Zabihi Morteza, Kiranyaz Serkan, Rad Ali Bahrami, Katsaggelos Aggelos K, Gabbouj Moncef, Ince Turker
IEEE Trans Neural Syst Rehabil Eng. 2016 Mar;24(3):386-98. doi: 10.1109/TNSRE.2015.2505238. Epub 2015 Dec 18.
In this paper, the performance of the phase space representation in interpreting the underlying dynamics of epileptic seizures is investigated and a novel patient-specific seizure detection approach is proposed based on the dynamics of EEG signals. To accomplish this, the trajectories of seizure and nonseizure segments are reconstructed in a high dimensional space using time-delay embedding method. Afterwards, Principal Component Analysis (PCA) was used in order to reduce the dimension of the reconstructed phase spaces. The geometry of the trajectories in the lower dimensions is then characterized using Poincaré section and seven features were extracted from the obtained intersection sequence. Once the features are formed, they are fed into a two-layer classification scheme, comprising the Linear Discriminant Analysis (LDA) and Naive Bayesian classifiers. The performance of the proposed method is then evaluated over the CHB-MIT benchmark database and the proposed approach achieved 88.27% sensitivity and 93.21% specificity on average with 25% training data. Finally, we perform comparative performance evaluations against the state-of-the-art methods in this domain which demonstrate the superiority of the proposed method.
本文研究了相空间表示在解释癫痫发作潜在动力学方面的性能，并基于脑电图（EEG）信号的动力学提出了一种新颖的针对特定患者的癫痫发作检测方法。为实现这一目标，使用时延嵌入方法在高维空间中重建癫痫发作和非癫痫发作片段的轨迹。之后，运用主成分分析（PCA）来降低重建相空间的维度。然后使用庞加莱截面来表征低维轨迹的几何形状，并从获得的相交序列中提取七个特征。一旦形成特征，就将其输入到由线性判别分析（LDA）和朴素贝叶斯分类器组成的两层分类方案中。然后在CHB - MIT基准数据库上评估所提方法的性能，该方法在使用25%训练数据时平均实现了88.27%的灵敏度和93.21%的特异性。最后，我们与该领域的现有方法进行了比较性能评估，结果表明了所提方法的优越性。
