基于加权复杂网络度熵和的癫痫发作检测方法

癫痫发作检测一直是一项富有挑战性的工作,随着癫痫发病率的增加,高性能癫痫自动检测算法在临床上可以减轻医务工作者的工作量,具有重要的临床医学研究意义。提出基于加权水平可视图的癫痫检测新方法。首先利用加权水平可视图将单通道脑电信号转化为复杂网络,并提取生成的复杂网络的度的平方和权重度分布熵两个特征;最后将两个特征之和作为单特征输入到线性分类器中,用来识别癫痫间歇期和发作期信号。对波恩大学的癫痫脑电数据集进行实验,评价所提出的检测算法的性能。使用该癫痫脑电数据集间歇期和发作期各100个实验样本,样本长度为1 024。实验结果表明,所提出的方法具有较高的分类精度,可达到98.5%。由于分类的特征为单特征,所以更加简单高效,可用于癫痫发作在线自动检测。

Epileptic Seizure Detection Based on theSum of Degree and Entropy of Weighted Complex Network

Epileptic seizure detection has always been a challenging task. With the increasing of epilepsy, high-performance epileptic automatic detection algorithm can reduce the workload of medical workers and has important clinical research significance. In this paper we proposed a new seizure detection method based on weighted horizontal visibility graph (WHVG). Firstly, the single channel electroencephalogram (EEG) signal was transformed into complex network by using WHVG. Then, the square of degree and weighted degree entropy of the complex network was extracted. Finally, the sum of this two extracted features was used as a single feature. The single feature was inputted into a linear classifier to identify interictal and ictal signals. The experiment evaluating the performance of proposed method was conducted on the epileptic EEG dataset of the University of Bonn. This experiment used 100 samples in interictal and 100 samples in ictal and each sample contains 1024 points. Experimental results showed that the proposed method had high classification accuracy, which was up to 98.5%. In addition, the feature used in the method was a single feature that was more simple and efficient. In conclusion, the proposed method was promising for uses in online automatic epileptic seizure detection.