ECG signatures of psychological stress

Stress influence on real-world driving identified by monitoring heart rate variability and morphologic variability of electrocardiogram signals: the case of intercity roads

Objectives. This study examines which of the heart rate variability (HRV) and morphologic variability (MV) metrics may have the highest accuracy in different stress detection during real-world driving. Methods. The cross-sectional study was carried out among 93 intercity mini-bus male drivers aged 22-67 years. The Trillium 5000 Holter Recorder and GARMIN Virb Elite camera were used to determine heart rate and vehicle speed measurements along the path, respectively. We considered the HRV and MV metrics of electrocardiogram (ECG) signals including the mean RR interval (mRR), mean heart rate (mHR), normalized low-frequency spectrum (nLF), normalized high-frequency spectrum (nHF), normalized very low-frequency spectrum (nVLF), difference of normalized low-frequency spectrum and normalized high-frequency spectrum (dLFHF), and sympathovagal balance index (SVI). Results. The analysis showed that the HRV metrics mHR, mRR, nVLF, nLF, nHF, dLFHF and SVI are effective in mental stress detection while driving as compared to rest time. We obtained a high accuracy of stress detection for MV metrics as compared to the traditional HRV analysis, of approximately 92%. Conclusions. Our findings indicate that driver stress could be detected with an accuracy of 92% using MV metrics as an accurate physiological index of the driver's state.

Parent Stress and Trauma, Autonomic Responses, and Negative Child Behaviors

Cumulative stress and trauma in parents may alter autonomic function. Both may negatively impact child behaviors, however these links have not been well established. We tested hypotheses that parent stress and trauma are associated with and interact with altered autonomic function during the toy wait task, an acute parent-child interaction challenge, to predict greater negative child behaviors. Sixty-eight parents and their 2-5 year old children were enrolled. More parent major and traumatic life events, and more parent recent life events coupled with increased heart rate and decreased heart rate variability (HRV), each related to more child disruptive/aggressive behavior. More major life and traumatic life events coupled with greater HRV predicted more child attention seeking behavior. Our novel approach to assessing parental life stress offers a unique perspective. Interventions mitigating parent stress and regulating physiological coping during parent-child interactions may both promote better parent health and improve child behavioral outcomes.

Frontiers of Wearable Biosensors for Human Health Monitoring

Wearable biosensors offer noninvasive, real-time, and continuous monitoring of diverse human health data, making them invaluable for remote patient tracking, early diagnosis, and personalized medicine [...].

Wearable Flexible Electronics Based Cardiac Electrode for Researcher Mental Stress Detection System Using Machine Learning Models on Single Lead Electrocardiogram Signal

In the modern world, wearable smart devices are continuously used to monitor people's health. This study aims to develop an automatic mental stress detection system for researchers based on Electrocardiogram (ECG) signals from smart T-shirts using machine learning classifiers. We used 20 subjects, including 10 from mental stress (after twelve hours of continuous work in the laboratory) and 10 from normal (after completing the sleep or without any work). We also applied three scoring techniques: Chalder Fatigue Scale (CFS), Specific Fatigue Scale (SFS), Depression, Anxiety, and Stress Scale (DASS), to confirm the mental stress. The total duration of ECG recording was 1800 min, including 1200 min during mental stress and 600 min during normal. We calculated two types of features, such as demographic and extracted by ECG signal. In addition, we used Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Logistic Regression (LR) to classify the intra-subject (mental stress and normal) and inter-subject classification. The DT leave-one-out model has better performance in terms of recall (93.30%), specificity (96.70%), precision (94.40%), accuracy (93.30%), and F1 (93.50%) in the intra-subject classification. Additionally, The classification accuracy of the system in classifying inter-subjects is 94.10% when using a DT classifier. However, our findings suggest that the wearable smart T-shirt based on the DT classifier may be used in big data applications and health monitoring. Mental stress can lead to mitochondrial dysfunction, oxidative stress, blood pressure, cardiovascular disease, and various health problems. Therefore, real-time ECG signals help assess cardiovascular and related risk factors in the initial stage based on machine learning techniques.

Relationship between Subjective and Biological Responses to Comfortable and Uncomfortable Sounds

Various kinds of biological sensors are now embedded in wearable devices and data on human biological information have recently become more widespread. Among various environmental stressors, sound has emotional and biological impacts on humans, and it is worthwhile to investigate the relationship between the subjective impressions of and biological responses to such sounds. In this study, the relationship between subjective and biological responses to acoustic stimuli with two contrasting kinds of sounds, a murmuring river sound and white noise, was investigated. The subjective and biological responses were measured during the presentation of the sounds. Compared with the murmuring river sound, the white noise had a significantly decreased EEG-related index of α-EEG and HRV-related index of SD2/SD1. The correlation between each index of subjective and biological responses indicated that α-EEG was highly correlated with the results of subjective evaluation. However, based on a more detailed analysis with clustering, some subjects showed different biological responses in each trial since they felt the sound was powerful when listening to the murmuring river sound, as well as feeling that it was beautiful. It was suggested that biological responses to sound exposure may be affected by the impression of the sound, which varies by individual.

Multi-Parameter Physiological State Monitoring in Target Detection Under Real-World Settings

Mental state changes induced by stimuli under experimental settings or by daily events in real life affect task performance and are entwined with physical and mental health. In this study, we developed a physiological state indicator with five parameters that reflect the subject's real-time physiological states based on online EEG signal processing. These five parameters are attention, fatigue, stress, and the brain activity shifts of the left and right hemispheres. We designed a target detection experiment modified by a cognitive attention network test for validating the effectiveness of the proposed indicator, as such conditions would better approximate a real chaotic environment. Results demonstrated that attention levels while performing the target detection task were significantly higher than during rest periods, but also exhibited a decay over time. In contrast, the fatigue level increased gradually and plateaued by the third rest period. Similar to attention levels, the stress level decreased as the experiment proceeded. These parameters are therefore shown to be highly correlated to different stages of the experiment, suggesting their usage as primary factors in passive brain-computer interfaces (BCI). In addition, the left and right brain activity indexes reveal the EEG neural modulations of the corresponding hemispheres, which set a feasible reference of activation for an active BCI control system, such as one executing motor imagery tasks. The proposed indicator is applicable to potential passive and active BCI applications for monitoring the subject's physiological state change in real-time, along with providing a means of evaluating the associated signal quality to enhance the BCI performance.

A Digital Electrocardiographic System for Assessing Myocardial Electrical Instability: Principles and Applications

The aim of the study was to develop an ECG hardware and software system for monitoring electrical instability of the myocardium and to assess the diagnostic and prognostic capabilities of this setup in a cardiology clinic.

Personal Resilience Can Be Well Estimated from Heart Rate Variability and Paralinguistic Features during Human-Robot Conversations

Mental health is as crucial as physical health, but it is underappreciated by mainstream biomedical research and the public. Compared to the use of AI or robots in physical healthcare, the use of AI or robots in mental healthcare is much more limited in number and scope. To date, psychological resilience—the ability to cope with a crisis and quickly return to the pre-crisis state—has been identified as an important predictor of psychological well-being but has not been commonly considered by AI systems (e.g., smart wearable devices) or social robots to personalize services such as emotion coaching. To address the dearth of investigations, the present study explores the possibility of estimating personal resilience using physiological and speech signals measured during human–robot conversations. Specifically, the physiological and speech signals of 32 research participants were recorded while the participants answered a humanoid social robot’s questions about their positive and negative memories about three periods of their lives. The results from machine learning models showed that heart rate variability and paralinguistic features were the overall best predictors of personal resilience. Such predictability of personal resilience can be leveraged by AI and social robots to improve user understanding and has great potential for various mental healthcare applications in the future.

INTEGRATED DESIGN OF PHYSIOLOGICAL MULTI-PARAMETER SENSORS ON A SMART GARMENT BY ULTRA-ELASTIC E-TEXTILE

The performance of electronic textile (E-textile)-based wearable sensors is largely determined by the wire and electrode contacting stability to the body, which is a multi-discipline challenge for smart garment designs. In this paper, an integrated design of wearable sensors on a smart garment is presented to concurrently measure the multi-channel electrocardiogram, respiration, and temperature signals in different regions of the body. Sensors in separative probe-controller schemes are introduced with full-textile designs of the electrodes and signal transmission wires. An ultra-elastic structure of E-textile wire is proposed with excellent electrical stability, high stretch ratio, and low tension under body dynamics. A complete garment integration solution of the probes, wires, and the sensors is presented. The design is evaluated by comparing the signal quality in static and moderate body movements, which shows clinical level comparable precision and stability. The proposed design may constitute a general solution of distributed noninvasive physiological multi-parameter detection and monitoring applications.

Academic stress detection on university students during COVID-19 outbreak by using an electronic nose and the galvanic skin response

Academic stress is an emotion that students experience during their time at the university, sometimes causing physical and mental health effects. Because of the COVID-19 pandemic, universities worldwide have left the classroom to provide the method of teaching virtually, generating challenges, adaptations, and more stress in students. In this pilot study, a methodology for academic stress detection in engineering students at the University of Pamplona (Colombia) is proposed by developing and implementing an artificial electronic nose system and the galvanic skin response. For the study, the student's stress state and characteristics were taken into account to make the data analysis where a set of measurements were acquired when the students were presenting a virtual exam. Likewise, for the non-stress state, a set of measurements were obtained in a relaxation state after the exam date. To carry out the pre-processing and data processing from the measurements obtained previously by both systems, a set of algorithms developed in Python software were used to perform the data analysis. Linear Discriminant Analysis (LDA), K-Nearest Neighbors (K-NN), and Support Vector Machine (SVM) classification methods were applied for the data classification, where a 96 % success rate of classification was obtained with the E-nose, and 100 % classification was achieved by using the Galvanic Skin Response.

Multiomics, virtual reality and artificial intelligence in heart failure

Aim: Multiomics delivers more biological insight than targeted investigations. We applied multiomics to patients with heart failure (HF) and reduced ejection fraction (HFrEF), with machine learning applied to advanced ECG (AECG) and echocardiography artificial intelligence (Echo AI). Patients & methods: In total, 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography-mass spectrometry and solid-phase microextraction volatilomics in plasma and urine. HFrEF was defined using left ventricular (LV) global longitudinal strain, EF and N-terminal pro hormone BNP. AECG and Echo AI were performed over 5 min, with a subset of patients undergoing a virtual reality mental stress test. Results: A-ECG had similar diagnostic accuracy as N-terminal pro hormone BNP for HFrEF (area under the curve = 0.95, 95% CI: 0.85-0.99), and correlated with global longitudinal strain (r = -0.77, p < 0.0001), while Echo AI-generated measurements correlated well with manually measured LV end diastolic volume r = 0.77, LV end systolic volume r = 0.8, LVEF r = 0.71, indexed left atrium volume r = 0.71 and indexed LV mass r = 0.6, p < 0.005. AI-LVEF and other HFrEF biomarkers had a similar discrimination for HFrEF (area under the curve AI-LVEF = 0.88; 95% CI: -0.03 to 0.15; p = 0.19). Virtual reality mental stress test elicited arrhythmic biomarkers on AECG and indicated blunted autonomic responsiveness (alpha 2 of RR interval variability, p = 1 × 10^-4) in HFrEF. Conclusion: Multiomics-related machine learning shows promise for the assessment of HF.

The Concept of Advanced Multi-Sensor Monitoring of Human Stress

Many people live under stressful conditions which has an adverse effect on their health. Human stress, especially long-term one, can lead to a serious illness. Therefore, monitoring of human stress influence can be very useful. We can monitor stress in strictly controlled laboratory conditions, but it is time-consuming and does not capture reactions, on everyday stressors or in natural environment using wearable sensors, but with limited accuracy. Therefore, we began to analyze the current state of promising wearable stress-meters and the latest advances in the record of related physiological variables. Based on these results, we present the concept of an accurate, reliable and easier to use telemedicine device for long-term monitoring of people in a real life. In our concept, we ratify with two synchronized devices, one on the finger and the second on the chest. The results will be obtained from several physiological variables including electrodermal activity, heart rate and respiration, body temperature, blood pressure and others. All these variables will be measured using a coherent multi-sensors device. Our goal is to show possibilities and trends towards the production of new telemedicine equipment and thus, opening the door to a widespread application of human stress-meters.

Neural Processing of Cognitive Control in an Emotionally Neutral Context in Anxiety Patients

Impaired cognitive control plays a crucial role in anxiety disorders and is associated with deficient neural mechanisms in the fronto-parietal network. Usually, these deficits were found in tasks with an emotional context. The present study aimed at investigating electrophysiological and vascular signatures from event-related brain potentials (ERPs) and functional near-infrared spectroscopy (fNIRS) in anxiety patients versus healthy controls during an inhibition task integrated in an emotionally neutral context. Neural markers were acquired during the completion of a classical Eriksen flanker task. The focus of data analysis has been the ERPs N200 and P300 and fNIRS activations in addition to task performance. No behavioral or neural group differences were identified. ERP findings showed a larger N2pc and a delayed and reduced P300 for incongruent stimuli. The N2pc modulation suggests the reorienting of attention to salient stimuli, while the P300 indicates longer lasting stimulus evaluation processes due to increased task difficulty. FNIRS did not result in any significant activation potentially suggesting a contribution from deeper brain areas not measurable with fNIRS. The missing group difference in our non-emotional task indicates that no generalized cognitive control deficit but rather a more emotionally driven deficit is present in anxiety patients.

Public health impact of daily life triggers of sudden cardiac death: A systematic review and comparative risk assessment

BACKGROUND

Sudden cardiac death (SCD) may be triggered by daily circumstances and activities such as stressful psycho-emotional events, physical exertion or substance misuse. We calculated population attributable fractions (PAFs) to estimate the public health relevance of daily life triggers of SCD and to compare their population impacts.

METHODS

We searched PubMed, Scopus and the Web of Science citation databases to retrieve studies of triggers of SCD and cardiac arrest that would enable a computation of PAFs. When more studies investigated the same trigger, a meta-analytical pooled risk random-effect estimate was used.

RESULTS

Of the retrieved studies, eight provided data enabling computation of PAFs. The prevalence of exposure within population for SCD triggers in the control periods ranged from 1.06% for influenza infection to 8.73% for recent use of cannabis. Triggers ordered from the highest to the lowest risk increase were: physical exertion, recent cocaine use, episodic alcohol consumption, recent amphetamine use, episodic coffee consumption, psycho-emotional stress within the previous month, influenza infection, and recent cannabis use. The relative risk increase ranged from 1.10 to 4.98. By accounting for both the magnitude of the risk increase and the prevalence in the population, the present estimates of PAF assign 14.5% (95% confidence interval [CI] 4.9-28.5) of all SCDs to episodic alcohol consumption, 9.4% (95% CI 1.2-29.3) to physical exertion, 6.9% (95% CI 0.3-25.0) to cocaine, 6% (95% CI 1.2-14.6) to episodic coffee consumption, 3% (95% CI 0.4-6.8) to psycho-emotional stress in the previous month, 1.7% (95% CI -0.9 to 12.9) to amphetamines, 0.9% (95% CI -4.9 to 12.5) to cannabis, and 0.3% (95% CI 0.2-0.4) to influenza infections.

CONCLUSIONS

In addition to episodic alcohol consumption, a trigger with the greatest public health importance for SCD, episodic physical exertion, cocaine use and coffee consumption also show a considerable population impact.

Breathing rate and heart rate as confounding factors in measuring T wave alternans and morphological variability in ECG

OBJECTIVE

High morphological variability magnitude (MVM) and microvolt T wave alternans (TWA) within an electrocardiogram (ECG) signifies increased electrical instability and risk of sudden cardiac death. However, the influence of breathing rate (BR), heart rate (HR), and signal-to-noise ratio (SNR) is unknown and may inflate measured values.

APPROACH

We synthesize ECGs with morphologies derived from the Physikalisch-Technische Bundesanstalt Database. We calculate MVM and TWA at varying BRs, HRs and SNRs. We compare the MVM and TWA of signal with versus without breathing at varying HRs and SNRs. We then quantify the percentage of MVM and TWA estimates affected by BR and HR in a healthy population and assess the effect of removing these affected estimates on a method for classifying individuals with and without post-traumatic stress disorder (PTSD).

MAIN RESULTS

For signals with high SNR (>15 dB), MVM is significantly increased when BRs are > 9 respirations/minute (rpm) and HRs are < 100 beats/minute (bpm). Increased TWAs are detected for HR/BR pairs of 60/15, 60/30 and 120/30 bpm/rpm. For 18 healthy participants, 8.33% of TWA windows and 66.76% of MVM windows are affected by BR and HR. On average, the number of windows with TWA elevations > 47 μV decreases by 23% after excluding regions with significant BR and HR effect. Adding HR and BR to a morphological variability feature increases the classification performance by 6% for individuals with and without PTSD.

SIGNIFICANCE

Physiological BR and HR significantly increase MVM and TWA , indicating that BR and HR should be considered separately as confounders. The code for this work has been released as part of an open-source toolbox.

Electronic Devices for Stress Detection in Academic Contexts during Confinement Because of the COVID-19 Pandemic

This article studies the development and implementation of different electronic devices for measuring signals during stress situations, specifically in academic contexts in a student group of the Engineering Department at the University of Pamplona (Colombia). For the research’s development, devices for measuring physiological signals were used through a Galvanic Skin Response (GSR), the electrical response of the heart by using an electrocardiogram (ECG), the electrical activity produced by the upper trapezius muscle (EMG), and the development of an electronic nose system (E-nose) as a pilot study for the detection and identification of the Volatile Organic Compounds profiles emitted by the skin. The data gathering was taken during an online test (during the COVID-19 Pandemic), in which the aim was to measure the student’s stress state and then during the relaxation state after the exam period. Two algorithms were used for the data process, such as Linear Discriminant Analysis and Support Vector Machine through the Python software for the classification and differentiation of the assessment, achieving 100% of classification through GSR, 90% with the E-nose system proposed, 90% with the EMG system, and 88% success by using ECG, respectively.

Design and rationale of an intelligent algorithm to detect BuRnoUt in HeaLthcare workers in COVID era using ECG and artificiaL intelligence: The BRUCEE-LI study

Background

There is no large contemporary data from India to see the prevalence of burnout in HCWs in covid era. Burnout and mental stress is associated with electrocardiographic changes detectable by artificial intelligence (AI).

Objective

The present study aims to estimate the prevalence of burnout in HCWs in COVID-19 era using Mini Z-scale and to develop predictive AI model to detect burnout in HCWs in COVID-19 era.

Methods

This is an observational and cross-sectional study to evaluate the presence of burnout in HCWs in academic tertiary care centres of North India in the COVID-19 era. At least 900 participants will be enrolled in this study from four leading premier government-funded/public-private centres of North India. Each study centre will be asked to recruit HCWs by approaching them through various listed ways for participation in the study. Interested participants after initial screening and meeting the eligibility criteria, will be asked to fill the questionnaire (having demographic and work related with Mini Z questionnaire) to assess burnout. The healthcare workers will include physicians at all levels of training, nursing staff and paramedical staff who are involved directly or indirectly in COVID-19 care. The analysis of the raw electrocardiogram (ECG) data and development of algorithm using convolutional neural networks (CNN) will be done by experts.

Conclusions

In Summary, we propose that ECG data generated from the people with burnout can be utilized to develop AI-enabled model to predict the presence of stress and burnout in HCWs in COVID-19 era.

The Impact of Different Sounds on Stress Level in the Context of EEG, Cardiac Measures and Subjective Stress Level: A Pilot Study

Everyone experiences stress at certain times in their lives. This feeling can motivate, however, if it persists for a prolonged period, it leads to negative changes in the human body. Stress is characterized, among other things, by increased blood pressure, increased pulse and decreased alpha-frequency brainwave activity. An overview of the literature indicates that music therapy can be an effective and inexpensive method of improving these factors. The objective of this study was to analyze the impact of various types of music on stress level in subjects. The conducted experiment involved nine females, aged 22. All participants were healthy and did not have any neurological or psychiatric disorders. The test included four types of audio stimuli: silence (control sample), rap, relaxing music and music triggering an autonomous sensory meridian response (ASMR) phenomenon. The impact of individual sound types was assessed using data obtained from four sources: a fourteen-channel electroencephalograph, a blood pressure monitor, a pulsometer and participant’s subjective stress perception. The conclusions from the conducted study indicate that rap music negatively affects the reduction of stress level compared to the control group (p < 0.05), whereas relaxing music and ASMR calms subjects much faster than silence (p < 0.05).

Management of paroxysmal atrial flutter that occurred in an outpatient prior to dental surgery: a case report

Background

It is essential to accomplish the appropriate emergency care particularly in patients undergoing stressful dento-oral surgical procedures. Atrial flutter may be induced by sympathetic hypertonia due to excessive mental and physical stress. There is no report regarding dental care in patients with atrial flutter. Herein, we describe a rare case of the antiarrhythmic management in an outpatient who presented with an electrocardiographic finding of paroxysmal atrial flutter before the initiation of the dento-oral surgical procedure.

Case presentation

A 60-year-old male patient was scheduled for a dental extraction. He had a history of angina pectoris, diabetes mellitus, and paroxysmal atrial fibrillation with medication. The preoperative electrocardiogram (ECG) revealed left ventricular hypertrophy and ST-T segment abnormality. Immediately before the dental extraction, II-lead ECG revealed atrial flutter; however, he complained of few subjective symptoms, such as precordial discomfort or palpitation. Observing the vital signs, ECG findings, and the general condition of the patient, low dose diltiazem was immediately administered by continuous infusion in order to control the heart rate and prevent atrial flutter-induced supraventricular tachyarrhythmia. Special attention was paid to prevent any critical cardiovascular condition under a preparation of intravenous disopyramide and verapamil and a defibrillator. The intravenous administration of diltiazem progressively restored the sinus rhythm after converting atrial flutter into atrial fibrillation, resulting in the prevention of tachycardia, and then was found to be appropriate as a prophylactic therapy of tachyarrhythmia.

Conclusions

The present case suggests that it is possible to successfully manage some of such patients using our method during dento-oral surgery which is likely to be associated with mental and physical stress. Therefore, it is essential to accomplish an initial emergency care in parallel to the differential diagnosis of unforeseen serious medical conditions or paroxysmal arrhythmia such as atrial flutter.

Atrial fibrillation and psychological factors: a systematic review

BACKGROUND

Psychological factors have been suggested to have an influence in Atrial Fibrillation (AF) onset, progression, severity and outcomes, but their role is unclear and mainly focused on anxiety and depression.

METHODS

A systematic electronic search had been conducted to identify studies exploring different psychological factors in AF. The search retrieved 832 articles that were reviewed according to inclusion criteria: observational study with a control/comparison group; use of standardized and validated instruments for psychological assessment. Results were summarized qualitatively and quantitatively by effect size measure (Cohen's d and its 95% confidence interval). Cochrane Collaboration guidelines and the PRISMA Statement were adopted.

RESULTS

Eight studies were included in the systematic review. Depression was the most studied construct/ but only one study showed a clear link with AF. The remaining studies showed small and non-significant (95% CI [-0.25-1.00]) differences between AF and controls, no differences in frequency of depression history (95% CI [-0.14-0.22]) or in case frequency (95% CI [-0.50-0.04]). Miscellaneous results were found as far as anxiety: AF patients showed higher levels when compared to healthy subjects (95% CI [2.05-2.95]), but findings were inconsistent when compared to other heart diseases. Considering personality and life-events preceding AF, we respectively found a large (95% CI [1.87-2.49]) and a moderate to large effect (95% CI [0.48-0.98]).

DISCUSSION

The small number of studies does not allow to draw clear-cut conclusions on the involvement of psychological factors in AF. Promising lines of research are related to personality and adverse life-events, and to the increase of longitudinal design studies. Some methodological problems could be overcome by including clinical psychologists in the implementation of research protocols.