title: EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery.
abstract: Electroencephalography (EEG) has been widely utilized to measure the depth of anaesthesia (DOA) during operation. However, the EEG signals are usually contaminated by artifacts which have a consequence on the measured DOA accuracy. In this study, an effective and useful filtering algorithm based on multivariate empirical mode decomposition and multiscale entropy (MSE) is proposed to measure DOA. Mean entropy of MSE is used as an index to find artifacts-free intrinsic mode functions. The effect of different levels of artifacts on the performances of the proposed filtering is analysed using simulated data. Furthermore, 21 patients' EEG signals are collected and analysed using sample entropy to calculate the complexity for monitoring DOA. The correlation coefficients of entropy and bispectral index (BIS) results show 0.14 ± 0.30 and 0.63 ± 0.09 before and after filtering, respectively. Artificial neural network (ANN) model is used for range mapping in order to correlate the measurements with BIS. The ANN method results show strong correlation coefficient (0.75 ± 0.08). The results in this paper verify that entropy values and BIS have a strong correlation for the purpose of DOA monitoring and the proposed filtering method can effectively filter artifacts from EEG signals. The proposed method performs better than the commonly used wavelet denoising method. This study provides a fully adaptive and automated filter for EEG to measure DOA more accuracy and thus reduce risk related to maintenance of anaesthetic agents.
authors.affiliation: Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Ministry of Education, Wuhan University of Technology, Wuhan, 430070, Hubei, China.|School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, Hubei, China.
authors.forename: Quan
authors.initials: Q
authors.lastname: Liu
authors.affiliation: School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, Hubei, China.
authors.forename: Yi-Feng
authors.initials: YF
authors.lastname: Chen
authors.affiliation: Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei, 100, Taiwan.
authors.forename: Shou-Zen
authors.initials: SZ
authors.lastname: Fan
authors.affiliation: Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UB8 3PH, UK.
authors.forename: Maysam F
authors.initials: MF
authors.lastname: Abbod
authors.affiliation: Department of Mechanical Engineering, Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Chung-Li District, Taoyuan City, 32003, Taiwan. jsshieh@saturn.yzu.edu.tw.
authors.forename: Jiann-Shing
authors.initials: JS
authors.lastname: Shieh
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D056805:Consciousness Monitors; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D019277:Entropy; D006801:Humans; D064795:Intraoperative Neurophysiological Monitoring / Q000379:methods*; D015233:Models, Statistical; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Depth of anaesthesia; Electroencephalography; Filtering; Mean entropy value; Multiscale entropy; Multivariate empirical mode decomposition