ObjectiveVideo electroencephalography (VEEG) monitoring for health education of elderly patients based on a process-based communication model, and explore the impact of this model on the success rate, negative emotions, nursing satisfaction, and active cooperation rate of such patients.MethodsFrom September 2017 to September 2019, 118 patients with suspected epilepsy, encephalitis and other diseases who required VEEG monitoring in Suining Central Hospital were selected for this study (patients aged 61 to 73 years; 54 males and 64 females). Patients were divided into 2 groups using a random number table method, 59 patients in each group.A group received routine nursing, and B group received health education based on the process communication model. The monitoring success rate, negative emotion, active cooperation rate, and nursing satisfaction were compared between the two groups.ResultsThe total effective rate in the B group was 86.44%, which was significantly higher than 76.27% in the A group (P<0.05). After nursing intervention, the scores of anxiety and depression in the two groups were significantly decreased, but the decline was greater in the B group (P<0.05). The active cooperation rate and nursing satisfaction of the B group were significantly higher than those of the A group (P<0.05).ConclusionCompared with conventional nursing, health education based on process communication mode can significantly improve the success rate of VEEG monitoring in elderly patients, alleviate the negative emotions of patients, improve the active cooperation rate and nursing satisfaction.
【摘要】 目的 探討單次癲癇發作是否會引起腦損傷。 方法 2007年6月-2009年11月，采用電化學發光法檢測癲癇發作后24 h內40例和對照組40例患者血清和腦脊液中神經元特異性烯醇化酶（neuron-specific enolase，NSE）水平，采用ELISA法測定其血清和腦脊液中髓鞘堿性蛋白（myebin bosic protein，MBP）水平。 結果 癲癇組血清和腦脊液中NSE水平明顯高于對照組（Plt;0.01）；癲癇組血清MBP水平與對照組比較差異無統計學意義（Pgt;0.05）；癲癇組腦脊液中MBP水平高于對照組（Plt;0.05）。 結論 單次癲癇患者血清和腦脊液中NSE明顯升高，腦脊液中MBP升高，提示單次癲癇發作可導致神經元損傷。【Abstract】 Objective To detect the possibility of brain damage in the epileptic patients after single episodes. Methods The levels of neuron-specific enolase (NSE) in serum and cerebrospinal fluid (CSF) in 40 patients with single episodes within 24 hours after seizures from June 2007 to November 2009 were determined respectively by electrochemiluminescence. Another 40 healthy individuals were enrolled as the control. The levels of myelin basic protein (MBP) were determined by enzyme-linked immunosorbent assay. Results The levels of NSE in the serum and CSF in epileptic group within 24 hours after seizures were significantly higher than those in the control group (Plt;0.01), and the levels of MBP in the serum in the two group didn′t differ much (Pgt;0.05). The levels of MBP in CSF in epileptic group were significantly higher than those in the control group (Plt;0.05). Conclusion After single episodes, the levels of NSE in serum and CSF and the levels of NSE in CSF increase,which suggests that single episodes may lead to neuronal damage.
Objective
To explore the status of smoking and passive smoking of the population with the high risk of stroke in the community and their attitude towards smoking control.
Methods
In March 2015, under the direction of Stroke Screening and Prevention Projection, the residents with the high risk of stroke were sought out in Longfeng Community, Suining City, Sichuan Province. And then their status of smoking and passive smoking and their attitude towards smoking control was investigated by Passive Smoking Questionnaire for Adults from National Smoking Control Office.
Results
A total of 354 residents with the high risk of stroke were sought out, in whom 152 (42.9%) were smokers, and the smoking rate of males (70.1%) and females (1.4%) was significantly different (P<0.001). Those aged 40-49 had the highest smoking rate (55.0%), followed by those aged 50-59 (51.7%), and smokers of the two age groups accounted for 73.0% of all smokers. There was significant difference in smoking rate among different age groups (P<0.001). The smoking rate of those with a lower education level of primary school (57.9%) was the highest, and there were significant differences in smoking rates among the population with different education levels (P<0.001). The smoking rate of the solitary (95.7%) was higher than that of the non solitary (34.9%) (P<0.001). In 202 non-smokers, 67 (33.2%) was suffered from passive smoking, and the rate of passive smoking was 31.3% in males and 62.3% in females with a significant difference (P<0.001). The proportion of the female non-smokers against passive smoking (84.1%) was higher than that of the male non-smokers (57.8%). According to the participants report, 79.9% of participants approved completely non-smoking in hospital, school and public transport, 66.4% approved non-smoking in the office and traffic station, and only 10.2% approved non-smoking in the restaurants.
Conclusions
The rates of smoking and passive smoking among the population with the high risk of stroke are high, and most of the population are supportive to smoke prohibition in public places except restaurants. The population with a low cultural level is short of smoking harm knowledge.
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.
Acute respiratory distress syndrome (ARDS) is a serious threat to human life and health disease, with acute onset and high mortality. The current diagnosis of the disease depends on blood gas analysis results, while calculating the oxygenation index. However, blood gas analysis is an invasive operation, and can’t continuously monitor the development of the disease. In response to the above problems, in this study, we proposed a new algorithm for identifying the severity of ARDS disease. Based on a variety of non-invasive physiological parameters of patients, combined with feature selection techniques, this paper sorts the importance of various physiological parameters. The cross-validation technique was used to evaluate the identification performance. The classification results of four supervised learning algorithms using neural network, logistic regression, AdaBoost and Bagging were compared under different feature subsets. The optimal feature subset and classification algorithm are comprehensively selected by the sensitivity, specificity, accuracy and area under curve (AUC) of different algorithms under different feature subsets. We use four supervised learning algorithms to distinguish the severity of ARDS (P/F ≤ 300). The performance of the algorithm is evaluated according to AUC. When AdaBoost uses 20 features, AUC = 0.832 1, the accuracy is 74.82%, and the optimal AUC is obtained. The performance of the algorithm is evaluated according to the number of features. When using 2 features, Bagging has AUC = 0.819 4 and the accuracy is 73.01%. Compared with traditional methods, this method has the advantage of continuously monitoring the development of patients with ARDS and providing medical staff with auxiliary diagnosis suggestions.
