Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia

Jeng-Rung Huang,Kuo-Kuang Jen,Shou-Zen Fan,Maysam Abbod,Jeng-Fu Wu,Jiann-Shing Shieh

doi:10.3390/e15093325

Abstract

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.

Highlights

Accurate and non-invasive monitoring of depth of anesthesia (DOA) [1] is very desirable during surgery
The EEG signals are collected from thirty patients, whose ages are ranged from 20 to 80 and under ear nose and throat (ENT) surgery with general anesthesia at the National Taiwan University
artificial neural network (ANN) is utilized to do the modelling, where 70% of the thirty cases are used for training, 15% are used for validation, and the rest for testing

Summary

Introduction

Accurate and non-invasive monitoring of depth of anesthesia (DOA) [1] is very desirable during surgery. To achieve this purpose, many of the techniques or devices have been examined or used as methods to indicate the DOA, such as heart rate, blood pressure, and electroencephalogram (EEG). Signals [2,3] Among these methods, the analysis of EEG is very intuitive, because the main action of general anesthetic agents takes place in the brain. Except for bispectral analysis, these methods use linear computational algorithms. None of these methods have been proved to completely express the EEG message.

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