# Variation Trends of Fractal Dimension in Epileptic EEG Signals
[PERSON]  
1 Division of Advanced Manufacturing, Tsinghua Shenzhen International Graduate School,  
Tsinghua University, Shenzhen 518055, China; [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (Y.X. ); [EMAIL_ADDRESS] (P.F.) [PERSON]  
1 Division of Advanced Manufacturing, Tsinghua Shenzhen International Graduate School,  
Tsinghua University, Shenzhen 518055, China; [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (Y.X. ); [EMAIL_ADDRESS] (P.F.) [PERSON]  
1 Division of Advanced Manufacturing, Tsinghua Shenzhen International Graduate School,  
Tsinghua University, Shenzhen 518055, China; [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (Y.X. ); [EMAIL_ADDRESS] (P.F.) [PERSON]  
1 Division of Advanced Manufacturing, Tsinghua Shenzhen International Graduate School,  
Tsinghua University, Shenzhen 5180555, China; [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (Y.X. ); [EMAIL_ADDRESS] (P.F.) [PERSON]  
1 Division of Advanced Manufacturing, Tsinghua Shenzhen International Graduate School,  
Tsinghua University, Shenzhen 5180555, China; [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (Y.X. ); [EMAIL_ADDRESS] (P.F.) [PERSON]  
1 Division of Advanced Manufacturing, Tsinghua Shenzhen International Graduate School,  
Tsinghua University, Shenzhen 5180555, China; [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (Y.X. ); [EMAIL_ADDRESS] (P.F.) [PERSON]  
1 Division of Advanced Manufacturing, Tsinghua Shenzhen International Graduate School,  
Tsinghua University, Shenzhen 5180555, China; [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (Y.X. ); [EMAIL_ADDRESS] (P.F.) [PERSON]  
1 Division of Advanced Manufacturing, Tsinghua Shenzhen International Graduate School,  
Tsinghua University, Shenzhen 5180555, China; [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (Y.X. ); [EMAIL_ADDRESS] (P.F.) [PERSON]  
1 Division of Advanced Manufacturing, Tsinghua Shenzhen International Graduate School,  
Tsinghua University, Shenzhen 518055, China; [EMAIL_ADDRESS]. the algorithms that could accurately analyze EEG signals for the automatic detection of epileptic seizures. Using the fractal algorithm to detect epileptic seizures was an important method and had been demonstrated [18; 19]. Currently, there were many commonly used fractal algorithms, among which the Higuchi algorithm was highly recommended because of its accuracy and high speed [20; 21]. In our previous studies, roughness scaling extraction (RSE) algorithm was proposed to detect epileptic seizures, which could be more accurate to calculate fractal dimension (\(D\)) relative to the traditional algorithms including Higuchi algorithm [22; 23]. An important basis for using the fractal algorithms to detect epileptic seizures was the \(D\) variation during seizure status compared with normal status. However, it was found that some publications reported that \(D\) in the seizure status had a downward trend [21; 24; 25], while other literature reported an upward trend of \(D\) variation [20; 22; 23; 26; 27], and such a significant divergence and its cause had not been studied in the available literature. Such a significant difference seriously undermined the feasibility and even the reliability of fractal algorithms in epileptic detection. Therefore, it would be of great importance to carry out an investigation on which trend of \(D\) variation in the seizure status should own and the underlying mechanism of the divergence mentioned above. In this study, based on EEG signals of the CHB-MIT Scalp EEG Database, both Higuchi and RSE algorithms with high accuracy in \(D\) calculation were used to calculate \(D\) values of epileptic EEG signals and study the \(D\) variation during seizure status compared with normal status. The denoising process for EEG signals was essential and there were lots of noise reduction methods, such as wavelet decomposition [28; 29] and empirical mode decomposition (EMD) [30; 31].