Depth classification algorithm of anesthesia based on model fusion

• Published in: Multimedia tools and applications 2024-03, Vol.83 (33), p.79589-79605
• Main Authors: Wang, Miaorong, Zhu, Fugui, Hou, Changjun, Huo, Danqun, Lei, Yinglan, Long, Qin, Luo, Xiaogang
• Format: Article
• Language: English
• Subjects: Algorithms; Anesthesia; Artificial intelligence; Artificial neural networks; Back propagation networks; Central nervous system; Classification; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Decision trees; Machine learning; Monitoring; Multimedia Information Systems; Neural networks; Special Purpose and Application-Based Systems; Track 2: Medical Applications of Multimedia
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-024-18727-6

Accurate monitoring of anesthesia status is very important in surgery, as it can guide anesthesiologists, reduce drug usage, and reduce postoperative adverse effects. However, due to the complex interactions between anesthetic drugs and the central nervous system, there is no perfect monitoring method. In recent years, the development of artificial intelligence technology has offered the possibility of using machine learning algorithms to achieve more accurate monitoring of anesthesia depth. In this paper, four levels of anesthesia states were classified and multifaceted feature values were extracted from Electroencephalogram (EEG) signals, a convolutional neural network-based KRDGB-CNN model was constructed, which was based on K-nearest neighbor (KNN), Random Forest (RF), Decision Tree (DT), Gaussian Naive Baye (GNB), and Back propagation Neural Network (BP), and fused by Convolutional Neural Network (CNN) algorithm for decision layers. By evaluating the model performance on the collected data, the results show that the model outperforms existing algorithms in terms of classification accuracy and specificity, and can effectively improve the robustness and accuracy of the algorithm.