured":"Zhang D, Yao L, Chen K et al (2019) Making sense of spatio-temporal preserving representations for EEG-based human intention recognition[J]. IEEE Trans Cybern 50(7):3033\u20133044","journal-title":"IEEE Trans Cybern"},{"key":"16280_CR31","doi-asserted-by":"publisher","first-page":"103338","DOI":"10.1016\/j.bspc.2021.103338","volume":"72","author":"X Zhao","year":"2022","unstructured":"Zhao X, Liu D, Ma L et al (2022) Deep CNN model based on serial-parallel structure optimization for four-class motor imagery EEG classification[J]. Biomed Signal Process Control 72:103338","journal-title":"Biomed Signal Process Control"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16280-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16280-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16280-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T08:45:46Z","timestamp":1706690746000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16280-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,17]]},"references-count":31,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["16280"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16280-2","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,17]]},"assertion":[{"value":"24 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 May 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2023","order":4,"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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}}]}}