Medical & Biological Engineering & Computing

, Volume 55, Issue 8, pp 1435–1450 | Cite as

EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery

  • Quan Liu
  • Yi-Feng Chen
  • Shou-Zen Fan
  • Maysam F. Abbod
  • Jiann-Shing Shieh
Original Article


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.


Depth of anaesthesia Electroencephalography Multivariate empirical mode decomposition Filtering Multiscale entropy Mean entropy value 


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Copyright information

© International Federation for Medical and Biological Engineering 2016

Authors and Affiliations

  • Quan Liu
    • 1
    • 2
  • Yi-Feng Chen
    • 2
  • Shou-Zen Fan
    • 3
  • Maysam F. Abbod
    • 4
  • Jiann-Shing Shieh
    • 5
  1. 1.Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Ministry of EducationWuhan University of TechnologyWuhanChina
  2. 2.School of Information EngineeringWuhan University of TechnologyWuhanChina
  3. 3.Department of Anesthesiology, College of MedicineNational Taiwan UniversityTaipeiTaiwan
  4. 4.Department of Electronic and Computer EngineeringBrunel University LondonUxbridgeUK
  5. 5.Department of Mechanical Engineering, Innovation Center for Big Data and Digital ConvergenceYuan Ze UniversityChung-Li District, Taoyuan CityTaiwan

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