Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm - Zhu, Guohun; Li, Yan; Wen, Peng (Paul) (Computer methods and programs in biomedicine, 2014)
•Developing a fast algorithm for constructing a network from a time series in linear time.•Discriminating between healthy and seizure EEG signals with 100% accuracy with only two features.•Extracted features from a time series is faster and more robust to against noise than those based on FFT. 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.