Utility of the Full ECG Waveform for Stress Classification.

The detection of psychological stress using the electrocardiogram (ECG) signal is most commonly based on the detection of the R peak-the most prominent part of the ECG waveform-and the heart rate variability (HRV) measurements derived from it. For stress detection algorithms focused on short-duration time windows, there is potential benefit in including HRV features derived from the detection of smaller peaks within the ECG waveform: the P, Q, S, and T waves. However, the potential drawback of using these small peaks is their smaller magnitude and subsequent susceptibility to noise, making them more difficult to reliably detect. In this work, we demonstrate the potential benefits of including smaller waves within binary stress classification using a pre-existing data set of ECG recordings from 57 participants (aged 18-40) with a self-reported fear of spiders during exposure to videos of spiders. We also present an analysis of the performance of an automated peak detection algorithm and the reliability of detection for each of the smaller parts of the ECG waveform. We compared two models, one with only R peak features and one with small peak features. They were similar in precision, recall, F1, area under ROC curve (AUC), and accuracy, with the greatest differences less than the standard deviations of each metric. There was a significant difference in the Akaike Information Criterion (AIC), which represented the information loss of the model. The inclusion of novel small peak features made the model 4.29×1028 times more probable to minimize the information loss, and the small peak features showed higher regression coefficients than the R peak features, indicating a stronger relationship with acute psychological stress. This difference and further analysis of the novel features suggest that small peak intervals could be indicative of independent processes within the heart, reflecting a psychophysiological response to stress that has not yet been leveraged in stress detection algorithms.

Isolation and confinement due to the COVID-19 pandemic: Lessons for human spaceflight.

Background: Astronauts live and work in isolated, confined, extreme (ICE) environments that create both high stress and the need for high performance. The COVID-19 pandemic created ICE-like conditions across the globe by confining people to their homes under the ever-present threat of disease. Our goal is to understand the impact of prior experience in ICE on coping, using the pandemic as a pseudo space analog environment. Methods: We administered a survey three times with 7 days between administrations. A total of 82 participants completed all three survey sessions, and these participants were divided into three groups for analysis. The first group is those with prior experience in an ICE environment (n = 17; 7F/10 M), the second is those aged 30-55 with a master's or doctoral degree and without prior experience (n = 22; 10F/12 M), and the third is the general population (n = 43; 27F/16 M). Linear mixed models were used for statistical analysis of the results, given the unequal sample sizes. Results: The experienced group did not show healthier mental health scores than the astronaut-like group, but both groups displayed higher scores than the general population. However, work productivity scores for the experienced group were higher on average than the other two groups. Discussion: Results suggest that prior experience in ICE may improve the capability to maintain productivity-corresponding to the idea of resilience. However, experience may not improve mental health maintenance, suggesting that other approaches are needed to prepare astronauts for the mental health stressors of long-duration exploration missions.

Detection of the Complete ECG Waveform with Woven Textile Electrodes.

Wearable physiological monitoring systems are becoming increasingly prevalent in the push toward autonomous health monitoring and offer new modalities for playful and purposeful interaction within human computer interaction (HCI). Sensing systems that can be integrated into garments and, therefore, daily activities offer promising pathways toward ubiquitous integration. The electrocardiogram (ECG) signal is commonly monitored in healthcare and is increasingly utilized as a method of determining emotional and psychological state; however, the complete ECG waveform with the P, Q, R, S, and T peaks is not commonly used, due to the challenges associated with collecting the full waveform with wearable systems. We present woven textile electrodes as an option for garment-integrated ECG monitoring systems that are capable of capturing the complete ECG waveform. In this work, we present the changes in the peak detection performance caused by different sizes, patterns, and thread types with data from 10 human participants. These testing results provide empirically-derived guidelines for future woven textile electrodes, present a path forward for assessing design decisions, and highlight the importance of testing novel wearable sensor systems with more than a single individual.

Wearable 3-Lead Electrocardiogram Placement Model for Fleet Sizing of Medical Devices.

BACKGROUND: Electrocardiography (ECG) provides valuable information on astronaut physiological and psychological health. ECG monitoring has been conducted during crewed missions since the beginning of human spaceflight and will continue during astronauts upcoming long-duration exploration missions (LDEMs) in support of automated health monitoring systems. ECG monitoring is traditionally performed in clinical environments with single-use, adhesive electrodes in a 3, 6, or 12-lead configuration placed by a trained clinician. In the space exploration environment, astronauts self-place electrodes without professional assistance. Wearable ECG systems are an attractive option for automated health monitoring, but electrode placement has not been quantified to a high enough degree to avoid artifacts within the data due to position changes. This variability presents challenges for physician-limited, autonomous health monitoring, so quantifying electrode placement is key in the development of reliable, wearable ECG monitoring systems.METHODS: We present a method of quantifying electrode placement for 3-lead, chest-mounted ECG using easy-to-measure, two-dimensional chest measurements.RESULTS: We find that male and female dimensions require different electrode positioning computations, but there is overlap in positioning between men and women. The distribution of electrodes vertical positions is wider than their horizontal positions.DISCUSSION: These results can be translated directly to ECG wearable design for the individual and for the size range and adjustability required for the astronaut fleet. Implementation of this method will improve the reliability in placement and fit of future wearables, increasing comfort and usability of these systems and subsequently augmenting autonomous health monitoring capabilities for exploration medicine.Arquilla K, Leary S, Webb AK, Anderson AP. Wearable 3-lead electrocardiogram placement model for fleet sizing of medical devices. Aerosp Med Hum Perform. 2020; 91(11):868875.

Woven electrocardiogram (ECG) electrodes for health monitoring in operational environments.

Electrical signals produced within the human body can reveal information about a wide variety of physiological processes including physical activity, cardiac health, and psychological state. The industry standard for physiological signal detection is the use of adhesive electrodes that stick onto the skin. These electrodes can irritate the skin over long periods of time and are not reusable, making them a challenge for use in operational environments. Further, these electrodes often require gel to improve signal transduction, leading to changes in signal quality as these gels dry over time. Wearable sensors for operational environments should be comfortable, unobtrusive, and non-stigmatizing while maintaining signal quality high enough to allow the detection of health states. Here, we present the development and test of a set of woven textile electrodes of 8 different sizes for chest-mounted, 3-lead electrocardiogram (ECG) monitoring. Ten male subjects were tested with each of the woven electrode sizes and with one set of adhesive electrodes. A derived performance metric and signal-to-noise ratio were calculated for each set of electrodes for comparison between them. The smallest sized electrodes were found to be least effective, while the 6th of the 8 sizes were found to be most effective.

Textile Electrocardiogram (ECG) Electrodes for Wearable Health Monitoring.

Wearable health-monitoring systems should be comfortable, non-stigmatizing, and able to achieve high data quality. Smart textiles with electronic elements integrated directly into fabrics offer a way to embed sensors into clothing seamlessly to serve these purposes. In this work, we demonstrate the feasibility of electrocardiogram (ECG) monitoring with sewn textile electrodes instead of traditional gel electrodes in a 3-lead, chest-mounted configuration. The textile electrodes are sewn with silver-coated thread in an overlapping zig zag pattern into an inextensible fabric. Sensor validation included ECG monitoring and comfort surveys with human subjects, stretch testing, and wash cycling. The electrodes were tested with the BIOPAC MP160 ECG data acquisition module. Sensors were placed on 8 subjects (5 males and 3 females) with double-sided tape. To detect differences in R peak detectability between traditional and sewn sensors, effect size was set at 10% of a sample mean for heart rate (HR) and R-R interval. Paired student's t-tests were run between adhesive and sewn electrode data for R-R interval and average HR, and a Wilcoxon signed-rank test was run for comfort. No statistically significant difference was found between the traditional and textile electrodes (R-R interval: t = 1.43, p > 0.1; HR: t = - 0.70, p > 0.5; comfort: V = 15,p > 0.5).

Psychophysiological Monitoring: An Approach for the Diagnosis and Treatment of Mental Health Disorders.

The ultimate goal of this work is to improve patient outcomes by increasing the ability to accurately diagnose, efficiently treat, and effectively monitor the response to treatment over time.

Reduced emotional and cardiovascular reactivity to emotionally evocative stimuli in major depressive disorder.

Major Depressive Disorder (MDD) is a highly debilitating mental health concern that affects a large number of adults in the United States. The emotional context insensitivity (ECI) hypothesis argues that individuals with MDD disengage from the environment to defend themselves from futile activity. In the current study, electrocardiogram and pupillometry were recorded from 50 participants (MDD n=25, never depressed control n=25) during the display of emotionally evocative images, sounds, and movie clips. Individuals with MDD reported reduced change in happiness to positively- and negatively-valenced images and sounds. Heart rate reactivity also was reduced in individuals with MDD when viewing images and watching movie clips. These results suggest that individuals with MDD may have some difficulty engaging with certain environmental stimuli.

Physiological reactivity to nonideographic virtual reality stimuli in veterans with and without PTSD.

BACKGROUND: Post-traumatic stress disorder (PTSD) currently is diagnosed via clinical interview in which subjective self reports of traumatic events and associated experiences are discussed with a mental health professional. The reliability and validity of diagnoses can be improved with the use of objective physiological measures. METHODS: In this study, physiological activity was recorded from 58 male veterans (PTSD Diagnosis n = 16; Trauma Exposed/No PTSD Diagnosis: n = 23; No Trauma/No PTSD Diagnosis: n = 19) with and without PTSD and combat trauma exposure in response to emotionally evocative non-idiographic virtual reality stimuli. RESULTS: Statistically significant differences among the Control, Trauma, and PTSD groups were present during the viewing of two virtual reality videos. Skin conductance and interbeat interval features were extracted for each of ten video events (five events of increasing severity per video). These features were submitted to three stepwise discriminant function analyses to assess classification accuracy for Control versus Trauma, Control versus PTSD, and Trauma versus PTSD pairings of participant groups. Leave-one-out cross-validation classification accuracy was between 71 and 94%. CONCLUSIONS: These results are promising and suggest the utility of objective physiological measures in assisting with PTSD diagnosis.

Lyin' eyes: ocular-motor measures of reading reveal deception.

Our goal was to evaluate an alternative to current methods for detecting deception in security screening contexts. We evaluated a new cognitive-based test of deception that measured participants' ocular-motor responses (pupil responses and reading behaviors) while they read and responded to statements on a computerized questionnaire. In Experiment 1, participants from a university community were randomly assigned to either a "guilty" group that committed one of two mock crimes or an "innocent" group that only learned about the crime. Participants then reported for testing, where they completed the computer-administered questionnaire that addressed their possible involvement in the crimes. Experiment 2 also manipulated participants' incentive to pass the test and difficulty of statements on the test. In both experiments, guilty participants had increased pupil responses to statements answered deceptively; however, they spent less time fixating on, reading, and rereading those statements than statements answered truthfully. These ocular-motor measures were optimally weighted in a discrimination function that correctly classified 85% of participants as either guilty or innocent. Findings from Experiment 2 indicated that group discrimination was improved with greater incentives to pass the test and the use of statements with simple syntax. The present findings suggest that two cognitive processes are involved in deception-vigilance and strategy-and that these processes are reflected in different ocular-motor measures. The ocular-motor test reported here represents a new approach to detecting deception that may fill an important need in security screening contexts.

Multilevel models for repeated measures research designs in psychophysiology: an introduction to growth curve modeling.

Psychophysiologists often use repeated measures analysis of variance (RMANOVA) and multivariate analysis of variance (MANOVA) to analyze data collected in repeated measures research designs. ANOVA and MANOVA are nomothetic approaches that focus on group means. Newer multilevel modeling techniques are more informative than ANOVA because they characterize both group-level (nomothetic) and individual-level (idiographic) effects, yielding a more complete understanding of the phenomena under study. This article was written as an introduction to growth curve modeling for applied researchers. A growth model is defined that can be used in place of RMANOVAs and MANOVAs for single-group and mixed repeated measures designs. The model is expanded to test and control for the effects of baseline levels of physiological activity on stimulus-specific responses. Practical, conceptual, and statistical advantages of growth curve modeling are discussed.