基于加权复杂网络度熵和的癫痫发作检测方法

doi:10.3969/j.issn.0258-8021.2019.03.003

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Abstract 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.

Key words： epileptic seizure detection EEG signal complex network weighted horizontal visibility graph

Received: 22 March 2018

PACS:

R318

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	Articles by authors
	Zhang Hanyong
	Meng Qingfang
	Du Lei
	Liu Mingmin

Cite this article:

Zhang Hanyong,Meng Qingfang,Du Lei, et al. Epileptic Seizure Detection Based on theSum of Degree and Entropy of Weighted Complex Network[J]. Chinese Journal of Biomedical Engineering, 2019, 38(3): 273-280.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2019.03.003 OR http://cjbme.csbme.org/EN/Y2019/V38/I3/273

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