The effects of mood state and psychological well-being on heart rate variability during mental stress

2014 ◽  
Vol 94 (2) ◽  
pp. 232 ◽  
Author(s):  
Elena Saperova ◽  
Dmitry Dimitriev
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Mental Stress ◽  
Mood State ◽  
Well Being ◽  
Psychological Well Being

scholarly journals The Effect of Heart Rate Variability Biofeedback Training on Mental Health of Pregnant and Non-Pregnant Women: A Randomized Controlled Trial

2019 ◽  
Vol 16 (6) ◽  
pp. 1051 ◽  
Author(s):  
Judith van der Zwan ◽  
Anja Huizink ◽  
Paul Lehrer ◽  
Hans Koot ◽  
Wieke de Vente
Keyword(s):  
Mental Health ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Beneficial Effect ◽  
Pregnant Women ◽  
Mental Health Problems ◽  
Controlled Trial ◽  
Well Being ◽  
Biofeedback Training ◽  
Psychological Well Being

In this study, we examined the efficacy of heart rate variability (HRV)-biofeedback on stress and stress-related mental health problems in women. Furthermore, we examined whether the efficacy differed between pregnant and non-pregnant women. Fifty women (20 pregnant, 30 non-pregnant; mean age 31.6, SD = 5.9) were randomized into an intervention (n = 29) or a waitlist condition (n = 21). All participants completed questionnaires on stress, anxiety, depressive symptoms, sleep, and psychological well-being on three occasions with 6-week intervals. Women in the intervention condition received HRV-biofeedback training between assessment 1 and 2, and women in the waitlist condition received the intervention between assessment 2 and 3. The intervention consisted of a 5-week HRV-biofeedback training program with weekly 60–90 min. sessions and daily exercises at home. Results indicated a statistically significant beneficial effect of HRV-biofeedback on psychological well-being for all women, and an additional statistically significant beneficial effect on anxiety complaints for pregnant women. No significant effect was found for the other stress-related complaints. These findings support the use of HRV-biofeedback as a stress-reducing technique among women reporting stress and related complaints in clinical practice to improve their well-being. Furthermore, it supports the use of this technique for reducing anxiety during pregnancy.

Complexity and time asymmetry of heart rate variability are altered in acute mental stress

2014 ◽  
Vol 35 (7) ◽  
pp. 1319-1334 ◽  
Author(s):  
Z Visnovcova ◽  
M Mestanik ◽  
M Javorka ◽  
D Mokra ◽  
M Gala ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Mental Stress ◽  
Time Asymmetry

Effect of short-term mental stress on time- domain indices of heart rate variability in obese individuals – A case- control study

Biomedicine ◽  
2021 ◽  
Vol 41 (2) ◽  
pp. 274-277
Author(s):  
Priya S.A. ◽  
R. Rajalakshmi
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Neuronal Activity ◽  
Time Domain ◽  
Mental Stress ◽  
Mental Arithmetic ◽  
High Body Mass Index ◽  
Control Group ◽  
Stress Task ◽  
The Individual

  Introduction and Aim: Mental stress may impact dramatically on dynamic autonomic control on heart. Many studies have demonstrated association of high body mass index (BMI) with greater risk for cardiovascular disease with disturbance in autonomic neuronal activity. Analysis of Heart rate variability (HRV)during acute mental stress assesses the autonomic status of the individual. Hence, we aimed to study the effect of acute mental stress on time domain measures in obese adults.   Materials and Methods:Sixty male volunteers of 30 each in study group (obese individuals) and control group (non-obese individuals) were recruited for the study. A basal recording of ECG in lead II was done on all the individuals. Then they underwent mental arithmetic stress task for 5 minutes during which again ECG was recorded. The change in time domain measures of HRV during rest and stress task was analyzed and compared between both the groups.   Results: Analysis of time domain measures of HRV revealed a statistically significant increase (p ? 0.001) in mean heart rate in both obese and non-obese individuals, while rMSSD(root mean square differences of successive RR interval) and SDNN (standard deviation of all NN intervals) showed a statistically significant (p? 0.001) decrease in obese individuals and non-obese individuals did not show any statistically significant change during the mental stress task.   Conclusion: In response to acute mental stress there was increased heart rate in both the groups. But the autonomic neuronal activity differed by way of sympathetic dominance in non-obese individuals and parasympathetic withdrawal in obese individuals.  

scholarly journals Estimation of heart rate variability from finger photoplethysmography during rest, mild exercise and mild mental stress

2021 ◽  
Vol 12 (1) ◽  
pp. 89-102
Author(s):  
Bjørn-Jostein Singstad ◽  
Naomi Azulay ◽  
Andreas Bjurstedt ◽  
Simen S. Bjørndal ◽  
Magnus F. Drageseth ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Physical Exercise ◽  
Mental Stress ◽  
Fiducial Point ◽  
Point Selection ◽  
Maximum Slope ◽  
Robust Parameter ◽  
Signal Processing Methods ◽  
Selection Of

Abstract Due to the possibilities in miniaturization and wearability, photoplethysmography (PPG) has recently gained a large interest not only for heart rate measurement, but also for estimating heart rate variability, which is derived from ECG by convention. The agreement between PPG and ECG-based HRV has been assessed in several studies, but the feasibility of PPG-based HRV estimation is still largely unknown for many conditions. In this study, we assess the feasibility of HRV estimation based on finger PPG during rest, mild physical exercise and mild mental stress. In addition, we compare different variants of signal processing methods including selection of fiducial point and outlier correction. Based on five minutes synchronous recordings of PPG and ECG from 15 healthy participants during each of these three conditions, the PPG-based HRV estimation was assessed for the SDNN and RMSSD parameters, calculated based on two different fiducial points (foot point and maximum slope), with and without outlier correction. The results show that HRV estimation based on finger PPG is feasible during rest and mild mental stress, but can give large errors during mild physical exercise. A good estimation is very dependent on outlier correction and fiducial point selection, and SDNN seems to be a more robust parameter compared to RMSSD for PPG-based HRV estimation.

scholarly journals Trends in Heart-Rate Variability Signal Analysis

2021 ◽  
Vol 3 ◽  
Author(s):  
Syem Ishaque ◽  
Naimul Khan ◽  
Sri Krishnan
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Electrodermal Activity ◽  
Well Being ◽  
Good Health ◽  
Machine Learning Techniques ◽  
Video Gaming ◽  
Clinical Events ◽  
Learning Techniques

Heart rate variability (HRV) is the rate of variability between each heartbeat with respect to time. It is used to analyse the Autonomic Nervous System (ANS), a control system used to modulate the body's unconscious action such as cardiac function, respiration, digestion, blood pressure, urination, and dilation/constriction of the pupil. This review article presents a summary and analysis of various research works that analyzed HRV associated with morbidity, pain, drowsiness, stress and exercise through signal processing and machine learning methods. The points of emphasis with regards to HRV research as well as the gaps associated with processes which can be improved to enhance the quality of the research have been discussed meticulously. Restricting the physiological signals to Electrocardiogram (ECG), Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) analysis resulted in 25 articles which examined the cause and effect of increased/reduced HRV. Reduced HRV was generally associated with increased morbidity and stress. High HRV normally indicated good health, and in some instances, it could signify clinical events of interest such as drowsiness. Effective analysis of HRV during ambulatory and motion situations such as exercise, video gaming, and driving could have a significant impact toward improving social well-being. Detection of HRV in motion is far from perfect, situations involving exercise or driving reported accuracy as high as 85% and as low as 59%. HRV detection in motion can be improved further by harnessing the advancements in machine learning techniques.

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