Ecg Waveforms Based On Wavelet Transform And Coronary Heart Disease Automatic Diagnosis

Nowadays, Coronary Heart Disease (CHD) is one of main cardiovascular diseases which are harmful for human health. Dynamic electrocardiogram is an currently important method of clinical diagnosis for cardiovascular disease. It is a popular research which use artificial intelligence technology to analyze ECG accurately at home and abroad. However, the accurate classification of the ST section is the most important technology of it, which plays a critical role to improve the performance of the automatic diagnosis system of CHD.In this paper, it adopted a more accurate localization method of ECG detection which used ANFIS (adaptive neuron-fuzzy unference system) to establish mathematical model of graphics classification, and then the improved model was built. The automatic diagnosis for pathologic characteristics of early CHD was realized. And, the main innovative works are shown as follows:1, Using no extraction translation invariance of wavelet transformation with ATrous for the irregularity discrete signal sampling points and the high order smooth characteristics of the cubic B-spline, the cubic B-spline wavelet was embedded into wavelet transform with ATrous to detect and position accurately for the characteristics of QRS wave points. The simulation results verified that this method accuracy can reach 99.8%.2, According to the mathematical characteristics of various forms in ST section, four parameters were defined:curve type parameter d, offset level parameter c, Linear sloping direction parameter k, and curve bump direction parameter p. The ANFIS was used to establish the mathematical graphics classification model, and then the input parameters was judged and combined with the judgments to complete the discrimination for the ST section's forms. The simulation results proved that the method's accuracy can reach 92% above.3, According to the above four parameters and pathologic characteristics of early CHD, the model for the automatic diagnosis was improved by the conjugate gradient method. The size and direction of the weight vector calculation in each reverse was worked out to adjust operation and determine the optimal value of the weights, and the operation speed and the convergence speed of the system were optimized. Finally, the accuracy in automatic diagnosis of early CHD was improved.