ArticleThe open biomedical engineering journal2013
A Predictive Model of Anesthesia Depth Based on SVM in the Primary Visual Cortex.
Article in The open biomedical engineering journal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Photoplethysmography temporal marker-based machine learning classifier for anesthesia drug detection.Medical & biological engineering & computing · 2022Article
- Association Between Burst-Suppression Latency and Burst-Suppression Ratio Under Isoflurane or Adjuvant Drugs With Isoflurane Anesthesia in Mice.Frontiers in pharmacology · 2021Article
- Alternating Periods of High and Low-Entropy Neural Ensemble Activity During Image Processing in the Primary Visual Cortex of Rats.The open biomedical engineering journal · 2016Article
- Quality metrics of spike sorting using neighborhood components analysis.The open biomedical engineering journal · 2014Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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