Comput Methods Programs Biomed - Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm.

Tópicos

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Resumo

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.

Resumo Limpo

paper propos fast weight horizont visibl graph construct algorithm fwhva identifi seizur eeg signal perform fwhva evalu compar fast fourier transform fft sampl entropi sampen method two noiserobust graph featur base fwhva mean degre mean strength investig use two chao signal five group eeg signal experiment result show featur extract use fwhva faster sampen fft mean strength featur associ ictal eeg signific higher healthi interict eeg addit classif accuraci identifi seizur healthi show featur base fwhva promis frequenc featur base fft entropi indic base sampen time seri classif

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