scholarly journals Multilevel Assessment of Mental Stress using SVM with ECOC: An EEG Approach

2019 ◽  
Author(s):  
Fares Al-Shargie
Keyword(s):  
Prefrontal Cortex ◽  
Stress Level ◽  
Mental Stress ◽  
Alpha Rhythm ◽  
Support Vector ◽  
P Values ◽  
Assessment Protocol ◽  
Comparison Results ◽  
Stress Levels ◽  
Self Reporting Questionnaire

Mental stress has been identified as one of the major contributing factors that leads to various diseases such as heart attack, depression and stroke. To avoid this, stress quantification is important for clinical intervention and disease prevention. This study aims to investigate the feasibility of exploiting Electroencephalography (EEG) signals to discriminate between different stress levels. We propose a new assessment protocol whereby the stress level is represented by the complexity of mental arithmetic (MA) task for example, at three levels of difficulty, and the stressors are time pressure and negative feedback. Using 18-male subjects, the experimental results showed that there were significant differences in EEG response between the control and stress conditions at different levels of MA task with p-values < 0.001. Furthermore, we found a significant reduction in alpha rhythm power from one stress level to another level, p-values <0.05. In comparison, results from self-reporting questionnaire NASA-TLX approach showed no significant differences between stress levels. In addition, we developed a discriminant analysis method based on multiclass support vector machine (SVM) with error-correcting output code (ECOC). Different stress levels were detected with an average classification accuracy of 94.79%. The Lateral Index (LI) results further showed dominant right prefrontal cortex (PFC) to mental stress (reduced alpha rhythm). The study demonstrated the feasibility of using EEG in classifying multilevel mental stress, and reported alpha rhythm power at right prefrontal cortex as a suitable index.

scholarly journals Assessing Waterlogging Stress Level of Winter Wheat from Hyperspectral Imagery Based on Harmonic Analysis

2021 ◽  
Vol 14 (1) ◽  
pp. 122
Author(s):  
Feifei Yang ◽  
Shengping Liu ◽  
Qiyuan Wang ◽  
Tao Liu ◽  
Shijuan Li
Keyword(s):  
Winter Wheat ◽  
Harmonic Analysis ◽  
Stress Level ◽  
Hyperspectral Imagery ◽  
Wheat Variety ◽  
Freezing Stress ◽  
Support Vector ◽  
Maturation Stage ◽  
Waterlogging Stress ◽  
Stress Levels

Frequent waterlogging disasters can have serious effects on regional ecology, food safety, and socioeconomic sustainable development. Early monitoring of waterlogging stress levels is vital for accurate production input management and reduction of crop production-related risks. In this study, a pot experiment on winter wheat was designed using three varieties and seven gradients of waterlogging stress. Hyperspectral imagery of the winter wheat canopy in the jointing stage, heading stage, flowering stage, filling stage, and maturation stage were measured and then classified. Wavebands of imaging data were screened. Waterlogging stress level was assessed by a combined harmonic analysis method, and application of this method at field scale was discussed preliminarily. Results show that compared to the k-nearest neighbor and support vector machine algorithms, the random forest algorithm is the best batch classification method for hyperspectral imagery of potted winter wheat. It can recognize waterlogging stress well in the wavebands of red absorption valley (RW: 640–680 nm), red-edge (RE: 670–737 nm), and near-infrared (NIR: 700–900 nm). In the RW region, amplitudes of the first three harmonic sub-signals (c1, c2, and c3) can be used as indexes to recognize the waterlogging stress level that each winter wheat variety undertakes. The third harmonic sub-signal amplitude c3 of the RE region is also suitable for judging stress levels of JM31 (one of the three varieties which is highly sensitive to water content). This study has important theoretical significance and practical application values related to the accurate control of waterlogging stress, and functions as a new method to monitor other types of environmental stress levels such as drought stress, freezing stress, and high-temperature stress levels.

scholarly journals Classification of Mental Stress Levels by Analyzing fNIRS Signal Using Linear and Non-linear Features

2018 ◽  
Vol 5 (2) ◽  
pp. 55-61 ◽  
Author(s):  
Reza Arefi Shirvan ◽  
Seyed Kamaledin Setarehdan ◽  
Ali Motie Nasrabadi
Keyword(s):  
Prefrontal Cortex ◽  
Mental Stress ◽  
Near Infrared ◽  
Mental Arithmetic ◽  
Influential Factors ◽  
Functional Near Infrared Spectroscopy ◽  
Arithmetic Task ◽  
Stress Levels ◽  
Nonlinear Features ◽  
Linear And Nonlinear

Background: Mental stress is known as one of the main influential factors in development of different diseases including heart attack and stroke. Thus, quantification of stress level can be very important in preventing many diseases and in human health. Methods: The prefrontal cortex is involved in body regulation in response to stress. In this research, functional near infrared spectroscopy (fNIRS) signals were recorded from FP2 position in the international electroencephalographic 10–20 system during a stressful mental arithmetic task to be calculated within a limited period of time. After extracting the brain’s hemodynamic response from fNIRS signal, different linear and nonlinear features were extracted from the signal which are then used for stress levels classification both individually and in combination. Results: In this study, the maximum accuracy of 88.72% was achieved in classification between high and low stress levels, and 96.92% was obtained for the stress and rest states. Conclusion: Our results showed that using the proposed linear and nonlinear features it is possible to effectively classify stress levels from fNIRS signals recorded from only one site in the prefrontal cortex. Comparing to other methods, it is shown that the proposed algorithm outperforms other previously reported methods using the nonlinear features extracted from the fNIRS signal. These results clearly show the potential of fNIRS signal as a useful tool for early diagnosis and quantify stress.

scholarly journals Stress Detection Methodology based on Social Media Network: A Proposed Design

2020 ◽  
Vol 9 (3) ◽  
pp. 3489-3492
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Mental Disorders ◽  
Stress Level ◽  
Machine Learning Algorithms ◽  
Working Environment ◽  
Support Vector ◽  
Stress Detection ◽  
Lower Accuracy ◽  
Stress Levels

Mental disorders can be recognized by how a person behaves, feels, perceives, or thinks over a period of a lifetime. Nowadays, a large number of people are feeling stressed with the rapid pace of life. Stress and depression may lead to mental disorders. Work pressure, working environment, people we interact, schedule of the day, food habits, etc. are some of the major reasons behind building stress among the people. Thus, stress can be detected through some conventional medical symptoms such as headache, rapid heartbeats, feeling low energy, chest pain, frequent colds, infections, etc. The stress also may reflect in normal behavior while carrying out day-to-day activities. Individuals may share their day-to-day activities and interact with friends on social media. Thus, it may be possible to detect stress through social network data. There are many ways to detect stress levels. Some of the instruments are used to detect stress while there is a medical test to know the stress level. Also, there are apps that analyze the behavior of the person to detect stress. Many researchers had tried to use machine learning techniques including the use of various algorithms such as Decision Tree, Naïve Bayes, Random Forest, etc. which gives a lower accuracy of 70% on average. In this paper, we are using a closeness of stress levels with social media data shared by many users. In our proposed system design, Facebook posts are being accessed using a token. Further, we recommend the use of machine learning algorithms such as Conventional Neural Network (CNN) to extract Facebook posts, Transductive Support Vector Machine (TSVM) to classify posts and K-Nearest Neighbors (KNN) to recommend nearby hospitals. With the help of these algorithms, we predict the stress level of the person as positive, negative. Thus, we are expecting more accuracy to detect the stress along with the preventive recommendation. We have proposed a methodology to detect stress because severe stress may lead to self-harming activities and also it may affect the lives of people around us. Thus, stress detection has become extremely important and we are expecting that our proposed model may detect it with more accuracy.

Conjectures, Hypotheses, and Theories of Drumlin Formation (In memory of Stanislaw Baranowski)

1981 ◽  
Vol 27 (97) ◽  
pp. 503-505 ◽  
Author(s):  
Ian J. Smalley
Keyword(s):  
Shear Strength ◽  
Pore Water ◽  
Stress Level ◽  
Critical Stress ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Glacial Till ◽  
Forming Process ◽  
Stress Levels

AbstractRecent investigations have shown that various factors may affect the shear strength of glacial till and that these factors may be involved in the drumlin-forming process. The presence of frozen till in the deforming zone, variation in pore-water pressure in the till, and the occurrence of random patches of dense stony-till texture have been considered. The occurrence of dense stony till may relate to the dilatancy hypothesis and can be considered a likely drumlin-forming factor within the region of critical stress levels. The up-glacier stress level now appears to be the more important, and to provide a sharper division between drumlin-forming and non-drumlin-forming conditions.

Stress evaluation in seven-wire strands based on singular value feature of ultrasonic guided waves

2021 ◽  
pp. 147592172110053
Author(s):  
Qian Ji ◽  
Li Jian-Bin ◽  
Liu Fan-Rui ◽  
Zhou Jian-Ting ◽  
Wang Xu
Keyword(s):  
Dispersion Curve ◽  
Support Vector Regression ◽  
Stress Level ◽  
Guided Waves ◽  
Guided Wave ◽  
Singular Value ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Various Boundary Conditions ◽  
Wave Methods

The seven-wire strands are the crucial components of prestressed structures, though their performance inevitably degrades with the passage of time. The ultrasonic guided wave methods have been intensely studied, owing to its tremendous potential for full-scale applications, among the existing nondestructive testing methods, for evaluating the stress status of strands. We have employed the theoretical and finite element methods to solve the dispersion curve of single wire and steel strands under various boundary conditions. Thereafter, the singular value decomposition was adopted to work with the simulated and experimental signals for extracting a feature vector that carries valuable stress status information. The effectiveness of the vector was verified by analyzing the relationship between the vector and the stress level. The vector was also used as an input to establish a support vector regression model. The accuracy of the model has been discussed for different sample sizes. The results show that the fundamental mode dispersion curve offset on the high-frequency part and cut-off frequency increases as the boundary constraints enhance. Simulated and experimental results have demonstrated the effectiveness and potential of the proposed support vector regression method for evaluating the stress level in the strands. This method performs well even at low stress levels and the reliability can be enhanced by adding more samples.

scholarly journals Classification of Prefrontal Cortex Activity Based on Functional Near-Infrared Spectroscopy Data upon Olfactory Stimulation

2021 ◽  
Vol 11 (6) ◽  
pp. 701
Author(s):  
Cheng-Hsuan Chen ◽  
Kuo-Kai Shyu ◽  
Cheng-Kai Lu ◽  
Chi-Wen Jao ◽  
Po-Lei Lee
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Kernel Functions ◽  
Olfactory Stimulation ◽  
Support Vector ◽  
Hemodynamic Response Function ◽  
Functional Near Infrared Spectroscopy ◽  
Data Set

The sense of smell is one of the most important organs in humans, and olfactory imaging can detect signals in the anterior orbital frontal lobe. This study assessed olfactory stimuli using support vector machines (SVMs) with signals from functional near-infrared spectroscopy (fNIRS) data obtained from the prefrontal cortex. These data included odor stimuli and air state, which triggered the hemodynamic response function (HRF), determined from variations in oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) levels; photoplethysmography (PPG) of two wavelengths (raw optical red and near-infrared data); and the ratios of data from two optical datasets. We adopted three SVM kernel functions (i.e., linear, quadratic, and cubic) to analyze signals and compare their performance with the HRF and PPG signals. The results revealed that oxyHb yielded the most efficient single-signal data with a quadratic kernel function, and a combination of HRF and PPG signals yielded the most efficient multi-signal data with the cubic function. Our results revealed superior SVM analysis of HRFs for classifying odor and air status using fNIRS data during olfaction in humans. Furthermore, the olfactory stimulation can be accurately classified by using quadratic and cubic kernel functions in SVM, even for an individual participant data set.

scholarly journals Convergence of heteromodal lexical retrieval in the lateral prefrontal cortex

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexander A. Aabedi ◽  
Sofia Kakaizada ◽  
Jacob S. Young ◽  
Jasleen Kaur ◽  
Olivia Wiese ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Picture Naming ◽  
Cognitive Rehabilitation ◽  
Tumor Model ◽  
Brain Regions ◽  
Support Vector ◽  
Lexical Retrieval ◽  
Lateral Prefrontal Cortex ◽  
Lesion Symptom Mapping ◽  
Single Construct

AbstractLexical retrieval requires selecting and retrieving the most appropriate word from the lexicon to express a desired concept. Few studies have probed lexical retrieval with tasks other than picture naming, and when non-picture naming lexical retrieval tasks have been applied, both convergent and divergent results emerged. The presence of a single construct for auditory and visual processes of lexical retrieval would influence cognitive rehabilitation strategies for patients with aphasia. In this study, we perform support vector regression lesion-symptom mapping using a brain tumor model to test the hypothesis that brain regions specifically involved in lexical retrieval from visual and auditory stimuli represent overlapping neural systems. We find that principal components analysis of language tasks revealed multicollinearity between picture naming, auditory naming, and a validated measure of word finding, implying the existence of redundant cognitive constructs. Nonparametric, multivariate lesion-symptom mapping across participants was used to model accuracies on each of the four language tasks. Lesions within overlapping clusters of 8,333 voxels and 21,512 voxels in the left lateral prefrontal cortex (PFC) were predictive of impaired picture naming and auditory naming, respectively. These data indicate a convergence of heteromodal lexical retrieval within the PFC.

الضغوط المهنية لدى معلمي ومعلمات التربية المهنية في الأردن في ضوء بعض المتغيرات

2019 ◽  
Vol 7 (2) ◽  
pp. 59-67
Author(s):  
Sameer Shdaifat ◽  
Jaafar Abusaa
Keyword(s):  
Public Schools ◽  
Occupational Stress ◽  
Stress Level ◽  
Primary Teachers ◽  
Male Teachers ◽  
Ministry Of Education ◽  
Occupational Education ◽  
The Public ◽  
Significant Difference ◽  
Stress Levels

The present study aimed to identify the occupational stress level of occupational education female and male teachers. It also aimed to identify whether there is any difference between the respondents’ occupational stress levels which can be attributed to their (gender, experience or school stage). The study’s population consists from all the all the occupational education female and male teachers who work at the public schools affiliated with the first and second directorates of education in Irbid (i.e. 320 teachers). As for the sample, it consists from 100 female and male teachers. Those teachers were selected through using the random stratified sampling method. Those teachers were selected from the public schools affiliated with the first and second directorates of education in Irbid. The researchers chose a descriptive survey research design. They developed an instrument (i.e. a questionnaire) for measuring the occupational stress level of teachers. It was found that the occupational stress level of the occupational education female and male teachers is high. It was found that there is a statistically significant difference between the respondents’ occupational stress levels which can be attributed to gender. The latter difference is for the favor of males.  It was found that there is a statistically significant difference between the respondents’ occupational stress levels which can be attributed to experience. The latter difference is for the favor of the ones who possess moderate experience. It was found that there is a statistically significant difference between the respondents’ occupational stress levels which can be attributed to the school stage. The latter difference is for the favor of the lower primary teachers. In the light of the aforementioned results, the researchers recommend exerting effort to reduce the occupational stress level of occupational education female and male teachers. Such efforts include creating convenient psychological and occupational environments. The researchers also recommend providing the lower primary teachers with attention by the Ministry of Education in Jordan. That can be done through providing those teachers with training & development programs. That can be also done through raising their socio-economic levels and providing them with financial & moral incentives & rewards.

Relationship of Fiber Intake, Stress Level, Physical Activity with Blood Pressure of Pre Elderly and Elderly in Lubang Buaya and Kampung Tengah

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Eti Cahya Fitrianti ◽  
Sintha Fransiske Simanungkalit
Keyword(s):  
Physical Activity ◽  
Blood Pressure ◽  
Stress Level ◽  
Blood Pressure Measurement ◽  
Bivariate Analysis ◽  
P Value ◽  
Fiber Intake ◽  
Chi Square ◽  
Stress Levels ◽  
Chi Square Test

High blood pressure is defined as systolic blood pressure that is equal to or above 140 mm Hg or diastolic blood pressure equal to or above 90 mm Hg (JNC VIII, 2013). In 2018, the prevalence of hypertension in Indonesia is based on the characteristics of the age 45-75 years and above with an average of 58.33% (Riskesdas, 2018). The aimed of this study was to determine of fiber intake, stress levels, and physical activity with blood pressure in pre elderly and elderly at RW 03 Lubang Buaya and RW 09 Kampung Tengah, East Jakarta. This research method is observational with cross sectional approach followed by 80 respondents with simple random sampling technique Data collection was taken, namely blood pressure measurement using a Sphygmomanometer, fiber intake using the Food Recall form 2 x 24 hours (Weekend and Weekday), stress levels with the DASS-14 questionnaire, and physical activity with the Baecke questionnaire. Data processing was analyzed by univariate and bivariate using Chi-Square test. The results of bivariate analysis with chi-square test showed a significant relationship between fiber intake (p value = 0.007), stress level (p value = 0,000), and physical activity (p value = 0.022) with blood pressure. There is a relationship between fiber intake, stress level, and physical activity with blood pressure in the elderly and elderly in Lubang Buaya and Kampung Tengah.

Fatigue Characteristics of Open-End Thick-Walled Cylinders Under Cyclic Internal Pressure

1963 ◽  
Vol 85 (4) ◽  
pp. 555-565 ◽  
Author(s):  
T. E. Davidson ◽  
R. Eisenstadt ◽  
A. N. Reiner
Keyword(s):  
Fatigue Crack ◽  
Internal Pressure ◽  
Stress Level ◽  
Cyclic Stress ◽  
Diameter Ratio ◽  
Tensile Fatigue ◽  
Stress Levels ◽  
Fatigue Characteristics ◽  
Using Data ◽  
Rate Of Improvement

Thick-walled cylinder fatigue data due to cyclic internal pressure for open-end cylinders in the range of 103 to 105 cycles to failure and having a diameter ratio of 1.4 to 2.0 at a nominal yield strength of 160,000 pounds per square inch is presented. Discussed and also presented are the effects of autofrettage on the fatigue characteristics of thick-walled cylinders. Autofrettage substantially enhances fatigue characteristics at stress levels below the corresponding overstrain pressure, the degree of improvement increasing the decreasing stress levels. The rate of improvement in fatigue characteristics increases significantly with diameter ratio in autofrettaged cylinders up to a diameter ratio of 1.8–2.0 and to a much smaller degree in the nonautofrettaged condition. The rate of improvement of fatigue characteristics above 2.0 is the same for both the autofrettaged and nonautofrettaged cases. It is shown that thermal treatment of 675 F for 6 hours after autofrettage does not affect fatigue characteristics and that there is a correlation between the cyclic-stress level and the area and depth of the fatigue crack to the point of ductile rupture. The depth of the fatigue crack decreases with increasing cyclic-stress level. A means for using data from a unidirectional tensile fatigue test to predict the fatigue characteristics of thick-walled cylinders is discussed.

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