Based on bioelectrical impedance theory and pattern recognition algorithm, we in this study measured varieties of people's bioelectrical impedance in hands and identified different people according to their bioelectrical impedance. We designed a bioelectrical impedance collection circuit with AD5933 chip to measure the impedance in different people's hands, and we obtained the bioelectrical impedance spectrum for each person under 1-100 kHz electrical stimulation. We calculated the segmentation slopes of bioelectrical impedance spectrum, and took the slopes as characteristic parameters. In order to promote the recognition rate and prevent the overfitting of the model, we divided the people into the training set and the test set, and designed a 3 layer back propagation neural network model to train and test the samples. The results showed that back propagation neural network model could identify the test set effectively. The recognition rate of the training sets was as high as 97.62%, recognition rate of validation sets was 88.79%, recognition rate of test sets was 86.34%, and the synthetical recognition rate was 94.22%. It gives a clue that the network can perfectly recognize people in the training network as well as strangers that comes from the outside of the tests. Our work can verify the feasibility and reliability of using bioelectrical impedance and pattern recognition algorithm for identification, and can provide a simple and supplementary way to identify people.
Identification of real-time uterine contraction status is very significant to labor analgesia, but the traditional uterine contraction analysis algorithms and systems cannot meet the requirement. According to the situations mentioned above, this paper designs a set of algorithms for the real-time analysis of uterine contraction status. The algorithms include uterine contraction signal preprocessing, uterine contraction baseline extraction based on histogram and linear iteration and an algorithm for the real-time analysis of uterine contraction status based on finite state machines theory. It uses the last uterine status and a series of state transfer conditions to identify the current uterine contraction status, as well as a buffer mechanism to avoid false status transitions. To evaluate the performance of the algorithm, we compare it with an existing uterine contraction analysis algorithm used in the electronic fetal monitor. The experiments show that our algorithm can analyze the uterine contraction status while monitoring the uterine contraction signal in a real-time. Its sensitivity reaches 0.939 9 and its positive predictive value is 0.869 3, suggesting that the algorithm has high accuracy and meets the need of clinical monitoring.
Delirium is a common complication in elderly inpatients which could result in cognitive impairment, and increase the risk of disability, fall and mortality. Moreover, it could cause heavy social burden. Even with multiple bedside screening scales to detect delirium, the rate of missed diagnosis is still high. Maybe it is associated with the acute fluctuation and nocturnal onset of delirium. With the development of the intelligence and automation of the electronic medical record (EMR), previous studies have explored the use of EMR to identify delirium patients, and this method provides help for delirium diagnosis and prevention. In this paper, we reviewed and summarized the current situation of research on delirium recognition by EMR, and put forward the development prospect in this method in order to provide basis and lay a foundation for intelligent diagnosis of delirium.
Real-time free breathing cardiac cine imaging is a reproducible method with shorter acquisition time and without breath-hold for cardiac magnetic resonance imaging. However, the detection of end-diastole and end-systole frames of real-time free breathing cardiac cine imaging for left ventricle function analysis is commonly completed by visual identification, which is time-consuming and laborious. In order to save processing time, we propose a method for semi-automatic identification of end-diastole and end-systole frames. The method fits respiratory motion signal and acquires the expiration phase, end-diastole and end-systole frames by cross correlation coefficient. The procedure successfully worked on ten healthy volunteers and validated by the analysis of left ventricle function compared to the standard breath-hold steady-state free precession cardiac cine imaging without any significant statistical differences. The results demonstrated that the present method could correctly detect end-diastole and end-systole frames. In the future, this technique may be used for rapid left ventricle function analysis in clinic.
ObjectiveTo investigate the status of knowledge, attitude, and practice of patient identification in nurses, and provide a basis for clinical managers to carry out targeted training.MethodsA total of 3 696 nurses of tertiary, secondary, and primary hospitals in Guizhou Province were recruited and investigated for the status of knowledge, attitude, and practice of patient identification with a questionnaire by using convenient sampling in May 2019.ResultsThe scores of identification knowledge, attitude, and practice of the 3 696 nurses were 47.87±6.10, 27.39±3.15, and 57.19±4.86, respectively. Logistic regression analysis showed that the higher the educational level was, the higher the score of nurses’ knowledge of patient identification was [odds ratio (OR)=1.592, 95% confidence interval (CI) (1.084, 2.338), P=0.018]; the higher the personal monthly income was, the more positive the nurses’ attitude towards patient identification was [OR=1.570, 95%CI (1.005, 2.453), P=0.048].ConclusionsThe general situation of patient identification in nurses is good, but there are still differences among nurses with different characteristics. It is suggested that managers should pay special attention to the training of nurses with low educational level and low income, make them master the knowledge of patient identification, at the same time, improve their enthusiasm and standardize their behavior, so as to ensure the safety of patients.
In order to develop safe training intensity and training methods for the passive balance rehabilitation training system, we propose in this paper a mathematical model for human standing balance adjustment based on T-S fuzzy identification method. This model takes the acceleration of a multidimensional motion platform as its inputs, and human joint angles as its outputs. We used the artificial bee colony optimization algorithm to improve fuzzy C-means clustering algorithm, which enhanced the efficiency of the identification for antecedent parameters. Through some experiments, the data of 9 testees were collected, which were used for model training and model results validation. With the mean square error and cross-correlation between the simulation data and measured data, we concluded that the model was accurate and reasonable.
Carbapenemase producing Enterobacteriaceae (CPE) has emerged as a significant global public health challenge and placing infected patients at risk of potentially untreatable infections. When resistance to carbapenems occurs, there are often few alternative treatments available. Numerous international guidelines have performed systematic and evidence review to identify new strategies to prevent the entry and spread of CPE in healthcare settings. Several key strategies have been shown to be highly effective. Firstly a new strategy that is proven to be effective is the early identification of the CPE carrier patients through active surveillance cultures. While waiting for the screening results, suspected CPE carriers will be put on preemptive isolation in single room and healthcare worker will at the same time practice contact precautions. The active surveillance culture and prompt preemptive isolation will limit the entry and spread of CPE from getting into hospital. Secondly, it is of utmost importance to incorporate enforcement of the basic infection prevention and control best practices in the hospital including, full compliance to hand hygiene, appropriate use of personal protective equipment, execute antibiotic stewardship program to control abuse of antibiotics, effective environmental cleaning and decontamination, staff education and feedback, as well as surveillance of healthcare-associated infections. Such a holistic approach has been shown to be effective in inhibiting CPE from gaining foothold in the hospital.
The early damage detection and evaluation are of great significance in treatment and prognosis to the left ventricular function for children with tumor. In this paper, it is reported that the early damage of the left ventricular function was observed by pulsed wave Doppler (PWD) and tissue Doppler imaging (TDI) in our laboratory. Eighty children half a year to fourteen years old were included in this study. The cardiac function indices in chemotherapy group and control group were measured and compared. The results showed that there was significant difference in mitral and tricuspid annulus flow spectrum between the two groups. Compared with PWD, TDI is more prompt, objective and accurate in detecting early damage of left ventricular function in children with tumor.And TDI is a good method for early identification of ventricular function damage in children with tumor.
ObjectiveTo summarize the latest progress of parathyroid gland identification in thyroid surgery, and to provide some reference for improving the clinical efficacy.MethodThe literatures about the identification of parathyroid gland in thyroid surgery in recent years were collected to make an review.ResultsThere were many methods for identifying parathyroid gland in thyroid surgery, such as naked eye identification method, intraoperative frozen section, intraoperative staining identification method, intraoperative optical identification method, intraoperative parathyroid hormone assay, γ-detector, and histological identification, each method had its own advantages and disadvantages.ConclusionThe identification of parathyroid gland does not only depend on a certain method, but also require surgeons to enhance their ability to distinguish parathyroid gland.
In this research a strain of isolated Pseudomonas alcaligenes which causes degradation of dexamethasone was acclimated further and its proteins of every position in the bacterium were separated by the osmotic shock method. The separated intracellular proteins which had the highest enzyme activity were extracted by the salting out with ammonium sulfate and were purified with the cation exchange chromatography and gel chromatography. The purified proteins which was active to cause degradation of dexamethasone had been detected were cut with enzyme and were analyzed with mass spectrometry. The results showed that the degradation rate to dexamethasone by acclimated Pseudomonas alcaligenes were increased from 23.63% to 52.84%. The degrading enzymes were located mainly in the intracellular of the bacteria and its molecular weight was about 41 kD. The specific activity of the purified degrading enzymes were achieved to 1.02 U·mg-1. Its 5-peptide amino acid sequences were consistent with some sequences of the isovaleryl-CoA dehydrogenase. The protein enzyme may be a new kind degrading enzyme of steroidal compounds. Our experimental results provided new strategies for cleanup of dexamethasone in water environment with microbial bioremediation technique.