{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T05:33:04Z","timestamp":1671427984955},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"S12","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,12,24]],"date-time":"2020-12-24T00:00:00Z","timestamp":1608768000000},"content-version":"vor","delay-in-days":23,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Sudden death in epilepsy (SUDEP) is a rare disease in US, however, they account for 8\u201317% of deaths in people with epilepsy. This disease involves complicated physiological patterns and it is still not clear what are the physio-\/bio-makers that can be used as an indicator to predict SUDEP so that care providers can intervene and treat patients in a timely manner. For this sake, UTHealth School of Biomedical Informatics (SBMI) organized a machine learning Hackathon to call for advanced solutions <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/sbmi.uth.edu\/hackathon\/archive\/sept19.htm\">https:\/\/sbmi.uth.edu\/hackathon\/archive\/sept19.htm<\/jats:ext-link>.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>In recent years, deep learning has become state of the art for many domains with large amounts data. Although healthcare has accumulated a lot of data, they are often not abundant enough for subpopulation studies where deep learning could be beneficial. Taking these limitations into account, we present a framework to apply deep learning to the detection of the onset of slow activity after a generalized tonic\u2013clonic seizure, as well as other EEG signal detection problems exhibiting data paucity.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We conducted ten training runs for our full method and seven model variants, statistically demonstrating the impact of each technique used in our framework with a high degree of confidence.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Our findings point toward deep learning being a viable method for detection of the onset of slow activity provided approperiate regularization is performed.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-020-01308-6","type":"journal-article","created":{"date-parts":[[2020,12,24]],"date-time":"2020-12-24T03:03:57Z","timestamp":1608779037000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Learning to detect the onset of slow activity after a generalized tonic\u2013clonic seizure"],"prefix":"10.1186","volume":"20","author":[{"given":"Carroll","family":"Vance","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yejin","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoqiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samden","family":"Lhatoo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiqiang","family":"Tao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Licong","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqian","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,12,24]]},"reference":[{"key":"1308_CR1","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Proceedings of the 25th international conference on neural information processing systems\u2014volume 1. NIPS\u201912. Red Hook: Curran Associates Inc.; 2012. p. 1097\u2013105."},{"key":"1308_CR2","unstructured":"Rajpurkar P, Hannun AY, Haghpanahi M, Bourn C, Ng AY. Cardiologist-level arrhythmia detection with convolutional neural networks. CoRR abs\/1707.01836 (2017). arxiv:1707.01836."},{"issue":"141","key":"1308_CR3","doi-asserted-by":"publisher","first-page":"20170387","DOI":"10.1098\/rsif.2017.0387","volume":"15","author":"T Ching","year":"2018","unstructured":"Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, Ferrero E, Agapow P-M, Zietz M, Hoffman MM, Xie W, Rosen GL, Lengerich BJ, Israeli J, Lanchantin J, Woloszynek S, Carpenter AE, Shrikumar A, Xu J, Cofer EM, Lavender CA, Turaga SC, Alexandari AM, Lu Z, Harris DJ, DeCaprio D, Qi Y, Kundaje A, Peng Y, Wiley LK, Segler MHS, Boca SM, Swamidass SJ, Huang A, Gitter A, Greene CS. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 2018;15(141):20170387. https:\/\/doi.org\/10.1098\/rsif.2017.0387.","journal-title":"J R Soc Interface"},{"key":"1308_CR4","unstructured":"Marcus G. Deep learning: a critical appraisal. 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CoRR abs\/1508.02788 2015. arxiv:1508.02788"},{"key":"1308_CR11","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. CoRR abs\/1512.03385 2015. arxiv:1512.03385"},{"key":"1308_CR12","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1007\/3-540-46805-6_19","volume-title":"Shape, contour and grouping in computer vision","author":"Y LeCun","year":"1999","unstructured":"LeCun Y, Haffner P, Bottou L, Bengio Y. Object recognition with gradient-based learning. In: Forsyth DA, et al., editors. Shape, contour and grouping in computer vision. Heidelberg: Springer; 1999. p. 319."},{"key":"1308_CR13","unstructured":"Chollet F, et al. Keras. https:\/\/keras.io. Accessed on 2020-09-20 2015."},{"key":"1308_CR14","unstructured":"Jiang X, Kim Y. SBMI Healthcare Machine Learning Hackathon. School of Biomedical Informatics. Accessed on 2020-09-20 2019. https:\/\/sbmi.uth.edu\/hackathon\/archive\/sept19.htm."},{"key":"1308_CR15","unstructured":"Zhao Z, Zheng P, Xu S, Wu X. Object detection with deep learning: a review. CoRR abs\/1807.05511 2018. arxiv:1807.05511."},{"issue":"1","key":"1308_CR16","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data. 2019;6(1):60. https:\/\/doi.org\/10.1186\/s40537-019-0197-0.","journal-title":"J Big Data"},{"key":"1308_CR17","unstructured":"Lathuili\u00e8re S, Mesejo P, Alameda-Pineda X, Horaud R. A comprehensive analysis of deep regression. CoRR abs\/1803.08450 2018. arxiv:1803.08450."}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-020-01308-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12911-020-01308-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-020-01308-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,24]],"date-time":"2020-12-24T03:04:30Z","timestamp":1608779070000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-020-01308-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12]]},"references-count":17,"journal-issue":{"issue":"S12","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["1308"],"URL":"https:\/\/doi.org\/10.1186\/s12911-020-01308-6","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12]]},"assertion":[{"value":"24 December 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This study was approved by the Institutional Review Board of University of Texas Health Science Center at Houston (HSC-MS-19-0045).","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"330"}}