@inproceedings{d31afbecbd1b434084b8a248753ac3a3,
title = "Investigation on Anesthesia Depth Monitoring Based on Electroencephalogram",
abstract = "The depth of anesthesia is an important indicator for determining the clinical dosage of anesthesia. Currently available monitoring methods have problems with accuracy relying on human experience for judgment and poor precision. Electroencephalogram (EEG) signals have the characteristic of reflecting the mental state of the cerebral cortex. Therefore, this paper proposes a feature extraction method combining EEG signal symbolic entropy and short-time Fourier transform, as well as an anesthesia depth monitoring method based on least squares-support vector machines (LS-SVM) classification. In addition, this paper uses the anesthesia depth measurement standard based on the energy ratio of various rhythmic signals to divide the anesthesia depth into four levels. An anesthesia depth monitoring experiment was conducted using a self-built EEG signal collection platform. According to the experimental results, the proposed method can accurately classify the depth of anesthesia, with a classification accuracy of 86.87\%.",
keywords = "Depth of anesthesia, EEG signals, LS-SVM",
author = "Yili Cheng and Jing Shi and Jiguang Lu and Dan Liu and Hong Tang and Qisong Wang and Jinwei Sun",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE.; 2nd International Conference on Biomedical and Intelligent Systems, IC-BIS 2023 ; Conference date: 28-04-2023 Through 30-04-2023",
year = "2023",
doi = "10.1117/12.2688191",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Ming Chen and Gangmin Ning",
booktitle = "Second International Conference on Biomedical and Intelligent Systems, IC-BIS 2023",
address = "美国",
}