Radiotherapy patients with identification study on continuous quality improvement / 中华放射肿瘤学杂志

Objective To study the recogniting patients identity for the safety and reliability of radiotherapy. Methods Through PDCA 4 footwork, namely, plan, do, check, action the technicians in the hospital to improve patients' identity verification.Results After 4 months of PDCA cycle,the patient identity verification qualified rate increase gradually,from 88.17% up to 99.07%,the privacy of patients satisfaction rate rose from 52. 69% to 98. 15%. The patients identification accuracy rate of 100%, technicians working efficiency has been greatly improved. Conclusions The measure of patient identification can improve the working process of radiotherapy for safety and efficiency and can get better privacy protection.

Brain Vigilance Analysis Based on the Measure of Complexity / 生物医学工程学杂志

Vigilance is defined as the ability to maintain attention for prolonged periods of time. In order to explore the variation of brain vigilance in work process, we designed addition and subtraction experiment with numbers of three digits to induce the vigilance to change, combined it with psychomotor vigilance task (PVT) to measure this process of electroencephalogram (EEG), extracted and analyzed permutation entropy (PE) of 11 cases of subjects' EEG and made a brief comparison with nonlinear parameter sample entropy (SE). The experimental results showed that: PE could well reflect the dynamic changes of EEG when vigilance decreases, and has advantages of fast arithmetic speed, high noise immunity, and low requirements for EEG length. This can be used as a measure of the brain vigilance indicators.

Research Progress on the Interaction Effects and Its Neural Mechanisms between Physical Fatigue and Mental Fatigue / 生物医学工程学杂志

Fatigue is an exhaustion state caused by prolonged physical work and mental work, which can reduce working efficiency and even cause industrial accidents. Fatigue is a complex concept involving both physiological and psychological factors. Fatigue can cause a decline of concentration and work performance and induce chronic diseases. Prolonged fatigue may endanger life safety. In most of the scenarios, physical and mental workloads co-lead operator into fatigue state. Thus, it is very important to study the interaction influence and its neural mechanisms between physical and mental fatigues. This paper introduces recent progresses on the interaction effects and discusses some research challenges and future development directions. It is believed that mutual influence between physical fatigue and mental fatigue may occur in the central nervous system. Revealing the basal ganglia function and dopamine release may be important to explore the neural mechanisms between physical fatigue and mental fatigue. Future effort is to optimize fatigue models, to evaluate parameters and to explore the neural mechanisms so as to provide scientific basis and theoretical guidance for complex task designs and fatigue monitoring.

Sample entropy analysis of EEG in ischemic stroke patients / 国际生物医学工程杂志

Objective To explore the nonlinear complexity characteristics of electroencephalogram (EEG) in ischemic stroke patients with different course. Methods Sample entropy of all bands of EEG signals in 20 ischemic stroke patients and 10 healthy controls was extracted and analyzed using statistical analysis methods. Results The full-band EEG in sample entropy of stroke patients was significantly lower than that of healthy controls in most locations. Theα-band sample entropy of different course had significant differences in the frontal, temporal and occipital lobe (P<0.05), and the parameters had significant negative linear correlation with the post-stroke time in some locations. Conclusions There is an abnormal neural electrical activity in post-stroke patients. It is feasible to detect the aberrant EEG complexity using sample entropy, which is worth of further research.

Research on Mental Fatigue Detecting Method Based on Sleep Deprivation Models / 生物医学工程学杂志

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 R' could reach to 0.811. It can meet the daily applicatioi accuracy of mental fatigue prediction.

Non-linear research of alertness levels under sleep deprivation / 生物医学工程学杂志

We applied Lempel-Ziv complexity (LZC) combined with brain electrical activity mapping (BEAM) to study the change of alertness under sleep deprivation in our research. Ten subjects were involved in 36 hours sleep deprivation (SD), during which spontaneous electroencephalogram (EEG) experiments and auditory evoked EEG experiments-Oddball were recorded once every 6 hours. Spontaneous and evoked EEG data were calculated and BEAMs were structured. Results showed that during the 36 hours of SD, alertness could be divided into three stages, i. e. the first 12 hours as the high stage, the middle 12 hours as the rapid decline stage and the last 12 hours as the low stage. During the period SD, LZC of Spontaneous EEG decreased over the whole brain to some extent, but remained consistent with the subjective scales. By BEAMs of event related potential, LZC on frontal cortex decreased, but kept consistent with the behavioral responses. Therefore, LZC can be effective to reflect the change of brain alertness. At the same time LZC could be used as a practical index to monitor real-time alertness because of its simple computation and fast calculation.

Bi-modal enhancement effect from combination of visual and auditory stimulus / 国际生物医学工程杂志

Objective To investigate the cognitive difference between uni-modal (V,A) and bi-modal (VA)target stimuli from both vision and audition,and then to study the neural mechanisms of bi-modal enhancement.Methods This experiment adopted a speeded target stimuli detection task, both behavioral and electroencephalographic responses to uni-modal and bi-modal target stimuli which were combined from visual and auditory target stimuli,were recorded from 14 normal subjects using a 64-channel EEG NeuroScan system.The differences of cognitive between uni-modal and bi-modal stimulus were tested from both behavioral (reaction time (RT) and error rate (ER)) and event-related potentials (ERPs) (P2 latency and amplitude,P3 latency and amplitude)data,and the correlation between behavioral and ERPs results were analyzed.Results As a result,the RT,ER and P3 latency has significant difference between uni-modal and bi-modal target stimuli.In addition,there were significant correlation between behavioral data and P3 latency,especially from the RT and P3 latency.Conclusion By comparing the difference between uni-modal and bi-modal from both behavioral and ERPs results,we could reached the conclusion that the neural mechanism of bi-modal target detection was predominant over that of vision and audition uni-modal target detection,the enhancement take place not only involved in early ERP components (such as P1 and N1),but engaged at the late ERP components (such as P2 and P3).