Fuzzy Evaluation of Heart Rate Signals for Mental Stress Assessment

Mental stress is accompanied by dynamic changes in autonomic nervous system (ANS) activity. Heart rate variability (HRV) analysis is a popular tool for assessing the activities of autonomic nervous system. This paper presents a novel method of HRV analysis for mental stress assessment using fuzzy clustering and robust identification techniques. The approach consists of 1) online monitoring of heart rate signals, 2) signal processing (e.g., using the continuous wavelet transform to extract the local features of HRV in time-frequency domain), 3) exploiting fuzzy clustering and fuzzy identification techniques to render robustness in HRV analysis against uncertainties due to individual variations, and 4) monitoring the functioning of autonomic nervous system under different stress conditions. Our experiments involved 38 physically fit subjects (26 male, 12 female, aged 18-29 years) in air traffic control task simulations. The subjective rating scores of mental workload were assessed using NASA task load index. Fuzzy clustering methods have been used to model the experimental data. Further, a robust fuzzy identification technique has been used to handle the uncertainties due to individual variations for the assessment of mental stress. [ All rights reserved Elsevier].     

Automatic identification of cardiac health using modeling techniques: A comparative study

Heart rate variability (HRV), a widely adopted quantitative marker of the autonomic nervous system can be used as a predictor of risk of cardiovascular diseases. Moreover, decreased heart rate variability (HRV) has been associated with an increased risk of cardiovascular diseases. Hence in this work HRV signal is used as the base signal for predicting the risk of cardiovascular diseases. The present study concerns nine cardiac classes that include normal sinus rhythm (NSR), congestive heart failure (CHF), atrial fibrillation (AF), ventricular fibrillation (VF), preventricular contraction (PVC), left bundle branch block (LBBB), complete heart block (CHB), ischemic/dilated cardiomyopathy (ISCH) and sick sinus syndrome (SSS). A total of 352 cardiac subjects belonging to the nine classes were analyzed in the frequency domain. The fast Fourier transforms (FFT) and three other modeling techniques namely, autoregressive (AR) model, moving average (MA) model and the autoregressive moving average (ARMA) model are used to estimate the power spectral densities of the RR interval variability. The spectral parameters obtained from the spectral analysis of the HRV signals are used as the input parameters to the artificial neural network (ANN) for classification of the different cardiac classes. Our findings reveal that the ARMA modeling technique seems to give better resolution and would be more promising for clinical diagnosis.     

Heart rate variability in relation to psychological factors

Fluctuations in heart rate are related to both physiological and psychological factors. A possible link between those factors has been investigated by examining heart rate variability (HRV)

In two groups of subjects characterized by a large difference in psychic state, psychiatric patients and normals, an investigation was conducted into what extent factors of neural cardiovascular control (for example, respiratory arrhythmia and blood pressure oscillations) are reflected in HRV. With help of cluster analysis methods applied to parameters extracted from the HRV power spectra, it was found that four different groups could be identified. The results indicated likely differences in neural cardiovascular control activity in psychiatric patients and normal subjects     

Cardiovascular state changes during performance of a simulated ambulance dispatchers' task: Potential use for adaptive support

Adaptive support has the potential to keep the operator optimally motivated, involved, and able to perform a task. In order to use such support, the operator's state has to be determined from physiological parameters and task performance measures. In an environment where the task of an ambulance dispatcher was simulated, two studies have been carried out to evaluate the feasibility of using cardiovascular measures for adaptive support. During performance of this 2–3 h lasting planning task, a pattern of results is found that can be characterized by an initial increase of blood pressure and heart rate and a decrease of heart rate variability (defense reaction pattern) followed by an ongoing increase of blood pressure counteracted by a decrease in heart rate. This pattern can be explained by an augmented short-term blood pressure control (baroreflex), which is reflected in an increase of baroreflex sensitivity. Additionally, in this latter phase heart rate variability (HRV) increases as a function of time, while blood pressure variability decreases. In the two studies performed, the baroreflex pattern was consistent for all the relevant variables.In both studies there were periods with high and low workload. Effects of task load are mainly reflected in the variability measures, while in the second study, additionally, blood pressure level was higher during periods with high task demands.The conclusion of the studies is that consistent cardiovascular response patterns can be recognized during this semi-realistic planning task, where variability measures are most sensitive to task demand changes, while blood pressure and baroreflex sensitivity are most informative with respect to cardiovascular state changes. These findings can be seen as a great potential benefit for future use in adaptive support applications.     

Heart rate variability related to effort at work

Changes in autonomic nervous system function have been related to work stress induced increases in cardiovascular morbidity and mortality. Our purpose was to examine whether various heart rate variability (HRV) measures and new HRV-based relaxation measures are related to self-reported chronic work stress and daily emotions. The relaxation measures are based on neural network modelling of individual baseline heart rate and HRV information. Nineteen healthy hospital workers were studied during two work days during the same work period. Daytime, work time and night time heart rate, as well as physical activity were recorded. An effort–reward imbalance (ERI) questionnaire was used to assess chronic work stress. The emotions of stress, irritation and satisfaction were assessed six times during both days. Seventeen subjects had an ERI ratio over 1, indicating imbalance between effort and reward, that is, chronic work stress. Of the daily emotions, satisfaction was the predominant emotion. The daytime relaxation percentage was higher on Day 2 than on Day 1 (4 ± 6% vs. 2 ± 3%, p < 0.05) and the night time relaxation (43 ± 30%) was significantly higher than daytime or work time relaxation on the both Days. Chronic work stress correlated with the vagal activity index of HRV. However, effort at work had many HRV correlates: the higher the work effort the lower daytime HRV and relaxation time. Emotions at work were also correlated with work time (stress and satisfaction) and night time (irritation) HRV. These results indicate that daily emotions at work and chronic work stress, especially effort, is associated with cardiac autonomic function. Neural network modelling of individual heart rate and HRV information may provide additional information in stress research in field conditions.     

Estimation of heart rate signals for mental stress assessment using neuro fuzzy technique

This paper presents the evaluation of mental stress assesment using heart-rate variability (HRV). The activity of the autonomic nervous system (ANS) is studied by means of time-frequency analysis (TFA) of the heart-rate variability signal. Spectral decomposition of the heart-rate variability before smoking and after smoking was obtained. Mental stress is accompanied by dynamic changes in ANS activity. HRV analysis is a popular tool for assessing the activities of autonomic nervous system. The approach consists of (1) monitoring of heart rate signals, (2) signal processing using wavelet transform (WT) (different wavelets), (3) neuro fuzzy evaluation techniques to provide robustness in HRV analysis, (4) monitoring the function of ANS under different stress conditions. Our experiment involves 20 physically fit persons under different times (before smoking and after smoking). Nero fuzzy technique have been used to model the experimental data.     

Autonomic function assessment using analysis of heart rate variability

Analysis of changes in heart rate can be useful in determining the state of various body systems. In particular the analysis of heart rate variability (HRV) is used in the assessment of autonomic function. This paper uses the discrete harmonic wavelet transform for a time-frequency analysis of HRV data to show changes in spectral power over time. Signals representing patient heart rate are presented, and methods for spectral and time-frequency analysis are described. Three sets of patient data are then analysed using these methods. The results show the potential of time-frequency analysis in the assessment of medical disorders, such as the sleep apnoea syndrome, where transient alterations in autonomic function occur.     

Fighter pilots’ heart rate,heart rate variation and performance during instrument approaches

Fighter pilots’ heart rate (HR), heart rate variation (HRV) and performance during instrument approaches were examined. The subjects were required to fly instrument approaches in a high-fidelity simulator under various levels of task demand. The task demand was manipulated by increasing the load on the subjects by reducing the range at which they commenced the approach. HR and the time domain components of HRV were used as measures of pilot mental workload (PMWL). The findings of this study indicate that HR and HRV are sensitive to varying task demands. HR and HRV were able to distinguish the level of PMWL after which the subjects were no longer able to cope with the increasing task demands and their instrument landing system performance fell to a sub-standard level. The major finding was the HR/HRV’s ability to differentiate the sub-standard performance approaches from the high-performance approaches.

Practitioner Summary:

This paper examined if HR and HRV were sensitive to varying task demands in a fighter aviation environment and if these measures were related to variations in pilot’s performance.     

Driving in an urban environment,the stress response and effects of exercise

Driving may be detrimental to health, with one hypothesis suggesting that driving may elicit an acute stress response and, with repeated exposures, may become a chronic stressor. The present study examined the stress response to driving and the effectiveness of a prior exercise bout in dampening this response. Twenty healthy adults performed three tasks: control, driving and exercise plus driving. Heart rate (HR), heart rate variability (HRV), blood pressure (BP) and cortisol were measured to quantify the acute stress response to each condition. Data indicated a stress response to driving: HR was elevated and HRV was reduced during the driving task compared with control. HR was elevated and HRV was reduced comparing the exercise plus driving with the driving condition. BP and cortisol were not different among conditions. The potential of interventions, such as exercise, to counter daily stressors should be evaluated to safeguard long-term health.

Practitioner Summary: this study confirms that driving induces a stress response, with the exercise intervention providing mixed results (an increase in cardiovascular measures and a decrease in cortisol measure trending significance). Given the known consequences of stress and evidence that exercise can mitigate acute stress, further evaluation of exercise interventions is recommended.     

Preprocessing effects in time–frequency distributions and spectral analysis of heart rate variability

Heart rate variability (HRV) is very significance noninvasive tool for autonomic nervous system (ANS) analysis. HRV signal includes both slowly changing components and rapidly changing transient events. This study presents effects of preprocessing of HRV in time–frequency analysis and spectral estimations. Preprocessing includes two levels as detrending of trend using smoothness prior method and correction of ectopics using integral pulse frequency modulation (IPFM). The datasets used in this study are obtained from the Spontaneous Ventricular Tachyarrhythmia (VTA) database. Datasets include least one ventricular tachyarrhythmia (VT) or ventricular fibrillation (VF) episode. Effects of preprocessing are investigated for time–frequency analysis using continuous wavelet transform (CWT) and spectrogram and for spectral analysis using periodogram, Welch's periodogram and Burg's periodogram. Performance of these methods in determination of VT or VF episode is analyzed. Importance of preprocessing is explained comparing of obtained results.     

The effects of physical vibration on heart rate variability as a measure of drowsiness

We investigated the effects of low frequency whole body vibration on heart rate variability (HRV), a measure of autonomic nervous system activation that differentiates between stress and drowsiness. Fifteen participants underwent two simulated driving tasks for 60?min each: one involved whole-body 4–7?Hz vibration delivered through the car seat, and one involved no vibration. The Karolinska Sleepiness Scale (KSS), a subjective measure of drowsiness, demonstrated a significant increase in drowsiness during the task. Within 15–30?min of exposure to vibration, autonomic (sympathetic) activity increased (p?p?Practitioner summary: The effects of physical vibration on driver drowsiness have not been well investigated. This laboratory-controlled study found characteristic changes in heart rate variability (HRV) domains that indicated progressively increasing neurological effort in maintaining alertness in response to low frequency vibration, which becomes significant within 30?min.

Abbreviations: ANS: autonomic nervous system; Ctrl: control; EEG: electroencephalography; HF: the power in high frequency range (0.15 Hz-0.4Hz) in the PSD relected parasympathetic activity only; HRV: heart rate variability; KSS: karolinska sleepiness scale; LF: the power in low frequency range (0.04 Hz-0.15Hz) in the PSD reflected both sympathetic and parasympathetic activity of the autonomic nervous system; LF/HF ratio: the ratio of LF to HF indicated the balance between sympathetic and parasympathetic activity; RMSSD: the root mean square of difference of adjacent RR interval; pNN50: the number of successive RR interval pairs that differed by more than 50 ms divided by the total number of RR intervals; RR interval: the differences between successive R-wave occurrence times; PSD: power spectral density; RTP: research training program; SD: standard deviation; SEM: standard error of the Mean; Vib: vibration     

Sensitivity and diagnosticity of the 0.1-Hz component of heart rate variability as an indicator of mental workload

We investigated whether the 0.1-Hz component of heart rate variability (HRV) allows one to discriminate among levels of mental work stress induced by different types of tasks (diagnosticity) as well as among those induced by different levels of difficulty (sensitivity). Our 14 participants were presented 14 tasks of the Advisory Group for Aerospace Research and Development Standardized Tests for Research with Environmental Stressors battery in a repeated-measures design. Sufficient sensitivity was obtained for a discrimination between work and rest, but we found no support for a more fine-grained sensitivity. Concerning diagnosticity, only the grammatical reasoning task could be discriminated from all other tasks, indicating for this task a level of mental strain comparable to rest, which was in contrast with the results both for perceived difficulty and performance. We propose that HRV is an indicator for time pressure or emotional strain, not for mental workload, given that it seems to allow discrimination between tasks with and without pacing. Application of this research argues against using HRV as a measure of mental and especially cognitive workload, particularly where system safety or occupational risks may be at stake (e.g., when evaluating operator tasks or interface design in control room operations).     

Development of a decision support system for neuro-electrostimulation: Diagnosing disorders of the cardiovascular system and evaluation of the treatment efficiency

The study describes a preliminary stage of the decision support system development for physician performing neuro-electrostimulation of neck neural formations for patients suffering from cardiovascular system disorders. The arterial hypertension was used as the clinical model of the disorders. The study consisted of two steps: diagnosing of the arterial hypertension and an evaluation of the treatment efficiency during the neuro-electrostimulation application. For the diagnosing part, a clinical study was conducted involving heart rate variability signals recorded while performing tilt-test functional load. Heart rate variability signal is an indirect mean of accessing autonomic nervous system functioning. Disturbances of the autonomic nervous system are essential in pathology of arterial hypertension. Performance of different machine learning techniques and feature selection strategies in task of binary classification (healthy volunteers and patients suffering from arterial hypertension) were compared. The genetic programming feature selection and quadratic discriminant analysis classifier reached the highest classification accuracy. Best feature combinations were used to evaluate treatment efficiency. Predictions based on the selected heart rate variability features have a high level of agreement with the arterial pressure dynamics. The results indicate the potential of the proposed decision support system.     

Cardiovascular and eye activity measures as indices for momentary changes in mental effort during simulated flight

This study examines effects of momentary changes in mental effort on cardiovascular and eye activity measures. A total of 19 male pilots performed an instrument flight task. Task load was manipulated by having the pilots perform flight manoeuvres of varying complexity. Multilevel analyses demonstrated clear effects of momentary changes in mental effort on both the cardiovascular and the eye activity measures. An increase in task load resulted in an increase of heart rate and a decrease in heart rate variability, mean dwell time and fixation duration. Heart rate differentiated between resting period and task execution. Heart rate variability from short data segments provided more insight in intermediate levels of mental effort. The eye activity measures were sensitive to intermediate levels of mental effort as well. Attitude changes resulted in an increase of mean dwell time and mean fixation duration. Task analysis is required to use eye measures as valid indices of mental effort. Having indications of the effects of changing mental demands during daily work of operators is of great importance nowadays. This paper presents an approach to estimate such effects on the basis of heart rate and eye activity measures. In particular, the use of averaged short-term heart rate variability measures is a relatively new aspect.     

Cardiovascular and eye activity measures as indices for momentary changes in mental effort during simulated flight

This study examines effects of momentary changes in mental effort on cardiovascular and eye activity measures. A total of 19 male pilots performed an instrument flight task. Task load was manipulated by having the pilots perform flight manoeuvres of varying complexity. Multilevel analyses demonstrated clear effects of momentary changes in mental effort on both the cardiovascular and the eye activity measures. An increase in task load resulted in an increase of heart rate and a decrease in heart rate variability, mean dwell time and fixation duration. Heart rate differentiated between resting period and task execution. Heart rate variability from short data segments provided more insight in intermediate levels of mental effort. The eye activity measures were sensitive to intermediate levels of mental effort as well. Attitude changes resulted in an increase of mean dwell time and mean fixation duration. Task analysis is required to use eye measures as valid indices of mental effort. Having indications of the effects of changing mental demands during daily work of operators is of great importance nowadays. This paper presents an approach to estimate such effects on the basis of heart rate and eye activity measures. In particular, the use of averaged short-term heart rate variability measures is a relatively new aspect.     

A study on differences in human perception between a real and an AR scene viewed in an OST‐HMD

With the recent growth in the development of augmented reality (AR) technologies, it is becoming important to study human perception of AR scenes. In order to detect whether users will suffer more from visual and operator fatigue when watching virtual objects through optical see‐through head‐mounted displays (OST‐HMDs), compared with watching real objects in the real world, we propose a comparative experiment including a virtual magic cube task and a real magic cube task. The scores of the subjective questionnaires (SQ) and the values of the critical flicker frequency (CFF) were obtained from 18 participants. In our study, we use several electrooculogram (EOG) and heart rate variability (HRV) measures as objective indicators of visual and operator fatigue. Statistical analyses were performed to deal with the subjective and objective indicators in the two tasks. Our results suggest that participants were very likely to suffer more from visual and operator fatigue when watching virtual objects presented by the OST‐HMD. In addition, the present study provides hints that HRV and EOG measures could be used to explore how visual and operator fatigue are induced by AR content. Finally, three novel HRV measures are proposed to be used as potential indicators of operator fatigue.     

Mining Physiological Conditions from Heart Rate Variability Analysis

This article presents a successfully developed methodology for mining physiological conditions from heart rate variability (HRV) analysis. The application of HRV analysis in both research and clinical settings has seen rapid development in the past decades. Unlike previous research, this study employed features derived from longterm monitoring of HRV indices, as these trends can best reflect the autonomic nervous system dynamics influenced by various physiological conditions. We proposed two methods for mining physiological conditions from HRV trends: a decision-tree learning method and a hybrid learning method that combines feature selection, feature extraction, and classifier construction processes. The proposed methods have been validated through a clinical case study: severity classification for Parkinson's disease. Our approach yielded classification accuracy greater than 90.0%, and high sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV).     

Sensitivity of candidate markers of psychophysiological strain to cyclical changes in manual control load during simulated process control

Complex systems are vulnerable to unpredictable breakdowns in operator performance. Although primary task goals are typically protected by compensatory effort, such protection may break down under fatigue and high strain. Detection of strain states would enable prediction of increased operational risk through adaptive automation, triggering a switch of control from human to computer. A simulated process control task was used to identify markers of strain under a cyclic loading procedure, which forced performance breakdown through stepwise changes in control load. Four trained participants provided data on control performance and a range of candidate psychophysiological markers of strain (two EEG power ratios and HRV). Within-individual analyses showed the strongest sensitivity for ‘task load index’ (TLI), an EEG measure based on executive control activity in frontal brain areas, though all measures were sensitive for some participants. The implications of such findings for the development of a closed loop system for adaptive automation are discussed.     

The effects of air temperature on office workers’ well-being,workload and productivity-evaluated with subjective ratings

Productivity bears a close relationship to the indoor environmental quality (IEQ), but how to evaluate office worker’s productivity remains to be a challenge for ergonomists. In this study, the effect of indoor air temperature (17 °C, 21 °C, and 28 °C) on productivity was investigated with 21 volunteered participants in the laboratory experiment. Participants performed computerized neurobehavioral tests during exposure in the lab; their physiological parameters including heart rate variation (HRV) and electroencephalograph (EEG) were also measured. Several subjective rating scales were used to tap participant’s emotion, well-being, motivation and the workload imposed by tasks. It was found that the warm discomfort negatively affected participants’ well-being and increased the ratio of low frequency (LF) to high frequency (HF) of HRV. In the moderately uncomfortable environment, the workload imposed by tasks increased and participants had to exert more effort to maintain their performance and they also had lower motivation to do work. The results indicate that thermal discomfort caused by high or low air temperature had negative influence on office workers’ productivity and the subjective rating scales were useful supplements of neurobehavioral performance measures when evaluating the effects of IEQ on productivity.