Diagnosis Of Myocardial Infarction Based On Nonlinear Dynamics Of ECG Vector Diagram

Cardiovascular disease,especially myocardial infarction,is one of the main diseases that is harmful to human health,and its mortality and disability are high.Because its not obviously the pre-onset symptoms,if some patients are not rescued in time,it will cause permanent damage to myocardial cells,heart failure,shock and even life-threatening,so the timely and accurate diagnosis of myocardial infarction will be very important for patients.Vectorcardiogram(VCG)is a comprehensive manifestation of the electrical activity of the heart on the body surface.It contains abundant physiological and pathological information and is of great significance in the diagnosis and evaluation of myocardial infarction.Compared with other invasive detection methods,vectorcardiogram are non-invasive and easy to perform,and its detection price is low.At present,it is still one of the most effective non-invasive ECG detection methods in myocardial infarction detection.Thanks to the development of signal processing and analysis technology,more and more features that cannot be seen by the naked eye can be mined from vectorcardiogram,but are important reminders of myocardial infarction.After decades of research and development,a lot of valuable work has emerged in the field of intelligent diagnosis of myocardial infarction with vectorcardiogram.At the same time,complex individual differences and pathophysiological changes in patients have also brought many challenges to the research.In view of the important theoretical significance and practical application value of intelligent diagnosis of myocardial infarction by vectorcardiogram,this paper will conduct in-depth research on it,focusing on the nonlinear dynamic characteristics of vectorcardiogram and its application in myocardial infarction based on previous work,the main tasks are:(1)Mining the inherent nonlinear dynamics of vectorcardiogram time series.Starting from the theory of non-linear dynamics,using phase space reconstruction technology to analyze the non-linear dynamic characteristics of the non-linear time series of the vectorcardiogram,respectively using the Poincare section method,principal component analysis method,and power spectrum chart method,observe the geometric structure of the reconstructed trajectory,analyze the distribution and periodicity of the frequency spectrum,determine the existence of non-linear chaotic characteristics in the vectorcardiogram,and observe the difference in non-linear dynamic characteristics between normal vectorcardiogram and myocardial infarction vectorcardiogram signals.(2)Research on the extraction method of nonlinear dynamic features based on vectorcardiogram.From the perspective of dynamic evolution of dynamic system information,three typical entropy characteristics are extracted:approximate entropy,sample entropy and fuzzy entropy;the complexity of nonlinear dynamic systems is studied from the process of the internal structure of the system,respectively LZ complexity and C0 complexity;from the perspective of chaotic dynamics,extract the maximum Lyapunov exponent,delay time,embedding dimension,Kolmogorov entropy and correlation dimension to characterize the vectorcardiogram of myocardial infarction and normal individuals value distribution analysis to determine the effectiveness of the non-linear dynamic characteristics of the vectorcardiogram in the diagnosis of myocardial infarction.(3)The study of myocardial infarction detection method based on the non-linear dynamic characteristics of vectorcardiogram.Firstly,the extracted nonlinear dynamic characteristics are combined with a machine learning classification model to optimize the parameters of the classifier.The normal and myocardial infarction samples in the PTB database are set to 5 folds cross-validation to achieve a single nonlinear dynamic characteristic and to determine the accuracy of each feature in myocardial infarction disease.Then,the classification test under multi-feature fusion was performed using a combination of pre-fusion and post-fusion,and finally achieved an accuracy of 93%.It is difficult for a single feature to comprehensively and accurately describe a complex VCG model.The proposed nonlinear dynamic features of the vectorcardiogram describe the inherent characteristics of the system from different angles,so the classification accuracy rate under the fusion of multiple features sensitivity and specificity are better than the classification results of any single feature.It is difficult for a single feature to comprehensively and accurately describe a complex VCG model.The extracted nonlinear dynamic features of the vectorcardiogram describe the inherent characteristics of the system from different angles,so the classification accuracy rate,sensitivity and specificity under the fusion of multiple features are better than the classification results of any single feature.(4)Developed an online analysis system for nonlinear dynamics of the vectorcardiogram.The online analysis system is built using MATLAB GUI module and the AIKD ECG acquisition module used interacts with the interface through the USB interface to realize the sending of commands and the transmission of data.The system can support the online collection and analysis of the vectorcardiogram,feature extraction,analysis and diagnosis of the vectorcardiogram,and finally provide graphical and textual output results,providing a practical auxiliary diagnostic tool for the diagnosis of myocardial infarction.