{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:51:32Z","timestamp":1701478292962},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684444","type":"print"},{"value":"9781643684451","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>Neonatal seizures are a common emergency in neonatal intensive care unit (NICU). Neonatal epilepsy detection is generally the doctor through the naked eye reading electroencephalogram (EEG) to judge, this way is more subjective easy to produce differences. In this paper, a novel algorithm combining sparse representation and deep learning for automatic detection of neonatal seizures is proposed. After the EEG data is preprocessed, the features are extracted and sent to the Deep Sparse Representation Classification (DSRC) network. The performance of the algorithm was evaluated by sensitivity (sen) and specificity (spe) values in a publicly available dataset of 36 neonates with continuous EEG signals. Testing is done using cross-validation, so performance accurately represents the ability to classify architectures and test generalizations in a clinical setting. The sensitivity and specificity of cross detection were 89.33 and 90.2, respectively.<\/jats:p>","DOI":"10.3233\/faia230846","type":"book-chapter","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:54:44Z","timestamp":1701446084000},"source":"Crossref","is-referenced-by-count":0,"title":["Neonatal Seizure Detection Combining Sparse Representation and Deep Learning"],"prefix":"10.3233","author":[{"given":"Guangmiao","family":"Gao","sequence":"first","affiliation":[{"name":"School of Physics and Electronics, Shandong Normal University, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Pediatric Intensive Care Unit, Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Physics and Electronics, Shandong Normal University, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiwei","family":"Nie","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Shandong First Medical University, Shandong First Medical University, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Advances in Artificial Intelligence, Big Data and Algorithms"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230846","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:54:55Z","timestamp":1701446095000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230846"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"ISBN":["9781643684444","9781643684451"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230846","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,30]]}}}