Complexlty Analysis Of Heart Rate Varlahlllty During Anaesthesia

Heart rate variability (HRV) refers to minor differences between cardiac cycles. Recently, analysis of HRV is a hot point in ECG analysis. HRV contains a lot of information about the cardiovascular regulation, while subtracting and analyzing of the information of HRV can offer a quantitative evaluation of the tenseness and the balance of the sympathetic and the vagus nerve activities and their effect on cardiovascular motion, as well as indirectly obtaining some information about the high-level nervous function and thermoregulation, peripheral vascular activity. Analysis of HRV is a non-invasive method in detecting the function of the heart and understanding the concerned physiology mechanism. Nowadays, analysis of HRV is widely applied to the research of cardiovascular diseases, diabetes and so on.In recent years, along with the development of anaesthesia, particularly of its techniques, methods, equipments, and of informatics, more and more attentions have been payed to HRV. Anathesia drug, lose of consciousness, stimulation of surgery can affect on the function of autonomic nerves, HRV evidently alters as well. Many researches show that analysis of HRV can offer the important information and means for monitoring the depth of anaesthesia, studying the characteristic of anaesthesia drugs, forecasting the patient after surgery and so on.Heart rate variability is nonlinear in nature, and takes on great complexity. However, presently the main analyzing methods of HRV are spectral analysis, while complexity analysis of HRV is less, and complexity analysis of HRV signal during anaesthesia is much more less. In this dissertation, complexity analyzing methods of short length time series, correlation dimension, approximate entropy (ApEn), complexity, fractal analysis, detrended fluctuation analysis(DFA) are summarized, and also use these methods to analyze HRV signal obtained during anaesthesia. The results show, correlation dimension, ApEn, complexity Kc and C1, analysis of fractal based on CGSA, DFA can detect the physiological transient complexity change of HRV during anaesthesia, while analysis of fractal based on wavelet transform and complexity C2 can' t detect it. At the same time, according to the fractal characteristics of HRV, wavelet transform method is used to decompose HRV signalinto fractal component and non-fractal component in the paper, and also analyzes the complexity of these decomposed signal by the above-mentioned complexity analyzing methods, the results show, ApEn, complexityKc, Ci, Cz can all remarkably reflect the complexity change of non-fractal part of HRV during anaesthesia. In comparison with non-fractal part of HRV, the complexity change of fractal part during anaesthesia embodies when analyzing it by ApEn, complexity Kc and DFA. At last, according to the inherence physiology factors that influence on the nonlinear characteristic of HRV, the dissertation makes use of independent component analysis (ICA) to analyze the experimental data, the results show that the values of complexity Kc, C1, C2 of independent components are greater than their values of HRV, while the values of DFA of independent components are all smaller than their values of HRV,In conclusion, the complexity difference between anaesthesia and well-balanced state can be detected -by the complexity analyzing methods, while physiological mechanism can be obtained by different decomposed methods according to the different aims. Meawhile, the dissertation obtains the quantitative indexes which subtly detect the complexity change of HRV during anaesthesia, fractal analysis, DFA and complexity Kc can offer real-time indexes for analysis of HRV in monitoring the depth of anaesthesia in clinic.So the paper offers a methodology guidance for monitoring the depth of anesthesia.