This study is aimed to investigate objective indicators of mental fatigue evaluation to improve the accuracy of mental fatigue evaluation. Mental fatigue was induced by a sustained cognitive task. The brain functional networks in two states (normal state and mental fatigue state) were constructed based on electroencephalogram (EEG) data. This study used complex network theory to calculate and analyze nodal characteristics parameters (degree, betweenness centrality, clustering coefficient and average path length of node), and served them as the classification features of support vector machine (SVM). Parameters of the SVM model were optimized by gird search based on 6-fold cross validation. Then, the subjects were classified. The results show that characteristic parameters of node of brain function networks can be divided into normal state and mental fatigue state, which can be used in the objective evaluation of mental fatigue state.
ObjectiveTo investigate the fatigue of asthma patients, and to analyze its influencing factors, and provide a reference for clinical intervention.MethodsThe convenience sampling method was adopted to select asthma patients who were in clinic of the First Affiliated Hospital of Guangxi Medical University from November 2018 to March 2019. The patients’ lung function were measured. And questionnaires were conducted, including general data questionnaire, Chinese version of Checklist Individual Strength-Fatigue, Asthma Control Test, Chinese version of Self-rating Depression Scale. Relevant data were collected for multiple stepwise linear regression analysis.ResultsFinally, 120 patients were enrolled. The results of multiple stepwise linear regression analysis showed that age, education level, place of residence, time period of frequent asthma symptoms, degree of small airway obstruction, Asthma Control Test score and degree of depression were the influencing factors of fatigue in asthma patients (P≤0.05). Multivariate linear stepwise regression analysis showed that degree of small airway obstruction, degree of depression and time period of frequent asthma symptoms were the main influencing factors of fatigue in asthma patients, which could explain 51.8% of the variance of fatigue (ΔR2=0.518).ConclusionsThe incidence of fatigue in asthma patients is at a relatively high level. Medical staff should pay attention to the symptoms of fatigue in asthma patients. For asthma patients, it is recommended to strengthen standardized diagnosis and treatment, reduce the onset of symptoms at night and eliminate small airway obstruction. Psychological intervention methods are needed to improve patients’ depression, reduce fatigue symptoms, and improve quality of life.
The pace of modern life is accelerating, the pressure of life is gradually increasing, and the long-term accumulation of mental fatigue poses a threat to health. By analyzing physiological signals and parameters, this paper proposes a method that can identify the state of mental fatigue, which helps to maintain a healthy life. The method proposed in this paper is a new recognition method of psychological fatigue state of electrocardiogram signals based on convolutional neural network and long short-term memory. Firstly, the convolution layer of one-dimensional convolutional neural network model is used to extract local features, the key information is extracted through pooling layer, and some redundant data is removed. Then, the extracted features are used as input to the long short-term memory model to further fuse the ECG features. Finally, by integrating the key information through the full connection layer, the accurate recognition of mental fatigue state is successfully realized. The results show that compared with traditional machine learning algorithms, the proposed method significantly improves the accuracy of mental fatigue recognition to 96.3%, which provides a reliable basis for the early warning and evaluation of mental fatigue.
Mental fatigue is the subjective state of people after excessive consumption of information resources. Its impact on cognitive activities is mainly manifested as decreased alertness, poor memory and inattention, which is highly related to the performance after impaired working memory. In this paper, the partial directional coherence method was used to calculate the coherence coefficient of scalp electroencephalogram (EEG) of each electrode. The analysis of brain network and its attribute parameters was used to explore the changes of information resource allocation of working memory under mental fatigue. Mental fatigue was quickly induced by the experimental paradigm of adaptive N-back working memory. Twenty-five healthy college students were randomly recruited as subjects, including 14 males and 11 females, aged from 20 to 27 years old, all right-handed. The behavioral data and resting scalp EEG data were collected simultaneously. The results showed that the main information transmission pathway of the brain changed under mental fatigue, mainly in the frontal lobe and parietal lobe. The significant changes in brain network parameters indicated that the information transmission path of the brain decreased and the efficiency of information transmission decreased significantly. In the causal flow of each electrode and the information flow of each brain region, the inflow of information resources in the frontal lobe decreased under mental fatigue. Although the parietal lobe region and occipital lobe region became the main functional connection areas in the fatigue state, the inflow of information resources in these two regions was still reduced as a whole. These results indicated that mental fatigue affected the information resources allocation of working memory, especially in the frontal and parietal regions which were closely related to working memory.
Aiming at the problem that the feature extraction ability of forehead single-channel electroencephalography (EEG) signals is insufficient, which leads to decreased fatigue detection accuracy, a fatigue feature extraction and classification algorithm based on supervised contrastive learning is proposed. Firstly, the raw signals are filtered by empirical modal decomposition to improve the signal-to-noise ratio. Secondly, considering the limitation of the one-dimensional signal in information expression, overlapping sampling is used to transform the signal into a two-dimensional structure, and simultaneously express the short-term and long-term changes of the signal. The feature extraction network is constructed by depthwise separable convolution to accelerate model operation. Finally, the model is globally optimized by combining the supervised contrastive loss and the mean square error loss. Experiments show that the average accuracy of the algorithm for classifying three fatigue states can reach 75.80%, which is greatly improved compared with other advanced algorithms, and the accuracy and feasibility of fatigue detection by single-channel EEG signals are significantly improved. The results provide strong support for the application of single-channel EEG signals, and also provide a new idea for fatigue detection research.
ObjectiveTo explore the effect of spiritual care on improving the psychological stress levels of relatives of patients with terminal cancer. MethodsDuring January 2013 and January 2014, 220 relatives of patients with terminal cancer were selected. Convenience sampling method was adopted to select 100 relatives out of 190 who were agreed to be participated in the investigation, who were divided into the trial group and the control group with 50 in each according to the random alphabet method. The control group was given routine care and psychological counseling, and the trial group was given spiritual care intervention additionally. Before intervention, all of the individuals in both of the two groups should conduct the questionnaire of general demographic data, caregiver stress scale, fatigue rating scale, quality of life scale (QLS), social support scale (SSS), and relatives stress scale (RSS). ResultsAfter one month's intervention, caregiver stress scale score (52.14±4.75), fatigue rating score (76.75±8.69), RSS score (15.71±3.97), SSS score (22.59±2.22), the QLS score (66.9±7.5) in the trial group were significant better than those in the control group (P < 0.05). After intervention, all the scores in the trial group were significant better than whose before the intervention (P < 0.05). ConclusionFor the relatives of the patients with terminal cancer, spiritual care can reduce the occurrence rate of stress and fatigue, relieve the psychological stress level, and improve the social support and quality of life.
Heart rate variability (HRV) is an important point to judge a person’s state in modern medicine. This paper is aimed to research a person’s fatigue level connected with vagal nerve based on the HRV using the improved Welch method. The process of this method is that it firstly uses a time window function on the signal to be processed, then sets the length of time according to the requirement, and finally makes frequency domain analysis. Compared with classical periodogram method, the variance and consistency of the present method have been improved. We can set time span freely using this method (at present, the time of international standard to measure HRV is 5 minutes). This paper analyses the HRV’s characteristics of fatigue crowd based on the database provided by PhysioNet. We therefore draw the conclusion that the accuracy of Welch analyzing HRV combining with appropriate window function has been improved enormously, and when the person changes to fatigue, the vagal activity is diminished and sympathetic activity is raised.
ObjectiveTo systematically review the clinical features of chronic fatigue syndrome (CFS) cases with pathogens infection. MethodsWe electronically searched databases including VIP, WanFang Data, CNKI, CBM, PubMed, MEDLINE, EMbase, The Cochrane Library, Web of Science, Elsevier and Google Scholar from 1994 to 2014 for CFS-related studies. Two reviewers independently screened literature and extracted data. Then we systematically reviewed and analyzed the information on demographic characteristics, clinical manifestations, types of infected pathogens, and results of some biochemical examinations. ResultsA total of 84 studies (case reports and case series) involving 2 565 CFS cases from 18 countries were included. The major infected pathogens of included CFS cases were mycoplasma, EB virus, intestinal virus, Bernat rickettsia, human-herpes virus, and Gram-negative intestinal bacteria. Fifty-seven studies reported that there might be associations between the pathogenic infection and CFS pathogenesis. Although there were different types of CFS-related pathogens, almost all the studies inferred that pathogens infection linked with immune dysfunction, which might cause CFS symptoms. ConclusionThere may be associations between the pathogenic infection and CFS pathogenesis.
At present, fatigue state monitoring of upper limb movement generally relies solely on surface electromyographic signal (sEMG) to identify and classify fatigue, resulting in unstable results and certain limitations. This paper introduces the sEMG signal recognition and motion capture technology into the fatigue state monitoring process and proposes a fatigue analysis method combining an improved EMG fatigue threshold algorithm and biomechanical analysis. In this study, the right upper limb load elbow flexion test was used to simultaneously collect the biceps brachii sEMG signal and upper limb motion capture data, and at the same time the Borg Fatigue Subjective and Self-awareness Scale were used to record the fatigue feelings of the subjects. Then, the fatigue analysis method combining the EMG fatigue threshold algorithm and the biomechanical analysis was combined with four single types: mean power frequency (MPF), spectral moments ratio (SMR), fuzzy approximate entropy (fApEn) and Lempel-Ziv complexity (LZC). The test results of the evaluation index fatigue evaluation method were compared. The test results show that the method in this paper has a recognition rate of 98.6% for the overall fatigue state and 97%, 100%, and 99% for the three states of ease, transition and fatigue, which are more advantageous than other methods. The research results of this paper prove that the method in this paper can effectively prevent secondary injury caused by overtraining during upper limb exercises, and is of great significance for fatigue monitoring.