The Analysis Of ECG And EEG Based On Modified Wavelet Permutation Entropy

ECG and EEG can effectively reflect the state of the human body, and it is of great significance to distinguish different kinds of ECG and EEG signals which is good for clinical diagnosis. Thus in order to distinguish these physiological signals effectively, we proposed the method of modified wavelet permutation entropy. This thesis mainly focus on the following three aspects:Firstly, we proposed the method of modified wavelet permutation entropy which can distinguish ventricular tachycardia(VT), sudden cardiac death(SCD) and normal sinus rhythm(NSR) effectively in different series length, embedding dimension and delay time respectively. The results show that the algorithm of wavelet permutation entropy can distinguish the three physiological signals to some degree and the result is not satisfactory. However, the algorithm we proposed can distinguish three physiological signals effectively.Secondly, we apply original and modified wavelet permutation entropy respectively to analyze the tense EEG of students and nurses. The outcomes shows that both of algorithm can distinguish the two signals, but the improved permutation entropy effect is much better(p value is far less than 0.01) under the vast majority of cases.Thirdly, we developed an analysis system of ECG and EEG based on LabVIEW. The entire system including four major modules, including reading data, displaying waveforms pictures, data analyzing and displaying results. The system works well, and the simulation result is similar to the result by using the tool of MATLAB, which indicate that the system achieved the desired results.