Comparison of FFT and marginal spectra of EEG using empirical mode decomposition to monitor anesthesia

A method for monitoring the anesthetized patients during surgery is proposed.The analysis characterizes the state of consciousness using electroencephalogram.There is agreement between the performance of two frequency analysis techniques (EMD+FFT and HHT).This work establishes a suitable classifier of anesthesia through the optimal frequency threshold. Background and objectiveIntraoperative awareness refers that patients can recall aspects of their surgery after being put under general anesthesia. This distressing complication causes affected patients to be conscious and probably feel pain, leading to emotional trauma or other sequelae. Monitoring and administrating the depth of anesthesia is necessary to prevent patients from awareness during a medical operation. In this paper, we analyzed the electroencephalograms (EEGs) of patients to characterize their anesthesia. The data set, awareness and anesthesia groups, each contained 558 samples, including patients who had undergone different types of surgeries. MethodsEEG signals acquired from patients in an aware state or under anesthesia were decomposed into a set of intrinsic mode functions (IMFs) through empirical mode decomposition (EMD). Fast Fourier transform (FFT) and Hilbert transform (HT) analyses were then performed on each IMF to determine the frequency spectra. The probability distributions of expected values of frequencies were generated for the same IMF in the two groups of patients. The corresponding statistical data, including analysis of variance tests, were also calculated. A receiver operating characteristic curve was used to identify optimal frequency value to discriminate between the two states of consciousness. ResultsThe frequencies of the IMFs for aware patients were found to be higher than those for anesthetized patients. The optimal frequency threshold by using FFT (or HT) for IMF 1 was 21.08 (or 25.00) Hz. IMF1 performed the highest with respect to the area under the curve (AUC) of 0.993 for FFT (or 0.989 for HT); hence it can be applied as a useful classifier to distinguish between fully anesthetized patients and aware patients. ConclusionsThis paper proposes a method for identifying whether patients' state of consciousness during a range of surgery types is under anesthesia or aware. Our method involves using EEG to characterize the depth of anesthesia through two frequency analysis techniques. On the basis of our analyses, we conclude that the performance of IMF1 is satisfactory in distinguishing between patients' states of consciousness during surgery requiring general anesthesia.