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

Shih-Jui Chen; Chia-Ju Peng; Yi-Chun Chen; Yean-Ren Hwang; Ying-Sian Lai; Shou-Zen Fan; Kuo-Kuang Jen

doi:10.1016/j.cmpb.2016.08.024

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

Comput Methods Programs Biomed. 2016 Dec:137:77-85. doi: 10.1016/j.cmpb.2016.08.024. Epub 2016 Sep 13.

Authors

Shih-Jui Chen¹, Chia-Ju Peng¹, Yi-Chun Chen², Yean-Ren Hwang³, Ying-Sian Lai¹, Shou-Zen Fan⁴, Kuo-Kuang Jen⁵

Affiliations

¹ Department of Mechanical Engineering, National Central University, Taoyuan, Taiwan, ROC.
² Department of Optics and Photonics, National Central University, Taoyuan, Taiwan, ROC.
³ Department of Mechanical Engineering, National Central University, Taoyuan, Taiwan, ROC. Electronic address: yhwang@cc.ncu.edu.tw.
⁴ Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei, Taiwan, ROC.
⁵ National Chung-Shan Institute of Science and Technology, Taoyuan, Taiwan, ROC.

PMID: 28110742
DOI: 10.1016/j.cmpb.2016.08.024

Abstract

Background and objective: Intraoperative 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.

Methods: EEG 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.

Results: The 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.

Conclusions: This 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.

Keywords: Anesthesia; Electroencephalogram; Empirical mode decomposition.

Publication types

Comparative Study

MeSH terms

Analysis of Variance
Anesthesia, General*
Electroencephalography*
Empirical Research
Humans
Monitoring, Physiologic / methods*
ROC Curve