Detection of subjects with higher self-reporting stress scores using heart rate variability patterns during the day

Kim, Desok; Seo, Yunhwan; Cho, Jaegeol; Cho, Chul-Ho

doi:10.1109/iembs.2008.4649244

Cited by 42 publications

(26 citation statements)

References 16 publications

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“…In [22] a mental arithmetic task was used to induce mental workload and the recovery patterns of physiological responses as indicators of stress were investigated. Kim et al [12,13] studied heart rate variability (HRV) features of subjects under chronic stress. Subjects were divided into a high-stress group and a low-stress group based on their self-reporting stress scores.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of mental workload levels during an everyday life office-work scenario

Cinaz

Arnrich

Marca

et al. 2011

Pers Ubiquit Comput

192

123

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Personal and ubiquitous healthcare applications offer new opportunities to prevent long-term health damage due to increased mental workload by continuously monitoring physiological signs related to prolonged high workload and providing just-in-time feedback. In order to achieve a quantification of mental load, different load levels that occur during a workday have to be discriminated. In this work, we present how mental workload levels in everyday life scenarios can be discriminated with data from a mobile ECG logger by incorporating individual calibration measures. We present an experiment design to induce three different levels of mental workload in calibration sessions and to monitor mental workload levels in everyday life scenarios of seven healthy male subjects. Besides the recording of ECG data, we collect subjective ratings of the perceived workload with the NASA Task Load Index (TLX), whereas objective measures are assessed by collecting salivary cortisol. According to the subjective ratings, we show that all participants perceived the induced load levels as intended from the experiment design. The heart rate variability (HRV) features under investigation can be classified into two distinct groups. Features in the first group, representing markers associated with parasympathetic nervous system activity, show a decrease in their values with increased workload. Features in the second group, representing markers associated with sympathetic nervous system activity or predominance, show an increase in their values with increased workload. We employ multiple regression analysis to model the relationship between relevant HRV features and the subjective ratings of NASA-TLX in order to predict the mental workload levels during office-work. The resulting predictions were correct for six out of the seven subjects. In addition, we compare the performance of three classification methods to identify the mental workload level during office-work. The best results were obtained with linear discriminant analysis (LDA) that yielded a correct classification for six out of the seven subjects. The k-nearest neighbor algorithm (k-NN) and the support vector machine (SVM) resulted in a correct classification of the mental workload level during office-work for five out of the seven subjects.

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Section: Introductionmentioning

confidence: 99%

Monitoring of mental workload levels during an everyday life office-work scenario

Cinaz

Arnrich

Marca

et al. 2011

Pers Ubiquit Comput

192

123

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“…The negative consequences of stress as a risk factor for cardiovascular disease and reduced human performances are well studied [1]. Stress is known to change the balance existing between the sympathetic and parasympathetic divisions of the autonomic nervous system [2]. Gutkha chewers are likely to suffer from mental and physical exhaustion leading to stress.…”

Section: Introductionmentioning

confidence: 99%

A Study of Heart Rate Variability among Gutkha Chewers from a Rural Farming Background in India

Pakkala¹,

T²

2013

J Alcohol Drug Depend

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Background:Modernisation of life style has affected the population even in rural areas leading to addiction.Smokeless tobacco is presumed to be less of an evil by the rural folk. Gutkha addicts are likely to suffer from mental and physical exhaustion leading to stress. This is important in view of the prevailing socio-economic as well as the healthcare system available in any developing country. Therefore the present study is designed to measure heart rate variability among this special group of population.Methods: 15 male subjects were enrolled from among the rural farm labors who regularly chewgutkha. They were free from any type of physical and mental health issues, non smokers. Each individual volunteer was subjected to HRV analysis on three occasions: the first record was performed on the first day of their arrival in the hospital as subjects during the lean season. The second observation was recorded after 3 months of farming activity. The final HRV analysis was done after 6 months of farming activity. Two types of parameters were analysed: time domain and frequency domain. Statistical analysis was done using paired t-test.Results: Both the HRV parameters -time and frequency domain, showed decreased values during the third month and sixth month recording as compared to the first week recording. The decrease is much more during the sixth month recording as compared to the third month recording. Statistically significant decrease is observed in mean RR interval, heart rate, VLF and LF only when the first week recording is compared with the third month recording but when the first week recording was compared with the sixth month recording significant decrease was found in mean RR interval and heart rate.Conclusion: Subjects involved in taking gutkha are likely to undergo a lot of physical and mental stress, thus affecting their autonomic status. This adds up to the stress induced by their occupation. HRV analysis using short term ECG recording was used to detect changes consequent to this stress.It was found that almost all HRV parameters measuring heart rate complexity were decreased in the period of farming activity. J o ur nal of A lc o h o lis m and D ru g D ep enden c e

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“…If we compare our algorithm with other algorithms for stress detection using only HR and HRV parameters, we can say that we achieved higher identification rate than the algorithm in [ 43 ] and a worse result than [ 48 ] or [ 45 ] and around 15% worse compared to [ 44 , 47 ] but we used only time-domain features for stress identification instead of frequency-domain features or using nonlinear analysis which are much more expensive to calculate.…”

Section: Discussionmentioning

confidence: 97%

“…For automated stress detection, several methods have been published which use only HRV. In 2008, Kim et al collected HRV data from sixty-eight subjects [ 43 ]. HRV data were collected during three different time periods.…”

Section: Introductionmentioning

confidence: 99%

Stress Detection Using Low Cost Heart Rate Sensors

Salai

Vassányi

Kósa

2016

Journal of Healthcare Engineering

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The automated detection of stress is a central problem for ambient assisted living solutions. The paper presents the concepts and results of two studies targeted at stress detection with a low cost heart rate sensor, a chest belt. In the device validation study (n = 5), we compared heart rate data and other features from the belt to those measured by a gold standard device to assess the reliability of the sensor. With simple synchronization and data cleaning algorithm, we were able to select highly (>97%) correlated, low average error (2.2%) data segments of considerable length from the chest data for further processing. The protocol for the clinical study (n = 46) included a relax phase followed by a phase with provoked mental stress, 10 minutes each. We developed a simple method for the detection of the stress using only three time-domain features of the heart rate signal. The method produced accuracy of 74.6%, sensitivity of 75.0%, and specificity of 74.2%, which is impressive compared to the performance of two state-of-the-art methods run on the same data. Since the proposed method uses only time-domain features, it can be efficiently implemented on mobile devices.

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Detection of subjects with higher self-reporting stress scores using heart rate variability patterns during the day

Cited by 42 publications

References 16 publications

Monitoring of mental workload levels during an everyday life office-work scenario

Monitoring of mental workload levels during an everyday life office-work scenario

A Study of Heart Rate Variability among Gutkha Chewers from a Rural Farming Background in India

Stress Detection Using Low Cost Heart Rate Sensors

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