Key result
Filtering EEG signals with multivariate empirical mode decomposition and artificial neural networks yielded an AUC of 0.969 ± 0.028 for assessing depth of anesthesia, compared to 0.733 ± 0.123 without filtering.
Why the study?
Does the use of MEMD and ANN improve the accuracy of EEG sample entropy in interpreting the depth of anesthesia?
Does the use of MEMD and ANN improve the accuracy of EEG sample entropy in interpreting the depth of anesthesia?
Absolute Event Rate: 0.969% vs 0.733%
The application of MEMD and ANN significantly improves the accuracy of EEG sample entropy in assessing the depth of anesthesia compared to unfiltered signals.
May enhance EEG anesthesia depth monitoring; hypothesis-generating and leaves validation open.
EEG (Electroencephalography) signals can express the human awareness activities and consequently it can indicate the depth of anesthesia. On the other hand, Bispectral-index (BIS) is often used as an indicator to assess the depth of anesthesia. This study is aimed at using an advanced signal processing method to analyze EEG signals and compare them with existing BIS indexes from a commercial product (i.e., IntelliVue MP60 BIS module). Multivariate empirical mode decomposition (MEMD) algorithm is utilized to filter the EEG signals. A combination of two MEMD components (IMF2 + IMF3) is used to express the raw EEG. Then, sample entropy algorithm is used to calculate the complexity of the patients’ EEG signal. Furthermore, linear regression and artificial neural network (ANN) methods were used to model the sample entropy using BIS index as the gold standard. ANN can produce better target value than linear regression. The correlation coefficient is 0.790 ± 0.069 and MAE is 8.448 ± 1.887. In conclusion, the area under the receiver operating characteristic (ROC) curve (AUC) of sample entropy value using ANN and MEMD is 0.969 ± 0.028 while the AUC of sample entropy value without filter is 0.733 ± 0.123. It means the MEMD method can filter out noise of the brain waves, so that the sample entropy of EEG can be closely related to the depth of anesthesia. Therefore, the resulting index can be adopted as the reference for the physician, in order to reduce the risk of surgery.
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Huang et al. (2013) studied Depth of anesthesia. Multivariate empirical mode decomposition (MEMD) and sample entropy via artificial neural networks vs. Sample entropy without filter was evaluated on Area under the receiver operating characteristic (ROC) curve (AUC) for depth of anesthesia. Filtering EEG signals with multivariate empirical mode decomposition and artificial neural networks yielded an AUC of 0.969 ± 0.028 for assessing depth of anesthesia, compared to 0.733 ± 0.123 without filtering.
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