{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T13:52:32Z","timestamp":1744206752394},"reference-count":0,"publisher":"American Scientific Publishers","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["j med imaging hlth inform"],"published-print":{"date-parts":[[2021,1,1]]},"abstract":"Clinical Electroencephalogram (EEG) data is of great significance to realize automatable detection, recognition and diagnosis to reduce the valuable diagnosis time. To make a classification of epilepsy, we constructed convolution support vector machine (CSVM) by integrating the advantages\n of convolutional neural networks (CNN) and support vector machine (SVM). To distinguish the focal and non-focal epilepsy EEG signals, we firstly reduced the dimensionality of EEG signals by using principal component analysis (PCA). After that, we classified the epilepsy EEG signals by the\n CSVM. The accuracy, sensitivity and specificity of our method reach up to 99.56%, 99.72% and 99.52% respectively, which are competitive than the widely acceptable algorithms. The proposed automatic end to end epilepsy EEG signals classification algorithm provides a better reference for clinical\n epilepsy diagnosis.<\/jats:p>","DOI":"10.1166\/jmihi.2021.3259","type":"journal-article","created":{"date-parts":[[2020,10,18]],"date-time":"2020-10-18T04:33:52Z","timestamp":1602995632000},"page":"25-32","source":"Crossref","is-referenced-by-count":10,"title":["Epilepsy EEG Classification Based on Convolution Support Vector Machine"],"prefix":"10.1166","volume":"11","author":[{"given":"Qi","family":"Xin","sequence":"first","affiliation":[]},{"given":"Shaohai","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Shuaiqi","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xiaole","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Lv","sequence":"additional","affiliation":[]},{"given":"Yu-Dong","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"17","container-title":["Journal of Medical Imaging and Health Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ingentaconnect.com\/content\/asp\/jmihi\/2021\/00000011\/00000001\/art00004","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T04:31:56Z","timestamp":1604637116000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ingentaconnect.com\/content\/10.1166\/jmihi.2021.3259"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,1]]},"references-count":0,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1,1]]}},"alternative-id":["2156-7018(20210101)11:1L.25;1-"],"URL":"https:\/\/doi.org\/10.1166\/jmihi.2021.3259","relation":{},"ISSN":["2156-7018"],"issn-type":[{"value":"2156-7018","type":"print"}],"subject":[],"published":{"date-parts":[[2021,1,1]]}}}