Analysis of epileptic seizure detection method based on improved genetic algorithm optimization back propagation neural network / 生物医学工程学杂志

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 Itelligent System for Diagnosis of Coronary Artery Disease with BP Neural Networks / 대한의료정보학회지

OBJECTIVE: In this paper, an intelligent system using BP neural networks (BPNN) is presented for early detection coronary artery disease (CAD). METHODS: Based on the four features of ECG signals and six basic parameters of patients, BPNN was built and trained. Especially the method which combined feature extraction and classification was discussed. RESULTS: The performance of the intelligent system has been evaluated in 20 samples. The test results showed that this system was effective in detecting CAD. The correct classification rate was about 90% for normal subjects and 100% for abnormal subjects. CONCLUSION: BPNN could quite accurately detect abnormal subjects. Because it is not expensive and noninvasive, it is fit to examine health of the elderly and has good application foreground.

Application of BP neural network to medical equipment demand forecast / 医疗卫生装备

An initial forecast model of BP neural networks is established, and is trained using the history data of our hospital inventory. This model is applied to forecasting the demand of medical equipment in Daping Hospital. The result indicates that the inventory cost is reduced enormously. The model is useful in the purchase and inventory management of medical equipment.