Epilepsy is caused by sudden abnormal discharges of neurons in the brain. This paper constructs an automatic seizure detection system, which combines the predicting result of multi-domain feature with the predicting result of spike rate feature to detect the occurrence of epileptic seizures. After segmenting EEG data into 5[Formula: see text]s with 80% overlap epochs, the paper extracts time domain features, frequency domain features and hurst exponents (HE) from each epoch and these features are reduced by linear discriminant analysis (LDA) to be input parameters of the random forest (RF) classifier, which provides classification of the EEG epochs concerning the existence of seizures. In parallel, the paper extracts spikes from EEG data with morphological filter and calculates the spike rate to determine whether there is seizure. Then the results obtained by these two methods are merged as the final detection result. The paper shows that the accuracy (AC), sensitivity (SE), specificity (SP) and the false positive ratio based on event (FPRE) obtained by hybrid method are 98.94%, 76.60%, 98.99% and 2.43 times/h, respectively. Finally, the paper applies the seizure detection method to do seizure warning and recording to help the family member to take care of the patients and the doctor to adjust the antiepileptic drugs (AEDs).

中文翻译：

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基于脑电多域特征和尖峰特征的癫痫发作检测系统

癫痫是由大脑中神经元突然异常放电引起的。本文构建了一个癫痫发作自动检测系统，将多域特征的预测结果与尖峰率特征的预测结果相结合，检测癫痫发作的发生。将EEG数据分割成5个[公式：见正文]，重叠80%的epoch，从每个epoch中提取时域特征、频域特征和hurst指数（HE），并通过线性判别分析（LDA）对这些特征进行缩减作为随机森林（RF）分类器的输入参数，该分类器提供关于癫痫发作存在的​​脑电图时期的分类。同时，论文利用形态滤波器从脑电数据中提取尖峰，并计算尖峰率以确定是否有癫痫发作。然后将这两种方法得到的结果合并为最终的检测结果。论文表明，混合法得到的准确率（AC）、灵敏度（SE）、特异性（SP）和基于事件的假阳性率（FPRE）分别为98.94%、76.60%、98.99%和2.43次/h . 最后，应用癫痫发作检测方法进行癫痫发作预警和记录，以帮助家属照顾患者和医生调整抗癫痫药物（AEDs）。