Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia
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 process...
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| Format: | Article |
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MDPI AG
2013-08-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/15/9/3325 |
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| author | Jiann-Shing Shieh Jeng-Fu Wu Kuo-Kuang Jen Jeng-Rung Huang Maysam F. Abbod Shou-Zen Fan |
| author_facet | Jiann-Shing Shieh Jeng-Fu Wu Kuo-Kuang Jen Jeng-Rung Huang Maysam F. Abbod Shou-Zen Fan |
| author_sort | Jiann-Shing Shieh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T20:38:13Z |
| format | Article |
| id | doaj.art-c5fb9be8633d44a7baa583344a79d357 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T20:38:13Z |
| publishDate | 2013-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-c5fb9be8633d44a7baa583344a79d3572022-12-22T04:04:18ZengMDPI AGEntropy1099-43002013-08-011593325333910.3390/e15093325Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of AnesthesiaJiann-Shing ShiehJeng-Fu WuKuo-Kuang JenJeng-Rung HuangMaysam F. AbbodShou-Zen FanEEG (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.http://www.mdpi.com/1099-4300/15/9/3325sample entropyelectroencephalographydepth of anesthesiamultivariate empirical mode decompositionartificial neural networksreceiver operating characteristic curve |
| spellingShingle | Jiann-Shing Shieh Jeng-Fu Wu Kuo-Kuang Jen Jeng-Rung Huang Maysam F. Abbod Shou-Zen Fan Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia Entropy sample entropy electroencephalography depth of anesthesia multivariate empirical mode decomposition artificial neural networks receiver operating characteristic curve |
| title | Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia |
| title_full | Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia |
| title_fullStr | Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia |
| title_full_unstemmed | Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia |
| title_short | Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia |
| title_sort | application of multivariate empirical mode decomposition and sample entropy in eeg signals via artificial neural networks for interpreting depth of anesthesia |
| topic | sample entropy electroencephalography depth of anesthesia multivariate empirical mode decomposition artificial neural networks receiver operating characteristic curve |
| url | http://www.mdpi.com/1099-4300/15/9/3325 |
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