In order to improve the accuracy and efficiency of automatic seizure detection, the paper proposes a method based on improved genetic algorithm optimization back propagation (IGA-BP) neural network for epilepsy diagnosis, and uses the method to achieve detection of clinical epilepsy rapidly and effectively. Firstly, the method extracted the linear and nonlinear features of the epileptic electroencephalogram (EEG) signals and used a Gaussian mixture model (GMM) to perform cluster analysis on EEG features. Next, expectation maximization (EM) algorithm was used to estimate GMM parameters to calculate the optimal parameters for the selection operator of genetic algorithm (GA). The initial weights and thresholds of the BP neural network were obtained through using the improved genetic algorithm. Finally, the optimized BP neural network is used for the classification of the epileptic EEG signals to detect the epileptic seizure automatically. Compared with the traditional genetic algorithm optimization back propagation (GA-BP), the IGA-BP neural network can improve the population convergence rate and reduce the classification error. In the process of automatic detection of epilepsy, the method improves the detection accuracy in the automatic detection of epilepsy disorders and reduced inspection time. It has important application value in the clinical diagnosis and treatment of epilepsy.
According to the "Regulations on Clinical Application Management of Medical Technologies," physicians intending to carry out restricted technologies must undergo standardized training and pass assessments in accordance with the clinical application management standards for the respective technology. As ventricular assist technology is classified as a nationally restricted technology, standardized training is one of the essential conditions for its application. This paper primarily explores the standardized training for the clinical application of ventricular assist technology in Shanghai, in light of its background, clinical application, and current training status, with the aim of promoting the standardized and high-quality development of ventricular assist technology locally.