Classifying Stress From Heart Rate Variability Using Salivary Biomarkers as Reference

Sleep-phasic heart rate variability predicts stress severity: Building a machine learning-based stress prediction model

We propose a novel approach for predicting stress severity by measuring sleep phasic heart rate variability (HRV) using a smart device. This device can potentially be applied for stress self-screening in large populations. Using a Holter electrocardiogram (ECG) and a Huawei smart device, we conducted 24-h dual recordings of 159 medical workers working regular shifts. Based on photoplethysmography (PPG) and accelerometer signals acquired by the Huawei smart device, we sorted episodes of cyclic alternating pattern (CAP; unstable sleep), non-cyclic alternating pattern (NCAP; stable sleep), wakefulness, and rapid eye movement (REM) sleep based on cardiopulmonary coupling (CPC) algorithms. We further calculated the HRV indices during NCAP, CAP and REM sleep episodes using both the Holter ECG and smart-device PPG signals. We later developed a machine learning model to predict stress severity based only on the smart device data obtained from the participants along with a clinical evaluation of emotion and stress conditions. Sleep phasic HRV indices predict individual stress severity with better performance in CAP or REM sleep than in NCAP. Using the smart device data only, the optimal machine learning-based stress prediction model exhibited accuracy of 80.3 %, sensitivity 87.2 %, and 63.9 % for specificity. Sleep phasic heart rate variability can be accurately evaluated using a smart device and subsequently can be used for stress predication.

Gated Recurrent Unit Network for Psychological Stress Classification Using Electrocardiograms from Wearable Devices

In recent years, research on human psychological stress using wearable devices has gradually attracted attention. However, the physical and psychological differences among individuals and the high cost of data collection are the main challenges for further research on this problem. In this work, our aim is to build a model to detect subjects' psychological stress in different states through electrocardiogram (ECG) signals. Therefore, we design a VR high-altitude experiment to induce psychological stress for the subject to obtain the ECG signal dataset. In the experiment, participants wear smart ECG T-shirts with embedded sensors to complete different tasks so as to record their ECG signals synchronously. Considering the temporal continuity of individual psychological stress, a deep, gated recurrent unit (GRU) neural network is developed to capture the mapping relationship between subjects' ECG signals and stress in different states through heart rate variability features at different moments, so as to build a neural network model from the ECG signal to psychological stress detection. The experimental results show that compared with all comparison methods, our method has the best classification performance on the four stress states of resting, VR scene adaptation, VR task and recovery, and it can be a remote stress monitoring solution for some special industries.

Symmetric Convolutional and Adversarial Neural Network Enables Improved Mental Stress Classification from EEG

Electroencephalography (EEG) is widely used for mental stress classification, but effective feature extraction and transfer across subjects remain challenging due to its variability. In this paper, a novel deep neural network combining convolutional neural network (CNN) and adversarial theory, named symmetric deep convolutional adversarial network (SDCAN), is proposed for stress classification based on EEG. The adversarial inference is introduced to automatically capture invariant and discriminative features from raw EEG, which aims to improve the classification accuracy and generalization ability across subjects. Experiments were conducted with 22 human subjects, where each participant's stress was induced by the Trier Social Stress Test paradigm while EEG was collected. Stress states were then calibrated into four or five stages according to the changing trend of salivary cortisol concentration. The results show that the proposed network achieves improved accuracies of 87.62% and 81.45% on the classification of four and five stages, respectively, compared to conventional CNN methods. Euclidean space data alignment approach (EA) was applied and the improved generalization ability of EA-SDCAN across subjects was also validated via the leave-one-subject-out-cross-validation, with the accuracies of four and five stages being 60.52% and 48.17%, respectively. These findings indicate that the proposed SDCAN network is more feasible and effective for classifying the stages of mental stress based on EEG compared with other conventional methods.

Analysis of Physiological Signals for Stress Recognition with Different Car Handling Setups

When designing a car, the vehicle dynamics and handling are important aspects, as they can satisfy a purpose in professional racing, as well as contributing to driving pleasure and safety, real and perceived, in regular drivers. In this paper, we focus on the assessment of the emotional response in drivers while they are driving on a track with different car handling setups. The experiments were performed using a dynamic professional simulator prearranged with different car setups. We recorded various physiological signals, allowing us to analyze the response of the drivers and analyze which car setup is more influential in terms of stress arising in the subjects. We logged two skin potential responses (SPRs), the electrocardiogram (ECG) signal, and eye tracking information. In the experiments, three car setups were used (neutral, understeering, and oversteering). To evaluate how these affect the drivers, we analyzed their physiological signals using two statistical tests (t-test and Wilcoxon test) and various machine learning (ML) algorithms. The results of the Wilcoxon test show that SPR signals provide higher statistical significance when evaluating stress among different drivers, compared to the ECG and eye tracking signals. As for the ML classifiers, we count the number of positive or “stress” labels of 15 s SPR time intervals for each subject and each particular car setup. With the support vector machine classifier, the mean value of the number of positive labels for the four subjects is equal to 13.13% for the base setup, 44.16% for the oversteering setup, and 39.60% for the understeering setup. In the end, our findings show that the base car setup appears to be the least stressful, and that our system enables us to effectively recognize stress while the subjects are driving in the different car configurations.

Machine Learning for personalised stress detection: Inter-individual variability of EEG-ECG markers for acute-stress response

Stress appears as a response for a broad variety of physiological stimuli. It does vary among individuals in amplitude, phase and frequency. Thus, the necessity for personalised diagnosis is key to prevent stress-related diseases. In order to evaluate stress levels, a multi-sensing system is proposed based on non-invasive EEG and ECG signals. A target population of 24 individuals which age range between 18-23 years old are intentionally exposed to control-induced stress tests while EEG and ECG are simultaneously recorded. The acquired signals are processed by using semisupevised Machine Learning techniques as those provide a patient-specific approach due to key characteristics such as adaptiveness and robustness. In here, a stress metric is proposed that jointly with each individual medical history provide mechanisms to prevent and avoid possible chronic-health issues for individuals whom are more sensitive to stressors. Finally, supervised learning techniques are used to classify the obtained featured clusters to evaluate specific and general subject models in order to pave the way for real time stress monitoring.

Gated temporal convolutional neural network and expert features for diagnosing and explaining physiological time series: A case study on heart rates

BACKGROUND AND OBJECTIVE

Physiological time series are common data sources in many health applications. Mining data from physiological time series is crucial for promoting healthy living and reducing governmental medical expenditure. Recently, research and applications of deep learning methods on physiological time series have developed rapidly because such data can be continuously recorded by smart wristbands or smartwatches. However, existing deep learning methods suffer from excessive model complexity and a lack of explanation. This paper aims to handle these issues.

METHODS

We propose TEG-net, which is a novel deep learning method for accurately diagnosing and explaining physiological time series. TEG-net constructs T-net (a multi-scale bi-directional temporal convolutional neural network) to model physiological time series directly, E-net (personalized linear model) to model expert features extracted from physiological time series, and G-net (gating neural network) to combine T-net and E-net for diagnosis. The combination of T-net and E-net through G-net improves diagnosis accuracy and E-net can be utilized for explanation.

RESULTS

Experimental results demonstrate that TEG-net outperforms the second-best baseline by 13.68% in terms of area under the receiver operating characteristic curve and 11.49% in terms of area under the precision-recall curve. Additionally, intuitive justifications can be provided to explain model predictions.

CONCLUSIONS

This paper develops an ensemble method to combine expert features and deep learning method for modeling physiological time series. Improvements in diagnostic accuracy and explanation make TEG-net applicable to many real-world health applications.

Effect of an active learning methodology combined with formative assessments on performance, test anxiety, and stress of university students

The aim of this study was to evaluate the effect of an active methodology combined with a lecture on undergraduate student learning and levels of stress and anxiety. The active learning methodology consisted of a lecture of 50-min duration, study at home with a textbook, an educational game activity, and three formative assessments on the topic of the cardiac cycle. In a following class, the students provided saliva samples to evaluate their levels of stress, received an anxiety test, and then undertook an exam to assess their understanding of the cardiac cycle. The traditional teaching methodology consisted of two lectures (∼2-h duration) on blood pressure control systems, delivered orally. In the third class, the students provided saliva samples, received an anxiety test, and then undertook an exam to assess their understanding of blood pressure control systems. The level of stress was assessed with the concentrations of the stress biomarkers cortisol and alpha-amylase in saliva. Anxiety was assessed with the State-Trait Anxiety Inventory (STAI) questionnaire. The students achieved significantly higher average scores in exams when the active learning strategy was applied compared with the use of traditional theoretical classes. The active methodology resulted in significantly lower levels of stress and anxiety, as well as improved student performance, compared with the use of traditional lectures.

Car Driver's Sympathetic Reaction Detection Through Electrodermal Activity and Electrocardiogram Measurements

OBJECTIVE

in this paper we propose a system to detect a subject's sympathetic reaction, which is related to unexpected or challenging events during a car drive.

METHODS

we use the Electrocardiogram (ECG) signal and the Skin Potential Response (SPR) signal, which has several advantages with respect to other Electrodermal (EDA) signals. We record one SPR signal for each hand, and use an algorithm that, selecting the smoother signal, is able to remove motion artifacts. We extract statistical features from the ECG and SPR signals in order to classify signal segments and identify the presence or absence of emotional events via a Supervised Learning Algorithm. The experiments were carried out in a company which specializes in driving simulator equipment, using a motorized platform and a driving simulator. Different subjects were tested with this setup, with different challenging events happening on predetermined locations on the track.

RESULTS

we obtain an Accuracy as high as 79.10% for signal blocks and as high as 91.27% for events.

CONCLUSION

results demonstrate the good performance of the presented system in detecting sympathetic reactions, and the effectiveness of the motion artifact removal procedure.

SIGNIFICANCE

our work demonstrates the possibility to classify the emotional state of the driver, using the ECG and EDA signals and a slightly invasive setup. In particular, the proposed use of SPR and of the motion artifact removal procedure are crucial for the effectiveness of the system.

A systematic review of the Trier Social Stress Test methodology: Issues in promoting study comparison and replicable research

Since its development in 1993, the Trier Social Stress Test (TSST) has been used widely as a psychosocial stress paradigm to activate the sympathetic nervous system and hypothalamic-pituitary-adrenal axis (HPAA) stress systems, stimulating physiological functions (e.g. heart rate) and cortisol secretion. Several methodological variations introduced over the years have led the scientific community to question replication between studies. In this systematic review, we used the Preferred Reporting Items of Systematic Reviews and Meta-Analysis (PRISMA) to synthesize procedure-related data available about the TSST protocol to highlight commonalities and differences across studies. We noted significant discrepancies across studies in how researchers applied the TSST protocol. In particular, we highlight variations in testing procedures (e.g., number of judges, initial number in the arithmetic task, time of the collected saliva samples for cortisol) and discuss possible misinterpretation in comparing findings from studies failing to control for variables or using a modified version from the original protocol. Further, we recommend that researchers use a standardized background questionnaire when using the TSST to identify factors that may influence physiological measurements in tandem with a summary of this review as a protocol guide. More systematic implementation and detailed reporting of TSST methodology will promote study replication, optimize comparison of findings, and foster an informed understanding of factors affecting responses to social stressors in healthy people and those with pathological conditions.

Extended Central Tendency Measure and difference plot for heart rate variability analysis

Heart rate variability (HRV) is a non-invasive alternative to analyze the role of the autonomic nervous system (ANS) on heart functioning. Many tools have been developed to analyze collected cardiac data. Among them, the Central Tendency Measure (CTM) is a quantitative method for variability analysis of RR intervals. The values of the CTM must be between 0 and 1 (inclusive) for different radius, which follows the intrinsic characteristics of each time series. Using the conventional CTM, the successive differences of the time series may be calculated, and it can classify and differentiate the disturbances in the ANS involving HRV. This method was extended (e-CTM) to analyze the differences between RR interval time series. In this extension, a new parameter is added, which allows analysis of long time intervals, instead of successive and adjacent RR intervals. The ability of the e-CTM to differentiate the groups of the RR interval time series was verified with 145 RR interval time series divided into three groups: subjects with congestive heart failure, healthy subjects, and nurses during one hour of their workday. Results evidence that the new parameter added differentiates the group with pathology (and subsequent impairment of ANS) and group under stress at work (temporary impairment of ANS). These results suggest that the e-CTM is capable of detection long-term variations in the HRV according to the ANS impairment.

Spectro-Temporal Electrocardiogram Analysis for Noise-Robust Heart Rate and Heart Rate Variability Measurement

The last few years has seen a proliferation of wearable electrocardiogram (ECG) devices in the market with applications in fitness tracking, patient monitoring, athletic performance assessment, stress and fatigue detection, and biometrics, to name a few. The majority of these applications rely on the computation of the heart rate (HR) and the so-called heart rate variability (HRV) index via time-, frequency-, or non-linear-domain approaches. Wearable/portable devices, however, are highly susceptible to artifacts, particularly those resultant from movement. These artifacts can hamper HR/HRV measurement, thus pose a serious threat to cardiac monitoring applications. While current solutions rely on ECG enhancement as a pre-processing step prior to HR/HRV calculation, existing artifact removal algorithms still perform poorly under extremely noisy scenarios. To overcome this limitation, we take an alternate approach and propose the use of a spectro-temporal ECG signal representation that we show separates cardiac components from artifacts. More specifically, by quantifying the rate-of-change of ECG spectral components over time, we show that heart rate estimates can be reliably obtained even in extremely noisy signals, thus bypassing the need for ECG enhancement. With such HR measurements in hands, we then propose a new noise-robust HRV index termed MD-HRV (modulation-domain HRV) computed as the standard deviation of the obtained HR values. Experiments with synthetic ECG signals corrupted at various different signal-to-noise levels, as well as recorded noisy signals show the proposed measure outperforming several HRV benchmark parameters computed post wavelet-based enhancement. These findings suggest that the proposed HR measures and derived MD-HRV metric are well-suited for ambulant cardiac monitoring applications, particularly those involving intense movement (e.g., elite athletic training).

Evaluation of an Integrated System of Wearable Physiological Sensors for Stress Monitoring in Working Environments by Using Biological Markers

OBJECTIVE

The objectives of this paper are to develop and test the ability of a wearable physiological sensors system, based on ECG, EDA, and EEG, to capture human stress and to assess whether the detected changes in physiological signals correlate with changes in salivary cortisol level, which is a reliable, objective biomarker of stress.

METHODS

15 healthy participants, eight males and seven females, mean age 40.8 ± 9.5 years, wore a set of three commercial sensors to record physiological signals during the Maastricht Acute Stress Test, an experimental protocol known to elicit robust physical and mental stress in humans. Salivary samples were collected throughout the different phases of the test. Statistical analysis was performed using a support vector machine (SVM) classification algorithm. A correlation analysis between extracted physiological features and salivary cortisol levels was also performed.

RESULTS

15 features extracted from heart rate variability, electrodermal, and electroencephalography signals showed a high degree of significance in disentangling stress from a relaxed state. The classification algorithm, based on significant features, provided satisfactory outcomes with 86% accuracy. Furthermore, correlation analysis showed that the observed changes in physiological features were consistent with the trend of salivary cortisol levels (R² = 0.714).

CONCLUSION

The tested set of wearable sensors was able to successfully capture human stress and quantify stress level.

SIGNIFICANCE

The results of this pilot study may be useful in designing portable and remote control systems, such as medical devices used to turn on interventions and prevent stress consequences.