ObjectiveTo investigate the methodological and reporting quality of clinical trials involving Xiaoyao San for chronic fatigue syndrome. MethodsWe searched PubMed, CBM, CNKI, VIP and WanFang Data to identify randomized controlled trials (RCTs) about Xiaoyao San for chronic fatigue syndrome. The methodological and reporting quality of included RCTs was respectively evaluated according to the assessment tool of risk of bias of the Cochrane Handbook 5.1.0 and the CONSORT 2010 statement, combined with complementary assessment by the characteristic indicators of traditional Chinese medicine (TCM). The methodological and reporting quality of included case series study was respectively assessed by the methods recommended by the Britain's National Institute for Clinical Excellence (NICE) and the STROBE statement. ResultsA total of 27 clinical trials were included, involving 11 RCTs and 16 case series studies. According to the assessment tool of risk of bias of the Cochrane Handbook, 54.5% of the RCTs performed proper random method, 9.1% conducted allocation concealment and blinding, 72.7% selected intention-to-treat (ITT) analysis without the report of loss to follow-up, and no RCT existed selective reports. Corresponding to the characteristic indicators of TCM, 54.5% of the RCTs did not conduct TCM syndrome diagnosis, the curative effect standard of TCM syndrome was discrepant, and no RCT was multi-center study. The CONSORT 2010 statement indicated that no RCT explained sample size estimation, implementation details of randomization, flow diagram of participant, use of ITT and clinical trial registration. According to the items recommended by Britain's NICE, 6.25% of the case series studies were multi-center, 81.25% did not report clear inclusion and exclusion criteria, and no case series study performed continuous patient recruitment and stratification analysis of outcome. The STROBE statement indicated that no case series study reported research design, sample size, flow chart, bias, limitations and generalizability. ConclusionThe quality of clinical trials about Xiaoyao San for chronic fatigue syndrome is still low in methodological and reporting aspects. It is suggested that the future clinical trials should be conducted with references of CONSORT statement and STROBE statement, to propel the modernization and internationalization of TCM.
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.
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.
This study investigated the effect of prolonged walking with load carriage on body posture, muscle fatigue, heart rate and blood pressure of the tested subjects. Ten healthy volunteers performed 30 min walking trials on treadmill (speed=1.1 m/s) with different backpack loads [0% body weight (BW), 10%BW, 15%BW and 20%BW]. The change of body posture, muscle fatigue, heart rate and blood pressure before and after walking and the recovery of muscle fatigue during the rest time (0, 5, 10 and 15 min) were collected using the Bortec AMT-8 and the NDI Optotrak Certus. Results showed that the forward trunk and head angle, muscle fatigue, heart rate and blood pressure increased with the increasing backpack loads and bearing time. With the 20%BW load, the forward angle, muscle fatigue and systolic pressure were significantly higher than with lighter weights. No significantly increased heart rate and diastolic pressure were found. Decreased muscle fatigue was found after removing the backpack in each load trial. But the recovery of the person with 20%BW load was slower than that of 0%BW,10%BW and 15%BW. These findings indicated that the upper limit of backpack loads for college-aged students should be between 15% BW and 20%BW according to muscle fatigue and forward angle. It is suggested that backpack loads should be restricted to no more than 15%BW for walks of up to 30 min duration to avoid irreversible muscle fatigue.
We proposed a new deep learning model by analyzing electroencephalogram signals to reduce the complexity of feature extraction and improve the accuracy of recognition of fatigue status of pilots. For one thing, we applied wavelet packet transform to decompose electroencephalogram signals of pilots to extract the δ wave (0.4–3 Hz), θ wave (4–7 Hz), α wave (8–13 Hz) and β wave (14–30 Hz), and the combination of them was used as de-nosing electroencephalogram signals. For another, we proposed a deep contractive auto-encoding network-Softmax model for identifying pilots' fatigue status. Its recognition results were also compared with other models. The experimental results showed that the proposed deep learning model had a nice recognition, and the accuracy of recognition was up to 91.67%. Therefore, recognition of fatigue status of pilots based on deep contractive auto-encoding network is of great significance.
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.
With the wide application of virtual reality technology and the rapid popularization of virtual reality devices, the problem of brain fatigue caused by prolonged use has attracted wide attention. Sixteen healthy subjects were selected in this study. And electroencephalogram (EEG) signals were acquired synchronously while the subjects watch videos in similar types presented by traditional displayer and virtual reality separately. Two questionnaires were conducted by all subjects to evaluate the state of fatigue before and after the experiment. The mutual correlation method was selected to construct the mutual correlation brain network of EEG signals before and after watching videos in two modes. We also calculated the mutual correlation coefficient matrix and the mutual correlation binary matrix and compared the average of degree, clustering coefficient, path length, global efficiency and small world attribute during two experiments. The results showed that the subjects were easier to get fatigue by watching virtual reality video than watching video presented by traditional displayer in a certain period of time. By comparing the characteristic parameters of brain network before and after watching videos, it was found that the average degree value, the average clustering coefficient, the average global efficiency and the small world attribute decreases while the average path length value increased significantly. In addition, compared to traditional plane video, the characteristic parameters of brain network changed more greatly after watching the virtual reality video with a significant difference (P < 0.05). This study can provide theoretical basis and experimental reference for analyzing and evaluating brain fatigue induced by virtual reality visual experience.
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.
This study aims to optimize surface electromyography-based gesture recognition technique, focusing on the impact of muscle fatigue on the recognition performance. An innovative real-time analysis algorithm is proposed in the paper, which can extract muscle fatigue features in real time and fuse them into the hand gesture recognition process. Based on self-collected data, this paper applies algorithms such as convolutional neural networks and long short-term memory networks to provide an in-depth analysis of the feature extraction method of muscle fatigue, and compares the impact of muscle fatigue features on the performance of surface electromyography-based gesture recognition tasks. The results show that by fusing the muscle fatigue features in real time, the algorithm proposed in this paper improves the accuracy of hand gesture recognition at different fatigue levels, and the average recognition accuracy for different subjects is also improved. In summary, the algorithm in this paper not only improves the adaptability and robustness of the hand gesture recognition system, but its research process can also provide new insights into the development of gesture recognition technology in the field of biomedical engineering.
The mechanical properties of nitinol iliac vein stent (NIVS) have been studied by many scholars at home and abroad, but the study on the mechanical properties of iliac vein stent under different release scales has not been reported yet. Based on the finite element analysis method, the mechanical properties of three self-developed NIVS were studied to reveal the influence of stent diameters (12, 14, 16 mm) and different release scales (80%, 90%) on its strength, fatigue life and vein wall biomechanical properties. With an increases in the release scales, the equivalent elastic strain, fatigue strength safety factors, and vessel wall equivalent stress exhibited a downward trend, while the most stressed cross-section coincided with the arc of stent-connecting rods. Through 30, 60 and 90 days’ animal test, a narrowed vascular model was established in the iliac veins of 12 pigs, and the developed iliac vein stents were implanted to comprehensively evaluate the safety and effectiveness of the stent, and at the same time the mechanical properties of stents were verified to provide important reference for the type inspection and clinical trials of follow-up products.