Abstract:
The electroencephalogram (EEG) signals with different brain states show different nonlinear dynamics. Recently the statistical properties of complex networks theory have ...Show MoreMetadata
Abstract:
The electroencephalogram (EEG) signals with different brain states show different nonlinear dynamics. Recently the statistical properties of complex networks theory have been applied to explore the nonlinear dynamics of time series, which studies the dynamics of time series via its organization. This study combines the complex networks theory with epileptic EEG analysis and applies the statistical properties of complex networks to the automatic epileptic EEG detection. We construct the complex networks from the epileptic EEG series and then calculate the entropy of the degree distribution of the network (NDDE). The NDDE corresponding to the ictal EEG is lower than interictal EEG's. The experiment result shows that the approach using the NDDE as a classification feature obtains robust performance of epileptic seizure detection and the accuracy is up to 95.75%.
Published in: 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
Date of Conference: 29 October 2013 - 01 November 2013
Date Added to IEEE Xplore: 02 January 2014
Electronic ISBN:978-986-90006-0-4
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- IEEE Keywords
- Index Terms
- EEG Signals ,
- Degree Distribution ,
- Seizure Detection ,
- Epileptic EEG ,
- Time Series ,
- Complex Network ,
- Feature Classification ,
- Nonlinear Dynamics ,
- Automatic Detection ,
- Brain States ,
- Epileptic Seizures ,
- EEG Analysis ,
- Dynamic Time Series ,
- Complex Network Theory ,
- Changes In Values ,
- Classification Accuracy ,
- Distance Matrix ,
- Phase Space ,
- EEG Data ,
- Approximate Entropy ,
- Largest Lyapunov Exponent ,
- Nonlinear Time Series ,
- Lyapunov Exponent ,
- Signal Shape
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- EEG Signals ,
- Degree Distribution ,
- Seizure Detection ,
- Epileptic EEG ,
- Time Series ,
- Complex Network ,
- Feature Classification ,
- Nonlinear Dynamics ,
- Automatic Detection ,
- Brain States ,
- Epileptic Seizures ,
- EEG Analysis ,
- Dynamic Time Series ,
- Complex Network Theory ,
- Changes In Values ,
- Classification Accuracy ,
- Distance Matrix ,
- Phase Space ,
- EEG Data ,
- Approximate Entropy ,
- Largest Lyapunov Exponent ,
- Nonlinear Time Series ,
- Lyapunov Exponent ,
- Signal Shape