Decreased Long Term Variations of Heart Rate Variability in Subjects with Higher Self Reporting Stress Scores

Kim, Desok; Seo, Yunhwan; Salahuddin, Lizawati

doi:10.4108/icst.pervasivehealth2008.2526

Cited by 9 publications

(10 citation statements)

References 3 publications

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“…Subjects were divided into high and low stress group based on their self reporting stress scores (stress response inventory) [18] and their HRV features were measured at multiple time points [19]. In this study, short term analysis has been performed for heartbeat data obtained at five different time points from two subject groups.…”

Section: Introductionmentioning

confidence: 99%

Short Term Analysis of Long Term Patterns of Heart Rate Variability in Subjects under Mental Stress

Kim¹,

Seo²,

Kim³

et al. 2008

2008 International Conference on BioMedical Engineering and Informatics

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Long term patterns of heart rate variability (HRV) features were decreased in subjects with higher self reporting stress scores. For mobile applications, short term analysis of HRV features may be ideal since conventional heartbeat recordings (3~5 min) might be inadequately long. In this study, short term analysis has been performed for heartbeat data obtained at five different time points from two subject groups (15 under high and 18 under low mental stress). The reliability of short term heartbeat data was demonstrated by detecting significant differences in long term patterns of HRV features between two groups.Fifteen to thirty second heartbeat measurements were long enough to produce reliable long term patterns of HRV features. Thus, short and intermittent recordings of heartbeats could be used to detect long term HRV patterns and offer a convenient method to monitor mental stress in mobile environments.

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Section: Introductionmentioning

confidence: 99%

Short Term Analysis of Long Term Patterns of Heart Rate Variability in Subjects under Mental Stress

Kim¹,

Seo²,

Kim³

et al. 2008

2008 International Conference on BioMedical Engineering and Informatics

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“…Previous studies showed that a minimum of 60 seconds ECG signal is needed for stress detection [2][4][5] [13][17] [18]; however, in our study, we attempted to show that a 30 sec long ECG signal is sufficient for stress analysis. To generate RR-intervals from the ECG signal, we performed the following steps: 1) applied Butterworth bandpass filter [0.2Hz to 5Hz] to filter low and high frequency components ( Figure Then for each RR-interval, 103 HRV features suggested by Boonnithi and Phongsuphap [4] were computed using the HRVAS software [15].…”

Section: Data Processingmentioning

confidence: 92%

“…Recent work has shown that some of the HRV features can potentially be used for distinguishing a subject's normal mental state from a stressed one [4], [13] & [14]. Besides many civilian applications of detecting and monitoring disease processes [1], HRV might also be relevant to security and military applications such as: border patrol, stress detection for deception [3], [17], and Wounded-Warrior triage [12].…”

Section: Introductionmentioning

confidence: 99%

“…While HRV is usually detected as low-and ultra low-frequency activity in the RR-interval time series, time domain techniques have been developed that overcome the stationary assumption of the Fourier transform analysis, allowing better identification of both the presence of stress and stressors [4], [13] & [14]. Recent work has shown that heart rate variability can be used to determine stress by analyzing RR-interval features both in the time and frequency domain [2], [4], [5], [14], [17] & [18].…”

Section: Introductionmentioning

confidence: 99%

“…They claim that subjects with higher baseline heart rates show an increased HRV in response to the stressor, and therefore suggest that HRV can be used as an indicator to determine stress. A group of researchers have studied the effect of long-term variations in the HRV on chronic stress levels [13]. They collected ECG data for 3 minutes at 5 different times of the day on 23 subjects in the 20-30 age range.…”

Section: Introductionmentioning

confidence: 99%

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Heart rate variability (HRV): an indicator of stress

et al. 2014

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Heart rate variability (HRV) can be an important indicator of several conditions that affect the autonomic nervous system, including traumatic brain injury, post-traumatic stress disorder and peripheral neuropathy [3], [4], [10] & [11]. Recent work has shown that some of the HRV features can potentially be used for distinguishing a subject's normal mental state from a stressed one [4], [13] & [14]. In all of these past works, although processing is done in both frequency and time domains, few classification algorithms have been explored for classifying normal from stressed RRintervals. In this paper we used 30 s intervals from the Electrocardiogram (ECG) time series collected during normal and stressed conditions, produced by means of a modified version of the Trier social stress test, to compute HRV-driven features and subsequently applied a set of classification algorithms to distinguish stressed from normal conditions. To classify RR-intervals, we explored classification algorithms that are commonly used for medical applications, namely 1) logistic regression (LR) [16] and 2) linear discriminant analysis (LDA) [6]. Classification performance for various levels of stress over the entire test was quantified using precision, accuracy, sensitivity and specificity measures. Results from both classifiers were then compared to find an optimal classifier and HRV features for stress detection. This work, performed under an IRB-approved protocol, not only provides a method for developing models and classifiers based on human data, but also provides a foundation for a stress indicator tool based on HRV. Further, these classification tools will not only benefit many civilian applications for detecting stress, but also security and military applications for screening such as: border patrol, stress detection for deception [3], [17], and wounded-warrior triage [12].

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