Epileptic EEG detection using the linear prediction error energy
Epileptic EEG detection using the linear prediction error energy
复制标题
DOI：
10.1016/j.eswa.2010.02.045
复制
发表时间：
2010-08-01
影响因子：
8.5
通讯作者：
Erogul, Osman
中科院分区：
文献类型：
--
作者：
Altunay, Semih;Telatar, Ziya;Erogul, Osman
在这项研究中，提出了一种通过脑电图信号检测癫痫发作的方法。为此，使用线性预测滤波器来观察癫痫发作脑电图记录中尖峰和尖波的存在。线性预测分析计算每个窗口的系数集，它可以最好地对所应用的时间序列信号进行建模。在预测误差信号上观察到建模成功。信号上尖峰和其他癫痫发作特异性尖波的存在会降低建模成功率并增加滤波器的预测误差。可以清楚地观察到，癫痫发作期间预测误差信号的能量远高于无癫痫发作间隔的能量，这表明了能量值，可以用来定位癫痫发作间隔。该方法适用于 250 个不同的 EEG 记录，每个记录的持续时间为 23.6 秒。所提出算法的结果通过 ROC 分析进行评估，表明检测癫痫发作的成功率为 93.6%。总之，线性预测误差能量法可以被认为是在脑电图记录上检测癫痫发作的有效方法。 (c) 2010 Elsevier Ltd. 保留所有权利。
In this study, a method is proposed to detect epileptic seizures over EEG signal. For this purpose, a linear prediction filter is used to observe the presence of spikes and sharp waves on seizure EEG recordings. Linear prediction analysis calculates a coefficient set for each window, which can best model the applied time series signal. Modeling success is observed on the prediction error signal. The presence of spikes and other seizure-specific sharp waves on the signal reduces the modeling success and increases the prediction error of the filter. It is clearly observed that, the energy of prediction error signal during seizures is much higher than that of the seizure free intervals, which indicates the energy value and can be used to locate the seizure interval. The method is applied to 250 distinct EEG records, each of which has 23.6 s duration. The results of the proposed algorithm are evaluated with the ROC analysis which indicates 93.6% success in detecting the presence of seizures. As a conclusion, the linear prediction error energy method can be considered as an efficient way to detect epileptic seizures on EEG records. (c) 2010 Elsevier Ltd. All rights reserved.