Seizure detection using earth movers' distance and SVM in intracranial EEG
基于推土机距离和支持向量机的脑电癫痫检测算法(英文)作者机构:山东大学信息科学与工程学院山东济南250100 山东大学苏州研究院江苏苏州215123 山东大学齐鲁医院山东济南250100
出 版 物:《Journal of Measurement Science and Instrumentation》 (测试科学与仪器(英文版))
年 卷 期:2014年第5卷第3期
页 面:94-102页
学科分类:1002[医学-临床医学] 100204[医学-神经病学] 10[医学]
基 金:Key Program of Natural Science Foundation of Shandong Province(No.ZR2013FZ002) Program of Science and Technology of Suzhou(No.ZXY2013030) Independent Innovation Foundation of Shandong University(No.2012DX008)
主 题:electroencephalograph (EEG)signals earth movers' distance (EMD) EMD-L1 support vector machine(SVM) wavelet decomposition seizure detection
摘 要:Seizure detection is extremely essential for long-term monitoring of epileptic patients. This paper investigates the detection of epileptic seizures in multi-channel long-term intracranial electroencephalogram (iEEG). The algorithm conducts wavelet decomposition of iEEGs with five scales, and transforms the sum of the three frequency bands into histogram for computing the distance. The proposed method combines a novel feature called EMD-L1, which is an efficient algorithm of earth movers' distance (EMD), with support vector machine (SVM) for binary classification between seizures and non-sei- zures. The EMD-LI used in this method is characterized by low time complexity and high processing speed by exploiting the L~ metric structure. The smoothing and collar technique are applied on the raw outputs of SVM classifier to obtain more ac- curate results. Several evaluation criteria are recommended to compare our algorithm with other conventional methods using the same dataset from the Freiburg EEG database. Experiment results show that the proposed method achieves a high sensi- tivity, specificity and low false detection rate, which are 95.73 %, 98.45 % and 0.33/h, respectively. This algorithm is char- acterized by its robustness and high accuracy with the possibility of performing real-time analysis of EEG data, and may serve as a seizure detection tool for monitoring long-term EEG.