Epilepsy is a neurological disorder that happens because of the propagation of abnormal signals produced by clusters of neurons in the brain. The majority of those with epileptic seizures can be treated by drug therapies and surgery. However, 25% of the patients with diagnosed epilepsy continue to have seizures. Seizures can cause serious injuries and limit the independence and mobility of an individual. Seizure detection and prediction could lead to a better understanding of seizures and with that help preventing patient injury.This paper discusses extraction and evaluation of nonlinear multivariate features using the cross-bispectral method to help predict epileptic seizure occurrences. These ten statistic features were employed to discriminate pre-ictal from interictal states. Therefore, the features were given to the support vector machine classifier as the input. Outputs were then processed in order to evaluate the sensitivity, false positive rate (FPR) and the prediction time. The proposed method obtained sensitivity of 100% and average FPR of 0.044 per hour by using the “Freiburg epileptic seizure prediction” dataset. This high sensitivity index and low FPR index compared with other studies show the ability of cross-higher-order spectral method to analyze epileptic EEG signals. The proposed method is also fast and easy and may be helpful in other applications of EEG analysis such as sleep stage identification and brain–computer interface.

中文翻译：

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使用 EEG 信号的交叉高阶频谱分析进行癫痫发作预测

癫痫是一种神经系统疾病，由于大脑中神经元簇产生的异常信号的传播而发生。大多数癫痫发作可以通过药物治疗和手术治疗。然而，25% 的确诊癫痫患者继续癫痫发作。癫痫发作会造成严重伤害并限制个人的独立性和活动能力。癫痫发作检测和预测可以更好地了解癫痫发作并有助于防止患者受伤。本文讨论了使用交叉双谱法提取和评估非线性多变量特征以帮助预测癫痫发作的发生。这十个统计特征用于区分发作前和发作间状态。所以，这些特征作为输入提供给支持向量机分类器。然后处理输出以评估灵敏度、假阳性率 (FPR) 和预测时间。通过使用“弗莱堡癫痫发作预测”数据集，所提出的方法获得了 100% 的灵敏度和每小时 0.044 的平均 FPR。与其他研究相比，这种高灵敏度指数和低 FPR 指数显示了交叉高阶频谱方法分析癫痫脑电信号的能力。所提出的方法也快速简便，可能有助于脑电图分析的其他应用，如睡眠阶段识别和脑机接口。通过使用“弗莱堡癫痫发作预测”数据集，所提出的方法获得了 100% 的灵敏度和每小时 0.044 的平均 FPR。与其他研究相比，这种高灵敏度指数和低 FPR 指数显示了交叉高阶谱方法分析癫痫脑电信号的能力。所提出的方法也快速简便，可能有助于脑电图分析的其他应用，如睡眠阶段识别和脑机接口。通过使用“弗莱堡癫痫发作预测”数据集，所提出的方法获得了 100% 的灵敏度和每小时 0.044 的平均 FPR。与其他研究相比，这种高灵敏度指数和低 FPR 指数显示了交叉高阶频谱方法分析癫痫脑电信号的能力。所提出的方法也快速简便，可能有助于脑电图分析的其他应用，如睡眠阶段识别和脑机接口。