Abstract

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Title:: Use of permutation wavelet entropy to evaluate EEG burst suppression

Author(s):: YUAN Sinian1; 2; DAN Guo1; 2; 3; YE Jilun1; 2; 3; ZHANG Xu1; 2; 3; NIU Hangduo1; 2; MA Shengcai1; 2; LI Ruowei1; 2; ZHU Zifu1; 2; 1. Department of Biomedical Engineering, School of Medicine, Shenzhen University, Shenzhen 518060, China 2. Shenzhen Key Laboratory for Biomedical Engineering, Shenzhen 518060, China 3. Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China

Keywords:: Keywords: electroencephalogram burst suppression level permutation entropy wavelet entropy

Abstract:: Abstract: From the perspective of nonlinear dynamics, the permutation entropy of electroencephalogram (EEG) signal is calculated, and then the wavelet entropy of the obtained permutation entropyis calculated to obtain a new parameter, namely permutation wavelet entropy (PEWE), for quantifying the burst suppression level of EEG signal. The results show that in the test of 4 cases of data, the correlation coefficient between PEWE and SR index output by the BIS module is 0.942 5, indicating that PEWE can be used as a measure to quantify the burst suppression level of EEG signal, which provides a new idea to evaluate EEG burst suppression.