Heart Rate Variability and Electrodermal Activity in Mental Stress Aloud: Predicting the Outcome

Lima, Rodrigo Monteiro de; Osório, Daniel; Gamboa, Hugo

doi:10.5220/0007355200420051

Cited by 13 publications

(25 citation statements)

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“…Moreover, thorough statistical analysis was performed to examine the level of correlation between the analyzed signals and their capability to indicate stress. A similar work [76] describes a process of analyzing and extracting features of PPG and EDA signals, while developing a machine learning algorithm to categorize the stress levels of the participants in the experiment. Time domain and frequency domain features of HRV and EDA were calculated underlining the significance of these two signals in the attempt to detect and characterize stress.…”

Section: -Implementations Of the Analysis Methodsmentioning

confidence: 99%

Overview of Biosignal Analysis Methods for the Assessment of Stress

Ladakis

Chouvarda

2021

Emerg Sci J

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Objectives: Stress is a normal reaction of the human organism induced in situations that demand a level of activation. This reaction has both positive and negative impact on the life of each individual. Thus, the problem of stress management is vital for the maintenance of a person’s psychological balance. This paper aims at the brief presentation of stress definition and various factors that can lead to augmented stress levels. Moreover, a brief synopsis of biosignals that are used for the detection and categorization of stress and their analysis is presented. Methods: Several studies, articles and reviews were included after literature research. The main questions of the research were: the most important and widely used physiological signals for stress detection/assessment, the analysis methods for their manipulation and the implementation of signal analysis for stress detection/assessment in various developed systems. Findings: The main conclusion is that current researching approaches lead to more sophisticated methods of analysis and more accurate systems of stress detection and assessment. However, the lack of a concrete framework towards stress detection and assessment remains a great challenge for the research community. Doi: 10.28991/esj-2021-01267 Full Text: PDF

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Section: -Implementations Of the Analysis Methodsmentioning

confidence: 99%

Overview of Biosignal Analysis Methods for the Assessment of Stress

Ladakis

Chouvarda

2021

Emerg Sci J

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“…Ideally, the SNS activity should increase during cardiovascular pathology and mental stress. (Lima et al,2019) revealed that research experiment is sometimes unpredictable as LF and LF/HF activity during stress decreased for certain circumstances where stress was detected. Delineating the changes in ANS activity plays a significant role towards preventing CVD and stress [69].…”

Section: 4g Contradictions Associated With Stress Classification and Analysis Using Features From Ppg And Eda Signalsmentioning

confidence: 99%

“…(Lima et al,2019) revealed that research experiment is sometimes unpredictable as LF and LF/HF activity during stress decreased for certain circumstances where stress was detected. Delineating the changes in ANS activity plays a significant role towards preventing CVD and stress [69]. ANS is regulated by the CNS, it is comprised of multiple neuroanatomical structures [69].…”

Section: 4g Contradictions Associated With Stress Classification and Analysis Using Features From Ppg And Eda Signalsmentioning

confidence: 99%

“…Delineating the changes in ANS activity plays a significant role towards preventing CVD and stress [69]. ANS is regulated by the CNS, it is comprised of multiple neuroanatomical structures [69]. CNS sends ouTPut to SA node in order to adjust to physiological arousal, it's also responsible for responding and adapting to environmental changes and the structures of the brain influences the activity of the heart [ 69].…”

Section: 4g Contradictions Associated With Stress Classification and Analysis Using Features From Ppg And Eda Signalsmentioning

confidence: 99%

“…Cholinergic fibers are terminal nerve endings associated with PNS which secrete/release acetylcholine which reduces the strength of contraction, which reduces heart rate. Adrenergic fibers are terminal nerve endings associated with SNS activity in order to release epinephrine resulting in increased contraction and HR [69]. EDA signal assess the activity of eccrine sweat gland which is primarily associated with emotional response due to external stimulus and reflects the SNS activity only, since there is no innervation of PNS within the glands.…”

Section: 4g Contradictions Associated With Stress Classification and Analysis Using Features From Ppg And Eda Signalsmentioning

confidence: 99%

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Heart-rate Variability Analysis for Stress Assessment in a Video-Game Setup

Ishaque¹

2021

Preprint

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<div>Chronic stress makes a person vulnerable to diseases leading to the evolution of various physical and mental health conditions including chronic fatigue, diabetes, obesity, depression and other symptoms associated with immune disorders. This study investigates the impact of virtual reality video games to reduce stress and increase resistance to stress. The study consisted of 4 phases: (1) a baseline phase which was used to assess the subjects normal physiological function, (2) a virtual reality roller coaster phase which was meant to induce stress through the elicitation of affective emotions (3) a cognitive color stroop test was also used to induce stress and increase anxiety, and (4) a VR fish game was required to understand the impact of playing video games to reduce stress and anxiety. The physiological variation associated with stress was analyzed through physiological signals such as Electrocardiogram (ECG), respiratory signal (RESP), and Galvanic Skin Response (GSR). Specific features such as pNN50, RMSSD, ApEn, LF, HF, LF/HF ratio and respiration rate were extracted from the corresponding signals. ECG derived features were used as input features for various machine learning models. Poincare plots were used to indicate abnormal HRV and stress through a visual representation. SD1 and SD2 share a high correlation with SNS and PNS activity, ApEn shares a 0.811 correlation with LF/HF ratio, proving that it is an effective method to assess stress. Average results from the users indicate that LF/HF reduced from 1.2 to 0.93, ApEn reduced from 0.0.695 to 0.562 after playing the VR fish game, demonstrating the positive influence of the game towards stress reduction. Statistical analysis using the t-test verified that the data were statistically significant, p-value < 0.05 rejected that the null hypothesis that the data was statistically significant. The Ensemble Gradient Boosting model was able to classify binary classes associated with stress/relaxation with 100% accuracy, various other models were able to classify with 90% accuracy. RUC curve, precision/recall curve and TP, FP, TN, FN revealed that few models such as Naive Bayes were inadequate for stress classification. The research study effectively demonstrated the impact of VR fish game for stress reduction.</div>

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