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.
Early accurate detection of inferior myocardial infarction is an important way to reduce the mortality from inferior myocardial infarction. Regrading the existing problems in the detection of inferior myocardial infarction, complex model structures and redundant features, this paper proposed a novel inferior myocardial infarction detection algorithm. Firstly, based on the clinic pathological information, the peak and area features of QRS and ST-T wavebands as well as the slope feature of ST waveband were extracted from electrocardiogram (ECG) signals leads Ⅱ, Ⅲ and aVF. In addition, according to individual features and the dispersion between them, we applied genetic algorithm to make judgement and then input the feature with larger degree into support vector machine (SVM) to realize the accurate detection of inferior myocardial infarction. The proposed method in this paper was verified by Physikalisch-Technische Bundesanstalt (PTB) diagnostic electrocardio signal database and the accuracy rate was up to 98.33%. Conforming to the clinical diagnosis and the characteristics of specific changes in inferior myocardial infarction ECG signal, the proposed method can effectively make precise detection of inferior myocardial infarction by morphological features, and therefore is suitable to be applied in portable devices development for clinical promotion.
This paper presents a unit free-form deformation (FFD) method applied to rapid three-dimensioanl (3D) bone reconstruction, which was based on traditional FFD. With the femur as an example, we reconstructed a 3D model of femur from two X-ray images and a standardized model by taking advantage of unit FFD algorithm. The X-ray images and its parameters were taken by C-arm device. Those parameters and X-ray contour are contributed to 3D reconstruction. The out contours of X-ray image and standard model were connected by point matching algorithm. The unit-FFD lattice was built to reconstruct standard model and finally made the contour of X-ray image and standard model exactly the same. Experiments on shape accuracy, robustness and time consuming, carried out by 35 specimen from cadaver, showed that mean error of shape (0.52 mm) and mean construction time (112 s) were lower than those using traditional method (0.7-2.6 mm, 8-20 min). The method proposed in this paper shows a good prospect in clinical application and related research.
Detection and classification of malignant arrhythmia are key tasks of automated external defibrillators. In this paper, 21 metrics extracted from existing algorithms were studied by retrospective analysis. Based on these metrics, a back propagation neural network optimized by genetic algorithm was constructed. A total of 1,343 electrocardiogram samples were included in the analysis. The results of the experiments indicated that this network had a good performance in classification of sinus rhythm, ventricular fibrillation, ventricular tachycardia and asystole. The balanced accuracy on test dataset reached up to 99.06%. It illustrates that our proposed detection algorithm is obviously superior to existing algorithms. The application of the algorithm in the automated external defibrillators will further improve the reliability of rhythm analysis before defibrillation and ultimately improve the survival rate of cardiac arrest.
In this paper, we present a 3D virtual phantom design software, which was developed based on object-oriented programming methodology and dedicated to medical physics research. This software was named Magical Phantom (MPhantom), which is composed of 3D visual builder module and virtual CT scanner. The users can conveniently construct any complex 3D phantom, and then export the phantom as DICOM 3.0 CT images. MPhantom is a user-friendly and powerful software for 3D phantom configuration, and has passed the real scene's application test. MPhantom will accelerate the Monte Carlo simulation for dose calculation in radiation therapy and X ray imaging reconstruction algorithm research.
Non-rigid medical image registration is a popular subject in the research areas of the medical image and has an important clinical value. In this paper we put forward an improved algorithm of Demons, together with the conservation of gray model and local structure tensor conservation model, to construct a new energy function processing multi-modal registration problem. We then applied the L-BFGS algorithm to optimize the energy function and solve complex three-dimensional data optimization problem. And finally we used the multi-scale hierarchical refinement ideas to solve large deformation registration. The experimental results showed that the proposed algorithm for large deformation and multi-modal three-dimensional medical image registration had good effects.
The study was intended to introduce a novel method for aided diagnosis of cardiovascular diseases based on photoplethysmography (PPG). For this purpose, 40 volunteers were recruited in this study, of whom the physiological and pathological information was collected, including blood pressure and simultaneous PPG data on fingertips, by using a sphygmomanometer and a smart fingertip sensor. According to the PPG signal and its first and second derivatives, 52 features were defined and acquired. The Relief feature selection algorithm was performed to calculate the contribution of each feature to cardiovascular diseases. And then 10 core features which had the greatest contribution were selected as an optimal feature subset. Finally, the efficiency of the Relief feature selection algorithm was demonstrated by the results of k-nearest neighbor (kNN) and support vector machine (SVM) classifier applications of the features. The prediction accuracy of kNN model and SVM reached 66.67% and 83.33% respectively, indicating that: ① Age was the foremost feature for aided diagnosis of cardiovascular diseases; ② The optimal feature subset provided an important evaluation of cardiovascular health status. The obtained results showed that the application of the Relief feature selection algorithm provided high accuracy in aided diagnosis of cardiovascular diseases.
The deoxyribonucleic acid (DNA) molecule damage simulations with an atom level geometric model use the traversal algorithm that has the disadvantages of quite time-consuming, slow convergence and high-performance computer requirement. Therefore, this work presents a density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm based on the spatial distributions of energy depositions and hydroxyl radicals (·OH). The algorithm with probability and statistics can quickly get the DNA strand break yields and help to study the variation pattern of the clustered DNA damage. Firstly, we simulated the transportation of protons and secondary particles through the nucleus, as well as the ionization and excitation of water molecules by using Geant4-DNA that is the Monte Carlo simulation toolkit for radiobiology, and got the distributions of energy depositions and hydroxyl radicals. Then we used the damage probability functions to get the spatial distribution dataset of DNA damage points in a simplified geometric model. The DBSCAN clustering algorithm based on damage points density was used to determine the single-strand break (SSB) yield and double-strand break (DSB) yield. Finally, we analyzed the DNA strand break yield variation trend with particle linear energy transfer (LET) and summarized the variation pattern of damage clusters. The simulation results show that the new algorithm has a faster simulation speed than the traversal algorithm and a good precision result. The simulation results have consistency when compared to other experiments and simulations. This work achieves more precise information on clustered DNA damage induced by proton radiation at the molecular level with high speed, so that it provides an essential and powerful research method for the study of radiation biological damage mechanism.
Dual-energy computed tomography (CT) reconstruction imaging technology is an important development direction in the field of CT imaging. The mainstream model of dual-energy CT reconstruction algorithm is the basis material decomposition model, and the projection decomposition is the crucial technique. The projection decomposition algorithm based on projection matching was a general method. With establishing the energy spectrum lookup table, we can obtain the stable solution by the least squares matching method. But the computation cost will increase dramatically when size of lookup table enlarges and it will slow down the computer. In this paper, an acceleration algorithm based on projection matching is proposed. The proposed algorithm makes use of linear equations and plane equations to fit the lookup table data, so that the projection value of the decomposition coefficients can be calculated quickly. As the result of simulation experiment, the acceleration algorithm can greatly shorten the running time of the program to get the stable and correct solution.
Recent years, convolutional neural network (CNN) is a research hot spot in machine learning and has some application value in computer aided diagnosis. Firstly, this paper briefly introduces the basic principle of CNN. Secondly, it summarizes the improvement on network structure from two dimensions of model and structure optimization. In model structure, it summarizes eleven classical models about CNN in the past 60 years, and introduces its development process according to timeline. In structure optimization, the research progress is summarized from five aspects (input layer, convolution layer, down-sampling layer, full-connected layer and the whole network) of CNN. Thirdly, the learning algorithm is summarized from the optimization algorithm and fusion algorithm. In optimization algorithm, it combs the progress of the algorithm according to optimization purpose. In algorithm fusion, the improvement is summarized from five angles: input layer, convolution layer, down-sampling layer, full-connected layer and output layer. Finally, CNN is mapped into the medical image domain, and it is combined with computer aided diagnosis to explore its application in medical images. It is a good summary for CNN and has positive significance for the development of CNN.