Bandarabadi Mojtaba, Rasekhi Jalil, Teixeira César A, Karami Mohammad R, Dourado António
CISUC/DEI, Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Polo II, 3030-290 Coimbra, Portugal.
Department of Biomedical Engineering, Faculty of Engineering, Babol Noshirvani University of Technology, Babol, Iran.
Epilepsy Behav. 2015 May;46:158-66. doi: 10.1016/j.yebeh.2015.03.010. Epub 2015 May 3.
Supervised machine learning-based seizure prediction methods consider preictal period as an important prerequisite parameter during training. However, the exact length of the preictal state is unclear and varies from seizure to seizure. We propose a novel statistical approach for proper selection of the preictal period, which can also be considered either as a measure of predictability of a seizure or as the prediction capability of an understudy feature. The optimal preictal periods (OPPs) obtained from the training samples can be used for building a more accurate classifier model. The proposed method uses amplitude distribution histograms of features extracted from electroencephalogram (EEG) recordings. To evaluate this method, we extract spectral power features in different frequency bands from monopolar and space-differential EEG signals of 18 patients suffering from pharmacoresistant epilepsy. Furthermore, comparisons among monopolar channels with space-differential channels, as well as intracranial EEG (iEEG) and surface EEG (sEEG) signals, indicate that while monopolar signals perform better in iEEG recordings, no significant difference is noticeable in sEEG recordings.
基于监督式机器学习的癫痫发作预测方法在训练过程中将发作前期视为一个重要的前提参数。然而，发作前期的确切时长并不明确，且每次发作都有所不同。我们提出了一种新颖的统计方法来合理选择发作前期，该方法既可以被视为癫痫发作可预测性的一种度量，也可以被视为一个待研究特征的预测能力。从训练样本中获得的最佳发作前期（OPPs）可用于构建更准确的分类器模型。所提出的方法使用从脑电图（EEG）记录中提取的特征的幅度分布直方图。为了评估该方法，我们从18例药物难治性癫痫患者的单极和空间差分EEG信号中提取不同频段的频谱功率特征。此外，单极通道与空间差分通道以及颅内EEG（iEEG）和表面EEG（sEEG）信号之间的比较表明，虽然单极信号在iEEG记录中表现更好，但在sEEG记录中没有明显差异。
