{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T03:39:58Z","timestamp":1778211598364,"version":"3.51.4"},"reference-count":50,"publisher":"Association for Computing Machinery (ACM)","issue":"1s","license":[{"start":{"date-parts":[[2019,1,31]],"date-time":"2019-01-31T00:00:00Z","timestamp":1548892800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia","award":["RGP-228"],"award-info":[{"award-number":["RGP-228"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2019,1,31]]},"abstract":"<jats:p>Deep Convolutional Neural Network (CNN) has achieved remarkable results in computer vision tasks for end-to-end learning. We evaluate here the power of a deep CNN to learn robust features from raw Electroencephalogram (EEG) data to detect seizures. Seizures are hard to detect, as they vary both inter- and intra-patient. In this article, we use a deep CNN model for seizure detection task on an open-access EEG epilepsy dataset collected at the Boston Children's Hospital. Our deep learning model is able to extract spectral, temporal features from EEG epilepsy data and use them to learn the general structure of a seizure that is less sensitive to variations. For cross-patient EEG data, our method produced an overall sensitivity of 90.00%, specificity of 91.65%, and overall accuracy of 98.05% for the whole dataset of 23 patients. The system can detect seizures with an accuracy of 99.46%. Thus, it can be used as an excellent cross-patient seizure classifier. The results show that our model performs better than the previous state-of-the-art models for patient-specific and cross-patient seizure detection task. The method gave an overall accuracy of 99.65% for patient-specific data. The system can also visualize the special orientation of band power features. We use correlation maps to relate spectral amplitude features to the output in the form of images. By using the results from our deep learning model, this visualization method can be used as an effective multimedia tool for producing quick and relevant brain mapping images that can be used by medical experts for further investigation.<\/jats:p>","DOI":"10.1145\/3241056","type":"journal-article","created":{"date-parts":[[2019,2,19]],"date-time":"2019-02-19T20:54:15Z","timestamp":1550609655000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":259,"title":["Applying Deep Learning for Epilepsy Seizure Detection and Brain Mapping Visualization"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5906-9422","authenticated-orcid":false,"given":"M. Shamim","family":"Hossain","sequence":"first","affiliation":[{"name":"Research Chair of Pervasive and Mobile Computing, and Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia"}]},{"given":"Syed Umar","family":"Amin","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia"}]},{"given":"Mansour","family":"Alsulaiman","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia"}]},{"given":"Ghulam","family":"Muhammad","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia"}]}],"member":"320","published-online":{"date-parts":[[2019,2,17]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"World Health Organization. 2017. Epilepsy. Retrieved from http:\/\/www.who.int\/mediacentre\/factsheets\/fs999\/en\/.  World Health Organization. 2017. Epilepsy. Retrieved from http:\/\/www.who.int\/mediacentre\/factsheets\/fs999\/en\/."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(02)00003-0"},{"key":"e_1_2_1_3_1","volume-title":"Mediterranean Conference on Control 8 Automation. 1--6.","author":"Echauz J."},{"key":"e_1_2_1_4_1","volume-title":"Carney","author":"Greenfield L. John","year":"2012"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awl241"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2011.06.001"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10439-009-9755-5"},{"key":"e_1_2_1_8_1","volume-title":"International Conference on Natural Computation. 186--191","author":"Fatichah C."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1097\/WNP.0b013e3181e0a9b6"},{"key":"e_1_2_1_10_1","doi-asserted-by":"crossref","unstructured":"I. Osorio and M. Frei. 2009. Real-time detection quantification warning and control of epileptic seizures: The foundations for a scientific epileptology. Epilepsy 8 Behavior 16 3 (2009) 391--396.  I. Osorio and M. Frei. 2009. Real-time detection quantification warning and control of epileptic seizures: The foundations for a scientific epileptology. Epilepsy 8 Behavior 16 3 (2009) 391--396.","DOI":"10.1016\/j.yebeh.2009.08.024"},{"key":"e_1_2_1_12_1","volume-title":"Proceedings of the Engineering in Medicine and Biology Society, Annual International Conference of the IEEE.","author":"Shoeb A."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1528-1167.2011.03138.x"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-2789(00)00087-7"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0013-9580.2003.12007.x"},{"key":"e_1_2_1_16_1","first-page":"113","article-title":"Statistical assessment of nonlinear causality: Application to epileptic EEG signals. Journal of Neuroscience","volume":"124","author":"Ch\u00e1vez Mario","year":"2003","journal-title":"Methods"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2004.08.025"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/13\/2\/026018"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-spr.2013.0288"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2015.2505238"},{"key":"e_1_2_1_21_1","volume-title":"Proceedings of the 1st Machine Learning for Healthcare Conference, PMLR 56","author":"Thodoroff Pierre","year":"2016"},{"key":"e_1_2_1_22_1","volume-title":"Tsalikakis","author":"Tzallas Alexandros T.","year":"2012"},{"key":"e_1_2_1_23_1","doi-asserted-by":"crossref","volume-title":"A Clinical Guide to Epileptic Syndromes and Their Treatment","author":"Panayiotopoulos C. P.","DOI":"10.1007\/978-1-84628-644-5"},{"key":"e_1_2_1_24_1","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky Alex","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_25_1","volume-title":"The Handbook of Brain Theory and Neural Networks","author":"LeCun Yann"},{"key":"e_1_2_1_26_1","volume-title":"2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). 1--6.","author":"Antoniades A."},{"key":"e_1_2_1_27_1","volume-title":"ICLR","author":"Bashivan P.","year":"2016"},{"key":"e_1_2_1_28_1","volume-title":"Bernstein Conference","author":"Stober S.","year":"2016"},{"key":"e_1_2_1_29_1","volume-title":"Detecting epileptic seizures from EEG data using neural networks. ArXiv Preprint arXiv:1412.6502","author":"Pramod Siddharth","year":"2014"},{"key":"e_1_2_1_30_1","volume-title":"2014 AAAI Spring Symposium Series.","author":"Turner J. T.","year":"2014"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/8\/3\/036015"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7471776"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP.2016.7738824"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CONTROL.2016.7737620"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2014.6944546"},{"key":"e_1_2_1_36_1","volume-title":"Robust deep network with maximum correntropy criterion for seizure detection. BioMed Research International","author":"Qi Yu","year":"2014"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCChina.2016.7636897"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2016.2631620"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-018-1113-0"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1128115"},{"key":"e_1_2_1_43_1","volume-title":"Fast and accurate deep network learning by exponential linear units (ELUs). ArXiv e-Prints","author":"Clevert Djork-Arne"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670313"},{"key":"e_1_2_1_45_1","volume-title":"Proceedings of the 32nd International Conference on Machine Learning. 448--456","author":"Ioffe S."},{"key":"e_1_2_1_46_1","volume-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2818--2826","author":"Szegedy Christian","year":"2015"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.23730"},{"key":"e_1_2_1_48_1","first-page":"228","article-title":"Seizure detection: Evaluation of the reveal algorithm","volume":"10","author":"Wilson S.","year":"2004","journal-title":"Clinical Neurophysiology"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aci.2015.01.001"},{"key":"e_1_2_1_50_1","doi-asserted-by":"crossref","unstructured":"A. Supratak L. Li and Y. Guo. 2014. Feature extraction with stacked autoencoders for epileptic seizure detection. In 36th annual International Conference of the IEEE Engineering in Medicine and Biology Society. 4184--4187.  A. Supratak L. Li and Y. Guo. 2014. Feature extraction with stacked autoencoders for epileptic seizure detection. In 36th annual International Conference of the IEEE Engineering in Medicine and Biology Society. 4184--4187.","DOI":"10.1109\/EMBC.2014.6944546"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM.2015.7359702"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0173138"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3241056","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3241056","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:43:46Z","timestamp":1750207426000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3241056"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,31]]},"references-count":50,"journal-issue":{"issue":"1s","published-print":{"date-parts":[[2019,1,31]]}},"alternative-id":["10.1145\/3241056"],"URL":"https:\/\/doi.org\/10.1145\/3241056","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,31]]},"assertion":[{"value":"2017-10-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-07-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-02-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}