Parvez Mohammad Zavid, Paul Manoranjan
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:2888-91. doi: 10.1109/EMBC.2015.7318995.
Epilepsy is a common neurological disorders characterized by sudden recurrent seizures. Electroencephalogram (EEG) is widely used to diagnose possible epileptic seizure. Many research works have been devoted to predict epileptic seizure by analyzing EEG signal. Seizure prediction by analyzing EEG signals are challenging task due to variations of brain signals of different patients. In this paper, we propose a new approach for feature extraction based on phase correlation in EEG signals. In phase correlation, we calculate relative change between two consecutive segments of an EEG signal and then combine the changes with neighboring signals to extract features. These features are then used to classify preictal/ictal and interictal EEG signals for seizure prediction. Experiment results show that the proposed method carries good prediction rate with greater consistence for the benchmark data set in different brain locations compared to the existing state-of-the-art methods.
癫痫是一种常见的神经系统疾病，其特征是突然反复发作。脑电图（EEG）被广泛用于诊断可能的癫痫发作。许多研究工作致力于通过分析脑电图信号来预测癫痫发作。由于不同患者脑信号的变化，通过分析脑电图信号进行癫痫发作预测是一项具有挑战性的任务。在本文中，我们提出了一种基于脑电图信号相位相关性的特征提取新方法。在相位相关性中，我们计算脑电图信号两个连续段之间的相对变化，然后将这些变化与相邻信号相结合以提取特征。然后，这些特征被用于对发作前/发作期和发作间期的脑电图信号进行分类以进行癫痫发作预测。实验结果表明，与现有的最先进方法相比，该方法在不同脑区的基准数据集上具有良好的预测率和更高的一致性。
