Research Area:  Machine Learning
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.
Author(s) Name:  GuohunZhu, YanLi,Peng (Paul)Wen
Journal name:  Computer Methods and Programs in Biomedicine
Publisher name:  Elsevier
Volume Information:  Volume 115, Issue 2, July 2014, Pages 64-75
Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0169260714001266