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.
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.
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.
ObjectivesTo systematically review the risk factors of acute fatigue in patients with stroke.MethodsPubMed, Web of Science, EMbase, The Cochrane Library, CNKI, VIP and WanFang Data databases were electronically searched to collect case-control studies, cohort studies and cross-sectional studies on the risk factors of acute fatigue in patients with stroke from inception to April, 2019. Two reviewers independently screened literature, extracted data and assessed risk of bias of included studies, then, meta-analysis was performed by using RevMan 5.3 software.ResultsA total of 14 studies involving 2 658 objects and 13 risk factors were included. The results of meta-analysis showed that: female (OR=1.54, 95%CI 1.23 to 1.94, P=0.000 2), rural residence (OR=1.46, 95%CI 1.11 to 1.91, P=0.007), diabetes mellitus (OR=1.54, 95%CI 1.24 to 1.92, P<0.000 1), hyperlipidemia (OR=1.41, 95%CI 1.10 to 1.80, P=0.007), coronary heart disease (OR=1.94, 95%CI 1.30 to 2.89, P=0.001), previous stroke history (OR=1.54, 95%CI 1.07 to 2.23, P<0.000 01), pre-stroke fatigue (OR=4.51, 95%CI 3.33 to 6.09, P<0.000 01), basal ganglia stroke (OR=2.76, 95%CI 1.21 to 6.29, P<0.000 01), NIHSS >3 (OR=2.11, 95%CI 1.59 to 2.79, P<0.000 01), admission glucose level (OR=1.08, 95%CI 0.38 to 1.78, P=0.003), post-stroke sleep disorder (OR=2.40, 95%CI 1.87 to 3.07, P<0.000 01), post-stroke pain (OR=2.32, 95%CI 1.56 to 3.45, P<0.000 1) and post-stroke depression (OR=3.31, 95%CI 1.94 to 5.66, P<0.000 1) were risk factors of acute fatigue in patients with stroke.ConclusionsCurrent evidence shows that female, rural residence, diabetes mellitus, hyperlipidemia, coronary heart disease, previous stroke history, pre-stroke fatigue, basal ganglia stroke, NIHSS>3, admission glucose level, post-stroke sleep disorder, post-stroke pain and post-stroke depression are the risk factors of acute fatigue in patients with stroke. Medical staff should strengthen targeted preventive care for high-risk patients with related risk factors in order to reduce the incidence of post-stroke fatigue and improve the clinical prognosis outcome of patients.
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.
The automatic recognition technology of muscle fatigue has widespread application in the field of kinesiology and rehabilitation medicine. In this paper, we used surface electromyography (sEMG) to study the recognition of leg muscle fatigue during circuit resistance training. The purpose of this study was to solve the problem that the sEMG signals have a lot of noise interference and the recognition accuracy of the existing muscle fatigue recognition model is not high enough. First, we proposed an improved wavelet threshold function denoising algorithm to denoise the sEMG signal. Then, we build a muscle fatigue state recognition model based on long short-term memory (LSTM), and used the Holdout method to evaluate the performance of the model. Finally, the denoising effect of the improved wavelet threshold function denoising method proposed in this paper was compared with the denoising effect of the traditional wavelet threshold denoising method. We compared the performance of the proposed muscle fatigue recognition model with that of particle swarm optimization support vector machine (PSO-SVM) and convolutional neural network (CNN). The results showed that the new wavelet threshold function had better denoising performance than hard and soft threshold functions. The accuracy of LSTM network model in identifying muscle fatigue was 4.89% and 2.47% higher than that of PSO-SVM and CNN, respectively. The sEMG signal denoising method and muscle fatigue recognition model proposed in this paper have important implications for monitoring muscle fatigue during rehabilitation training and exercise.
The objective of this study is to combine troponin and indicators of cardiac acoustics for synthetically evaluating cardiac fatigue of rabbits, analyzing exercise-induced cardiac fatigue (EICF) and exercise-induced cardiac damage (EICD). New Zealand white rabbits were used to conduct a multi-step swimming experiments with load, reaching an exhaustive state for evaluating if the amplitude ratio of the first to second heart sound (S1/S2) and heart rate (HR) during the exhaustive exercise would decrease or not and if they would be recovered 24-48 h after exhaustive exercise. The experimental end point was to complete 3 times of exhaustions or death from exhaustion. Circulating troponin I (cTnI) were detected from all of the experimental rabbits at rest [(0.02±0.01) ng/mL], which, in general, indicated that there existed a physiological release of troponin. After the first exhaustive swim, cTnI of the rabbits increased. However, with 24-hour rest, S1/S2, HR, and cTnI of the tested rabbits all returned toward baseline levels, which meant that the experimental rabbits experienced a cardiac fatigue process. After repeated exhaustion, overloading phenomena were observed, which led to death in 3 out of 11 rabbits, indicating their cardiac damage; the troponin elevation under this condition could be interpreted by pathological release. Evaluation of myocardial damage can not be based on the troponin levels alone, but can only be based on a comprehensive analysis.
Muscle fatigue has widespread application in the field of rehabilitation medicine. The paper studies the muscle fatigue using surface electromyogram (sEMG) in the background of rehabilitation training system. The sEMG and ventilatory threshold of vastus lateralis, rectus femoris and erector spinae are collected synchronously and the electromyogram fatigue threshold (EMGFT) of different sEMG was analyzed by increasing load cycling experiments of 10 healthy subjects. This paper also analyzes the effect of isotonic and isometric contraction on EMGFT. Results showed that the appeared time of EMGFT was earlier than that of ventilatory threshold in the incremental load cycling. While the differences were subtle and EMGFT was verified to be effective. EMGFT has been proven effective for different muscle contraction by comparing the EMGFT of vastus lateralis and erector spinae. EMGFT could be used to keep muscle injuries from overtraining in the process of rehabilitation. Therefore, EMGFT has a great significance for femoral shaft fractures’s fatigue monitoring in rehabilitation training.
The purpose of this study is to compare the differences among neck muscle fatigue evaluation algorithms and to find a more effective algorithm which can provide a human factor quantitative evaluation method for neck muscle fatigue during bending over the desk. We collected surface electromyography signal of sternocleidomastoid muscle of 15 subjects using wireless physiotherapy Bio-Radio when they bent over the desk using memory pillows for 12 minutes. Five algorithms including mean power frequency, spectral moments ratio, discrete wavelet transform, fuzzy approximation entropy and the complexity algorithms were used to calculate the corresponding muscle fatigue index. The least squares method was used to calculate the corresponding coefficient of determination R2 and slope k of the linear regression of the muscle fatigue metric. The coefficient of determination R2 evaluates anti-interference ability of algorithms. The maximum vertical distance Lmax which is obtained by the Kolmogorov-Smirnov test for the slopes k evaluates the ability to distinguish fatigue of algorithms. The results indicate that in the aspect of anti-interference ability, the fuzzy approximation entropy has the largest R2 when using memory pillows with different heights. When the fuzzy approximate entropy is compared with average power frequency or the discrete wavelet transform, the differences are significant (P < 0.05). In terms of distinguishing the degree of fatigue, the approximate entropy is still the largest, with a maximum of 0.496 7. Fuzzy approximation entropy is superior to other algorithms in ability of anti-interference and distinguishing fatigue. Therefore, fuzzy approximation entropy can be used as a better evaluation algorithm in the evaluation of cervical muscle fatigue.