Mental Stress Assessment Using Ultra Short Term HRV Analysis Based on Non-Linear Method

Agreement between Ultra-Short-Term and Standard Heart Rate Variability Analysis in Resting and Post-Exercise Conditions

BACKGROUND

Short-term (5 min) heart rate variability (HRV) analysis is widely used in assessing autonomic nervous system activity during exercise. While shortening the HRV measurement duration can help improve its application efficiency, its accuracy needs to be verified. This study investigated the agreement between ultra-short-term (UST) HRV (3 or 4 min) and standard 5 min HRV and explored the optimal recording duration under resting and post-exercise conditions.

METHODS

Fourteen participants exercised on a cycle ergometer at 60% of their maximum peak power. Data were collected during the rest condition (Pre-E) and three post-exercise conditions (Post-E1, Post-E2, and Post-E3), with indicators of the standard deviation (SDNN) of the ultra-short and short-term RR intervals and the root mean square (RMSSD) of the continuous difference between RR intervals. Repeated measures ANOVA, Cohen's d statistic, Bland-Altman analysis, and interclass correlation coefficients (ICC) assessed the agreement between UST-HRV and ST-HRV.

RESULTS

The consistency results of SDNN and RMSSD in resting and post-exercise were different. At the Pre-E, Post-E2, and Post-E3 phases, no statistical differences for SDNN and RMSSD were observed, with ICCs surpassing 0.9, indicating a high level of agreement. However, at Post-E2, there was a significant difference between 3 min RMSSD and 5 min RMSSD (p < 0.05), as well as between 3 min SDNN, 4 min SDNN, and 5 min SDNN (p < 0.05). Furthermore, the limits of agreement were observed to decrease as the time duration increased in Bland-Altman plots.

CONCLUSIONS

UST-HRV analysis is a reliable substitute for standard 5 min HRV assessment, particularly during resting conditions. For post-exercise measurements, assessing the appropriateness of a 3- or 4 min duration based on the exercise's length is recommended to ensure accuracy and reliability.

Developmental changes of bodily self-consciousness in adolescent girls

The body and the self change markedly during adolescence, but how does bodily self-consciousness, the pre-reflexive experience of being a bodily subject, change? We addressed this issue by studying embodiment towards virtual avatars in 70 girls aged 10-17 years. We manipulated the synchrony between participants' and avatars' touch or movement, as well as the avatar visual shape or size relative to each participant's body. A weaker avatar's embodiment in case of mismatch between the body seen in virtual reality and the real body is indicative of a more robust bodily self-consciousness. In both the visuo-tactile and the visuo-motor experiments, asynchrony decreased ownership feeling to the same extent for all participants, while the effect of asynchrony on agency feeling increased with age. In the visuo-tactile experiment, incongruence in visual appearance did not affect agency feeling but impacted ownership, especially in older teenage girls. These findings highlight the higher malleability of bodily self-consciousness at the beginning of adolescence and suggest some independence between body ownership and agency.

Impedance cardiogram based exploration of cardiac mechanisms in post-traumatic stress disorder during trauma recall

Post-traumatic stress disorder (PTSD) is an independent risk factor for developing heart failure; however, the underlying cardiac mechanisms are still elusive. This study aims to evaluate the real-time effects of experimentally induced PTSD symptom activation on various cardiac contractility and autonomic measures. We recorded synchronized electrocardiogram and impedance cardiogram from 137 male veterans (17 PTSD, 120 non-PTSD; 48 twin pairs, 41 unpaired singles) during a laboratory-based traumatic reminder stressor. To identify the parameters describing the cardiac mechanisms by which trauma reminders can create stress on the heart, we utilized a feature selection mechanism along with a random forest classifier distinguishing PTSD and non-PTSD. We extracted 99 parameters, including 76 biosignal-based and 23 sociodemographic, medical history, and psychiatric diagnosis features. A subject/twin-wise stratified nested cross-validation procedure was used for parameter tuning and model assessment to identify the important parameters. The identified parameters included biomarkers such as pre-ejection period, acceleration index, velocity index, Heather index, and several physiology-agnostic features. These identified parameters during trauma recall suggested a combination of increased sympathetic nervous system (SNS) activity and deteriorated cardiac contractility that may increase the heart failure risk for PTSD. This indicates that the PTSD symptom activation associates with real-time reductions in several cardiac contractility measures despite SNS activation. This finding may be useful in future cardiac prevention efforts.

Effects of Game-Related Tasks for the Diagnosis and Classification of Gaming Disorder

Gaming disorder (GD) is an addictive behavior characterized by an insatiable need to play video games and shares similar symptoms with the failure of self-control due to a decline in cognitive function. Current GD diagnostic and screening tools rely on questionnaires and behavioral observations related to cognitive functions to assess an individual's capacity to maintain self-control in everyday life. However, current GD screening approaches rely on subjective symptoms, and a reliable diagnosis requires long-term clinical follow-up. Recent studies have measured biosignals along with cognitive functional tasks to provide objectivity to GD diagnosis and to acquire immediate results. However, people with GD are hypersensitive to game-related cues, so their responses may vary depending on the type of stimuli, and the difference in response to stimuli might manifest as a difference in the degree of change in the biosignal. Therefore, it is critical to choose the correct stimulus type when performing GD diagnostic tasks. In this study, we investigated the task dependence of cognitive decline in GD by comparing two cognitive functional tasks: a continuous performance task (CPT) and video game play. For this study, 69 young male adults were classified into either the gaming disorder group (GD, n = 39) or a healthy control group (HC, n = 30). CPT score, EEG signal (theta, alpha, and beta), and HRV-HF power were assessed. We observed differences in the left frontal region (LF) of the brain between the GD and HC groups during online video game play. The GD group also showed a significant difference in HF power of HRV between CPT and online video gaming. Furthermore, LF and HRV-HF significantly correlated with Young's Internet Addiction Test (Y-IAT) score, which is positively associated with impulsivity score. The amount of change in theta band activity in LF and HRV-HF-both biomarkers for changes in cognitive function-during online video game play suggests that people with GD express task-dependent cognitive decline compared with HC. Our results demonstrate the feasibility of quantifying individual self-regulation ability for gaming and underscore its importance for GD classification.

Pulse rate variability estimation method based on imaging-photoplethysmography and application to telepsychiatry

The worldwide adoption of telehealth services may benefit people who otherwise would not be able to access mental health support. In this paper, we present a novel algorithm to obtain reliable pulse and respiration signals from non-contact facial image sequence analysis. The proposed algorithm involved a skin pixel extraction method in the image processing part and signal reconstruction using the spectral information of RGB signal in the signal processing part. The algorithm was tested on 15 healthy subjects in a laboratory setting. The results show that the proposed algorithm can accurately monitor respiration rate (RR), pulse rate (PR), and pulse rate variability (PRV) in rest conditions.Clinical Relevance- The main achievement of this study is enabling non-contact PR and RR signal extraction from facial image sequences, which has potential for future use and support for psychiatrists in telepsychiatry.

Mental Stress Detection Using a Wearable In-Ear Plethysmography

This study presents an ear-mounted photoplethysmography (PPG) system that is designed to detect mental stress. Mental stress is a prevalent condition that can negatively impact an individual's health and well-being. Early detection and treatment of mental stress are crucial for preventing related illnesses and maintaining overall wellness. The study used data from 14 participants that were collected in a controlled environment. The participants were subjected to stress-inducing tasks such as the Stroop color-word test and mathematical calculations. The raw PPG signal was then preprocessed and transformed into scalograms using continuous wavelet transform (CWT). A convolutional neural network classifier was then used to classify the transformed signals as stressed or non-stressed. The results of the study show that the PPG system achieved high levels of accuracy (92.04%) and F1-score (90.8%). Furthermore, by adding white Gaussian noise to the raw PPG signals, the results were improved even more, with an accuracy of 96.02% and an F1-score of 95.24%. The proposed ear-mounted device shows great promise as a reliable tool for the early detection and treatment of mental stress, potentially revolutionizing the field of mental health and well-being.

AI-Enabled Smart Wristband Providing Real-Time Vital Signs and Stress Monitoring

This work introduces the design, architecture, implementation, and testing of a low-cost and machine-learning-enabled device to be worn on the wrist. The suggested wearable device has been developed for use during emergency incidents of large passenger ship evacuations, and enables the real-time monitoring of the passengers' physiological state, and stress detection. Based on a properly preprocessed PPG signal, the device provides essential biometric data (pulse rate and oxygen saturation level) and an efficient unimodal machine learning pipeline. The stress detecting machine learning pipeline is based on ultra-short-term pulse rate variability, and has been successfully integrated into the microcontroller of the developed embedded device. As a result, the presented smart wristband is able to provide real-time stress detection. The stress detection system has been trained with the use of the publicly available WESAD dataset, and its performance has been tested through a two-stage process. Initially, evaluation of the lightweight machine learning pipeline on a previously unseen subset of the WESAD dataset was performed, reaching an accuracy score equal to 91%. Subsequently, external validation was conducted, through a dedicated laboratory study of 15 volunteers subjected to well-acknowledged cognitive stressors while wearing the smart wristband, which yielded an accuracy score equal to 76%.

Interactive Cardio System for Healthcare Improvement

The paper presents an interactive cardio system that can be used to improve healthcare. The proposed system receives, processes, and analyzes cardio data using an Internet-based software platform. The system enables the acquisition of biomedical data using various means of recording cardiac signals located in remote locations around the world. The recorded discretized cardio information is transmitted to the system for processing and mathematical analysis. At the same time, the recorded cardio data can also be stored online in established databases. The article presents the algorithms for the preprocessing and mathematical analysis of cardio data (heart rate variability). The results of studies conducted on the Holter recordings of healthy individuals and individuals with cardiovascular diseases are presented. The created system can be used for the remote monitoring of patients with chronic cardiovascular diseases or patients in remote settlements (where, for example, there may be no hospitals), control and assistance in the process of treatment, and monitoring the taking of prescribed drugs to help to improve people's quality of life. In addition, the issue of ensuring the security of cardio information and the confidentiality of the personal data of health users is considered.

Prediction of exam scores using a multi-sensor approach for wearable exam stress dataset with uniform preprocessing

BACKGROUND

Physiological signals, such as skin conductance, heart rate, and temperature, provide valuable insight into the physiological responses of students to stress during examination sessions.

OBJECTIVE

The primary objective of this research is to explore the effectiveness of physiological signals in predicting grades and to assess the impact of different models and feature selection techniques on predictive performance.

METHODS

We extracted a comprehensive feature vector comprising 301 distinct features from seven signals and implemented a uniform preprocessing technique for all signals. In addition, we analyzed different algorithmic selection features to design relevant features for robust and accurate predictions.

RESULTS

The study reveals promising results, with the highest scores achieved using 100 and 150 features. The corresponding values for accuracy, AUROC, and F1-Score are 0.9, 0.89, and 0.87, respectively, indicating the potential of physiological signals for accurate grade prediction.

CONCLUSION

The findings of this study suggest practical applications in the field of education, where the use of physiological signals can help students cope with exam stress and improve their academic performance. The importance of feature selection and the use of appropriate models highlight the importance of engineering relevant features for precise and reliable predictions.

Influences of Intense Physical Effort on the Activity of the Autonomous Nervous System and Stress, as Measured with Photoplethysmography

Background: The autonomic nervous system, which is composed of the sympathetic and parasympathetic nervous system, is closely related to the cardiovascular system. The temporal variation between each of the intervals between the consecutive “R” waves of an electrocardiogram is known as heart rate variability. Depending on the type of activity, both systems can be activated, and also influence the interval between “R” waves. Currently, with advancements in technology and electronic devices, photoplethysmography is used. Photoplethysmography detects changes in the intensity of reflected light that allow differentiation between systole and diastole and, therefore, determines the heart rate, its frequency and its variations. In this way, changes in the autonomic nervous system can be detected by devices such as the Max Pulse®. Objective: To determine whether the information provided by Max Pulse® on autonomic balance and stress is modified after intense physical exercise, thereby determining whether there is a relationship with body composition, and also whether there are differences with respect to gender. Materials and Methods: Fifty-three runners (38.9% female) with a mean age of 31.3 ± 8.1 years participated in the study. Two measurements (before and after intense physical effort) were performed with the Max Pulse® device. The flotoplethysmography measurement lasted 3 min, and was performed in the supine position. The exercise test was performed on a treadmill. It was initiated at a speed of 6 and 7 km/h for women and men, respectively. Subjects indicated the end of the test by making a hand gesture when unable to continue the test. Results: Autonomic nervous system activity and mental stress values decreased significantly (p < 0.05) in men and women, while autonomic nervous system balance decreased only in women. Physical stress increased (p < 0.05) in both sexes. Conclusions: Intense exercise causes changes in variables that assess autonomic nervous system balance and stress, as measured by a device based on photoplethysmography. The changes are evident in both sexes, and are not related to body composition.

Feasibility of Ultra-Short-Term Analysis of Heart Rate and Systolic Arterial Pressure Variability at Rest and during Stress via Time-Domain and Entropy-Based Measures

Heart Rate Variability (HRV) and Blood Pressure Variability (BPV) are widely employed tools for characterizing the complex behavior of cardiovascular dynamics. Usually, HRV and BPV analyses are carried out through short-term (ST) measurements, which exploit ~five-minute-long recordings. Recent research efforts are focused on reducing the time series length, assessing whether and to what extent Ultra-Short-Term (UST) analysis is capable of extracting information about cardiovascular variability from very short recordings. In this work, we compare ST and UST measures computed on electrocardiographic R-R intervals and systolic arterial pressure time series obtained at rest and during both postural and mental stress. Standard time-domain indices are computed, together with entropy-based measures able to assess the regularity and complexity of cardiovascular dynamics, on time series lasting down to 60 samples, employing either a faster linear parametric estimator or a more reliable but time-consuming model-free method based on nearest neighbor estimates. Our results are evidence that shorter time series down to 120 samples still exhibit an acceptable agreement with the ST reference and can also be exploited to discriminate between stress and rest. Moreover, despite neglecting nonlinearities inherent to short-term cardiovascular dynamics, the faster linear estimator is still capable of detecting differences among the conditions, thus resulting in its suitability to be implemented on wearable devices.

Diagnostic Efficacy of Ultra-Short Term HRV Analysis in Obstructive Sleep Apnea

Heart rate variability (HRV) is the standard method for assessing autonomic nervous system (ANS) activity and is considered a surrogate marker for sympathetic overactivity in obstructive sleep apnea (OSA). Although HRV features are usually obtained from the short-term segment method, it is impossible to evaluate rapid dynamic changes in ANS activity. Herein, we propose the ultra-short-term analysis to detect the balance of ANS activity in patients with OSA. In 1021 OSA patients, 10 min HRV target datasets were extracted from polysomnographic data and analyzed by shifting the 2 min (ultra-short-term) and 5 min (short-term) segments. We detected frequency-domain parameters, including total power (Ln TP), very low frequency (Ln VLF), low frequency (Ln LF), and high frequency (Ln HF). We found that overall HRV feature alterations indicated sympathetic overactivity dependent on OSA severity, and that this was more pronounced in the ultra-short-term methodology. The apnea-hypopnea index, oxygen desaturation index, and Epworth sleepiness scale correlated with increased sympathetic activity and decreased parasympathetic activity, regardless of the methodology. The Bland-Altman plot analyses also showed a higher agreement of HRV features between the two methodologies. This study suggests that ultra-short-term HRV analysis may be a useful method for detecting alterations in ANS function in OSA patients.