Multivariate time series problems widely exist in production and life in the society. Anomaly detection has provided people with a lot of valuable information in financial, hydrological, meteorological fields, and the research areas of earthquake, video surveillance, medicine and others. In order to quickly and efficiently find exceptions in time sequence so that it can be presented in front of people in an intuitive way, we in this study combined the Riemannian manifold with statistical process control charts, based on sliding window, with a description of the covariance matrix as the time sequence, to achieve the multivariate time series of anomaly detection and its visualization. We made MA analog data flow and abnormal electrocardiogram data from MIT-BIH as experimental objects, and verified the anomaly detection method. The results showed that the method was reasonable and effective.
Objective To explore the methods of early diagnosis of arteriosclerosis obliterans of lower extremity (ASOLE). Methods The related literatures on ASOLE detection means adopted clinically were reviewed, and their advantages and disadvantages were compared.Results Asymptomatic ASOLE could be discovered by determination of ankle brachial index (ABI) and toe brachial index (TBI), which was a good index for arterial function assessment of lower extremity. Pulse wave velocity (PWV) was more vulnerable and less sensitive than ABI, and therefore more suitable for screening of a large sample. ASI was an index to assess arterial structure and function, and it had a good correlation with PWV. Flow-mediated dilation (FMD) was a measurement evaluating the function of endothelial cell; Pulse wave measurement was simple, sensitive, and its result was reliable. Color Doppler ultrasonography could localizate the lesion and determine the degree of stenosis at the same time. Multiple-slice CT angiography (MSCTA) was more accurate than color Doppler ultrasonography, but its inherent shortcomings, such as nephrotoxicity of contrast agent, was still need to be resolved. 3D-contrast enhancement magnetic resonance angiography (CEMRA) had little nephrotoxicity, but a combination of other imaging methods was necessary. Microcirculation detections required high consistency of the measurement environment, but they were simple, sensitive and noninvasive, and therefore could be used for screening of ASO. Conclusion Publicity and education of highrisk groups, and reasonable selection of all kinds of detection means, are helpful to improve the early diagnosis of ASOLE.
In order to improve the efficiency of protein spots detection, a fast detection method based on CUDA was proposed. Firstly, the parallel algorithms of the three most time-consuming parts in the protein spots detection algorithm: image preprocessing, coarse protein point detection and overlapping point segmentation were studied. Then, according to single instruction multiple threads executive model of CUDA to adopted data space strategy of separating two-dimensional (2D) images into blocks, various optimizing measures such as shared memory and 2D texture memory are adopted in this study. The results show that the operative efficiency of this method is obviously improved compared to CPU calculation. As the image size increased, this method makes more improvement in efficiency, such as for the image with the size of 2 048×2 048, the method of CPU needs 5 2641 ms, but the GPU needs only 4 384 ms.
ObjectiveTo sort out the historical evolution of diagnostic screening methods for pancreatic cancer, and to explore how to achieve early diagnosis and treatment of pancreatic cancer in the context of China’s large population base and economic development.MethodsSystematic review was performed. The computer was used to search databases inchuding CNKI, VIP, WanFang Data, Web of Science, PubMed and EMbase. Two researchers independently searched Baidu search engine to collect relevant reports on screening methods and effect evaluation of pancreatic cancer published before February 28, 2018, and a qualitative descroption was made.ResultsA total of 117 articles were included in the study. The exploration of screening methods for pancreatic cancer in China has experienced three stages: germination, enlightenment and growth. Current screening methods include clinical manifestations, imaging screening methods, serum tumor markers and molecular biology gene diagnosis, each of which had its advantages and disadvantages. Single method could not achieve higher specificity and sensitivity, and joint detection had become an inevitable trend. Considering the benefit of practical application, screening among high-risk groups could effectively reduce the population size and achieve accurate screening.ConclusionThere was a big gap between domestic and foreign research in screening for pancreatic cancer. Screening methods are diverse, and joint detection is an inevitable trend. Screening for pancreatic cancer in high-risk population will become a breakthrough under the technical bottleneck.
Mental fatigue is an important factor of human health and safety. It is important to achieve dynamic mental fatigue detection by using electroencephalogram (EEG) signals for fatigue prevention and job performance improvement. We in our study induced subjects' mental fatigue with 30 h sleep deprivation (SD) in the experiment. We extracted EEG features, including relative power, power ratio, center of gravity frequency (CGF), and basic relative power ratio. Then we built mental fatigue prediction model by using regression analysis. And we conducted lead optimization for prediction model. Result showed that R2 of prediction model could reach to 0.932. After lead optimization, 4 leads were used to build prediction model, in which R2 could reach to 0.811. It can meet the daily application accuracy of mental fatigue prediction.
A fitting method of calculating local helix parameters of proteins based on dual quaternions registration fitting (DQRFit) is proposed in this paper. First, the C and N atom coordinates of each residue in the protein structure data are extracted. Then the unregistered data and reference data are constructed using the sliding windows. The square sum of the distance of the data points before and after registration is regarded as an optimization goal. We calculate the optimal rotation matrix and the translation vector using the dual quaternion registration algorithm, and get the helix parameters of the secondary structure which contain the number of residues per turn(τ), helix radius(ρ)and helix pitch(p). Furthermore, we can achieve the fitting of three-helix parameters of τ, ρ, p simultaneously with the dual quaternion registration, and can adjust the sliding windows to adapt to different error levels. Compared with the traditional helix fitting method, DQRFit has some advantages such as low computational complexity, strong anti-interference, and high fitting accuracy. It is proven that the precision of proposed DQRFit for α helix detection is comparable to that of the dictionary of secondary structure of proteins (DSSP), and is better than that of other traditional methods. This is of great significance for the protein structure classification and functional prediction, drug design, protein structure visualization and other fields in the future.
Calculation of cardiac hemodynamic parameters is based on accurate detection of feature points in impedance cardiogram. According to these parameters, doctors can determine heart conditions, so it is very important to accurately detect the feature point of impedance differential signals. This article presents a process in which we used wavelet threshold method to de-noise signals, and then detected the feature points after six layers wavelet decomposition by using bior3.7. The experimental data were collected from healthy persons in our laboratory and twenty two clinical patients in Chongqing Daping Hospital by using KF_ICG instrument. The results indicated that this method could precisely detect feature points whether it was from healthy people or clinical patients. This helps to achieve the application of noninvasive detection cardiac hemodynamic parameters in clinical treatments by using impedance method.
Epilepsy is characterized by abnormally synchronized firing of neuronal populations, which is presented as epileptiform spikes in neural electrical signal recordings. In order to investigate the epileptiform spikes quantitatively, we designed a new window-based algorithm to automatically detect population spikes (PS) in acute epilepsy models in rat hippocampus CA1 region, and to calculate characteristic parameters of PS. Results show that the algorithm could recognize PS waveforms directly in wideband recording signals in epilepsy models induced by 4-aminopyridine (4-AP), a potassium channel blocker, or by picrotoxin (PTX), an antagonist of γ-aminobutyric acid A-type receptor. The PS detection ratios of the two epilepsy models were 94.2%±1.6% (n=11) and 95.9%±1.9% (n=12), respectively. The false positive ratios were 3.5%±2.3% (n=11) and 4.8%±2.3% (n=12), which were significantly lower than those of the conventional threshold method. Comparisons of the PS patterns between the 4-AP model and the PTX model showed that the PS of the 4-AP model had wider waveforms and fired more dispersedly with intervals mainly in the range of 100–700 ms. The PS of the PTX model fired as Burst with a higher firing rate and with intervals mainly in the range of 2–20 ms, resulting in a larger sum of spike amplitudes per second than the 4-AP model. Thus, the synchronous firing of neuronal populations in the PTX model was more intense than that in the 4-AP model. In conclusion, the new algorithm of PS detection can correctly detect and analyze epileptiform population spikes. It provides a useful tool of data analysis for investigating the underlying mechanism of seizure generation and for evaluating new therapeutics of epilepsy.
Poisoning is a common cause of emergency room visits in China, contributing to the fifth leading cause of death among Chinese residents together with injury. This paper describes the development characteristics and morbidity tendency of poisoning in China, in the context of social development in a domestic and foreign view. In addition, the key points to the construction of the discipline and key research realms of poisoning are emphasized, including focusing on the major types of poisoning, evaluating the effectiveness of gastrointestinal decontamination techniques, developing and applying extracorporeal elimination techniques, poison detection techniques, and developing toxic bio-identification techniques.
Automatic detection of pulmonary nodule based on computer tomography (CT) images can significantly improve the diagnosis and treatment of lung cancer. However, there is a lack of effective interactive tools to record the marked results of radiologists in real time and feed them back to the algorithm model for iterative optimization. This paper designed and developed an online interactive review system supporting the assisted diagnosis of lung nodules in CT images. Lung nodules were detected by the preset model and presented to doctors, who marked or corrected the lung nodules detected by the system with their professional knowledge, and then iteratively optimized the AI model with active learning strategy according to the marked results of radiologists to continuously improve the accuracy of the model. The subset 5?9 dataset of the lung nodule analysis 2016(LUNA16) was used for iteration experiments. The precision, F1-score and MioU indexes were steadily improved with the increase of the number of iterations, and the precision increased from 0.213 9 to 0.565 6. The results in this paper show that the system not only uses deep segmentation model to assist radiologists, but also optimizes the model by using radiologists' feedback information to the maximum extent, iteratively improving the accuracy of the model and better assisting radiologists.
