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.
An unequal loss of peripheral vision may happen with high sustaining multi-axis acceleration, leading to a great potential flight safety hazard. In the present research, finite element method was used to study the mechanism of unequal loss of peripheral vision. Firstly, a 3D geometric model of skull was developed based on the adult computer tomography (CT) images. The model of double eyes was created by mirroring with the previous right eye model. Then, the double-eye model was matched to the skull model, and fat was filled between eyeballs and skull. Acceleration loads of head-to-foot (Gz), right-to-left (Gy), chest-to-back (Gx) and multi-axis directions were applied to the current model to simulate dynamic response of retina by explicit dynamics solution. The results showed that the relative strain of double eyes was 25.7% under multi-axis acceleration load. Moreover, the strain distributions showed a significant difference among acceleration loaded in different directions. It indicated that a finite element model of double eyes was an effective means to study the mechanism of an unequal loss of peripheral vision at sustaining high multi-axis acceleration.