CHEN Shuang-shuang1,2, ZHOU Wei-dong1,2, GENG Shu-juan1,2, YUAN Qi1,2, WANG Ji-wen3
（1. Suzhou Institute of Shandong University, Suzhou 215123, China; 2. School of Information Science and Engineering, Shandong University, Jinan 250100, China;3. Qilu Hospital, Shandong University, Jinan 250100, China）
Abstract： 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.
Key words： electroencephalogram (EEG)； seizure detection； wavelet transform； fluctuation index； gradient boosting
CLD number： TN911.7Document code： A
Article ID： 1674-8042（2015）01-0096-07 doi： 10.3969/j.issn.1674-8042.2015.01.017
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基于梯度boosting的癫痫脑电检测方法
陈爽爽1,2， 周卫东1,2， 耿淑娟1,2， 袁琦1,2， 王纪文3
（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）
摘要:自动癫痫脑电检测对癫痫的诊断具有重要意义， 可以减轻监测长期脑电的工作强度。 本文提出和探讨一种基于梯度boosting的长程脑电癫痫检测的新机器学习算法。 该算法提取长程脑电的相对波动指数作为特征， 采用梯度boosting算法训练分类器来识别发作和正常脑电。 最后采用平滑和“collar”技术作为后处理进一步提高检测准确率。 利用弗莱堡21位病人的脑电数据对该癫痫检测算法进行评估， 实验表明， 该算法的平均灵敏度为94.6%， 误检率为0.18/h。
关键词:脑电信号； 癫痫检测； 小波变换； 波动指数； 梯度boosting
引用格式：CHEN Shuang-shuang, ZHOU Wei-dong, GENG Shu-juan, et al. Approach for epileptic EEG detection based on gradient boosting. Journal of Measurement Science and Instrumentation, 2015, 6(1)： 96-102. ［doi： 10.3969／j.issn.1674-8042.2015.01.017］
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