Bandarabadi Mojtaba, Teixeira Cesar A, Direito Bruno, Dourado Antonio
Centre for Informatics and Systems (CISUC), University of Coimbra, Portugal.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:5943-6. doi: 10.1109/EMBC.2012.6347347.
The spectral power of 5 frequently considered frequency bands (Alpha, Beta, Gamma, Theta and Delta) for 6 EEG channels is computed and then all the possible pairwise combinations among the 30 features set, are used to create a 435 dimensional feature space. Two new feature selection methods are introduced to choose the best candidate features among those and to reduce the dimensionality of this feature space. The selected features are then fed to Support Vector Machines (SVMs) that classify the cerebral state in preictal and non-preictal classes. The outputs of the SVM are regularized using a method that accounts for the classification dynamics of the preictal class, also known as "Firing Power" method. The results obtained using our feature selection approaches are compared with the ones obtained using minimum Redundancy Maximum Relevance (mRMR) feature selection method. The results in a group of 12 patients of the EPILEPSIAE database, containing 46 seizures and 787 hours multichannel recording for out-of-sample data, indicate the efficiency of the bivariate approach as well as the two new feature selection methods. The best results presented sensitivity of 76.09% (35 of 46 seizures predicted) and a false prediction rate of 0.15(-1).
计算6个脑电图通道的5个常用频段（阿尔法、贝塔、伽马、西塔和德尔塔）的频谱功率，然后使用30个特征集之间所有可能的成对组合来创建一个435维的特征空间。引入了两种新的特征选择方法，以在这些特征中选择最佳候选特征，并降低该特征空间的维度。然后将所选特征输入支持向量机（SVM），对发作前期和非发作前期的脑状态进行分类。支持向量机的输出使用一种考虑发作前期分类动态的方法进行正则化，该方法也称为“激发功率”方法。将使用我们的特征选择方法获得的结果与使用最小冗余最大相关性（mRMR）特征选择方法获得的结果进行比较。在EPILEPSIAE数据库的一组12名患者中，包含46次发作和787小时的样本外数据多通道记录，结果表明了双变量方法以及两种新的特征选择方法的有效性。最佳结果显示敏感性为76.09%（预测出46次发作中的35次），错误预测率为0.15（-1）。
