期刊
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
卷 55, 期 8, 页码 1435-1450出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s11517-016-1598-2
关键词
Depth of anaesthesia; Electroencephalography; Multivariate empirical mode decomposition; Filtering; Multiscale entropy; Mean entropy value
类别
资金
- Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan
- Ministry of Science and Technology [NSC102-2911-I-008-001]
- Chung-Shan Institute of Science and Technology in Taiwan [CSIST-095-V301, CSIST-095-V302]
- National Natural Science Foundation of China [51475342]
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.
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