{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:07:08Z","timestamp":1775578028099,"version":"3.50.1"},"reference-count":42,"publisher":"Association for Computing Machinery (ACM)","issue":"3s","license":[{"start":{"date-parts":[[2021,10,31]],"date-time":"2021-10-31T00:00:00Z","timestamp":1635638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62172139 and 62172030"],"award-info":[{"award-number":["62172139 and 62172030"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"crossref","award":["F2020201025, F2019201151, and F2018210148"],"award-info":[{"award-number":["F2020201025, F2019201151, and F2018210148"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science Research Project of Hebei Province","award":["BJ2020030 and QN2017306"],"award-info":[{"award-number":["BJ2020030 and QN2017306"]}]},{"name":"Open Foundation of Guangdong Key Laboratory of Digital Signal and Image Processing Technology","award":["2020GDDSIPL-04"],"award-info":[{"award-number":["2020GDDSIPL-04"]}]},{"name":"High-Performance Computing Center of Hebei University"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2021,10,31]]},"abstract":"<jats:p>\n            As one of the important tools of epilepsy diagnosis, the electroencephalogram (EEG) is noninvasive and presents no traumatic injury to patients. It contains a lot of physiological and pathological information that is easy to obtain. The automatic classification of epileptic EEG is important in the diagnosis and therapeutic efficacy of epileptics. In this article, an explainable graph feature convolutional neural network named\n            <jats:italic>WTRPNet<\/jats:italic>\n            is proposed for epileptic EEG classification. Since WTRPNet is constructed by a recurrence plot in the wavelet domain, it can fully obtain the graph feature of the EEG signal, which is established by an explainable graph features extracted layer called\n            <jats:italic>WTRP block<\/jats:italic>\n            . The proposed method shows superior performance over state-of-the-art methods. Experimental results show that our algorithm has achieved an accuracy of 99.67% in classification of focal and nonfocal epileptic EEG, which proves the effectiveness of the classification and detection of epileptic EEG.\n          <\/jats:p>","DOI":"10.1145\/3460522","type":"journal-article","created":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T10:51:46Z","timestamp":1640861506000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["WTRPNet: An Explainable Graph Feature Convolutional Neural Network for Epileptic EEG Classification"],"prefix":"10.1145","volume":"17","author":[{"given":"Qi","family":"Xin","sequence":"first","affiliation":[{"name":"Beijing Jiaotong University, Haidian Qu, Beijing Shi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaohao","family":"Hu","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, Haidian Qu, Beijing Shi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7520-8226","authenticated-orcid":false,"given":"Shuaiqi","family":"Liu","sequence":"additional","affiliation":[{"name":"Heibei University, Baoding Shi, Hebei Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling","family":"Zhao","sequence":"additional","affiliation":[{"name":"Heibei University, Baoding Shi, Hebei Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuihua","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Leicester, Leicester, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,12,30]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065711002808"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1015075101937"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.86.046206"},{"key":"e_1_3_1_5_2","first-page":"332","volume-title":"Proceedings of the 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS\u201920)","author":"Begum Sav","year":"2020","unstructured":"Sav Begum and M. P. Rani. 2020. Recognition of neurodegenerative diseases with gait patterns using double feature extraction methods. In Proceedings of the 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS\u201920). 332\u2013338. https:\/\/doi.org\/10.1109\/ICICCS48265.2020.9120920"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/app7040385"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2646-4"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/HSI.2017.8005015"},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","unstructured":"Shih Tsung Chen Chia Yi Chou and Li Ho Tseng. 2014. Recurrence plot analysis of HRV for exposure to low-frequency noise. In Frontiers of Energy Materials and Information Engineering . Advanced Materials Research Vol. 1044. Trans Tech Publications Ltd. 1251\u20131257. https:\/\/doi.org\/10.4028\/www.scientific.net\/AMR.1044-1045.1251","DOI":"10.4028\/www.scientific.net\/AMR.1044-1045.1251"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.06.032"},{"issue":"1","key":"e_1_3_1_11_2","first-page":"107624","article-title":"BLOCK-DBSCAN: Fast clustering for large scale data","volume":"109","author":"Chen Yewang","year":"2020","unstructured":"Yewang Chen, Lida Zhou, Nizar Bouguila, Cheng Wang, Yi Chen, and Jixiang Du. 2020. BLOCK-DBSCAN: Fast clustering for large scale data. Pattern Recognition 109, 1 (Jan. 2020), 107624. https:\/\/doi.org\/10.1016\/j.patcog.2020.107624","journal-title":"Pattern Recognition"},{"key":"e_1_3_1_12_2","article-title":"KNN-BLOCK DBSCAN: Fast clustering for large-scale data","author":"Chen Y.","year":"2019","unstructured":"Y. Chen, L. Zhou, S. Pei, Z. Yu, Y. Chen, X. Liu, J. Du, and N. Xiong. 2019. KNN-BLOCK DBSCAN: Fast clustering for large-scale data. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51, 6 (Dec. 2019), 3939\u20133953. https:\/\/doi.org\/10.1109\/TSMC.2019.2956527","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"e_1_3_1_13_2","series-title":"World Scientific Series on Nonlinear Science Series A","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1142\/9789812833709_0030","volume-title":"Recurrence Plots of Dynamical Systems","author":"Eckmann J. P.","year":"1995","unstructured":"J. P. Eckmann, S. O. Kamphorst, and D. Ruelle. 1995. Recurrence Plots of Dynamical Systems. World Scientific Series on Nonlinear Science Series A, Vol. 16. World Scientific, 441\u2013445. https:\/\/doi.org\/10.1142\/9789812833709_0030"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1111\/epi.12550"},{"key":"e_1_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Shu Juan Geng Wei Dong Zhou Qing Mei Yao and Zhen Ma. 2012. Nonlinear analysis of EEG using fractal dimension and approximate entropy. In Materials Science and Information Technology II . Advanced Materials Research Vol. 532. Trans Tech Publications Ltd. 988\u2013992. https:\/\/doi.org\/10.4028\/www.scientific.net\/AMR.532-533.988","DOI":"10.4028\/www.scientific.net\/AMR.532-533.988"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/0013-4694(82)90038-4"},{"key":"e_1_3_1_17_2","first-page":"59","volume-title":"Chaotic behavior of transistor circuits","author":"Hanias M. P.","year":"2012","unstructured":"M. P. Hanias, H. E. Nistazakis, and G. S. Tombras. 2012. Chaotic behavior of transistor circuits. In Applications of Chaos and Nonlinear Dynamics in Science and Engineering, Vol. 2. Springer, Berlin, Germany, 59\u201391. https:\/\/doi.org\/10.1007\/978-3-642-29329-0_4"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2016.09.008"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40708-020-00114-0"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.4015\/S1016237220500295"},{"key":"e_1_3_1_21_2","series-title":"Interdisciplinary Mathematical Sciences","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1142\/5862","volume-title":"EMD and instantaneous phase detection of structural damage","author":"Huang Norden","year":"2005","unstructured":"Norden Huang and Samuel Shen. 2005. EMD and instantaneous phase detection of structural damage. In Hilbert-Huang Transform and Its Applications. Interdisciplinary Mathematical Sciences, Vol. 5. World Scientific, 227\u2013262. https:\/\/doi.org\/10.1142\/9789812703347_0011"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-014-0170-6"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs11060702"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2657602"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/CISP-BMEI48845.2019.8965693"},{"key":"e_1_3_1_26_2","first-page":"290","article-title":"Emotion classification in Parkinson\u2019s disease EEG using RQA and ELM","author":"Murugappan M.","year":"2020","unstructured":"M. Murugappan, Waleed Alshuaib, Ali Bourisly, Sai Sruthi, Wan Khairunizam, Bibin Shalini, and Yean Choong. 2020. Emotion classification in Parkinson\u2019s disease EEG using RQA and ELM. In Proceedings of the 2020 16th IEEE International Colloquium on Signal Processing (CSPA\u201920). 290\u2013295. https:\/\/doi.org\/10.1109\/CSPA48992.2020.9068709","journal-title":"Proceedings of the 2020 16th IEEE International Colloquium on Signal Processing (CSPA\u201920)"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0218127411029008"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physrep.2006.11.001"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.00593"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physleta.2016.02.024"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.04.149"},{"key":"e_1_3_1_32_2","first-page":"153","article-title":"Automated prediction of epileptic seizures in rats with recurrence quantification analysis","author":"Ouyang Gaoxiang","year":"2005","unstructured":"Gaoxiang Ouyang, Lijuan Xie, Huanwen Chen, Xiaoli Li, Xinping Guan, and Huihua Wu. 2005. Automated prediction of epileptic seizures in rats with recurrence quantification analysis. In Proceedings of the 2005 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.153\u2013156. https:\/\/doi.org\/10.1109\/iembs.2005.1616365","journal-title":"Proceedings of the 2005 Annual International Conference of the IEEE Engineering in Medicine and Biology Society."},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2017.03.023"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2016.2585661"},{"issue":"1","key":"e_1_3_1_35_2","first-page":"16","article-title":"Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals","volume":"8","author":"Shoka A. A. E.","year":"2021","unstructured":"A. A. E. Shoka, M. H. Alkinani, A. S. El-Sherbeny, A. El-Sayed, and M. M. Dessouky. 2021. Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals. Brain Informatics 8, 1 (Feb. 2021), 16. https:\/\/doi.org\/10.1186\/s40708-021-00123-7","journal-title":"Brain Informatics"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-017-0800-x"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.02.040"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-018-0693-8"},{"key":"e_1_3_1_39_2","first-page":"335","volume-title":"Biomedical data processing using HHT: A review","author":"Wu Ming-Chya","year":"2009","unstructured":"Ming-Chya Wu and Norden E. Huang. 2009. Biomedical data processing using HHT: A review. In New Advances in Biomedical Signal Processing. Springer, Berlin, Germany, 335\u2013352. https:\/\/doi.org\/10.1007\/978-3-540-89506-0_16"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1166\/jmihi.2021.3259"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2016.10.001"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9052948"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2018.00095"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3460522","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3460522","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:47:55Z","timestamp":1750193275000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3460522"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,31]]},"references-count":42,"journal-issue":{"issue":"3s","published-print":{"date-parts":[[2021,10,31]]}},"alternative-id":["10.1145\/3460522"],"URL":"https:\/\/doi.org\/10.1145\/3460522","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,31]]},"assertion":[{"value":"2021-01-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-04-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-12-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}