Investigation on anesthesia depth monitoring based on electroencephalogram

Yili Cheng; Jing Shi; Jiguang Lu; Dan Liu; Hong Tang; Qisong Wang; Jinwei Sun

doi:10.1117/12.2688191

28 August 2023 Investigation on anesthesia depth monitoring based on electroencephalogram

Yili Cheng, Jing Shi, Jiguang Lu, Dan Liu, Hong Tang, Qisong Wang, Jinwei Sun

Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 127241F (2023) https://doi.org/10.1117/12.2688191
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China

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%.

Citation Download Citation

Yili Cheng, Jing Shi, Jiguang Lu, Dan Liu, Hong Tang, Qisong Wang, and Jinwei Sun "Investigation on anesthesia depth monitoring based on electroencephalogram", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 127241F (28 August 2023); https://doi.org/10.1117/12.2688191

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