A Predictive Model of Anesthesia Depth Based on SVM in the Primary Visual Cortex

Li Shi, Xiaoyuan Li*, Hong Wan
School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan, China

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© Shi et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, No.100, Ke-Xue Rd., PR China; Tel: 86-13613717322; Fax: +86-0371-67783113; E-mail:


In this paper, a novel model for predicting anesthesia depth is put forward based on local field potentials (LFPs) in the primary visual cortex (V1 area) of rats. The model is constructed using a Support Vector Machine (SVM) to realize anesthesia depth online prediction and classification. The raw LFP signal was first decomposed into some special scaling components. Among these components, those containing higher frequency information were well suited for more precise analysis of the performance of the anesthetic depth by wavelet transform. Secondly, the characteristics of anesthetized states were extracted by complexity analysis. In addition, two frequency domain parameters were selected. The above extracted features were used as the input vector of the predicting model. Finally, we collected the anesthesia samples from the LFP recordings under the visual stimulus experiments of Long Evans rats. Our results indicate that the predictive model is accurate and computationally fast, and that it is also well suited for online predicting.

Keywords: : Anesthesia Depth, Local Field Potential, Complexity Analysis, Wavelet Transform, Support Vector Machine..