使用快速加权水平可见性算法检测脑电图信号中的癫痫发作。
Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm.
作者信息
Zhu Guohun, Li Yan, Wen Peng Paul
机构信息
Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
出版信息
Comput Methods Programs Biomed. 2014 Jul;115(2):64-75. doi: 10.1016/j.cmpb.2014.04.001. Epub 2014 Apr 15.
This paper proposes a fast weighted horizontal visibility graph constructing algorithm (FWHVA) to identify seizure from EEG signals. The performance of the FWHVA is evaluated by comparing with Fast Fourier Transform (FFT) and sample entropy (SampEn) method. Two noise-robustness graph features based on the FWHVA, mean degree and mean strength, are investigated using two chaos signals and five groups of EEG signals. Experimental results show that feature extraction using the FWHVA is faster than that of SampEn and FFT. And mean strength feature associated with ictal EEG is significant higher than that of healthy and inter-ictal EEGs. In addition, an 100% classification accuracy for identifying seizure from healthy shows that the features based on the FWHVA are more promising than the frequency features based on FFT and entropy indices based on SampEn for time series classification.
本文提出了一种快速加权水平可见性图构建算法（FWHVA），用于从脑电图（EEG）信号中识别癫痫发作。通过与快速傅里叶变换（FFT）和样本熵（SampEn）方法进行比较，评估了FWHVA的性能。使用两个混沌信号和五组EEG信号，研究了基于FWHVA的两个抗噪声图特征，即平均度和平均强度。实验结果表明，使用FWHVA进行特征提取比SampEn和FFT更快。并且与发作期EEG相关的平均强度特征显著高于健康和发作间期EEG的平均强度特征。此外，从健康状态中识别癫痫发作的100%分类准确率表明，基于FWHVA的特征在时间序列分类方面比基于FFT的频率特征和基于SampEn的熵指数更有前景。
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