The Study On The Key Technology Of ECG Signal Intelligent Analysis

With the gradual improvement of people's living standard, people have becomemore conscious about their health. The incidence of heart disease also increases year byyear, which seriously endangers human health and survival. The incidence ofcardiovascular disease does not occur regularly. And it is not easy to notice theincidence of the phenomenon. As a result, it is very difficult for people to make theearly prevention and treatment of cardiovascular disease. And the effective heartdisease treatments such as monitoring outside hospital, primary diagnosis, activeprevention and the timely treatment have higher demand toward the study on ECGsignal analysis, the diagnostic techniques and ECG monitoring products.Multiple Physiological Parameter Monitoring and Diagnostic Systems Outside theHospital (2010B020102021)is one of the science research projects and high-techindustrialization projects in Zhu Hai high-tech field of science and technology. In thecontext, based on the research content of this project, as far as the weakness of ECGsignal processing, analysis and the intelligent diagnosis algorithm is concerned, thisthesis makes the study on the key technology of ECG signal preprocessing (denoising),waveform detection, the selection and extraction of the waveform feature vector andthe waveform automatically classification. This thesis also makes the relevant study onthe key technology of the transplantation for the hardware platform of thealgorithm-oriented ECG application. It aims at improving the accuracy andpracticability of computer intelligence analysis. It also aims at improving the accuracyand performance of abnormal ECG waveform automatic classification. This can speedup the development of ECG medical devices in China and make intelligent analysis ofECG signals with independent intellectual property core technology. In addition, thiscan improve the quality of ECG intelligent guardianship and make the application ofECG intelligent guardianship become universal. The study in this thesis has veryimportant significance and can bring enormous economic benefits.The research on key technology of ECG signal intelligent analysis in this thesishas made some achievements. The achievements are focused on the following aspects. 1. The Study on the Algorithm of ECG Signal Preprocessing (Denoising)After making research on the characteristics of noise in ECG signal, based onwavelet denoising principle, a new threshold function based between the soft and hardthreshold is created. And a weighted threshold shrinkage function is created. Inaddition, this thesis puts forward an ECG signal denoising algorithm based on two newthreshold functions. The two denoising methods are used to make experiments on thetypical data in MIT-BIH Data Base. The experimental results indicate that, comparedwith the previous wavelet threshold denoising methods, the two methods improve a lotin the effect of denoising. The proposed based on weighted threshold shrinkagedenoising method can preserve much more details of the waveform of P wave and Twave in ECG signal, which is much easier to satisfy the need of recognizing thefeatures of ECG signal waveform.2. The Study on the Identification Algorithm of Features of ECG SignalWaveformThe QRS wave identification algorithm based on the continuous wavelettransform is proposed. This algorithm takes the first derivative of Gaussian function asthe wavelet basis function. The relative position of the modulus maximum is used todefine the range of searching the R wave vertex in QRS complex, by examining thewavelet transform in the corresponding levels. According to the position of R wavevertex and the average ECG cycle, a window width adaptive method of searching for Rand P wave is proposed. In this window, the differential values of the original signalsare used to identify the key points of the waveform of P and T wave. The detection rateof the key points in QRS complex, P wave and T wave improves a lot than the previousalgorithms.3. The Study on the Hardware Oriented Implementation of ECG SignalProcessing, Analysis of the Fast Algorithm and VLSI ImplementationThe fast algorithm of ECG signal processing and identification based on DB4wavelet lifting is created. The algorithm uses the wavelet lifting's characteristic ofpossessing fast speed, and greatly improves the algorithm's overall execution speed.After making the study on DB4wavelet lifting VLSI implementation issues, as far asthe key technical points of the algorithm's transplantation toward the hardwareplatform are concerned, the program of the lifting decomposition and reconstruction ofDB4wavelet by using FPGA is proposed. The effectiveness and the above algorithm and the feasibility of realizing FPGA program are proved by the experiments.4. The Study on the Sorting Algorithm of the Abnormal ECG SignalThe ECG waveform vector extraction algorithm which is self adaptive to theaverage length of ECG cycle is created. The judgment of using the logical judgment toexact the normal ECG waveform is given. Accordingly, LCFCM algorithm whichcombines logical judgment, cluster analysis and fuzzy clustering together to realize theaccurate clustering of the abnormal ECG heart rate is proposed. The algorithm has agood adaptability to the ECG signal with individual differences. And the clusteranalysis and fuzzy clustering analysis based on the extracted ECG vector waveform canguarantee the integrity of algorithm's object information, which makes the wholealgorithm become very accurate. Finally, the experiment by using MIT-BIH databaseas the sample is made. The accuracy rate of the classification of the abnormal heart rateby LCFCM algorithm reaches93%.