scholarly journals Continuous measurement of stress levels in naturalistic settings using heart rate variability: An experience-sampling study driving a machine learning approach

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 239
Author(s):  
Pietro Cipresso ◽  
Silvia Serino ◽  
Francesca Borghesi ◽  
Gennaro Tartarisco ◽  
Giuseppe Riva ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Psychological Stress ◽  
Stress Level ◽  
Experience Sampling ◽  
Mobile System ◽  
Stress Levels ◽  
Decision Module ◽  
Leave One Out ◽  
Research Challenge

<p class="Abstract"><span id="page629R_mcid43" class="markedContent"><span dir="ltr">Developing automatic methods to measure psychological stress in everyday life has become an important research challenge. Here, we describe the design and implementation of a personalized mobile system for the detection of psychological stress episodes based on Heart-Rate Variability (HRV) indices. The system’s architecture consists of three main modules: a mobile acquisition module; an analysis-decision module; and a visualization-reporting module. Once the stress level is calculated by the mobile system, the visualization-reporting module of the mobile application displays the current stress level of the user. We carried out an experience-sampling study, involving 15 participants, monitored longitudinally, for a total of 561 ECG analyzed, to select the HRV features which best correlate with self-reported stress levels. Drawing on these results, a personalized classification system is able to automatically detect stress events from those HRV features, after a training phase in which the system learns from the subjective responses given by the user. Finally, the performance of the classification task was evaluated on the empirical dataset using the leave one out cross-validation process. Preliminary findings suggest that incorporating self-reported psychological data in the system’s knowledge base allows for a more accurate and personalized definition of the stress response measured by HRV indices.</span></span></p>

scholarly journals Smartphone App Stress Assessments: Heart Rate Variability vs Perceived Stress in a Large Group of Adults

2020 ◽  
Author(s):  
Konstantin Tyapochkin ◽  
Marina Kovaleva ◽  
Evgeniya Smorodnikova ◽  
Pavel Pravdin
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Psychological Stress ◽  
Perceived Stress ◽  
Physiological Stress ◽  
Research Paper ◽  
The State ◽  
Heart Rate Variability Analysis ◽  
Variability Analysis ◽  
Stress Levels

ABSTRACTBackgroundMultiple studies have shown that the state of stress has a negative impact on decision-making, the cardiovascular system, and the autonomic nervous system [1]. In light of this, we have developed a mobile application in order to assess user stress levels based on the state of their physiological systems. This assessment is based on heart rate variability [2], [3], [4], [5], which many wearable devices such as Apple Watch have learned to measure in the background. We developed a proprietary algorithm that assesses stress levels based on heart rate variability analysis, and this research paper shows that assessments positively correlate with subjective feelings of stress experienced by users.ObjectiveThe objective of this paper is to study the relationship between HRV-based physiological stress responses and Perceived Stress Questionnaire self-assessments in order to validate Welltory measurements as a tool that can be used for daily stress measurements.SettingWe analyzed data from Welltory app users, which is publicly available and free of charge. The app allows users to complete the Perceived Stress Questionnaire and take heart rate variability measurements, either with Apple Watch or with their smartphone cameras.SubjectsTo conduct our study, we collected all questionnaire results from users between the ages of 25 and 60 who also took a heart rate variability measurement on the same day, after filling out the Questionnaire. In total, this research paper includes results from 1,471 participants (602 men and 869 women).MeasurementsWe quantitatively measured physiological stress based on AMo, pNN50, and MedSD values, which were calculated based on sequences of RR-intervals recorded with the Welltory app. We assessed psychological stress levels based on the Perceived Stress Questionnaire (PSQ) [6], [7].ResultsPhysiological stress reliably correlates with self-assessed psychological stress levels - low for subjects with low psychological stress levels, medium for subjects with medium psychological stress levels, and high for subjects with high psychological stress levels. On a scale of 0-100%, median physiological stress is 48.7 (95% CI of 45.2-50.7%), 56.4 (95% CI of 54.3-58.9), and 62.5 (95% CI of 59.7-66.3) for these groups, respectively.ConclusionsPhysiological stress response, which is calculated based on heart rate variability analysis, on average increases as psychological stress increases. Our results show that HRV measurements significantly correlate with perceived psychological stress, and can therefore be used as a stress assessment tool.

scholarly journals Effects on Heart Rate Variability of Stress Level Responses to the Properties of Indoor Environmental Colors: A Preliminary Study

2021 ◽  
Vol 18 (17) ◽  
pp. 9136
Author(s):  
Jiyoung Oh ◽  
Haengwoo Lee ◽  
Heykyung Park
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Stress Level ◽  
Well Being ◽  
Human Vision ◽  
Stress Factors ◽  
Indoor Space ◽  
Stress Levels ◽  
Measurement Record ◽  
Reliable Scale

Color is the most potent stimulating factor affecting human vision, and the environmental color of an indoor space is a spatial component that affects the environmental stress level. As one of the methods of assessing the physiological response of the autonomic nervous system that influences stress, heart rate variability (HRV) has been utilized as a tool for measuring the user’s stress response in color environments. This study aims to identify the effects of the changes of hue, brightness, and saturation in environmental colors on the HRV of two groups with different stress levels—the stress potential group (n = 15) and the healthy group (n = 12)—based on their stress level indicated by the Psychosocial Well-being Index (PWI). The ln(LF), ln(HF), and RMSSD values collected during the subjects’ exposure to 12 environments colors of red and yellow with adjusted saturation and brightness, were statistically analyzed using t-test and two-way ANOVA. The results show that the HRV values in the two groups did not significantly vary in response to the changes in hue, brightness and saturation. The two groups’ stress factors distinguished according to the stress levels by the PWI scale affected the In(LF) parameter, which demonstrates that the PWI index can be utilized as a reliable scale for measuring stress levels. The ultra-short HRV measurement record and the use of a sole In(LF) parameter for stress assessment are regarded as the limitations of this study.

scholarly journals The Change of Heart Rate Variability in Anxiety Disorder after Given Physical or Psychological Stress

2014 ◽  
Vol 21 (2) ◽  
pp. 69-73 ◽  
Author(s):  
Min-Kyung Cho ◽  
Doo-Heum Park ◽  
Jaehak Yu ◽  
Seung-Ho Ryu ◽  
Ji-Hyeon Ha
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Anxiety Disorder ◽  
Psychological Stress

scholarly journals Binary symbolic dynamics analysis to detect stress-associated changes of nonstationary heart rate variability

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Conrad Spellenberg ◽  
Peter Heusser ◽  
Arndt Büssing ◽  
Andreas Savelsbergh ◽  
Dirk Cysarz
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Psychological Stress ◽  
Symbolic Dynamics ◽  
Acute Stress ◽  
Stress Test ◽  
Autonomic Regulation ◽  
Rr Interval ◽  
Cardiac Autonomic Regulation ◽  
Acute Psychological Stress

Abstract Psychological stress may have harmful physiological effects and result in deteriorating health. Acute psychological stress acts also on cardiac autonomic regulation and may lead to nonstationarities in the interbeat interval series. We address the requirement of stationary RR interval series to calculate frequency domain parameters of heart rate variability (HRV) and use binary symbolic dynamics derived from RR interval differences to overcome this obstacle. 24 healthy subjects (12 female, 20–35 years) completed the following procedure: waiting period, Trier Social Stress Test to induce acute psychological stress, recovery period. An electrocardiogram was recorded throughout the procedure and HRV parameters were calculated for nine 5-min periods. Nonstationarities in RR interval series were present in all periods. During acute stress the average RR interval and SDNN decreased compared to rest before and after the stress test. Neither low frequency oscillations (LF), high frequency oscillations (HF) nor LF/HF could unambiguously reflect changes during acute stress in comparison to rest. Pattern categories derived from binary symbolic dynamics clearly identified acute stress and accompanying alterations of cardiac autonomic regulation. Methods based on RR interval differences like binary symbolic dynamics should be preferred to overcome issues related to nonstationarities.

Changes in heart rate variability during psychological stress in cardiorespiratory failure patients

2020 ◽  
Author(s):  
Guadalupe Lizzbett Luna Rodríguez ◽  
Viridiana Pelaéz- Hernandéz ◽  
Laura Arely Martínez-Bautista ◽  
Karla Leticia Rosales-Castillo ◽  
Karen Aidee Santillán-Reyes ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Psychological Stress ◽  
Cardiorespiratory Failure

scholarly journals Prolonged Non-metabolic Heart Rate Variability Reduction as a Physiological Marker of Psychological Stress in Daily Life

2016 ◽  
Vol 50 (5) ◽  
pp. 704-714 ◽  
Author(s):  
Bart Verkuil ◽  
Jos F. Brosschot ◽  
Marieke S. Tollenaar ◽  
Richard D. Lane ◽  
Julian F. Thayer
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Psychological Stress ◽  
Daily Life

scholarly journals Resting heart rate variability in professors: Impact of psychological stress, obesity and blood pressure

2021 ◽  
Vol 11 (5) ◽  
pp. 11
Author(s):  
Nacha Samadi Andrade Rosario ◽  
Perciliany Martins De Souza ◽  
Poliana Elisa Assunção ◽  
Fernando Luiz Pereira de Oliveira ◽  
Eduardo Bearzoti ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Psychological Stress ◽  
Resting Heart Rate ◽  
Resting Heart Rate Variability

scholarly journals Mental Stress Detection via Heart Rate Variability using Machine Learning

2020 ◽  
Vol 12 (3) ◽  
pp. 431-444
Author(s):  
Bimo Sunarfri Hantono ◽  
◽  
Lukito Edi Nugroho ◽  
Paulus Insap Santosa ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Working Memory ◽  
Heart Rate Variability ◽  
Mental Stress ◽  
Nearest Neighbor ◽  
Frequency Domain Analysis ◽  
K Nearest Neighbor ◽  
Stress Levels ◽  
Back Task

Mental stress is an undesirable condition for everyone. Increased stress can cause many problems, such as depression, heart attacks, and strokes. Psychophysiological conditions possible use as a reference to a person’s mental state of stress. The development of mobile device technology, along with the accompanying sensors, can be used to measure the psychophysiological condition of its users. Heart rate allows measured from the photoplethysmography signal utilizing a smartphone or smartwatch. The heart rate variability is currently one of the most studied methods for assessing mental stress. Our objective is to analyze stress levels on the subjects when performing tasks on the smartphone. This study involved 41 students as respondents. Their heart rate was recorded using a smartphone while they were doing the n-back tasks. The n-back task is one of the performance tasks used to measure working memory and working memory capacity. In this study, the n-back task was also used as a stressor. The heart rate dataset and n-back task results are then processed and analyzed using machine learning to determine stress levels. Compared with three other algorithms (neural network, discriminant analysis, and naïve Bayes), the k-nearest neighbor algorithm is most appropriate to use in the classification of time and frequency domain analysis.

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