Sample Entropy Analysis of EEG Signals via Artificial Neural Networks to Model Patients’ Consciousness Level Based on Anesthesiologists Experience

Abstract

Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.

EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks

“…Firstly, two research nurses keep observing the state of patients and recording the events and signs which happen during surgery in operation room and possibly have relationship with “the state of anesthetic depth” in detail and carefully [ 38 ], for example, the start and end time of the anesthetic events including induction and extubation, drugs administered time and their dose, MAC values recorded every five minutes during the whole period of anesthesia, and so on. Then, five experienced anesthesiologists need to make decision by the individual to plot the changes of “the state of anesthetic depth” of patients over the whole duration of operation based on anesthesia record and their previous experiences.…”

“…In order to be consistent with BIS, the range of these curves is from 0 to 100, and 100 indicates totally awake state and 0 is equivalent to EEG silence, and a value between 40 and 60 represents an appropriate anesthesia level during surgery for general anesthesia. Because the original curve was plotted by hand drawing, so, finally, it is digitalized and resampled with a frequency of 0.2 Hz like BIS index to a single dimensionless number series called expert assessment of conscious level (EACL) [ 38 ]. Each anesthesiologist with different experience may have a different perspective on EACL; therefore, in order to measure consciousness level more accurately, the mean values of EACL from five anesthesiologists were obtained as target instead of BIS index.…”

“…However, it would be more applicable and reasonable if there is a real gold standard of DOA as the target. So in comparison, “the state of anesthetic depth” called expert assessment of conscious level (EACL) [ 38 ] which is decided by five experienced anesthesiologists based on detailed recordings during surgery was used as the target to train ANN.…”

Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks

“…In this case, the output data was produced by 5 medical doctors who concluded the anesthesia level graphically after evaluating the vital signs. This 5-doctor output was first digitized [20] and resampled at 0.2 Hz, as well as BIS frequency and other input parameters, eventually being an averaged value. The whole system is shown in Figure 1.…”

Sample Entropy in Electrocardiogram During Atrial Fibrillation

“…are used as statistics to evaluate complex systems. SampEn can be evaluated from time-series data with irregular fluctuations 8 and has been used to examine the complexity of time-series data for biological signals such as electroencephalogram (EEG), 9,10 ECG of the R-R interval, [11][12][13][14][15] and respiratory movement. 16 For example, SampEn for EEG was proposed to monitor the depth of anesthesia for patients during surgery, and SampEn has been found to be more feasible for real-time detection.…”