Abstract

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Title:: Application of back-propagation network in algorithm for monitoring depth of anesthesia

Author(s):: GU Jiajun1; YE Jilun2; 3; CUI Yuhan1; CHEN Jin1; CHEN Lingling1; 1. School of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China 2. School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China 3. Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China

Keywords:: Keywords: depth of anesthesia electroencephalogram signal filter feedforward back-propagation neural network

Abstract:: Abstract: Anesthesia is an essential part of clinical operation, but the inappropriate depth of anesthesia (overdose or underdose) may cause harms to patients. Therefore, monitoring the depth of anesthesia has a high clinical value. Currently, electroencephalogram is the most potential method to detect the depth of anesthesia. After obtaining the pure electroencephalogram signal by filtering and other processing methods, the time-domain and frequency-domain characteristics are analyzed, and the corresponding parameters are calculated. Then the parameters are used as the input parameters of feedforward neural network, and a dimensionless constant which can evaluate the depth of anesthesia is obtained by back-propagation (BP) neural network fitting. The accuracy of using BP neural network fitting results to characterize the depth of anesthesia is generally more than 90%, which reflects that BP neural network has a high value in monitoring the depth of anesthesia.