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Objective To analyze the training results of electroencephalogram (EEG) for brain death determination and to improve the training program. Methods A total of 114 trainees received theoretical training, simulation skills training, bedside skills training and test analysis. The composition of the trainees and the results of EEG tests were analyzed. The error rates of 5 knowledge points of EEG tests were calculated. Univariate and multivariate backward Logistic regression analyses were used to analyze the influence of factors including sex, age, specialty, professional category, professional qualification and hospital level on the error rates. Results All of 114 trainees came from 72 hospitals. Among them, 91 trainees (79.82%) were between 30-49 years old, 108 trainees (94.74%) came from third grade, grade A hospitals, and most of them were from Department of Neurology (57.89% , 66/114) and Electrophysiology (19.30% , 22/114). There were 98 clinicians (85.96% ) and 52 trainees (45.61% ) had intermediate certificate. Of the 5 knowledge points, the total error rate was 9.19% (204/2221). Among them, the error rate of parameter setting was the highest (11.40% , 26/228), followed by those of result determination (10.44%, 80/766), recording techniques (10.25%, 69/673), environmental requirements (7.46%, 17/228) and pitfalls (3.68%, 12/326). The error rate of trainees who were older than 50 was significantly higher than that in other ages (P = 0.000, for all). The error rate of technicians was higher than that of clinicians (P = 0.039). Univariate and multivariate Logistic regression analyses showed that age was independent risk factor associated with high error rates (OR = 1.382, 95%CI: 1.156-1.652; P = 0.000). Conclusions Among the trainees, degree of mastering the knowledge points is different. The training program should be optimized according to the trainees. More attention should be paid to the difference of EEG between brain death determination and routine check to improve the quality of determination for brain death by EEG. DOI: 10.3969/j.issn.1672-6731.2015.12.008

Objective To analyze the training effects of transcranial Doppler (TCD) for brain death determination conducted by Brain Injury Evaluation Quality Control Centre of National Health and Family Planning Commission to optimize the training program and improve the training effects. Methods A total of 106 trainees received theoretical training, simulation skill training, bedside skill training and test analysis on TCD confirmatory test for brain death determination. The composition of trainees was analyzed and the error rates of 6 knowledge points were calculated. Univariate and multivariate backward Logistic regression analyses were used to analyze the influence of factors including sex, age, specialty, professional category professional qualification and hospital level on the error rates. Results The trainees including 42 males and 64 females, came from 69 hospitals. Trainees of 30-49 years old occupied 77.36% (82/106). In the trainees, 96.23% (102/106) were from third grade, grade A hospitals, and most of them were from Department of Neurology (64.15% , 68/106) and Ultrasound (19.81% , 21/106). There were 82 clinicians (77.36%). Thirty four (32.08%) trainees had senior certificate and 49 (46.23%) had intermediate certificate. Total error rate of 6 knowledge points was 7.26% (149/2052). Of the 6 knowledge points, the error rate of parameter setting was the highest (9.43%, 10/106), followed by checking position (8.73%, 37/424), artery recognition (8.67%, 43/496), result determination (7.41%, 55/742), equipment (1.89%, 2/106) and pitfalls (1.12%, 2/178). Univariate and multivariate Logistic regression analyses showed that specialty (OR = 1.313, 95% CI: 1.072-1.610; P = 0.009) and hospital level (OR = 2.943, 95% CI: 1.623-5.338; P = 0.000) were independent risk factors associated with high error rates. Conclusions Among the trainees, degree of mastering the knowledge points is different, and the characteristics of trainees influence the training effect. The training program should be optimized according to key and difficult points to make more and more trainees qualifying for TCD confirmatory test in brain death determination. DOI: 10.3969/j.issn.1672-6731.2015.12.009

Objective To analyze the training results of short-latency somatosensory-evoked potential (SLSEP) for brain death determination and to improve the training program. Methods A total of 101 trainees received theoretical training, simulation skills training, bedside skills training and test analysis for SLSEP in brain death determination. The composition of trainees was analyzed and the error rates of 6 knowledge points were calculated. Univariate and multivariate backward Logistic regression analyses were used to analyze the influence of factors including sex, age, specialty, professional category, professional qualification and hospital level, on the error rates. Results Among them, trainees of 30-49 years old occupied 76.24% (77/101), most of them were from third grade, grade A hospitals (98.02%, 99/101), and 78 trainees (77.23%) were from Department of Neurology. There were 82 clinicians (81.19%), 31 (30.69%) had senior certificate and 42 (41.58%) had intermediate certificate. Total error rate of 6 knowledge points was 4.50% (91/2020). Of the 6 knowledge points, the error rate of pitfalls was the highest (9.41%, 19/202), followed by result determination (5.94% , 12/202), recording techniques (4.75% , 24/505), procedures (3.96%, 32/808), sequence of confirmatory tests (1.98%, 2/101) and environmental conditions (0.99%, 2/202). Univariate and multivariate Logistic regression analyses showed that age (OR = 1.566, 95% CI: 1.116-2.197; P = 0.009) and professional qualification (OR = 1.669, 95% CI: 1.163-2.397; P = 0.005) were independent risk factors associated with high error rates. Conclusions The differences between brain death determination and routine check of SLSEP should be paid more attention to improve the quality of determination for brain death by SLSEP. DOI: 10.3969/j.issn.1672-6731.2015.12.007

Objective Clinical diagnosis is the most predominant in the criteria for determination of brain death. This paper aims to analyze the training results of clinical diagnosis for brain death determination and to improve the training program. Methods A total of 461 trainees received theoretical training, simulation skills training, bedside skills training and test analysis. The composition of trainees was analyzed and the error rates of knowledge points were calculated. Univariate and multivariate backward Logistic regression analyses were used to analyze the influence of factors including sex, age, specialty, professional qualification and hospital level, on the error rates. Results Four hundred and sixty-one trainees came from 161 hospitals. Among them, trainees of 30-49 years old occupied 77.87% (359/461), and most of them came from third grade, grade A hospitals (88.29%, 407/461). There were 200 trainees (43.39% ) from Department of Neurology, 109 trainees (23.64% ) from Department of Neurosurgery, and 88 trainees (19.09%) from Intensive Care Unit. Most of them (66.59%, 307/461) had senior certificate. Total error rate of 13 knowledge points was 5.81% (1054/18 128). The error rate of corneal reflex was the highest (24.64% , 104/422), followed by deep coma (11.59% , 365/3149), oculocephalogyric reflex (9.48%, 40/422), step and time of determination (7.48%, 138/1844), and pupillary light reflex (5.10% , 90/1766). Univariate and multivariate Logistic regression analyses showed that age (OR = 1.558, 95%CI: 1.435-1.693; P = 0.000), specialty (OR = 1.080, 95%CI: 1.021-1.143; P = 0.007) and hospital level (OR = 1.395, 95%CI: 1.174-1.659; P = 0.000) were independent risk factors associated with high error rates. Conclusions The training patterns and methods of clinical diagnosis for brain death determination should be further improved, especially the individual training, to rise the training quality. DOI: 10.3969/j.issn.1672-6731.2015.12.006

The dynamic localization is a kind of technology by which the mobile robot tries to localize the position by itself. According to the dynamic localization failure of mobile robots in indoor network blind areas, an autonomous-dynamic localization system which dynamically chooses beacon node and establishes grids is proposed in this paper. This method applies received signal strength indication (RSSI) for distance measurement. Furthermore, the proposed grid-based improved maximum likelihood estimation (GIMLE) fulfills the localization. Finally, the localization error correction is implemented by Kalman filter. The approach combines the classical Kalman filter with the other localization algorithms. The purpose is to smooth and optimize the results of the algorithms, in order to improve the localization accuracy. In particular, in network blind spots, the Kalman filter provides better performance than the other algorithms listed in the paper. Experimental results show the accuracy, adaptivity, and robustness of the dynamic self-localization of mobile robots.

In this paper, we propose a novel approach to mini-UAV localization in a wireless sensor network. We firstly employ the environment adaptive RSS parameters' estimation method to estimate the parameters of range estimation model. However, the direct path from the target to a beacon is blocked by obstacles in a complicated indoor environment. So the proposed method, which employs a sequential probability ratio test to identify whether the measurement contains non-line of sight (NLOS) errors, is tolerant to parameter fluctuations. Finally, a particle swarm optimization-based method is proposed to solve the established objective function. Simulation results show that the proposed method achieved relatively higher localization accuracy. In addition, the performance analyses, carried out for a realistic indoor environment, shows that the proposed method still preserves the same localization accuracy.