It is very important for epilepsy treatment to distinguish epileptic seizure and non-seizure. In this study, an automatic seizure detection algorithm based on dual density dual tree complex wavelet transform (DD-DT CWT) for intracranial electroencephalogram (iEEG) was proposed. The experimental data were collected from 15 719 competition data set up by the National Institutes of Health (NINDS) in Kaggle. The processed database consisted of 55 023 seizure epochs and 501 990 non-seizure epochs. Each epoch was 1 second long and contained 174 sampling points. Firstly, the signal was resampled. Then, DD-DT CWT was used for EEG signal processing. Four kinds of features include wavelet entropy, variance, energy and mean value were extracted from the signal. Finally, these features were sent to least squares-support vector machine (LS-SVM) for learning and classification. The appropriate decomposition level was selected by comparing the experimental results under different wavelet decomposition levels. The experimental results showed that the features selected in this paper were different between seizure and non-seizure. Among the eight patients, the average accuracy of three-level decomposition classification was 91.98%, the sensitivity was 90.15%, and the specificity was 93.81%. The work of this paper shows that our algorithm has excellent performance in the two classification of EEG signals of epileptic patients, and can detect the seizure period automatically and efficiently.
正确区分癫痫发作期（seizure）与非发作期（non-seizure）对癫痫治疗有着重要意义。本研究以颅内脑电信号（iEEG）作为研究对象，提出了一种基于双密度双树复小波变换（DD-DT CWT）的癫痫发作期自动检测算法。实验数据来自美国国家卫生研究所（NINDS）设立在Kaggle上的15 719个竞赛数据，处理后的数据库由55 023段发作期数据和501 990段非发作期数据组成，每段数据长1 s，包含174个采样点。首先对信号进行重采样；然后利用DD-DT CWT处理脑电信号，并从中提取小波熵、方差、能量和均值共四类特征；最后使用最小二乘支持向量机（LS-SVM）学习分类，并通过比较不同小波分解层数下的实验结果选取合适的分解层数。实验结果表明：所提取的四类特征在发作期与非发作期存在差异，八位患者中，采用3层分解时分类的平均准确率较高，达到91.98%，灵敏度为90.15%，特异性为93.81%。本文工作表明，我们提出的算法在癫痫患者脑电信号的二分类中有优良的性能，能够自动高效地检测出癫痫发作期。.
Keywords: dual density dual tree complex wavelet; epilepsy; epileptic seizure detection; intracranial electroencephalogram; wavelet entropy.