US 12,334,215 B2
Method for identifying anesthetic drug, and method and device for processing anesthesia electroencephalogram signal
Zhigang Ye, Shenzhen (CN); Xingliang Jin, Shenzhen (CN); Xianliang He, Shenzhen (CN); Ningling Zhang, Shenzhen (CN); Hanyuan Luo, Shenzhen (CN); Ming Li, Shenzhen (CN); and Zuming Yao, Shenzhen (CN)
Assigned to SHENZHEN MINDRAY BIO-MEDICAL ELECTRONICS CO., LTD., Shenzhen (CN)
Filed by SHENZHEN MINDRAY BIO-MEDICAL ELECTRONICS CO., LTD., Shenzhen (CN)
Filed on Jun. 29, 2020, as Appl. No. 16/914,648.
Application 16/914,648 is a continuation of application No. PCT/CN2017/120348, filed on Dec. 29, 2017.
Prior Publication US 2020/0327995 A1, Oct. 15, 2020
Int. Cl. G06F 11/30 (2006.01); A61B 5/00 (2006.01); G16H 40/63 (2018.01); G16H 50/20 (2018.01); G16H 70/40 (2018.01)
CPC G16H 40/63 (2018.01) [A61B 5/4821 (2013.01); G16H 50/20 (2018.01); G16H 70/40 (2018.01); G06F 2218/08 (2023.01)] 15 Claims
OG exemplary drawing
 
9. A method for processing an anesthesia electroencephalogram signal, the method comprising:
acquiring an electroencephalogram signal using an electroencephalogram sensor;
selecting a target anesthesia depth model from an anesthesia depth model database according to a drug type of an anesthesia drug, each anesthesia depth model in the anesthesia depth model database being trained according to a specific drug type, an electroencephalogram signal corresponding to the specific drug type, a marked anesthesia depth, and a correspondence relationship between signal characteristics of the corresponding electroencephalogram signal and the marked anesthesia depths; and
obtaining an anesthesia depth value based on the target anesthesia depth model and the acquired electroencephalogram signal, comprising: determining the anesthesia depth value according to signal characteristics of the acquired electroencephalogram signal and the correspondence relationship between the signal characteristics of the corresponding electroencephalogram signal and the marked anesthesia depths of the target anesthesia depth model,
wherein the drug type is automatically identified according to a signal characteristic of the electroencephalogram signal and a preset prediction model, and confirmed by a user input, the prediction model comprising a correspondence relationship between at least one signal characteristic of an electroencephalogram signal and a drug type.