EEG non-linear feature extraction using correlation dimension and Hurst exponent

Abstract In this work, we evaluated the differences between epileptic electroencephalogram (EEG) and interictal EEG by computing some non-linear features. Correlation dimension (CD) and Hurst exponent (H) were calculated for 100 segments of epileptic EEG and 100 segments of interictal EEG. A comparison was made between epileptic EEG and interictal EEG in those non-linear parameters. Results show that the mean values of CD are 2·64 for epileptic EEG and 4·55 for interictal EEG. We also calculated approximate entropy (ApEn) of those EEG signals. The mean values of ApEn are 0·90 for epileptic EEG and 4·55 for interictal EEG. The values of CD and ApEn of epileptic EEG are generally lower than those of interictal EEG, indicating less complexity of EEG signals during seizures. The mean values of Hurst exponent are 0·19 for epileptic EEG and 0·29 for interictal EEG. Hurst exponents for epileptic EEG and interictal EEG are both <0·5. This indicates that both epileptic and interictal EEGs show long-range anticorrelation. The value of Hurst exponent of epileptic EEG signals is lower than that of interictal EEG signals, showing that the degree of anticorrelation of epileptic EEG signals is larger than that of interictal EEG. Hence, the non-linear parameters such as CD and Hurst exponent can help interpret epileptic and interictal EEGs and their neurodynamics.