Ghaderyan Peyvand, Abbasi Ataollah, Sedaaghi Mohammad Hossein
Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
AIIA, Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
J Neurosci Methods. 2014 Jul 30;232:134-42. doi: 10.1016/j.jneumeth.2014.05.019. Epub 2014 May 26.
Seizure prediction based on analysis of electroencephalogram signals has generated considerable research interests. A reliable seizure prediction algorithm with minimal computational requirements is prominent issue for medical facilities; however, it has not been addressed correctly. In this study, an optimized novel method is proposed in order to remove computational complexity, and predict epileptic seizures clinically. It is based on the univariate linear features in eight frequency sub-bands. It also employs principal component analysis (PCA) for dimension reduction and optimal feature selection. Class unbalanced problem is tackled by K-nearest neighbor (KNN)-based undersampling combined with support vector machine (SVM) classifier. To find out the best results two types of postprocessing methods were studied. The proposed algorithm was evaluated on seizures and 434.9h of interictal data from 18 patients of Freiburg database. It predicted 100% of seizures with average false alarm rate of 0.13 per hour ranging between 0 and 0.39. Furthermore, G-Mean and F-measure were used for validation which were 0.97 and 0.90, respectively. These results confirmed the discriminative ability of the algorithm. In comparison with other studies, the proposed method improves trade-off between sensitivity and false prediction rate with linear features and low computational requirements and it can potentially be employed in implantable devices. Achieving high performance by linear features, PCA, KNN-based undersampling, and SVM demonstrates that this method can potentially be used in implantable devices.
基于脑电图信号分析的癫痫发作预测已引发了大量的研究兴趣。对于医疗机构而言，拥有最小计算需求的可靠癫痫发作预测算法是一个突出问题；然而，这一问题尚未得到妥善解决。在本研究中，提出了一种优化的新方法，以消除计算复杂性，并在临床上预测癫痫发作。该方法基于八个频率子带中的单变量线性特征。它还采用主成分分析（PCA）进行降维和最优特征选择。通过基于K近邻（KNN）的欠采样结合支持向量机（SVM）分类器来解决类不平衡问题。为了找出最佳结果，研究了两种后处理方法。所提出的算法在来自弗莱堡数据库的18名患者的癫痫发作和434.9小时的发作间期数据上进行了评估。它对癫痫发作的预测准确率为100%，平均误报率为每小时0.13，范围在0至0.39之间。此外，使用G均值和F度量进行验证，其值分别为0.97和0.90。这些结果证实了该算法的判别能力。与其他研究相比，所提出的方法通过线性特征和低计算需求改善了灵敏度和错误预测率之间的权衡，并且它有可能应用于植入式设备。通过线性特征、PCA、基于KNN的欠采样和SVM实现高性能表明该方法有可能用于植入式设备。
