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

TitleA Predictive Model of Anesthesia Depth Based on SVM in the Primary Visual Cortex.
Publication TypeJournal Article
Year of Publication2013
AuthorsShi L, Li X, Wan H
JournalThe open biomedical engineering journal
Volume7
Pagination71-80
Date Published2013
Abstract

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.

DOI10.2174/1874120720130701002
Alternate JournalOpen Biomed Eng J