Research On Methods Of Evaluating Depth Of Anesthesia Based On Analysis Of Physiological Signals

Anaesthsia is an indispensable key factor in the clinical surgery, and the way how to ensure the safty of the patients during the surgeries without pain is the core issue in the anaesthetic works. In the process of complex operation, in order to protect the safty of patients, the anesthesiologist must observe the various recordings of physiological signals detected by patients undergoing surgeries. And at the same time, the jugement of depth of anaeshesia (DOA) is provided by the anesthesiologists'own experiences, which is prone to be mistake when the received information and doctor's experiences are not enough, and the influence of accumulated fatigure, environmental interferency, or ever the potential factors and the difference between patients all would make the decision mistake. However, following the deep interaction of biomedical engineering and modern information processing technology, the measurement and analysis device for kinds of physiological signals are helpful to decrease the workload of anesthesiologists.In this thesis, based on the difference of patient's physiological signs in state of anaesthesia and consciousness, the features of electrocardiogram (ECG) and electroencephalogram (EEG) are processed respectively, and some new index and methods for analyzing the DOA are presented for monitoring the state of patients during the surgeries with the combination of signal processing and traditional medicine. And a system for analyzing the DOA in general anaesthesia at real time based on multiple physiological signals and multiple methods is developed and tested in clinical surgeries. The purpose of it is to enhance the accuracy of monitoring DOA in clinical surgeries, to reduce the diagnostic errors caused by human factors, to promote the clinical application to DOA monitoring and improve the technical development, to ensure the safety of patients during surgeries and rapid and good recovery after surgeries.The main contents of this thesis are as follows:(1) In order to overcome the interference of baseline drift, movement artifacts and power line interference in the ECG signals, algorithms for detecting QRS waves and filtering are proposed in this thesis. Based on the improvement on the current morphological filter in the problem of missing data in computation, a conditionally adaptive QRS waves dectection algorithm based on the improved morphological filter is presented, which utilizes the root mean square error (RMSE) as the parameter to detect the length of QRS waves in the ECG signals. This algorithm can provide the QRS waves from the ECG signals disturbed by baseline drift. And then a filtering algorithm for power line noise based on the empirical mode decomposition (EMD) and improved morphological filter is able to filter the base line drift, movement artifacts and decrease the power line interference effectively, according to the minimum of RMSE in each intrinsic mode function (IMF) decomposed by EMD.(2) In order to resolve the disturbance of electrooculography (EOG) mixed in the EEG signals, a filtering algorithm based on multiple empirical mode decomposition (MEMD) and sample entropy is presented. With respect to the high sensitivity of sample entropy value to the EOG with low frequency, the sample entropy is less than0.5if EEG signals involve the EOG. After comparison of the performance in decompose the intrinsic mode from original signals among the EMD, ensemble EMD, complementary ensemble EMD and MEMD, the MEMD with the sample entropy as the threshold is capable of reconstructing the EEG and EOG from original EEG signals.(3) According to heart rate variability (HRV) is affected in some particular conditions, a new biomarker blood flow variability (BFV) is presented as an estimate for monitoring the DOA based on the analysis of Hilbert-Huang transform (HHT). BFV is a better biomarker than HRV in aspects of means of detection and analytical processing. And its distribution of power spectrum in specific frequency band can reflect the function of human cardiovascular system and autonomic system indirectly. While the HRV is disable to monitoring the DOA, which causes by the influence of anesthetic drugs and diathermy effect, the ratio of sum of sympathetic division and sum of parasympathetic division in spectrum of BFV is a index for evaluate the state of patients and level of their consciousness. Proven by the clinical experiment, BFV is a good biomarker to replace the HRV in monitoring the DOA while useless of HRV. (4) Based on the entropy theory in nonlinear analysis, the index for monitoring DOA based on the analysis of EEG through approximate entropy(ApEn), sample entropy(SampEn) and multiscale entropy(MSE) is proposed. According to the characteristics of ApEn, SampEn and MSE, there are differences in analysis of EEG through them. In analysis of EEG at real time during the surgeries, SampEn is more sensitive and effective than ApEn in monitoring DOA, even though both of two methods are adaptive to analyze the complexity of EEG signals. Analysis of EEG by MSE can reveal the complexity of EEG in different scales, however, the limitation of number of data and sampling rate is the main problem we meet in monitoring DOA through MSE at realtime. Thus, the multiscale entropy based on the adaptive resampling is presented to estimate the complexity of EEG in more detailed small scales through changing the sampling rate in limited data, so as to establish the relationship between distribution of complexity of EEG in different scales and the DOA.(5) With the analysis of physiological signals of patients during surgeries and study on the estimate of DOA, a system for analyzing the DOA in general anaesthesia at real time based on multiple physiological signals and multiple methods is developed based on the Borland C++Builder6.0and tested in clinical surgeries.