Approach for epileptic EEG detection based on gradient boosting  

基于梯度boosting的癫痫脑电检测方法(英文)

作　　者：陈爽爽[1,2] 周卫东[1,2] 耿淑娟[1,2] 袁琦[1,2] 王纪文[3] 

机构地区：[1]山东大学苏州研究院,江苏苏州215123 [2]山东大学信息科学与工程学院,山东济南250100 [3]山东大学齐鲁医院,山东济南250100

出　　处：《Journal of Measurement Science and Instrumentation》2015年第1期96-102,共7页测试科学与仪器（英文版）

基　　金：Key Program of Natural Science Foundation of Shandong Province(No.ZR2013FZ002);The Program of Science and Technology of Suzhou(No.ZXY2013030);Independent Innovation Foundation of Shandong University(No.11170074611102)

摘　　要：The automatic seizure detection is significant for epilepsy diagnosis and it can alleviate the work intensity of inspecting prolonged electroencephalogram （EEG）. This paper presents and investigates a novel machine learning approach utilizing gradient boosting to detect seizures from long-term EEG. We apply relative fluctuation index to extract features of long-term intracranial EEG data. A classifier trained with the gradient boosting algorithm is adopted to discriminate the seizure and non-seizure EEG signals. Smoothing and collar technique are finally used as post-processing in order to improve the detection accuracy further. The seizure detection method is assessed on Freiburg EEG datasets from 21 patients. The experimental results indicate that the proposed method yields an average sensitivity of 94. 60% with a false detection rate of 0. 18/h.

关 键 词：electroencephalogram （EEG） seizure detection wavelet transform fluctuation index gradient boosting 

分 类 号：R742.1[医药卫生—神经病学与精神病学] R741.044[医药卫生—临床医学]