{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T19:38:56Z","timestamp":1774381136637,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":50,"publisher":"ACM","license":[{"start":{"date-parts":[[2017,8,20]],"date-time":"2017-08-20T00:00:00Z","timestamp":1503187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61672064"],"award-info":[{"award-number":["61672064"]}]},{"name":"China Scholarship Council Fund","award":["201606540008"],"award-info":[{"award-number":["201606540008"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2017,8,20]]},"DOI":"10.1145\/3107411.3107419","type":"proceedings-article","created":{"date-parts":[[2017,8,24]],"date-time":"2017-08-24T11:58:11Z","timestamp":1503575891000},"page":"213-222","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":83,"title":["A Multi-view Deep Learning Method for Epileptic Seizure Detection using Short-time Fourier Transform"],"prefix":"10.1145","author":[{"given":"Ye","family":"Yuan","sequence":"first","affiliation":[{"name":"Beijing University of Technology, Beijing, China"}]},{"given":"Guangxu","family":"Xun","sequence":"additional","affiliation":[{"name":"State University of New York at Buffalo, Buffalo, NY, USA"}]},{"given":"Kebin","family":"Jia","sequence":"additional","affiliation":[{"name":"Beijing University of Technology, Beijing, China"}]},{"given":"Aidong","family":"Zhang","sequence":"additional","affiliation":[{"name":"State University of New York at Buffalo, Buffalo, NY, USA"}]}],"member":"320","published-online":{"date-parts":[[2017,8,20]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1186\/1687-6180-2014-183"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP.2016.7738824"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"e_1_3_2_1_4_1","volume-title":"Greedy layer-wise training of deep networks. Advances in neural information processing systems","author":"Bengio Yoshua","year":"2007","unstructured":"Yoshua Bengio , Pascal Lamblin , Dan Popovici , Hugo Larochelle , and others 2007. Greedy layer-wise training of deep networks. Advances in neural information processing systems Vol. 19 ( 2007 ), 153. Yoshua Bengio, Pascal Lamblin, Dan Popovici, Hugo Larochelle, and others 2007. Greedy layer-wise training of deep networks. Advances in neural information processing systems Vol. 19 (2007), 153."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Boualem Boashash Larbi Boubchir and Ghasem Azemi. 2012. Improving the classification of newborn EEG time-frequency representations using a combined time-frequency signal and image approach Information Science Signal Processing and their Applications (ISSPA) 2012 11th International Conference on. IEEE 280--285.  Boualem Boashash Larbi Boubchir and Ghasem Azemi. 2012. Improving the classification of newborn EEG time-frequency representations using a combined time-frequency signal and image approach Information Science Signal Processing and their Applications (ISSPA) 2012 11th International Conference on. IEEE 280--285.","DOI":"10.1109\/ISSPA.2012.6310560"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICM.2014.7071799"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2014.6854733"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.30749"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022627411411"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0013-9580.2005.66104.x"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2014.03.007"},{"key":"e_1_3_2_1_12_1","volume-title":"Methods for seizure detection and prediction: an overview. Modern Electroencephalographic Assessment Techniques: Theory and Applications","author":"Giannakakis Giorgos","year":"2015","unstructured":"Giorgos Giannakakis , Vangelis Sakkalis , Matthew Pediaditis , and Manolis Tsiknakis 2015. Methods for seizure detection and prediction: an overview. Modern Electroencephalographic Assessment Techniques: Theory and Applications ( 2015 ), 131--157. Giorgos Giannakakis, Vangelis Sakkalis, Matthew Pediaditis, and Manolis Tsiknakis 2015. Methods for seizure detection and prediction: an overview. Modern Electroencephalographic Assessment Techniques: Theory and Applications (2015), 131--157."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.101.23.e215"},{"key":"e_1_3_2_1_14_1","volume-title":"Automatic seizure detection in the newborn: methods and initial evaluation. Electroencephalography and clinical neurophysiology","author":"Gotman J","year":"1997","unstructured":"J Gotman , D Flanagan , J Zhang , and B Rosenblatt . 1997. Automatic seizure detection in the newborn: methods and initial evaluation. Electroencephalography and clinical neurophysiology , Vol. 103 , 3 ( 1997 ), 356--362. J Gotman, D Flanagan, J Zhang, and B Rosenblatt. 1997. Automatic seizure detection in the newborn: methods and initial evaluation. Electroencephalography and clinical neurophysiology, Vol. 103, 3 (1997), 356--362."},{"key":"e_1_3_2_1_15_1","volume-title":"Reading EEGs: a practical approach","author":"Greenfield L John","unstructured":"L John Greenfield , James D Geyer , and Paul R Carney . 2012. Reading EEGs: a practical approach . Lippincott Williams & Wilkins . L John Greenfield, James D Geyer, and Paul R Carney. 2012. Reading EEGs: a practical approach. Lippincott Williams & Wilkins."},{"key":"e_1_3_2_1_16_1","volume-title":"Reducing the dimensionality of data with neural networks. science","author":"Hinton Geoffrey E","year":"2006","unstructured":"Geoffrey E Hinton and Ruslan R Salakhutdinov 2006natexlaba. Reducing the dimensionality of data with neural networks. science , Vol. 313 , 5786 ( 2006 ), 504--507. Geoffrey E Hinton and Ruslan R Salakhutdinov 2006natexlaba. Reducing the dimensionality of data with neural networks. science, Vol. 313, 5786 (2006), 504--507."},{"key":"e_1_3_2_1_17_1","volume-title":"Reducing the dimensionality of data with neural networks. science","author":"Hinton Geoffrey E","year":"2006","unstructured":"Geoffrey E Hinton and Ruslan R Salakhutdinov 2006natexlabb. Reducing the dimensionality of data with neural networks. science , Vol. 313 , 5786 ( 2006 ), 504--507. Geoffrey E Hinton and Ruslan R Salakhutdinov 2006natexlabb. Reducing the dimensionality of data with neural networks. science, Vol. 313, 5786 (2006), 504--507."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/BIBE.2014.26"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7471776"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2501978"},{"key":"e_1_3_2_1_21_1","volume-title":"Principal component analysis","author":"Jolliffe Ian","unstructured":"Ian Jolliffe . 2002. Principal component analysis . Wiley Online Library . Ian Jolliffe. 2002. Principal component analysis. Wiley Online Library."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2014.6854723"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2014.01.008"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CONTROL.2016.7737620"},{"key":"e_1_3_2_1_25_1","volume-title":"2013 IEEE International Conference on. IEEE, 305--310","author":"Li Kang","year":"2013","unstructured":"Kang Li , Xiaoyi Li , Yuan Zhang , and Aidong Zhang . 2013 . Affective state recognition from EEG with deep belief networks Bioinformatics and Biomedicine (BIBM) , 2013 IEEE International Conference on. IEEE, 305--310 . Kang Li, Xiaoyi Li, Yuan Zhang, and Aidong Zhang. 2013. Affective state recognition from EEG with deep belief networks Bioinformatics and Biomedicine (BIBM), 2013 IEEE International Conference on. IEEE, 305--310."},{"key":"e_1_3_2_1_26_1","volume-title":"Classification of Epileptic EEG Signals with Stacked Sparse Autoencoder Based on Deep Learning. In International Conference on Intelligent Computing. Springer, 802--810","author":"Lin Qin","year":"2016","unstructured":"Qin Lin , Shu-qun Ye, Xiu-mei Huang, Si-you Li, Mei-zhen Zhang, Yun Xue , and Wen-Sheng Chen 2016 . Classification of Epileptic EEG Signals with Stacked Sparse Autoencoder Based on Deep Learning. In International Conference on Intelligent Computing. Springer, 802--810 . Qin Lin, Shu-qun Ye, Xiu-mei Huang, Si-you Li, Mei-zhen Zhang, Yun Xue, and Wen-Sheng Chen 2016. Classification of Epileptic EEG Signals with Stacked Sparse Autoencoder Based on Deep Learning. In International Conference on Intelligent Computing. Springer, 802--810."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098088"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098129"},{"key":"e_1_3_2_1_29_1","volume-title":"Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals","author":"Majumdar Angshul","year":"2016","unstructured":"Angshul Majumdar , Anupriya Gogna , and Rabab Ward 2016. Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals . IEEE Transactions on Biomedical Engineering ( 2016 ). Angshul Majumdar, Anupriya Gogna, and Rabab Ward 2016. Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals. IEEE Transactions on Biomedical Engineering (2016)."},{"key":"e_1_3_2_1_30_1","volume-title":"A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics","author":"McCulloch Warren S","year":"1943","unstructured":"Warren S McCulloch and Walter Pitts 1943. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics Vol. 5 , 4 ( 1943 ), 115--133. Warren S McCulloch and Walter Pitts 1943. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics Vol. 5, 4 (1943), 115--133."},{"key":"e_1_3_2_1_31_1","volume-title":"Classification of patterns of EEG synchronization for seizure prediction. Clinical neurophysiology","author":"Mirowski Piotr","year":"2009","unstructured":"Piotr Mirowski , Deepak Madhavan , Yann LeCun , and Ruben Kuzniecky 2009. Classification of patterns of EEG synchronization for seizure prediction. Clinical neurophysiology Vol. 120 , 11 ( 2009 ), 1927--1940. Piotr Mirowski, Deepak Madhavan, Yann LeCun, and Ruben Kuzniecky 2009. Classification of patterns of EEG synchronization for seizure prediction. Clinical neurophysiology Vol. 120, 11 (2009), 1927--1940."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awl241"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awl241"},{"key":"e_1_3_2_1_34_1","unstructured":"World Health Organization. 2017. Epilepsy Fact Sheet. http:\/\/www.who.int\/mediacentre\/factsheets\/fs999\/en\/. (2017).  World Health Organization. 2017. Epilepsy Fact Sheet. http:\/\/www.who.int\/mediacentre\/factsheets\/fs999\/en\/. (2017)."},{"key":"e_1_3_2_1_35_1","volume-title":"Detecting Epileptic Seizures from EEG Data using Neural Networks. arXiv preprint arXiv:1412.6502","author":"Pramod Siddharth","year":"2014","unstructured":"Siddharth Pramod , Adam Page , Tinoosh Mohsenin , and Tim Oates 2014. Detecting Epileptic Seizures from EEG Data using Neural Networks. arXiv preprint arXiv:1412.6502 ( 2014 ). Siddharth Pramod, Adam Page, Tinoosh Mohsenin, and Tim Oates 2014. Detecting Epileptic Seizures from EEG Data using Neural Networks. arXiv preprint arXiv:1412.6502 (2014)."},{"key":"e_1_3_2_1_36_1","volume-title":"Robust deep network with maximum correntropy criterion for seizure detection. BioMed research international","author":"Qi Yu","year":"2014","unstructured":"Yu Qi , Yueming Wang , Jianmin Zhang , Junming Zhu , and Xiaoxiang Zheng 2014. Robust deep network with maximum correntropy criterion for seizure detection. BioMed research international Vol. 2014 ( 2014 ). Yu Qi, Yueming Wang, Jianmin Zhang, Junming Zhu, and Xiaoxiang Zheng 2014. Robust deep network with maximum correntropy criterion for seizure detection. BioMed research international Vol. 2014 (2014)."},{"key":"e_1_3_2_1_37_1","volume-title":"Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform","author":"Samiee Kaveh","year":"2015","unstructured":"Kaveh Samiee , Peter Kovacs , and Moncef Gabbouj . 2015. Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform . IEEE transactions on Biomedical Engineering , Vol. 62 , 2 ( 2015 ), 541--552. Kaveh Samiee, Peter Kovacs, and Moncef Gabbouj. 2015. Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform. IEEE transactions on Biomedical Engineering, Vol. 62, 2 (2015), 541--552."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40708-015-0029-8"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2015.05.007"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.5370\/JEET.2014.9.3.1060"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/2975167.2975208"},{"key":"e_1_3_2_1_43_1","volume-title":"2014 36th Annual International Conference of the IEEE. IEEE, 4184--4187","author":"Supratak Akara","year":"2014","unstructured":"Akara Supratak , Ling Li , and Yike Guo 2014 . Feature extraction with stacked autoencoders for epileptic seizure detection Engineering in Medicine and Biology Society (EMBC) , 2014 36th Annual International Conference of the IEEE. IEEE, 4184--4187 . Akara Supratak, Ling Li, and Yike Guo 2014. Feature extraction with stacked autoencoders for epileptic seizure detection Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE. IEEE, 4184--4187."},{"key":"e_1_3_2_1_44_1","volume-title":"Survey on Feature Extraction and Applications of Biosignals. Machine Learning for Health Informatics","author":"Supratak Akara","unstructured":"Akara Supratak , Chao Wu , Hao Dong , Kai Sun , and Yike Guo 2016. Survey on Feature Extraction and Applications of Biosignals. Machine Learning for Health Informatics . Springer , 161--182. Akara Supratak, Chao Wu, Hao Dong, Kai Sun, and Yike Guo 2016. Survey on Feature Extraction and Applications of Biosignals. Machine Learning for Health Informatics. Springer, 161--182."},{"key":"e_1_3_2_1_45_1","volume-title":"Quantitative EEG analysis methods and clinical applications","author":"Tong Shanbao","unstructured":"Shanbao Tong and Nitish Vyomesh Thakor 2009. Quantitative EEG analysis methods and clinical applications . Artech House . Shanbao Tong and Nitish Vyomesh Thakor 2009. Quantitative EEG analysis methods and clinical applications. Artech House."},{"key":"e_1_3_2_1_46_1","volume-title":"2014 AAAI Spring Symposium Series.","author":"Turner JT","year":"2014","unstructured":"JT Turner , Adam Page , Tinoosh Mohsenin , and Tim Oates . 2014 . Deep belief networks used on high resolution multichannel electroencephalography data for seizure detection . In 2014 AAAI Spring Symposium Series. JT Turner, Adam Page, Tinoosh Mohsenin, and Tim Oates. 2014. Deep belief networks used on high resolution multichannel electroencephalography data for seizure detection. In 2014 AAAI Spring Symposium Series."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2009.2017939"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/8\/3\/036015"},{"key":"e_1_3_2_1_49_1","unstructured":"Junyuan Xie Linli Xu and Enhong Chen 2012. Image denoising and inpainting with deep neural networks Advances in Neural Information Processing Systems. 341--349.   Junyuan Xie Linli Xu and Enhong Chen 2012. Image denoising and inpainting with deep neural networks Advances in Neural Information Processing Systems. 341--349."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCChina.2016.7636897"},{"key":"e_1_3_2_1_51_1","volume-title":"Body sensor networks","author":"Yang Guang-Zhong","unstructured":"Guang-Zhong Yang and Magdi Yacoub 2006. Body sensor networks . Vol. Vol. 1 . Springer . endthebibliography Guang-Zhong Yang and Magdi Yacoub 2006. Body sensor networks. Vol. Vol. 1. Springer. endthebibliography"}],"event":{"name":"BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","location":"Boston Massachusetts USA","acronym":"BCB '17","sponsor":["SIGBio ACM Special Interest Group on Bioinformatics"]},"container-title":["Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3107411.3107419","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3107411.3107419","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T03:30:21Z","timestamp":1750217421000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3107411.3107419"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,20]]},"references-count":50,"alternative-id":["10.1145\/3107411.3107419","10.1145\/3107411"],"URL":"https:\/\/doi.org\/10.1145\/3107411.3107419","relation":{},"subject":[],"published":{"date-parts":[[2017,8,20]]},"assertion":[{"value":"2017-08-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}