ElectroCardioGram (ECG) heart beat classification plays an important role in clinical diagnosis.However, there is a serious imbalance of the available data among four types of ECG, which restricts the improvement of heart beat classification performance. In order to solve this problem, a class information extracting method based on Convolutional Neural Network (CNN) was proposed. Firstly, an general CNN model based on equivalent data of four ECG types was constructed. And then based on the general CNN model, four CNN models that more effectively express the propensity information of the four heart beat categories were constructed. Finally, the outputs of the four categories of CNN models were combined to discriminate the heart beat type. The experimental results show that the average sensitivity of the proposed method is 99.68%, the average positive detection rate is 98.58%, and the comprehensive index is 99.12%; which outperform the two-stage cluster analysis method.

Chaos synchronization is the essential theoretical basis of chaotic secure communication. Since time delay of function projective synchronization had rarely been considered, the adaptive controllers and parameter update laws were designed based on Lyapunov stability theory and adaptive control method, and generalized function projective lag synchronization of a class of hyperchaotic system was achieved. Then, taking hyperchaotic Lorenz-Stenflo (LS) system and hyperchaotic Lü system with fully uncertain parameters as an example the correctness and effectiveness of the method was varified, and the influence of external disturbance and time delay on the effect of the synchronization control were studied. The numerical simulations show the effectiveness, feasibility and robustness of the proposed method.