Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm

•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...

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Vydáno v:	Computer methods and programs in biomedicine Ročník 115; číslo 2; s. 64 - 75
Hlavní autoři:	Zhu, Guohun, Li, Yan, Wen, Peng (Paul)
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Kidlington Elsevier Ireland Ltd 01.07.2014 Elsevier
Témata:	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Biological and medical sciences Computational complexity Computer science; control theory; systems Computer Simulation Databases, Factual - statistics & numerical data Diagnosis, Computer-Assisted - methods Diagnosis, Computer-Assisted - statistics & numerical data Electrodiagnosis. Electric activity recording Electroencephalography - statistics & numerical data Epilepsy Epilepsy - diagnosis Exact sciences and technology Fourier Analysis Headache. Facial pains. Syncopes. Epilepsia. Intracranial hypertension. Brain oedema. Cerebral palsy Humans Information retrieval. Graph Internal Medicine Investigative techniques, diagnostic techniques (general aspects) Mean degree Mean strength Medical sciences Nervous system Nervous system (semeiology, syndromes) Neurology Nonlinear Dynamics Other Theoretical computing Weighted horizontal visibility graph Mean degree Mean strength Computational complexity Weighted horizontal visibility graph Epilepsy Chaos Fast Fourier transformation Time series Electroencephalography Entropy Graph theory Pattern recognition Weighted graph Experimental result Classification Mental disorder Selection criterion Noise immunity Feature extraction Visibility Fast algorithm Pattern extraction Strength
ISSN:	0169-2607, 1872-7565, 1872-7565
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Shrnutí:	•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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0169-2607 1872-7565 1872-7565
DOI:	10.1016/j.cmpb.2014.04.001