Epilepsy is characterized by uncontrollable seizure during which consciousness of patient is disturbed. Prediction of the seizure in advance will increase the remedial possibilities for the patients suffering from epilepsy. An automated system for seizure prediction is important for seizure enactment, prevention of sudden unexpected deaths and to avoid seizure related injuries. This paper proposes the prediction of an upcoming seizure by analyzing the 23 channel non-stationary EEG signal. EEG signal is divided into smaller segments to change it into quasi-stationary data using an overlapping moving window. Brain region is marked into four regions namely left hemisphere, right hemisphere, central region and temporal region to identify the epileptogenic region. The epileptogenic region shows significant variations during pre-ictal state in comparison to the other regions. So, seizure prediction is carried out by analyzing EEG signals from this region. Seizure prediction is proposed using features extracted from both time and frequency domain. Relative entropy and relative energy are extracted from wavelet transform and Pearson correlation coefficient is obtained from time domain EEG signal. Extracted features have been smoothened using moving average filter. First order derivative of relative features have been used to normalize the intervariability before deciding the threshold for marking the prediction of seizure. Isolated seizures where pre-ictal duration of more than 1 h is reported has been detected with an accuracy of 92.18% with precursory warning 18 min in advance and seizure confirmation 12 min in advance. An overall accuracy of 83.33% with false positive alarm rate of 0.01/h has been obtained for all seizure cases with average prediction time of 9.9 min.

中文翻译：

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基于头皮脑电图（sEEG）的癫痫发作时间的高级预测和癫痫发生区域的识别。

癫痫的特征在于癫痫发作不可控制，在此期间患者的意识受到干扰。提前预测癫痫发作将增加癫痫患者的补救可能性。癫痫发作预测的自动化系统对于制定癫痫发作，防止突发意外死亡以及避免与癫痫发作有关的伤害非常重要。本文通过分析23通道非平稳EEG信号，提出了即将发作的预测。EEG信号被分成较小的段，以使用重叠的移动窗口将其更改为准平稳数据。将脑区域标记为四个区域，即左半球，右半球，中央区域和颞区域，以识别致癫痫区域。与其他区域相比，癫痫发生区域在发作前状态下显示出显着变化。因此，通过分析来自该区域的脑电信号来进行癫痫发作预测。使用从时域和频域提取的特征提出了癫痫发作预测。从小波变换中提取相对熵和相对能量，并从时域脑电信号中获得皮尔逊相关系数。使用移动平均滤波器对提取的特征进行了平滑处理。在确定标记癫痫发作的阈值之前，已使用相对特征的一阶导数来标准化互变异性。据报道，发作前持续时间超过1小时的孤立性癫痫发作的准确性为92。18％提前预警18分钟，并提前12分钟确认发作。对于所有癫痫病例，平均预测时间为9.9分钟，总体准确率为83.33％，误报率为0.01 / h。