[The sample entropy and its application in EEG based epilepsy detection].
It is of great importance for the detection of epilepsy in clinical applications. Based on the limitations of the common used approximate entropy (ApEn) in the epilepsy detection, this paper analyzes epileptic EEG signals with the sample entropy (SampEn) approach, a new method for signal analysis with much higher precision than that of the ApEn. Data analysis results show that the values from both ApEn and SampEn decrease significantly when the epilepsy is burst. Furthermore, the SampEn is more sensitive to EEG changes caused by the epilepsy, about 15%-20% higher than the results of the ApEn.
Bai
Dongmei
D
Department of Electronic Engineering, Dalian University of Technology, Dalian 116024, China.
Qiu
Tianshuang
T
Li
Xiaobing
X
D000465:Algorithms; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D019277:Entropy; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted