Analysis on the Depth of Anesthesia by Using EEG and ECG Signals

다국어 초록 (Multilingual Abstract)

Anesthesia, which started being used to remove pain during surgery, has become itself one of the major concerns to be considered during surgery. While actual anesthesia is being performed, patients tend to have unpleasant experiences,due to wakening that accompanies pain, or wakening that does not accompany pain. Since this awakening during anesthesia is a most unpleasant experience in a patient’s life, evaluating the depth of anesthesia during surgery is essential for patients to avoid this experience. Although there has been much effort on the understanding and measurement of the depth of anesthesia, while various researches were performed on the need of anesthesia, the development of an indicator that could objectively evaluate the depth of anesthesia, other than by using the patient’s vital signs, is still inadequate. Therefore, this study was to develop an objective indicator by using EEG and ECG, which are essentially measured during the surgery, to evaluate the depth of anesthesia. The experiment was performed by taking patients who require a relatively short operation time, and general inhalation anesthetics among surgical patients in obstetrics and gynecology as the subjects of experiment, to measure the EEG and ECG signals of patients under anesthetics. The result showed that SEF using EEG and LF, HF using ECG signal and correlation dimension analysis parameter were valuable parameters that could measure the depth of anesthesia, by the stage of anesthesia.

참고문헌 (Reference)

1 Liao F, "Using recurrence network approach to quantify nonlinear dynamics of skin blood flow in response to loading pressure" 2012 : 4196-4199, 2012

2 Freeman WJ, "Simulated power spectral density (PSD) of background electrocorticogram (ECoG)" 3 (3): 2009

3 C. K Karmakar, "Sensitivity of temporal heart rate variability in Poincaré plot to changes in parasympathetic nervous system activity" 10 : 17-, 2011

4 L. Gugino, "Quantitative EEG changes associated with loss and return of consciousness in healthy adult volunteers anaesthetized with propofol or sevoflurane" 87 : 421-428, 2001

5 Peter Grassberger, "Measuring the Strangeness of Strange Attractors" 9 (9): 189-208, 1983

6 Lewis MJ, "Influence of age and aerobic fitness on the multifractal characteristics of electrocardiographic RR time-series" 4 (4): 1-13, 2013

7 U. Kraus, "Individual Daytime Noise Exposure during Routine Activities and Heart Rate Variability in Adults: A Repeated Measures Study" 121 : 607-612, 2013

8 Xiuqing Zheng, "Improving Spatial Adaptivity of Nonlocal Means in Low-Dosed CT Imaging Using Pointwise Fractal Dimension" 2013 (2013): 2013

9 A. T. Mazzeo, "Heart rate variability: a diagnostic and prognostic tool in anesthesia and intensive care" 55 : 79-811, 2011

10 Recart A, "Effect of auditory evoked potential index monitoring on anesthetic drug requirements and recovery profile after laparoscopic surgery: a clinical utility study" 99 (99): 813-818, 2003

11 A. Miller, "Does bispectral analysis and anything but complexity? BIS sub-components may be superior to BIS for detection of awareness" 93 (93): 596-597, 2004

12 Janda M, "Design and implementation of a control system reflecting the level of analgesia during general anesthesia" 58 (58): 1-11, 2013

13 Benitez R, "Characterization of the nonlinear content of the heart rate dynamics during myocardial ischemia" 31 (31): 660-667, 2009

14 Curtis BM, "Autonomic Tone as a Cardiovascular RiskFactor" 77 (77): 45-54, 2002

15 Isgum I, "Automatic coronary calcium scoring in low-dose chest computed tomography" 31 (31): 2322-2334, 2012

16 Suzuki N, "Assessment of maternal heart-rate variability during labor using wavelet-based power spectral analysis" 74 (74): 35-40, 2012

17 McKeever S, "An observational study exploring amplitude-integrated electroencephalogram and spectral edge frequency during paediatric anaesthesia" 40 (40): 275-284, 2012

18 John ER, "A field theory of consciousness" 10 (10): 184-213, 2001