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.
Mental fatigue is a subjective fatigue state caused by long-term brain activity, which is the core of health problems among brainworkers. However, its influence on the process of brain information transmission integration is not clear. In this paper, phase amplitude coupling (PAC) between theta and gamma rhythm was used to study the electroencephalogram (EEG) data recorded before and after mental fatigue, so as to explain the effect of mental fatigue on brain information transmission mechanism. The experiment used a 4-hour professional English reading to induce brain fatigue. EEG signals of 14 male undergraduate volunteers before and after mental fatigue were recorded by Neuroscan EEG system. Phase amplitude coupling value was calculated and analyzed. t test was used to compare the results between two states. The results showed that theta phase of more than 90% of the electrodes in the whole brain area jointly modulated gamma amplitude of the right central area and the right parietal area, and the coupling effect among different brain regions significantly decreased (P < 0.05) when participants had felt mental fatigue. This paper shows that phase amplitude coupling can explain the influence of mental fatigue on information transmission mechanism. It could be an important indicator for mental fatigue detection. On the other hand, the results also provide a new measure to evaluate the effect of neuromodulation in relieving mental fatigue.
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.
In the present paper, the contribution of the largest principal component and the number of principal component needed for accumulative contribution 95% are selected as indices of electroencephalogram (EEG) in mental fatigue state in order to investigate the relationship between these parameters and mental fatigue. The experimental results showed that the contribution of the largest principal component of EEG signals increased in the prefrontal, frontal and central areas, while the number of principal component needed for accumulative contribution decreased by 95% with the increasing mental fatigue level. The parameters of singular system of EEG signals can be regarded as useful features for the estimation of mental fatigue and have larger application value in the study of mental fatigue.
ObjectiveTo assess the fatigue in patients with obstructive sleep apnea hypopnea syndrome (OSAHS), and analyze the factors caused fatigue and the relationship between quality of life (QOL) and fatigue. MethodsOne hundred and sixty-nine patients with OSAHS and 78 subjects without OSAHS diagnosed by polysomnography (PSG) between December 2010 and March 2011 in West China Hospital were recruited in the study. Fatigue was assessed by using multidimensional fatigue inventory (MFI), excessive daytime sleepiness by Epworth sleepiness scale(ESS), QOL by functional outcomes of sleep questionnaire (FOSQ). ResultsFatigue in the patients with OSAHS was more severe than that of the controls (51.06±13.39 vs. 44.82±9.81, P < 0.001), but no difference was revealed in the patients with different degree of OSAHS. Fatigue was positively correlated with ESS score(r=0.210), total sleep time intervals(r=0.156), and the ratio of time of SpO2 below 90% in total sleep time(r=0.153)(P < 0.05), and was negatively correlated with the average oxygen saturation(r=-0.171, P < 0.05) and all subscales of FOSQ(P < 0.01). ConclusionsFatigue in patients with OSAHS is more severe than that of controls. Fatigue can significantly reduce QOL, and the impact is greater than that of excessive daytime sleepiness.
Objective To explore the current situation and influencing factors of self-regulatory fatigue in maintenance hemodialysis (MHD) patients, so as to provide good dialysis treatment for MHD patients, reduce their level of self-regulated fatigue and improve their quality of life. Methods The convenient sampling method was used to select the MHD patients in the Wenjiang Hemodialysis Center of West China Hospital of Sichuan University between April 12 and April 30, 2022. The patients were investigated by self-made basic information scale and self-regulatory fatigue scale. Results A total of 131 patients were included. The average score of self-regulatory fatigue was 53.47±6.45, cognitive dimension was 20.21±2.39, emotional dimension was 20.85±2.85, behavioral dimension was 12.40±3.63. The results of multiple linear stepwise regression analysis showed that age, duration of dialysis and educational background could inversely predict the score of self-regulatory fatigue (P<0.05). Conclusions MHD patients have a high level of self-regulatory fatigue. Clinical nurses can make individual dialysis programs according to the actual situation of MHD patients, improve their self-regulated level and physical and mental health, and improve the quality of life of MHD patients.
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.
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 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 R2 could reach to 0.811. It can meet the daily application accuracy of mental fatigue prediction.
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.