Stress Analysis Based on Simultaneous Heart Rate Variability and EEG Monitoring

Wearable EEG-Based Brain-Computer Interface for Stress Monitoring

Detecting stress is important for improving human health and potential, because moderate levels of stress may motivate people towards better performance at cognitive tasks, while chronic stress exposure causes impaired performance and health risks. We propose a Brain-Computer Interface (BCI) system to detect stress in the context of high-pressure work environments. The BCI system includes an electroencephalogram (EEG) headband with dry electrodes and an electrocardiogram (ECG) chest belt. We collected EEG and ECG data from 40 participants during two stressful cognitive tasks: the Cognitive Vigilance Task (CVT), and the Multi-Modal Integration Task (MMIT) we designed. We also recorded self-reported stress levels using the Dundee Stress State Questionnaire (DSSQ). The DSSQ results indicated that performing the MMIT led to significant increases in stress, while performing the CVT did not. Subsequently, we trained two different models to classify stress from non-stress states, one using EEG features, and the other using heart rate variability (HRV) features extracted from the ECG. Our EEG-based model achieved an overall accuracy of 81.0% for MMIT and 77.2% for CVT. However, our HRV-based model only achieved 62.1% accuracy for CVT and 56.0% for MMIT. We conclude that EEG is an effective predictor of stress in the context of stressful cognitive tasks. Our proposed BCI system shows promise in evaluating mental stress in high-pressure work environments, particularly when utilizing an EEG-based BCI.

Correlating Personality Traits With Acute Stress Responses in Earthquake Simulations: An HRV and RESP Analysis

Earthquakes, as significant natural disasters, still cannot be accurately predicted today. Although current earthquake early warning systems can provide alerts several seconds in advance, acute stress responses (ASR) in emergency situations can waste these precious escape seconds. To investigate the correlation between personality and ASR, this study collected the temperament and character of all participants using the Chen Huichang-60 Temperament Scale and the DISC Personality Inventory. In addition, this study simulated growing earthquakes in an earthquake experience hall, collecting heart rate variability and respiration signal variations throughout the process from subjects. Multivariate analysis of variance (MANOVA) and Toeplitz Inverse Covariance-Based Clustering methods were used to analyse the differences and connections between them. Furthermore, this study employed a deep learning model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to predict ASR across personalities. This model used datasets from the majority dataset of a certain personality and a single participant, respectively, and showed different performance. The results are as follows. After categorising participants based on personality test results, MANOVA revealed significant differences between the personality groups Influence-Choleric and Influence-Sanguine (p = 0.001), Influence-Phlegmatic and Steadiness-Sanguine (p = 0.023), Influence-Sanguine and Steadiness-Sanguine (p < 0.001) and Influence-Sanguine and Steadiness-Phlegmatic (p < 0.001), as well as across different earthquake stages (p < 0.01). The clustering method quantified stress responses over time for different personalities and labelled ASR levels for use in supervised learning. Ultimately, the CNN-LSTM model performed predictions of ASR using both personality and individual datasets, achieving the AUC of 0.795 and 0.72, demonstrating better prediction and classification effectiveness with the former. This study provides a new personality-based method for earthquake stress management, creating possibilities for longitudinal stress research and prediction. It aids the general public in comprehending their own acute stress and allows authorities and communities to make practical, efficient disaster evacuation plans based on the overall situation of public ASR.

Enhancing Epilepsy Seizure Detection Through Advanced EEG Preprocessing Techniques and Peak-to-Peak Amplitude Fluctuation Analysis

Objectives: Naturally, there are several challenges, such as muscular artifacts, ocular movements and electrical interferences that depend on precise diagnosis and classification, which hamper exact epileptic seizure detection. This study has been conducted to improve seizure detection accuracy in epilepsy patients using an advanced preprocessing technique that could remove such noxious artifacts. Methods: In the frame of this paper, the core tool in the area of epilepsy, EEG, will be applied to record and analyze the electrical patterns of the brain. The dataset includes recordings of seven epilepsy patients taken by the Unit of Neurology and Neurophysiology, University of Siena. The preprocessing techniques employed include advanced artifact removal and signal enhancement methods. We introduced Peak-to-Peak Amplitude Fluctuation (PPAF) to assess amplitude variability within Event-Related Potential (ERP) waveforms. This approach was applied to data from patients experiencing 3-5 seizures, categorized into three distinct groups. Results: The results indicated that the frontal and parietal regions, particularly the electrode areas Cz, Pz and Fp2, are the main contributors to epileptic seizures. Additionally, the implementation of the PPAF metric enhanced the effectiveness of seizure detection and classification algorithms, achieving accuracy rates of 99%, 98% and 95% for datasets with three, four and five seizures, respectively. Conclusions: The present research extends the epilepsy diagnosis with clues on brain activity during seizures and further demonstrates the effectiveness of advanced preprocessing techniques. The introduction of PPAF as a metric could have promising potential in improving both the accuracy and reliability of epilepsy seizure detection algorithms. These observations provide important implications for control and treatment both in focal and in generalized epilepsy.

Analysis of perioperative autonomic nervous system activity to visualize stress in pediatric patients undergoing alveolar bone graft surgery

Perioperative stress in pediatric patients is often difficult to assess via interviews; thus, an objective measure to assess perioperative stress is needed. To visualize perioperative stress, we observed autonomic nervous system (ANS) activity, circulatory dynamics, and psychological status in pediatric patients undergoing alveolar bone grafting under general anesthesia. This prospective observational study included 40 patients aged 8-12 years who were scheduled for alveolar bone grafting in our hospital. ANS activity was analyzed using heart rate variability the day before surgery, during general anesthesia, 2 h postoperatively, 24 h postoperatively, and the day before discharge. ANS assessment included LF/HF (sympathetic nervous system activity) and HF (parasympathetic nervous system activity). Additionally, heart rate (HR), systolic blood pressure (SBP), face scale (FS) score were recorded. Data from 31 patients, excluding dropouts, were analyzed. The ratio of change to the preoperative value was compared. After surgery, the LF/HF, HR, SBP, and FS score significantly increased (P < 0.01) and HF significantly decreased (2 h postoperatively: P < 0.05, 24 h postoperatively, before discharge: P < 0.01). SBP recovered to preoperative values 24 h postoperatively, and HR and FS scores recovered to preoperative values before discharge. However, even before discharge, LF/HF remained significantly higher than preoperative values, and HF remained significantly lower than preoperative values (P < 0.01). Conclusion We observed perioperative stress from multiple perspectives. Circulatory dynamics and psychological status recovered by the day before discharge; however, ANS activity did not. Therefore, evaluating ANS activity may be useful in visualizing potential perioperative stress in pediatric patients.

A pilot study using the LASCA technique to analyze stress using heart rate variability

In the quest to uncover biological cues that help explain organic changes brought on by an external stimulus, like stress, new technologies have become necessary. The Laser Speckle Contrast Analysis (LASCA) approach is one of these technologies that may be used to analyze biological data, including respiratory rate (RR) intervals, and then use the results to determine heart rate variability (HRV Thus, to evaluate the stress brought on by physical activity, this study used the LASCA approach. A stress induction procedure involving physical exertion was employed, and the results were compared to other established techniques (cortisol analysis and ECG signal) to verify the LASCA methodology as a tool for measuring HRV and stress. The study sample comprised 27 willing participants. The technique involving LASCA allowed for the non-invasive (non-contact) acquisition of HRV and the study of stress. Furthermore, it made it possible to gather pertinent data, such as recognizing modifications to the thermoregulation, peripheral vasomotor tonus, and renin-angiotensin-aldosterone systems that were brought on by elevated stress and, as a result, variations in HRV readings.

Improving Prefrontal Oxygenation and Cardiac Autonomic Activity Following Meditation: A Functional Near-Infrared Spectroscopy (fNIRS) Study

OBJECTIVE

The empirical evidence explicitly demonstrates that meditation practice enhances both brain functions and mental well-being. A meditative relaxation approach called the mind sound resonance technique (MSRT) has shown promising effects on children, adolescents, and people with psychological illnesses. This study aimed to investigate the effects of MSRT practice on brain hemodynamics, heart rate variability (HRV), mindfulness, and anxiety levels in college students.

METHODS

Fifty volunteers in all genders (females, n = 30; males, n = 20) aged between 19 and 30 years were chosen from an educational institute and allocated into two groups, i.e., MSRT (n = 25) and supine rest (SR; n = 25). Enrolled participants were measured cerebral hemodynamics and HRV before, during, and after the MSRT or SR practice. The self-reported assessments including state anxiety and mindfulness were assessed before and after the intervention.

RESULTS

The results demonstrated that practicing MSRT significantly improved oxygenation (p < 0.05) in the right prefrontal cortex (PFC) and increased low-frequency (LF) (p < 0.05) and decreased high-frequency (HF) (p < 0.05) component of HRV when compared to the baseline. The between-group analysis showed a significant difference between MSRT and SR in the standard deviation of the normal-to-normal (SDNN) (p < 0.05) component of HRV.

CONCLUSION

These crumbs of evidence imply that MSRT sessions may foster the development of anxiety-related coping skills by elevating mindfulness, promoting PFC oxygenation, and modulating HRV in MSRT practitioners.

Exploring physiological stress response evoked by passive translational acceleration in healthy adults: a pilot study utilizing electrodermal activity and heart rate variability measurements

Passive translational acceleration (PTA) has been demonstrated to induce the stress response and regulation of autonomic balance in healthy individuals. Electrodermal activity (EDA) and heart rate variability (HRV) measurements are reliable indicators of the autonomic nervous system (ANS) and can be used to assess stress levels. The objective of this study was to investigate the potential of combining EDA and HRV measurements in assessing the physiological stress response induced by PTA. Fourteen healthy subjects were randomly assigned to two groups of equal size. The experimental group underwent five trials of elevator rides, while the control group received a sham treatment. EDA and HRV indices were obtained via ultra-short-term analysis and compared between the two groups to track changes in the ANS. In addition, the complexity of the EDA time series was compared between the 4 s before and the 2-6 s after the onset of PTA to assess changes in the subjects' stress levels in the experimental group. The results revealed a significant increase in the skin conductance response (SCR) frequency and a decrease in the root mean square of successive differences (RMSSD) and high frequency (HF) components of HRV. In terms of stress assessment, the results showed an increase in the complexity of the EDA time series 2-6 s after the onset of PTA. These results indicate an elevation in sympathetic tone when healthy subjects were exposed to a translational transport scenario. Furthermore, evidence was provided for the ability of EDA complexity to differentiate stress states in individual trials of translational acceleration.

Detection of driver drowsiness level using a hybrid learning model based on ECG signals

OBJECTIVES

Fatigue has a considerable impact on the driver's vehicle and even the driver's own operating ability.

METHODS

An intelligent algorithm is proposed for the problem that it is difficult to classify the degree of drowsiness generated by the driver during the driving process. By studying the driver's electrocardiogram (ECG) during driving, two models were established to jointly classify the ECG signals as awake, stress, and fatigue or drowsiness states for drowsiness levels. Firstly, the deep learning method was used to establish the model_1 to predict the drowsiness of the original ECG, and model_2 was developed using the combination of principal component analysis (PCA) and weighted K-nearest neighbor (WKNN) algorithm to classify the heart rate variability characteristics. Then, the drowsiness prediction results of the two models were weighted according to certain rules, and the hybrid learning model combining dilated convolution and bidirectional long short-term memory network with PCA and WKNN algorithm was established, and the mixed model was denoted as DiCNN-BiLSTM and PCA-WKNN (DBPW). Finally, the validity of the DBPW model was verified by simulation of the public database.

RESULTS

The experimental results show that the average accuracy, sensitivity and F1 score of the test model in the dataset containing multiple drivers are 98.79, 98.81, and 98.79 % respectively, and the recognition accuracy for drowsiness or drowsiness state is 99.33 %.

CONCLUSIONS

Using the proposed algorithm, it is possible to identify driver anomalies and provide new ideas for the development of intelligent vehicles.

Lumbar Paravertebral Muscle Pain Management Using Kinesitherapy and Electrotherapeutic Modalities

BACKGROUND

Low back pain is considered a public health problem internationally. Low back pain is a cause of disability that occurs in adolescents and causes negative effects in adults as well. The work environment and physical and psychosocial factors can influence the occurrence and evolution of low back pain.

METHODS

The purpose of this paper is to highlight the physiological and functional changes in young adults with painful conditions of the lumbar spine, after using exercise therapy. The study was of the longitudinal type and was carried out over a period 6 months in an outpatient setting. The rehabilitation treatment included electrotherapeutic modalities and kinesitherapy.

RESULTS

The results obtained when evaluating each parameter, for all moments, show statistically significant values in both groups. The results obtained regarding the relationship between the therapeutic modalities specific to rehabilitation medicine and low back pain are consistent with those reported in studies.

CONCLUSIONS

Depending on the clinical-functional status of each patient, kinesitherapy can accelerate the heart rate and increase the blood pressure and oxygen saturation of the arterial blood, values that can later return to their initial levels, especially through training.

Is Acute Lower Back Pain Associated with Heart Rate Variability Changes? A Protocol for Systematic Reviews

Acute lower back pain (ALBP) is an extremely common musculoskeletal problem. ALBP consists of a sudden onset of short-duration pain in the lower back. However, repeated attacks can make the pain chronic. It can be measured through a self-report scale as well as through physical and physiological evaluations. Heart Rate Variability (HRV) has been used to evaluate the body's response to pain. However, to the best of our knowledge, no clear consensus has been reached regarding the relationship between both variables and on an optimal protocol for ALBP evaluation based on HRV. The objective of this review is to analyze the relationship and effectiveness of HRV as an instrument for measuring ALBP. Furthermore, we consider the influence of different types of interventions in this relationship. The protocol of this review was previously recorded in the International Prospective Register of Systematic Reviews (number CRD42023437160). The PRISMA guidelines for systematic reviews and PubMed, WOS and Scopus databases are employed. Studies with samples of adults with ALBP are included. This study sets out a systematic review protocol to help identify the relationship between HRV and ALBP. Understanding this relationship could help in designing early detection or action protocols that alleviate ALBP.

Classification of Mental Stress Levels using EEG Connectivity and Convolutional Neural Networks

Classifying mental stress is important as it helps in identifying the type and severity of stress, which can inform the most appropriate treatment or intervention. In this study, we propose utilizing electroencephalography (EEG) signals with convolutional neural networks (CNNs) to classify four mental states: rest, control-alert, stress and stress mitigation. The mental stress state was induced using Stroop color word test (SCWT) with time constrains and was then mitigated using 16 Hz Binaural beat stimulation (BBs). We quantified the four mental states using the reaction time (RT) to stimuli, accuracy of target detection, subjective score, and functional connectivity images of EEG estimated by Phase Locking Value (PLV). Our results show that, the SCWT reduced the accuracy of target detection by 70% with (F= 24.56, p = .00001), and the BBs improved the accuracy by 28% (F= 4.54, p = .00470). The functional connectivity network showed different patterns between the frontal/occipital and parietal regions, under the four mental states. The proposed CNNs with PLV images differentiated between the four mental states with highest classification performance at beta frequency band with 80.95% accuracy, 80.36% sensitivity, 94.75% specificity, 83.63% precision and 81.96% F-score. The overall results suggest that 16 Hz BBs can be used as an effective method to mitigate stress and the proposed CNNs with EEG-PLV images as a promising method for classifying different mental states.

An Innovative Random-Forest-Based Model to Assess the Health Impacts of Regular Commuting Using Non-Invasive Wearable Sensors

Regular commutes to work can cause chronic stress, which in turn can cause a physical and emotional reaction. The recognition of mental stress in its earliest stages is very necessary for effective clinical treatment. This study investigated the impact of commuting on human health based on qualitative and quantitative measures. The quantitative measures included electroencephalography (EEG) and blood pressure (BP), as well as weather temperature, while qualitative measures were established from the PANAS questionnaire, and included age, height, medication, alcohol status, weight, and smoking status. This study recruited 45 (n) healthy adults, including 18 female and 27 male participants. The modes of commute were bus (n = 8), driving (n = 6), cycling (n = 7), train (n = 9), tube (n = 13), and both bus and train (n = 2). The participants wore non-invasive wearable biosensor technology to measure EEG and blood pressure during their morning commute for 5 days in a row. A correlation analysis was applied to find the significant features associated with stress, as measured by a reduction in positive ratings in the PANAS. This study created a prediction model using random forest, support vector machine, naive Bayes, and K-nearest neighbor. The research results show that blood pressure and EEG beta waves were significantly increased, and the positive PANAS rating decreased from 34.73 to 28.60. The experiments revealed that measured systolic blood pressure was higher post commute than before the commute. For EEG waves, the model shows that the EEG beta low power exceeded alpha low power after the commute. Having a fusion of several modified decision trees within the random forest helped increase the performance of the developed model remarkably. Significant promising results were achieved using random forest with an accuracy of 91%, while K-nearest neighbor, support vector machine, and naive Bayes performed with an accuracy of 80%, 80%, and 73%, respectively.

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.

Review of electroencephalography signals approaches for mental stress assessment

The innovation of electroencephalography (EEG) more than a century ago supports the technique to assess brain structure and function in clinical health and research applications. The EEG signals were identified on their frequency ranges as delta (from 0.5 to 4 Hz), theta (from 4 to 7 Hz), alpha (from 8 to 12 Hz), beta (from 16 to 31 Hz), and gamma (from 36 to 90 Hz). Stress is a sense of emotional tension caused by several life events. For example, worrying about something, being under pressure, and facing significant challenges are causes of stress. The human body is affected by stress in various ways. It promotes inflammation, which affects cardiac health. The autonomic nervous system is activated during mental stress. Posttraumatic stress disorder and Alzheimer's disease are common brain stress disorders. Several methods have been used previously to identify stress, for instance, magnetic resonance imaging, single-photon emission computed tomography and EEG. The EEG identifies the electrical activity in the human brain by applying small electrodes positioned on the scalp of the brain. It is a useful non-invasive method and collects feedback from stress hormones. In addition, it can serve as a reliable tool for measuring stress. Furthermore, evaluating human stress in real-time is complicated and challenging. This review demonstrates the power of frequency bands for mental stress and the behaviors of frequency bands based on medical and research experiencebands based on medical and research experience.