Atherosclerosis is a cardiovascular condition that can occur in any part of the vascular system. Especially, it can exist in bifurcated arteries such as the left and right coronary arteries, abdominal aortic bifurcation or carotid artery bifurcation. In our study, we examine the left coronary artery as an exemplification using wall shear stress (WSS) and wall pressure gradient (WPG). Then, we attempt to find the relationship between bifurcated arterial geometry and hemodynamics. Computational fluid dynamics (CFD) is a common technique applied to characterize blood flow accurately and assist us to gain an insight of atherosclerosis. In this paper, we used CFD as the computational hemodynamics analysis technique to examine flow through the left coronary artery that has variable angular bifurcation. Our results demonstrated that the region of low WSS area and magnitudes of maximum WPG increases with the angles of bifurcation. Such hemodynamic condition resulting from the large bifurcation angles has an effect on atherogenesis and is worthy of investigation for better understanding of atherosclerosis.
Heart disease is a common disease affecting human health. Electrocardiogram (ECG) classification is the most effective and direct method to detect heart disease, which is helpful to the diagnosis of most heart disease symptoms. At present, most ECG diagnosis depends on the personal judgment of medical staff, which leads to heavy burden and low efficiency of medical staff. Automatic ECG analysis technology will help the work of relevant medical staff. In this paper, we use the MIT-BIH ECG database to extract the QRS features of ECG signals by using the Pan-Tompkins algorithm. After extraction of the samples,
K
-means clustering is used to screen the samples, and then, RBF neural network is used to analyze the ECG information. The classifier trains the electrical signal features, and the classification accuracy of the final classification model can reach 98.9%. Our experiments show that this method can effectively detect the abnormality of ECG signal and implement it for the diagnosis of heart disease.
Epilepsy is a difficult problem that has puzzled the medical profession for a long time. The complexity, randomness, non-stationarity and nonlinearity of EEG signal of epilepsy bring great challenge to the detection of epilepsy. The study of epilepsy is an important subject of neutral
system diseases. For automatic epilepsy detection system, the accuracy of identifying epilepsy and predicting epilepsy is of great significance to the treatment of doctors and the recovery of patients. This paper proposes the mixed feature extraction to extract the feature by mixture of time-domain
method and nonlinear analysis method, and the extracted feature is optimized using evolutionary optimization algorithm, and finally train the epilepsy classifier by utilizing the optimized features through the Random forest algorithm. In the experiment, the accuracies of two-classification
problems and three-classification problems respectively reach 99.2% and 98.1%. The results of cross-over experiment for many times show that, the method is of effectiveness in the classified feature extraction aiming at epilepsy brain wave.
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