Abstract
In monitoring the depth of anesthesia (DOA), the electroencephalography (EEG) signals of patients have been utilized during surgeries to diagnose their level of consciousness. Different entropy methods were applied to analyze the EEG signal and measure its complexity, such as spectral entropy, approximate entropy (ApEn) and sample entropy (SampEn). However, as a weak physiological signal, EEG is easily subject to interference from external sources such as the electric power, electric knives and other electrophysiological signal sources, which lead to a reduction in the accuracy of DOA determination. In this study, we adopt the multivariate empirical mode decomposition (MEMD) to decompose and reconstruct the EEG recorded from clinical surgeries according to its best performance among the empirical mode decomposition (EMD), the ensemble EMD (EEMD), and the complementary EEMD (CEEMD) and the MEMD. Moreover, according to the comparison between SampEn and ApEn in measuring DOA, the SampEn is a practical and efficient method to monitor the DOA during surgeries at real time.
Highlights
Electroencephalography (EEG) is a technique used to evaluate the absence of cerebral cortex function for monitoring the depth of anesthesia (DOA) in clinical surgeries
Richman and Moorman [19] compared the approximate entropy (ApEn) with the sample entropy (SampEn) in the dependency on the length of time series, the results show that the ApEn is much slower than SampEn in the speed of stability convergence because of its reliance on the length N
Since extended empirical mode decomposition (EMD) methods have been applied to the analysis and processing of various human physiological signals, it is important to figure out the performances and advantages of these extended
Summary
Electroencephalography (EEG) is a technique used to evaluate the absence of cerebral cortex function for monitoring the depth of anesthesia (DOA) in clinical surgeries. The non-invasively detected EEG signals from the forehead of the human body are mixed with signals from external interferences such as the electric power, electric knives and other electrophysiological signal sources like electromyography (EMG), electrocardiography (ECG), electrooculography (EOG), etc Such interferences cause serious difficulties in obtaining pure EEG signals during surgeries because of their non-linear and non-stationary nature. In previous study [14,15,16,17], the approximate entropy (ApEn) and the sample entropy (SampEn) were proposed to monitor the DOA of patients during surgeries, which showed that the SampEn is more adaptive to the real time detection Both methods are disturbed by the interferences mentioned above, the series of extended EMD methods should be sufficient to reconstruct the EEG signals for better determination of DOA. Discussion of the results and future work are presented in section four with the conclusions
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