Ultra short term analysis of heart rate variability for monitoring mental stress in mobile settings

Driver Fatigue Detection Using Heart Rate Variability Features from 2-Minute Electrocardiogram Signals While Accounting for Sex Differences

Traffic accidents due to fatigue account for a large proportion of road fatalities. Based on simulated driving experiments with drivers recruited from college students, this paper investigates the use of heart rate variability (HRV) features to detect driver fatigue while considering sex differences. Sex-independent and sex-specific differences in HRV features between alert and fatigued states derived from 2 min electrocardiogram (ECG) signals were determined. Then, decision trees were used for driver fatigue detection using the HRV features of either all subjects or those of only males or females. Nineteen, eighteen, and thirteen HRV features were significantly different (Mann-Whitney U test, p < 0.01) between the two mental states for all subjects, males, and females, respectively. The fatigue detection models for all subjects, males, and females achieved classification accuracies of 86.3%, 94.8%, and 92.0%, respectively. In conclusion, sex differences in HRV features between drivers' mental states were found according to both the statistical analysis and classification results. By considering sex differences, precise HRV feature-based driver fatigue detection systems can be developed. Moreover, in contrast to conventional methods using HRV features from 5 min ECG signals, our method uses HRV features from 2 min ECG signals, thus enabling more rapid driver fatigue detection.

Camera-Based Seismocardiogram for Heart Rate Variability Monitoring

Heart rate variability (HRV) is a crucial metric that quantifies the variation between consecutive heartbeats, serving as a significant indicator of autonomic nervous system (ANS) activity. It has found widespread applications in clinical diagnosis, treatment, and prevention of cardiovascular diseases. In this study, we proposed an optical model for defocused speckle imaging, to simultaneously incorporate out-of-plane translation and rotation-induced motion for highly-sensitive non-contact seismocardiogram (SCG) measurement. Using electrocardiogram (ECG) signals as the gold standard, we evaluated the performance of photoplethysmogram (PPG) signals and speckle-based SCG signals in assessing HRV. The results indicated that the HRV parameters measured from SCG signals extracted from laser speckle videos showed higher consistency with the results obtained from the ECG signals compared to PPG signals. Additionally, we confirmed that even when clothing obstructed the measurement site, the efficacy of SCG signals extracted from the motion of laser speckle patterns persisted in assessing the HRV levels. This demonstrates the robustness of camera-based non-contact SCG in monitoring HRV, highlighting its potential as a reliable, non-contact alternative to traditional contact-PPG sensors.

Brain white matter changes and their associations with non-motor dysfunction in orthostatic hypotension in α-synucleinopathy: A NODDI study

BACKGROUND

The specific non-motor symptoms associated with α-synucleinopathies, including orthostatic hypotension (OH), cognitive impairment, and emotional abnormalities, have been a subject of ongoing controversy over the mechanisms underlying the development of a vicious cycle among them. The distinct structural alterations in white matter (WM) in patients with α-synucleinopathies experiencing OH, alongside their association with other non-motor symptoms, remain unexplored. This study employs axial diffusivity and density imaging (NODDI) to investigate WM damage specific to α-synucleinopathies with concurrent OH, delivering fresh evidence to supplement our understanding of the pathogenic mechanisms and pathological rationales behind the occurrence of a spectrum of non-motor functional impairments in α-synucleinopathies.

METHODS

This study recruited 49 individuals diagnosed with α-synucleinopathies, stratified into an α-OH group (n = 24) and an α-NOH group (without OH, n = 25). Additionally, 17 healthy controls were included for supine and standing blood pressure data collection, as well as neuropsychological assessments. Magnetic resonance imaging (MRI) was utilized for the calculation of NODDI parameters, and tract-based spatial statistics (TBSS) were employed to explore differential clusters. The fibers covered by these clusters were defined as regions of interest (ROI) for the extraction of NODDI parameter values and the analysis of their correlation with neuropsychological scores.

RESULTS

The TBSS analysis unveiled specific cerebral regions exhibiting disparities within the α-OH group as compared to both the α-NOH group and the healthy controls. These differences were evident in clusters that indicated a decrease in the acquisition of the neurite density index (NDI), a reduction in the orientation dispersion index (ODI), and an increase in the isotropic volume fraction (FISO) (p < 0.05). The extracted values from these ROIs demonstrated significant correlations with clinically assessed differences in supine and standing blood pressure, overall cognitive scores, and anxiety-depression ratings (p < 0.05).

CONCLUSION

Patients with α-synucleinopathies experiencing OH exhibit distinctive patterns of microstructural damage in the WM as revealed by the NODDI model, and there is a correlation with the onset and progression of non-motor functional impairments.

Enhancing Stress Detection: A Comprehensive Approach through rPPG Analysis and Deep Learning Techniques

Stress has emerged as a major concern in modern society, significantly impacting human health and well-being. Statistical evidence underscores the extensive social influence of stress, especially in terms of work-related stress and associated healthcare costs. This paper addresses the critical need for accurate stress detection, emphasising its far-reaching effects on health and social dynamics. Focusing on remote stress monitoring, it proposes an efficient deep learning approach for stress detection from facial videos. In contrast to the research on wearable devices, this paper proposes novel Hybrid Deep Learning (DL) networks for stress detection based on remote photoplethysmography (rPPG), employing (Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), 1D Convolutional Neural Network (1D-CNN)) models with hyperparameter optimisation and augmentation techniques to enhance performance. The proposed approach yields a substantial improvement in accuracy and efficiency in stress detection, achieving up to 95.83% accuracy with the UBFC-Phys dataset while maintaining excellent computational efficiency. The experimental results demonstrate the effectiveness of the proposed Hybrid DL models for rPPG-based-stress detection.

Synchronous monitoring of brain-heart electrophysiology using heart rate variability coupled with rapid quantitative electroencephalography in orthostatic hypotension patients with α-synucleinopathies: Rapid prediction of orthostatic hypotension and preliminary exploration of brain stimulation therapy

BACKGROUND

In α-synucleinopathies, the dysfunction of the autonomic nervous system which typically manifests as orthostatic hypotension (OH) often leads to severe consequences and poses therapeutic challenges. This study aims to discover the brain-cardiac electrophysiological changes in OH patients with α-synucleinopathies using the rapid quantitative electroencephalography (qEEG) coupled with heart rate variability (HRV) technique to identify rapid, noninvasive biomarkers for early warning and diagnosis, as well as shed new light on complementary treatment approaches such as brain stimulation targets.

METHODS

In this study, 26 subjects of α-synucleinopathies with OH (α-OH group), 21 subjects of α-synucleinopathies without OH (α-NOH group), and 34 healthy controls (control group) were included from September 2021 to August 2023 (NCT05527067). The heart rate-blood pressure variations in supine and standing positions were monitored, and synchronization parameters of seated resting-state HRV coupled with qEEG were collected. Time-domain and frequency-domain of HRV measures as well as peak frequency and power of the brainwaves were extracted. Differences between these three groups were compared, and correlations between brain-heart parameters were analyzed.

RESULTS

The research results showed that the time-domain parameters such as MxDMn, pNN50, RMSSD, and SDSD of seated resting-state HRV exhibited a significant decrease only in the α-OH group compared to the healthy control group (p < 0.05), while there was no significant difference between the α-NOH group and the healthy control group. Several time-domain and frequency-domain parameters of seated resting-state HRV were found to be correlated with the blood pressure changes within the first 5 min of transitioning from supine to standing position (p < 0.05). Differences were observed in the power of beta1 waves (F4 and Fp2) and beta2 waves (Fp2 and F4) in the seated resting-state qEEG between the α-OH and α-NOH groups (p < 0.05). The peak frequency of theta waves in the Cz region also showed a difference (p < 0.05). The power of beta2 waves in the Fp2 and F4 brain regions correlated with frequency-domain parameters of HRV (p < 0.05). Additionally, abnormal electrical activity in the alpha, theta, and beta1 waves was associated with changes in heart rate and blood pressure within the first 5 min of transitioning from supine to standing position (p < 0.05).

CONCLUSION

Rapid resting-state HRV with certain time-domain parameters below normal levels may serve as a predictive indicator for the occurrence of orthostatic hypotension (OH) in patients with α-synucleinopathies. Additionally, the deterioration of HRV parameters correlates with synchronous abnormal qEEG patterns, which can provide insights into the brain stimulation target areas for OH in α-synucleinopathy patients.

Comparing two relaxation procedures to ease fatigue in multiple sclerosis: a single-blind randomized controlled trial

BACKGROUND

Various relaxation procedures have been proposed to reduce fatigue in multiple sclerosis (MS). However, it is unknown, which type of relaxation has the largest effect on fatigue reduction and on autonomic nervous system (ANS) activity.

OBJECTIVE

We aimed to compare two biofeedback-supported relaxation exercises: a deep breathing (DB) exercise and progressive muscle relaxation (PMR), which may ameliorate MS fatigue and alter ANS activity.

METHODS

We performed a single-blind randomized clinical trial, introducing MS patients (n = 34) to the DB or PMR exercise. We first tested cardiovagal integrity, reflected by changes in heart rate variability (HRV) in response to DB. Participants then performed a fatigue-inducing vigilance task, followed by the DB or PMR. State fatigue was recorded consecutively at baseline, after the vigilance task, and after the relaxation exercise, along with HRV reflecting ANS activity.

RESULTS

Only patients assigned to the PMR group experienced a significant drop in fatigue, whereas both relaxation exercises changed ANS activity. MS patients showed the expected autonomic response during the cardiovagal reflex test. The vigilance task elevated short-term feelings of fatigue and significantly reduced HRV parameters of parasympathetic activity. Trait fatigue was negatively correlated with HRV during the second half of the vigilance task.

CONCLUSION

PMR alleviates short-term feelings of fatigue in persons with MS. The vigilance task in combination with HRV measurements may be helpful for evaluating relaxation procedures as a treatment of fatigue. Hereby, future studies should ensure longer and more frequent relaxation exercises and focus on patients with weak to moderate fatigue.

TRIAL REGISTRATION

Trial Registry: DRKS00024358.

Exploring Autonomic Alterations during Seizures in Temporal Lobe Epilepsy: Insights from a Heart-Rate Variability Analysis

Epilepsy's impact on cardiovascular function and autonomic regulation, including heart-rate variability, is complex and may contribute to sudden unexpected death in epilepsy (SUDEP). Lateralization of autonomic control in the brain remains the subject of debate; nevertheless, ultra-short-term heart-rate variability (HRV) analysis is a useful tool for understanding the pathophysiology of autonomic dysfunction in epilepsy patients. A retrospective study reviewed medical records of patients with temporal lobe epilepsy who underwent presurgical evaluations. Data from 75 patients were analyzed and HRV indices were extracted from electrocardiogram recordings of preictal, ictal, and postictal intervals. Various HRV indices were calculated, including time domain, frequency domain, and nonlinear indices, to assess autonomic function during different seizure intervals. The study found significant differences in HRV indices based on hemispheric laterality, language dominancy, hippocampal atrophy, amygdala enlargement, sustained theta activity, and seizure frequency. HRV indices such as the root mean square of successive differences between heartbeats, pNN50, normalized low-frequency, normalized high-frequency, and the low-frequency/high-frequency ratio exhibited significant differences during the ictal period. Language dominancy, hippocampal atrophy, amygdala enlargement, and sustained theta activity were also found to affect HRV. Seizure frequency was correlated with HRV indices, suggesting a potential relationship with the risk of SUDEP.

Behavioral and Biological Indicators of Risk and Well-Being in a Sample of South African Youth

Self report measures have been widely used in research to illustrate high rates of exposure to violence among youth in trauma-saturated regions, such as Cape Town, South Africa. To better understand the risk and resilience factors of youth who have been exposed to, witnessed, or directly experienced violence, the current study used a multi-method assessment in a naturalistic setting that included heart rate variability (an index of regulatory flexibility and cardiovascular health), a computerized risk-taking task, and self report measures. Youth (N = 83) from Cape Town, South Africa, participated in a psychobiological assessment. Findings suggest elevated age-adjusted heart rate variability compared to age related norms, which is indicative of overregulation of behavior and emotion. Additionally, youth, all of whom had witnessed or experienced violence at least once, demonstrated a low risk taking and reward seeking propensity. Low risk taking in the context of elevated heart rate variability may reflect youth's affective and behavioral inhibition, suggestive of stress among children who have an overgeneralized threat response. These results both demonstrate the feasibility of psychophysiological research in community youth settings, and counter the traditional narrative that there is an overarching lack of capacity to regulate and a high propensity to risk in violence-exposed youth.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40653-021-00426-1.

An Open-Source, Interoperable Architecture for Generating Real-Time Surgical Team Cognitive Alerts from Heart-Rate Variability Monitoring

Clinical alarm and decision support systems that lack clinical context may create non-actionable nuisance alarms that are not clinically relevant and can cause distractions during the most difficult moments of a surgery. We present a novel, interoperable, real-time system for adding contextual awareness to clinical systems by monitoring the heart-rate variability (HRV) of clinical team members. We designed an architecture for real-time capture, analysis, and presentation of HRV data from multiple clinicians and implemented this architecture as an application and device interfaces on the open-source OpenICE interoperability platform. In this work, we extend OpenICE with new capabilities to support the needs of the context-aware OR including a modularized data pipeline for simultaneously processing real-time electrocardiographic (ECG) waveforms from multiple clinicians to create estimates of their individual cognitive load. The system is built with standardized interfaces that allow for free interchange of software and hardware components including sensor devices, ECG filtering and beat detection algorithms, HRV metric calculations, and individual and team alerts based on changes in metrics. By integrating contextual cues and team member state into a unified process model, we believe future clinical applications will be able to emulate some of these behaviors to provide context-aware information to improve the safety and quality of surgical interventions.

Effects of Acoustic Environment Types on Stress Relief in Urban Parks

Urban public space environments are critical to the health of residents. In previous studies on urban park environments and health, landscape environment questionnaires have been the main method to evaluate the environmental quality and comfort of urban parks. The research on sound perception also focuses on the exploration of evaluation methods and evaluation indicators; there is little objective empirical evidence in these studies. To further explore the nature of the health role of urban parks, this study started with the sound types of urban parks, based on a field survey, combined the electrocardiogram (ECG) index with the sound type of the park through a portable intelligent device, and HR and RMSSD were selected as the ECG indicators to evaluate the stress relief status. The regression model between the type of acoustic environments and the ECG data was established through the analysis of relevant data. This paper tries to improve the physiological recovery benefit and influence mechanism of sound types in urban parks from an objective point of view and puts forward reasonable suggestions to improve the sound environment in urban parks. The preliminary results show that, in a short time frame, natural sound has a strong relieving effect on mental pressure, while mechanical sound has an obvious impediment effect on the recovery of mental pressure. The results also reveal that the human voice has no obvious impediment effect, and changes in wind and broadcast sound have little impact on the recovery of mental pressure.

Non-contact heart rate estimation based on singular spectrum component reconstruction using low-rank matrix and autocorrelation

The remote photoplethysmography (rPPG) based on cameras, a technology for extracting pulse wave from videos, has been proved to be an effective heart rate (HR) monitoring method and has great potential in many fields; such as health monitoring. However, the change of facial color intensity caused by cardiovascular activities is weak. Environmental illumination changes and subjects' facial movements will produce irregular noise in rPPG signals, resulting in distortion of heart rate pulse signals and affecting the accuracy of heart rate measurement. Given the irregular noises such as motion artifacts and illumination changes in rPPG signals, this paper proposed a new method named LA-SSA. It combines low-rank sparse matrix decomposition and autocorrelation function with singular spectrum analysis (SSA). The low-rank sparse matrix decomposition is employed to globally optimize the components of the rPPG signal obtained by SSA, and some irregular noise is removed. Then, the autocorrelation function is used to optimize the global optimization results locally. The periodic components related to the heartbeat signal are selected, and the denoised rPPG signal is obtained by weighted reconstruction with a singular value ratio. The experiment using UBFC-RPPG and PURE database is performed to assess the performance of the method proposed in this paper. The average absolute error was 1.37 bpm, the 95% confidence interval was -7.56 bpm to 6.45 bpm, and the Pearson correlation coefficient was 98%, superior to most existing video-based heart rate extraction methods. Experimental results show that the proposed method can estimate HR effectively.

Perceived Workload Using Separate (Filtering Facepiece Respirator and Face Shield) and Powered Air-Purifying Respirator and Integrated Lightweight Protective Air-Purifying Respirator: Protocol for an International Multisite Human Factors Randomized Crossover Feasibility Study

BACKGROUND

The design of personal protective equipment (PPE) may affect well-being and clinical work. PPE as an integrated item may improve usability and increase adherence by healthcare professionals. Human factors design and safety may reduce occupational-acquired diseases. As an integrated PPE, a lightweight protective air-purifying respirator (L-PAPR) could be used during health procedures where healthcare professionals are exposed to airborne pathogens. The human factors affecting the implementation of alternative PPE such as L-PAPR have not been thoroughly studied. The population of interest is health care professionals, the intervention is the performance by PPE during tasks across the three PPE types 1.) N95 respirators and face shields, 2.)traditional powered air-purifying respirator(PAPR), and 3.) L-PAPR. The outcomes are user error, communications, safety, and end-user preferences.

OBJECTIVE

This study will assess whether the L-PAPR improves health care professionals' comfort in terms of perceived workload and physical and psychological burden during direct patient care when compared with the traditional PAPR or N95 and face shield. This study also aims to evaluate human factors during the comparison of the use of L-PAPR with a combination of N95 respirators plus face shields or the traditional PAPRs.

METHODS

This is an interventional randomized crossover quality improvement feasibility study consisting of a 3-site simulation phase with 10 participants per site and subsequent field testing in 2 sites with 30 participants at each site. The 3 types of respiratory PPE will be compared across medical tasks and while donning and doffing. We will evaluate the user's perceived workload, usability, usage errors, and heart rate. We will conduct semistructured interviews to identify barriers and enablers to implementation across each PPE type over a single continuous wear episode and observe interpersonal communications across conditions and PPE types.

RESULTS

We expect the research may highlight communication challenges and differences in usability and convenience across PPE types along with error frequency during PPE use across PPE types, tasks, and time.

CONCLUSIONS

The design of PPE may affect overall well-being and hinder or facilitate clinical work. Combining 2 pieces of PPE into a single integrated item may improve usability and reduce occupational-acquired diseases. The human factors affecting the implementation of an alternative PPE such as L-PAPR or PAPR have not been thoroughly studied.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/36549.

Determination of Waste Management Workers' Physical and Psychological Load: A Cross-Sectional Study Using Biometric Data

Waste management workers experience high stress and physical strain in their work environment, but very little empirical evidence supports effective health management practices for waste management workers. Hence, this study investigated the effects of worker characteristics and biometric indices on workers' physical and psychological loads during waste-handling operations. A biometric measurement system was installed in an industrial waste management facility in Japan to understand the actual working conditions of 29 workers in the facility. It comprised sensing wear for data collection and biometric sensors to measure heart rate (HR) and physical activity (PA) based on electrocardiogram signals. Multiple regression analysis was performed to evaluate significant relationships between the parameters. Although stress level is indicated by the ratio of low frequency (LF) to high frequency (HF) or high LF power in HR, the results showed that compared with workers who did not handle waste, those who did had lower PA and body surface temperature, higher stress, and lower HR variability parameters associated with higher psychological load. There were no significant differences in HR, heart rate interval (RRI), and workload. The psychological load of workers dealing directly with waste was high, regardless of their PA, whereas others had a low psychological load even with high PA. These findings suggest the need to promote sustainable work relationships and a quantitative understanding of harsh working conditions to improve work quality and reduce health hazards.

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.

Measuring brain beats: Cardiac-aligned fast functional magnetic resonance imaging signals

Blood and cerebrospinal fluid (CSF) pulse and flow throughout the brain, driven by the cardiac cycle. These fluid dynamics, which are essential to healthy brain function, are characterized by several noninvasive magnetic resonance imaging (MRI) methods. Recent developments in fast MRI, specifically simultaneous multislice acquisition methods, provide a new opportunity to rapidly and broadly assess cardiac-driven flow, including CSF spaces, surface vessels and parenchymal vessels. We use these techniques to assess blood and CSF flow dynamics in brief (3.5 min) scans on a conventional 3 T MRI scanner in five subjects. Cardiac pulses are measured with a photoplethysmography (PPG) on the index finger, along with functional MRI (fMRI) signals in the brain. We, retrospectively, align the fMRI signals to the heartbeat. Highly reliable cardiac-gated fMRI temporal signals are observed in CSF and blood on the timescale of one heartbeat (test-retest reliability within subjects R²  > 50%). In blood vessels, a local minimum is observed following systole. In CSF spaces, the ventricles and subarachnoid spaces have a local maximum following systole instead. Slower resting-state scans with slice timing, retrospectively, aligned to the cardiac pulse, reveal similar cardiac-gated responses. The cardiac-gated measurements estimate the amplitude and phase of fMRI pulsations in the CSF relative to those in the arteries, an estimate of the local intracranial impedance. Cardiac aligned fMRI signals can provide new insights about fluid dynamics or diagnostics for diseases where these dynamics are important.

Investigating attentional scope as a novel indicator of emotional state in animals

In humans, contrasting emotional states can lead to a broadening or narrowing of attentional scope. Whether this is also the case in animals has yet to be investigated. If confirmed, measurement of attentional scope has potential as a novel cognitive method of welfare assessment. In this study, we therefore aimed to investigate a test of attentional scope as a measure of emotional state in animals. We did this by inducing four putatively different emotional states in dogs (N = 10), varying in valence (positive, negative) and arousal (high, low), in two different reward contexts (food rewards in Experiment 1, social rewards in Experiment 2) and then assessing dogs' behavioural responses in a test of attentional scope. We also recorded heart rate variability (HRV) parameters as additional confirmatory affective indicators. In Experiment 1, the dogs showed a narrowing of attentional scope after the induction of both positively valenced emotional states. That dogs were in a positive state was supported by the reduced Standard Deviation of normal-to-normal R-R intervals (SDNN) and the reduced Low Frequency (LF) and Very Low Frequency (VLF) HRV. In Experiment 2, when responses to social rewards were examined, we did not detect any statistically significant differences in attentional scope between the emotional states, but dogs had a slightly narrow attentional scope in the negatively valenced emotional states. The LF tended to be reduced in the high arousal positive treatment. In conclusion, our study provides the first indication that emotional states can also alter attentional scope in animals. The results justify further investigation of this approach for use in animal welfare assessment, although additional studies are needed to refine predictions.

Alignment Between Heart Rate Variability From Fitness Trackers and Perceived Stress: Perspectives From a Large-Scale In Situ Longitudinal Study of Information Workers

BACKGROUND

Stress can have adverse effects on health and well-being. Informed by laboratory findings that heart rate variability (HRV) decreases in response to an induced stress response, recent efforts to monitor perceived stress in the wild have focused on HRV measured using wearable devices. However, it is not clear that the well-established association between perceived stress and HRV replicates in naturalistic settings without explicit stress inductions and research-grade sensors.

OBJECTIVE

This study aims to quantify the strength of the associations between HRV and perceived daily stress using wearable devices in real-world settings.

METHODS

In the main study, 657 participants wore a fitness tracker and completed 14,695 ecological momentary assessments (EMAs) assessing perceived stress, anxiety, positive affect, and negative affect across 8 weeks. In the follow-up study, approximately a year later, 49.8% (327/657) of the same participants wore the same fitness tracker and completed 1373 EMAs assessing perceived stress at the most stressful time of the day over a 1-week period. We used mixed-effects generalized linear models to predict EMA responses from HRV features calculated over varying time windows from 5 minutes to 24 hours.

RESULTS

Across all time windows, the models explained an average of 1% (SD 0.5%; marginal R2) of the variance. Models using HRV features computed from an 8 AM to 6 PM time window (namely work hours) outperformed other time windows using HRV features calculated closer to the survey response time but still explained a small amount (2.2%) of the variance. HRV features that were associated with perceived stress were the low frequency to high frequency ratio, very low frequency power, triangular index, and SD of the averages of normal-to-normal intervals. In addition, we found that although HRV was also predictive of other related measures, namely, anxiety, negative affect, and positive affect, it was a significant predictor of stress after controlling for these other constructs. In the follow-up study, calculating HRV when participants reported their most stressful time of the day was less predictive and provided a worse fit (R2=0.022) than the work hours time window (R2=0.032).

CONCLUSIONS

A significant but small relationship between perceived stress and HRV was found. Thus, although HRV is associated with perceived stress in laboratory settings, the strength of that association diminishes in real-life settings. HRV might be more reflective of perceived stress in the presence of specific and isolated stressors and research-grade sensing. Relying on wearable-derived HRV alone might not be sufficient to detect stress in naturalistic settings and should not be considered a proxy for perceived stress but rather a component of a complex phenomenon.

Association of Heart Rate Variability with the Psychosocial Stress Level in Men 41-44 Years Old Living in Moscow

Aim. Research of the association of heart rate variability (HRV) with the level of psychosocial stress (PS) and other indicators of the risk of cardiovascular diseases in a sample of 41-44-year-old men living in Moscow.

Material and methods. A total of 299 men aged 41-44 years were examined. The study included a clinical examination and a survey using a standard questionnaire. The categorization of risk factors (RF) for cardiovascular diseases (CVD) was carried out in accordance with generally accepted criteria The psychosocial stress was assessed using the Reeder scale. Depending on the psychosocial stress level, all surveyed men were divided into 3 groups by terciles: group 1 (3,28-4,0 points) – mild stress, group 2 (2,71-3,14) – moderate stress, group 3 (1,28-2,57) – severe stress. The analysis of HRV was performed on the basis of a short recording of an electrocardiogram using the original software package.

Results. Nonparametric ANOVA showed that the mean [M (95% CI)] values of the HRV time domain (SDNN, rMSSD and the state of regulatory reserves) were lower in the group of men with high PS compared with the group with low PS [25.3 ms (20.9-29.7) versus 40.5 ms (30.7-50.3), p=0.007; 29.5 ms (24.6-34.3) versus 49.5 ms (36.7-62.3), p=0.030; and 46.7 (44.7-48.6) versus 49.7 (48.1-51.4), p=0.019; respectively]. On the contrary, the mean values [M (95% CI)] of the integral indicators of HRV (SI and IVR) were higher in the group of men with high PS [635.8 c.u. (556.2-715.4) versus 488.9 (423.8-554.1), p=0.005; 1172.6 (1045.1-1300.1) versus 904.7 (790.0-1019.4), p=0.003; respectively]. The results of correlation and multiple regression analysis confirmed that these HRV indicators are statistically significantly associated not only with PS, but also with other indicators (age, waist / hip ratio, diastolic blood pressure). However, their predictive value turned out to be low, and the proportion of the explained variance of HRV indices ranged from 2.5 to 13.1%.

Conclusion. The weakening of the autonomous regulation of the heart rate with a decrease in the activity of the parasympathetic link, the activation of the central circuit of regulation with the prevalence of sympathetic influences, a decrease in the functional reserves of the heart rate regulation system are associated with an increase in the level of PS and other indicators of the risk of cardiovascular diseases.

Multimodal Sensor-Based Identification of Stress and Compulsive Actions in Children with Obsessive-Compulsive Disorder for Telemedical Treatment

In modern psychotherapy, digital health technology offers advanced and personalized therapy options, increasing availability as well as ecological validity. These aspects have proven to be highly relevant for children and adolescents with obsessive-compulsive disorder (OCD). Exposure and Response Prevention therapy, which is the state-of-the-art treatment for OCD, builds on the reconstruction of everyday life exposure to anxious situations. However, while compulsive behavior pre-dominantly occurs in home environments, exposure situations during therapy are limited to clinical settings. Telemedical treatment allows to shift from this limited exposure reconstruction to exposure situations in real life. In the SSTeP KiZ study (smart sensor technology in telepsychotherapy for children and adolescents with OCD), we combine video therapy with wearable sensors delivering physiological and behavioral measures to objectively determine the stress level of patients. The setup allows to gain information from exposure to stress in a realistic environment both during and outside of therapy sessions. In a first pilot study, we explored the sensitivity of individual sensor modalities to different levels of stress and anxiety. For this, we captured the obsessive-compulsive behavior of five adolescents with an ECG chest belt, inertial sensors capturing hand movements, and an eye tracker. Despite their prototypical nature, our results deliver strong evidence that the examined sensor modalities yield biomarkers allowing for personalized detection and quantification of stress and anxiety. This opens up future possibilities to evaluate the severity of individual compulsive behavior based on multi-variate state classification in real-life situations. Clinical Relevance- Our results demonstrate the potential for efficient personalized psychotherapy by monitoring physiological and behavioral changes with multiple sensor modalities in ecologically valid real-life scenarios.

Effects of Changes in Environmental Color Chroma on Heart Rate Variability and Stress by Gender

With increasing time spent indoors during the coronavirus disease pandemic, occupants are increasingly affected by indoor space environmental factors. Environmental color stimulates human vision and affects stress levels. This study investigated how changing environmental color chroma affected heart rate variability (HRV) and stress. The HRV of nine males and fifteen females was measured during exposure to 12 color stimuli with changes in chroma under green/blue hues and high/low-value conditions, and a stress assessment was performed. The effect of chroma on the HRV of males and females was verified, but the interaction effect between chroma and gender was not. ln(LF) and RMSSD were valid parameters. ln(LF) of males and females decreased as chroma increased under the green hue and low-value conditions; RMSSD was reduced as chroma increased in the blue hue and low-value conditions. ln(LF) decreased as chroma increased under blue hue and high-value conditions in males. Color–stress evaluation revealed that the higher chroma under high-value conditions, the more positive the stress emotion, and the lower the chroma under low-value conditions, the more negative the stress emotion. As chroma increased under low-value conditions, color is a stress factor; for men, this effect was more evident in the blue hue.

A Multimodal Approach for Real Time Recognition of Engagement towards Adaptive Serious Games for Health

In this article, an unobtrusive and affordable sensor-based multimodal approach for real time recognition of engagement in serious games (SGs) for health is presented. This approach aims to achieve individualization in SGs that promote self-health management. The feasibility of the proposed approach was investigated by designing and implementing an experimental process focusing on real time recognition of engagement. Twenty-six participants were recruited and engaged in sessions with a SG that promotes food and nutrition literacy. Data were collected during play from a heart rate sensor, a smart chair, and in-game metrics. Perceived engagement, as an approximation to the ground truth, was annotated continuously by participants. An additional group of six participants were recruited for smart chair calibration purposes. The analysis was conducted in two directions, firstly investigating associations between identified sitting postures and perceived engagement, and secondly evaluating the predictive capacity of features extracted from the multitude of sources towards the ground truth. The results demonstrate significant associations and predictive capacity from all investigated sources, with a multimodal feature combination displaying superiority over unimodal features. These results advocate for the feasibility of real time recognition of engagement in adaptive serious games for health by using the presented approach.

Assessing Workplace Stress Among Nurses Using Heart Rate Variability Analysis With Wearable ECG Device–A Pilot Study

This study aims to measure workplace stress of nurses using heart rate variability (HRV) analysis based on data derived from wearable ECG heart rate monitors. The study population consists of 17 nurses at a major public hospital in China. Data was collected from 7 DON nurses (department of neurosurgery; all females; mean age: 31.43 ± 4.50), and 9 ICU nurses (intensive care unit; 8 females and 1 male; mean age: 31.33 ± 5.43). Each participant was asked to wear a wireless ECG heart rate monitor to measure stress level during work, and to complete the Chinese Nurses Stress Response Scale (CNSRS) after work as subjective response criteria. Demographic information, body posture, heart rate, R-R intervals (RRI), low frequency components (LF) and high frequency components (HF) were collected. LF%, LnHF and the squared root of the mean squared differences of successive NN intervals (RMSSD) based on HRV analysis were used to estimate the stress level of nurses. DON nurses reported a higher LF%, lower LnHF and lower RMSSD than ICU nurses. Work shifts were shown to have significant effects on LF%, LnHF and RMSSD respectively, with nurses in long shifts and night shifts reported high stress levels. Higher LF%, lower LnHF and lower RMSSD were found during work shift. Posture analysis revealed negative correlations with LnHF and RMSSD in walking and standing/sitting positions, and a significant negative correlation with LF% in lying-down position. Nurses with higher LF% reported higher CNSRS scores in all subscales, whereas nurses with lower LnHF or RMSSD reported higher CNSRS scores in social phobia and fatigue subscales. The results of this study support the idea that HRV can be used to investigate workplace stress among nurses under real work condition, and can serve as a preventive measure for identifying stress-related illnesses among nurses.

Relevant Physiological Indicators for Assessing Workload in Conditionally Automated Driving, Through Three-Class Classification and Regression

In future conditionally automated driving, drivers may be asked to take over control of the car while it is driving autonomously. Performing a non-driving-related task could degrade their takeover performance, which could be detected by continuous assessment of drivers' mental load. In this regard, three physiological signals from 80 subjects were collected during 1 h of conditionally automated driving in a simulator. Participants were asked to perform a non-driving cognitive task (N-back) for 90 s, 15 times during driving. The modality and difficulty of the task were experimentally manipulated. The experiment yielded a dataset of drivers' physiological indicators during the task sequences, which was used to predict drivers' workload. This was done by classifying task difficulty (three classes) and regressing participants' reported level of subjective workload after each task (on a 0–20 scale). Classification of task modality was also studied. For each task, the effect of sensor fusion and task performance were studied. The implemented pipeline consisted of a repeated cross validation approach with grid search applied to three machine learning algorithms. The results showed that three different levels of mental load could be classified with a f1-score of 0.713 using the skin conductance and respiration signals as inputs of a random forest classifier. The best regression model predicted the subjective level of workload with a mean absolute error of 3.195 using the three signals. The accuracy of the model increased with participants' task performance. However, classification of task modality (visual or auditory) was not successful. Some physiological indicators such as estimates of respiratory sinus arrhythmia, respiratory amplitude, and temporal indices of heart rate variability were found to be relevant measures of mental workload. Their use should be preferred for ongoing assessment of driver workload in automated driving.

The Impact of Control Interface on Features of Heart Rate Variability

Shared human-robot control for assistive machines can improve the independence of individuals with motor impairments. Monitoring elevated levels of workload can enable the assistive autonomy to adjust the control sharing in an assist-as-needed way, to achieve a balance between user fatigue, stress, and independent control. In this work, we aim to investigate how heart rate variability features can be utilized to monitor elevated levels of mental workload while operating a powered wheelchair, and how that utilization might vary under different control interfaces. To that end, we conduct a 22 person study with three commercial interfaces. Our results show that the validity and reliability of using the ultra-short-term heart-rate variability features as predictors of workload indeed are affected by the type of interface in use.

Effects of ECG Data Length on Heart Rate Variability among Young Healthy Adults

The relationship between the robustness of HRV derived by linear and nonlinear methods to the required minimum data lengths has yet to be well understood. The normal electrocardiography (ECG) data of 14 healthy volunteers were applied to 34 HRV measures using various data lengths, and compared with the most prolonged (2000 R peaks or 750 s) by using the Mann-Whitney U test, to determine the 0.05 level of significance. We found that SDNN, RMSSD, pNN50, normalized LF, the ratio of LF and HF, and SD1 of the Poincaré plot could be adequately computed by small data size (60-100 R peaks). In addition, parameters of RQA did not show any significant differences among 60 and 750 s. However, longer data length (1000 R peaks) is recommended to calculate most other measures. The DFA and Lyapunov exponent might require an even longer data length to show robust results. Conclusions: Our work suggests the optimal minimum data sizes for different HRV measures which can potentially improve the efficiency and save the time and effort for both patients and medical care providers.

Psychological Effects of Heart Rate and Physical Vibration on the Operation of Construction Machines: Experimental Study

BACKGROUND

A construction method has emerged in which a camera is installed around a construction machine, and the operator remotely controls the machine while synchronizing the vibration of the machine with the images seen from the operator's seat using virtual reality (VR) technology. Indices related to changes in heart rate (HR) and physical vibration, such as heart rate variability (HRV) and multiscale entropy (MSE), can then be measured among the operators. As these indices are quantitative measures of autonomic regulation in the cardiovascular system, they can provide a useful means of assessing operational stress.

OBJECTIVE

In this study, we aimed to evaluate changes in HR and body vibration of machine operators and investigate appropriate methods of machine operation while considering the psychological load.

METHODS

We enrolled 9 remote operators (18-50 years old) in the experiment, which involved 42 measurements. A construction machine was driven on a test course simulating a construction site, and three patterns of operation-riding operation, remote operation using monitor images, and VR operation combining monitor images and machine vibration-were compared. The heartbeat, body vibration, and driving time of the participants were measured using sensing wear made of a woven film-like conductive material and a three-axis acceleration measurement device (WHS-2). We used HRV analysis in the time and frequency domains, MSE analysis as a measure of the complexity of heart rate changes, and the ISO (International Standards Organization) 2631 vibration index. Multiple regression analysis was conducted to model the relationship among the low frequency (LF)/high frequency (HF) HRV, MSE, vibration index, and driving time of construction equipment. Efficiency in driving time was investigated with a focus on stress reduction.

RESULTS

Multiple comparisons conducted via the Bonferroni test and Kruskal-Wallis test showed statistically significant differences (P=.05) in HRV-LF/HF, the vibration index, weighted acceleration, motion sickness dose value (MSDVz), and the driving time among the three operation patterns. The riding operation was found to reduce the driving time of the machine, but the operation stress was the highest in this case; operation based on the monitor image was found to have the lowest operation stress but the longest operation time. Multiple regression analysis showed that the explanatory variables (LH/HF), RR interval, and vibration index (MSDVz by vertical oscillation at 0.5-5 Hz) had a negative effect on the driving time (adjusted coefficient of determination R2=0.449).

CONCLUSIONS

A new method was developed to calculate the appropriate operating time by considering operational stress and suppressing the physical vibration within an acceptable range. By focusing on the relationship between psychological load and physical vibration, which has not been explored in previous studies, the relationship of these variables with the driving time of construction machines was clarified.

Notification alert! Effects of auditory text alerts on attention and heart rate variability across three developmental periods

In a modern world saturated with cellphone-related stimuli, surprisingly little is known about their psychological effects. A small number of previous studies have found global distracting effects of cellphone rings on cognitive performance in undergraduate students. However, moment-to-moment reactions to cellphone sounds have not been investigated, nor have physiological changes that might accompany the cognitive effects. Developmental variations also remain unexamined. Thus, two experiments were conducted to examine the effects of cellphone notification sounds on cognitive performance (i.e., reaction time and accuracy on math problems) and heart rate variability in three age groups: adolescents (mean age: 15 years); young adults (mean age: 20 years); and mid-life adults (mean age: 48 years). Effects were most pronounced in the adolescent group, whose math problem accuracy and reaction time was compromised in response to notification sounds. These compromises were accompanied by increases in heart rate variability.

Autonomic factors do not underlie the elevated self-disgust levels in Parkinson's disease

INTRODUCTION

Parkinson's disease (PD) is manifested along with non-motor symptoms such as impairments in basic emotion regulation, recognition and expression. Yet, self-conscious emotion (SCEs) such as self-disgust, guilt and shame are under-investigated. Our previous research indicated that Parkinson patients have elevated levels of self-reported and induced self-disgust. However, the cause of that elevation-whether lower level biophysiological factors, or higher level cognitive factors, is unknown.

METHODS

To explore the former, we analysed Skin Conductance Response (SCR, measuring sympathetic activity) amplitude and high frequency Heart Rate Variability (HRV, measuring parasympathetic activity) across two emotion induction paradigms, one involving narrations of personal experiences of self-disgust, shame and guilt, and one targeting self-disgust selectively via images of the self. Both paradigms had a neutral condition.

RESULTS

Photo paradigm elicited significant changes in physiological responses in patients relative to controls-higher percentages of HRV in the high frequency range but lower SCR amplitudes, with patients to present lower responses compared to controls. In the narration paradigm, only guilt condition elicited significant SCR differences between groups.

CONCLUSIONS

Consequently, lower level biophysiological factors are unlikely to cause elevated self-disgust levels in Parkinson's disease, which by implication suggests that higher level cognitive factors may be responsible.

Co-ordination of brain and heart oscillations during non-rapid eye movement sleep

Oscillatory activities of the brain and heart show a strong variation across wakefulness and sleep. Separate lines of research indicate that non‐rapid eye movement (NREM) sleep is characterised by electroencephalographic slow oscillations (SO), sleep spindles, and phase–amplitude coupling of these oscillations (SO–spindle coupling), as well as an increase in high‐frequency heart rate variability (HF‐HRV), reflecting enhanced parasympathetic activity. The present study aimed to investigate further the potential coordination between brain and heart oscillations during NREM sleep. Data were derived from one sleep laboratory night with polysomnographic monitoring in 45 healthy participants (22 male, 23 female; mean age 37 years). The associations between the strength (modulation index [MI]) and phase direction of SO–spindle coupling (circular measure) and HF‐HRV during NREM sleep were investigated using linear modelling. First, a significant SO–spindle coupling (MI) was observed for all participants during NREM sleep, with spindle peaks preferentially occurring during the SO upstate (phase direction). Second, linear model analyses of NREM sleep showed a significant relationship between the MI and HF‐HRV (F = 20.1, r² = 0.30, p < 0.001) and a tentative circular‐linear correlation between phase direction and HF‐HRV (F = 3.07, r² = 0.12, p = 0.056). We demonstrated a co‐ordination between SO–spindle phase–amplitude coupling and HF‐HRV during NREM sleep, presumably related to parallel central nervous and peripheral vegetative arousal systems regulation. Further investigating the fine‐graded co‐ordination of brain and heart oscillations might improve our understanding of the links between sleep and cardiovascular health.

Effects on Heart Rate Variability of Stress Level Responses to the Properties of Indoor Environmental Colors: A Preliminary Study

Color is the most potent stimulating factor affecting human vision, and the environmental color of an indoor space is a spatial component that affects the environmental stress level. As one of the methods of assessing the physiological response of the autonomic nervous system that influences stress, heart rate variability (HRV) has been utilized as a tool for measuring the user’s stress response in color environments. This study aims to identify the effects of the changes of hue, brightness, and saturation in environmental colors on the HRV of two groups with different stress levels—the stress potential group (n = 15) and the healthy group (n = 12)—based on their stress level indicated by the Psychosocial Well-being Index (PWI). The ln(LF), ln(HF), and RMSSD values collected during the subjects’ exposure to 12 environments colors of red and yellow with adjusted saturation and brightness, were statistically analyzed using t-test and two-way ANOVA. The results show that the HRV values in the two groups did not significantly vary in response to the changes in hue, brightness and saturation. The two groups’ stress factors distinguished according to the stress levels by the PWI scale affected the In(LF) parameter, which demonstrates that the PWI index can be utilized as a reliable scale for measuring stress levels. The ultra-short HRV measurement record and the use of a sole In(LF) parameter for stress assessment are regarded as the limitations of this study.

A method to quantify autonomic nervous system function in healthy, able-bodied individuals

BACKGROUND

The autonomic nervous system (ANS) maintains physiological homeostasis in various organ systems via parasympathetic and sympathetic branches. ANS function is altered in common diffuse and focal conditions and heralds the beginning of environmental and disease stresses. Reliable, sensitive, and quantitative biomarkers, first defined in healthy participants, could discriminate among clinically useful changes in ANS function. This framework combines controlled autonomic testing with feature extraction during physiological responses.

METHODS

Twenty-one individuals were assessed in two morning and two afternoon sessions over two weeks. Each session included five standard clinical tests probing autonomic function: squat test, cold pressor test, diving reflex test, deep breathing, and Valsalva maneuver. Noninvasive sensors captured continuous electrocardiography, blood pressure, breathing, electrodermal activity, and pupil diameter. Heart rate, heart rate variability, mean arterial pressure, electrodermal activity, and pupil diameter responses to the perturbations were extracted, and averages across participants were computed. A template matching algorithm calculated scaling and stretching features that optimally fit the average to an individual response. These features were grouped based on test and modality to derive sympathetic and parasympathetic indices for this healthy population.

RESULTS

A significant positive correlation (p = 0.000377) was found between sympathetic amplitude response and body mass index. Additionally, longer duration and larger amplitude sympathetic and longer duration parasympathetic responses occurred in afternoon testing sessions; larger amplitude parasympathetic responses occurred in morning sessions.

CONCLUSIONS

These results demonstrate the robustness and sensitivity of an algorithmic approach to extract multimodal responses from standard tests. This novel method of quantifying ANS function can be used for early diagnosis, measurement of disease progression, or treatment evaluation.

TRIAL REGISTRATION

This study registered with Clinicaltrials.gov , identifier NCT04100486 . Registered September 24, 2019, https://www.clinicaltrials.gov/ct2/show/NCT04100486 .

Factors Associated with Longitudinal Psychological and Physiological Stress in Health Care Workers During the COVID-19 Pandemic: Observational Study Using Apple Watch Data

Background

The COVID-19 pandemic has resulted in a high degree of psychological distress among health care workers (HCWs). There is a need to characterize which HCWs are at an increased risk of developing psychological effects from the pandemic. Given the differences in the response of individuals to stress, an analysis of both the perceived and physiological consequences of stressors can provide a comprehensive evaluation of its impact.

Objective

This study aimed to determine characteristics associated with longitudinal perceived stress in HCWs and to assess whether changes in heart rate variability (HRV), a marker of autonomic nervous system function, are associated with features protective against longitudinal stress.

Methods

HCWs across 7 hospitals in New York City, NY, were prospectively followed in an ongoing observational digital study using the custom Warrior Watch Study app. Participants wore an Apple Watch for the duration of the study to measure HRV throughout the follow-up period. Surveys measuring perceived stress, resilience, emotional support, quality of life, and optimism were collected at baseline and longitudinally.

Results

A total of 361 participants (mean age 36.8, SD 10.1 years; female: n=246, 69.3%) were enrolled. Multivariate analysis found New York City’s COVID-19 case count to be associated with increased longitudinal stress (P=.008). Baseline emotional support, quality of life, and resilience were associated with decreased longitudinal stress (P<.001). A significant reduction in stress during the 4-week period after COVID-19 diagnosis was observed in the highest tertial of emotional support (P=.03) and resilience (P=.006). Participants in the highest tertial of baseline emotional support and resilience had a significantly different circadian pattern of longitudinally collected HRV compared to subjects in the low or medium tertial.

Conclusions

High resilience, emotional support, and quality of life place HCWs at reduced risk of longitudinal perceived stress and have a distinct physiological stress profile. Our findings support the use of these characteristics to identify HCWs at risk of the psychological and physiological stress effects of the pandemic.

Interactive Effects of Heart Rate Variability and P-QRS-T on the Power Density Spectra of ECG Signals

Different from the traditional methods of assessing the cardiac activities through heart rhythm statistics or P-QRS-T complexes separately, this study demonstrates their interactive effects on the power density spectrum (PDS) of ECG signal with applications for the diagnosis of ST-segment elevation myocardial infarction (STEMI) diseases. Firstly, a mathematical model of the PDS of ECG signal with a random pacing pulse train (PPT) mimicking S-A node firings was derived. Secondly, an experimental PDS analysis was performed on clinical ECG signals from 49 STEMI patients and 42 healthy subjects in PTB Diagnostic Database. It was found that besides the interactive effects which are consistent between theoretical and experimental results, the ECG PDSs of STEMI patients exhibited consistently significant power shift towards lower frequency range in ST-elevated leads in comparison with those of reference leads and leads of health subjects with the highest median frequency shift ratios at 51.39 12.94% found in anterior MI. Thirdly, the results of ECG simulation with systematic changes in PPT firing statistics over various lengths of ECG data ranging from 10s to 60 mins revealed that the mean and median frequency parameters were less affected by the heart rhythm statistics and the data length but more depended on the alterations of P-QRS-T complexes, which were further confirmed on 33 more STEMI patients in European ST-T Database, demonstrating that the frequency indexes could be potentially used as alternative indicators for STEMI diagnosis even with ultra-short-term ECG recordings suitable for wearable and mobile health applications in living-free environments.

Analysis of Dynamic Changes in Cognitive Workload During Cardiac Surgery Perfusionists' Interactions With the Cardiopulmonary Bypass Pump

OBJECTIVE

This novel preliminary study sought to capture dynamic changes in heart rate variability (HRV) as a proxy for cognitive workload among perfusionists while operating the cardiopulmonary bypass (CPB) pump during real-life cardiac surgery.

BACKGROUND

Estimations of operators' cognitive workload states in naturalistic settings have been derived using noninvasive psychophysiological measures. Effective CPB pump operation by perfusionists is critical in maintaining the patient's homeostasis during open-heart surgery. Investigation into dynamic cognitive workload fluctuations, and their relationship with performance, is lacking in the literature.

METHOD

HRV and self-reported cognitive workload were collected from three Board-certified cardiac perfusionists (N = 23 cases). Five HRV components were analyzed in consecutive nonoverlapping 1-min windows from skin incision through sternal closure. Cases were annotated according to predetermined phases: prebypass, three phases during bypass, and postbypass. Values from all 1min time windows within each phase were averaged.

RESULTS

Cognitive workload was at its highest during the time between initiating bypass and clamping the aorta (preclamp phase during bypass), and decreased over the course of the bypass period.

CONCLUSION

We identified dynamic, temporal fluctuations in HRV among perfusionists during cardiac surgery corresponding to subjective reports of cognitive workload. Not only does cognitive workload differ for perfusionists during bypass compared with pre- and postbypass phases, but differences in HRV were also detected within the three bypass phases.

APPLICATION

These preliminary findings suggest the preclamp phase of CPB pump interaction corresponds to higher cognitive workload, which may point to an area warranting further exploration using passive measurement.

Efficient methods for acute stress detection using heart rate variability data from Ambient Assisted Living sensors

Background

Using Ambient Assisted Living sensors to detect acute stress could help people mitigate the harmful effects of everyday stressful situations. This would help both the healthy and those affected more by sudden stressors, e.g., people with diabetes or heart conditions. The study aimed to develop a method for providing reliable stress detection based on heart rate variability features extracted from portable devices.

Methods

Features extracted from portable electrocardiogram sensor recordings were used for training various classification algorithms for stress detection purposes. Data were recorded in a clinical trial with 7 participants and two stressors, the Trier Social Stress Test and the Stroop colour word test, both validated by standardised questionnaires. Different heart rate variability feature sets (all, time-domain and non-linear only, frequency-domain only) were tested to investigate how classification performance is affected, in addition to various time window length setups and participant-wise training sessions. The accuracy and F1 score of the trained models were compared and analysed.

Results

The best results were achieved with models using time-domain and non-linear heart rate variability features with 5-min-long overlapping time windows, yielding 96.31% accuracy and 96.26% F1 score. Shorter overlapping windows had slightly lower performance, with 91.62–94.55% accuracy and 91.77–94.55% F1 score ranges. Non-overlapping window configurations were less effective, with both accuracy and F1 score below 88%. For participant-wise learning, average F1 scores of 99.47%, 98.93% and 96.1% were achieved for feature sets using all, time-domain and non-linear, and frequency-domain features, respectively.

Conclusion

The tested stress detector models based on heart rate variability data recorded by a single electrocardiogram sensor performed just as well as those published in the literature working with multiple sensors, or even better. This suggests that once portable devices such as smartwatches provide reliable hear rate variability recordings, efficient stress detection can be achieved without the need for additional physiological measurements.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12938-021-00911-6.

Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare-A Review

Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.

Comparing Ictal Cardiac Autonomic Changes in Patients with Frontal Lobe Epilepsy and Temporal Lobe Epilepsy by Ultra-Short-Term Heart Rate Variability Analysis

Background and Objectives: Abnormal epileptic discharges in the brain can affect the central brain regions that regulate autonomic activity and produce cardiac symptoms, either at onset or during propagation of a seizure. These autonomic alterations are related to cardiorespiratory disturbances, such as sudden unexpected death in epilepsy. This study aims to investigate the differences in cardiac autonomic function between patients with temporal lobe epilepsy (TLE) and frontal lobe epilepsy (FLE) using ultra-short-term heart rate variability (HRV) analysis around seizures. Materials and Methods: We analyzed electrocardiogram (ECG) data recorded during 309 seizures in 58 patients with epilepsy. Twelve patients with FLE and 46 patients with TLE were included in this study. We extracted the HRV parameters from the ECG signal before, during and after the ictal interval with ultra-short-term HRV analysis. We statistically compared the HRV parameters using an independent t-test in each interval to compare the differences between groups, and repeated measures analysis of variance was used to test the group differences in longitudinal changes in the HRV parameters. We performed the Tukey-Kramer multiple comparisons procedure as the post hoc test. Results: Among the HRV parameters, the mean interval between heartbeats (RRi), normalized low-frequency band power (LF) and LF/HF ratio were statistically different between the interval and epilepsy types in the t-test. Repeated measures ANOVA showed that the mean RRi and RMSSD were significantly different by epilepsy type, and the normalized LF and LF/HF ratio significantly interacted with the epilepsy type and interval. Conclusions: During the pre-ictal interval, TLE patients showed an elevation in sympathetic activity, while the FLE patients showed an apparent increase and decrease in sympathetic activity when entering and ending the ictal period, respectively. The TLE patients showed a maintained elevation of sympathetic and vagal activity in the pos-ictal interval. These differences in autonomic cardiac characteristics between FLE and TLE might be relevant to the ictal symptoms which eventually result in SUDEP.

Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial

The use of heart rate variability (HRV) in research has been greatly popularized over the past decades due to the ease and affordability of HRV collection, coupled with its clinical relevance and significant relationships with psychophysiological constructs and psychopathological disorders. Despite the wide use of electrocardiograms (ECG) in research and advancements in sensor technology, the analytical approach and steps applied to obtain HRV measures can be seen as complex. Thus, this poses a challenge to users who may not have the adequate background knowledge to obtain the HRV indices reliably. To maximize the impact of HRV-related research and its reproducibility, parallel advances in users' understanding of the indices and the standardization of analysis pipelines in its utility will be crucial. This paper addresses this gap and aims to provide an overview of the most up-to-date and commonly used HRV indices, as well as common research areas in which these indices have proven to be very useful, particularly in psychology. In addition, we also provide a step-by-step guide on how to perform HRV analysis using an integrative neurophysiological toolkit, NeuroKit2.

Impact of Water Temperature on Heart Rate Variability during Bathing

Background: Heart rate variability (HRV) is affected by many factors. This paper aims to explore the impact of water temperature (WT) on HRV during bathing. Methods: The bathtub WT was preset at three conditions: i.e., low WT (36–38 °C), medium WT (38–40 °C), and high WT (40–42 °C), respectively. Ten subjects participated in the data collection. Each subject collected five electrocardiogram (ECG) recordings at each preset bathtub WT condition. Each recording was 18 min long with a sampling rate of 200 Hz. In total, 150 ECG recordings and 150 WT recordings were collected. Twenty HRV features were calculated using 1-min ECG segments each time. The k-means clustering analysis method was used to analyze the rough trends based on the preset WT. Analyses of the significant differences were performed using the multivariate analysis of variance of t-tests, and the mean and standard deviation (SD) of each HRV feature based on the WT were calculated. Results: The statistics show that with increasing WT, 11 HRV features are significantly (p < 0.05) and monotonously reduced, four HRV features are significantly (p < 0.05) and monotonously rising, two HRV features are rising first and then reduced, two HRV features (fuzzy and approximate entropy) are almost unchanged, and vLF power is rising. Conclusion: The WT has an important impact on HRV during bathing. The findings in the present work reveal an important physiological factor that affects the dynamic changes of HRV and contribute to better quantitative analyses of HRV in future research works.

Is Ultra-Short-Term Heart Rate Variability Valid in Non-static Conditions?

In mobile healthcare, heart rate variability (HRV) is increasingly being used in dynamic patient states. In this situation, shortening of the measurement time is required. This study aimed to validate ultra-short-term HRV in non-static conditions. We conducted electrocardiogram (ECG) measurements at rest, during exercise, and in the post-exercise recovery period in 30 subjects and analyzed ultra-short-term HRV in time and frequency domains by ECG in 10, 30, 60, 120, 180, and 240-s intervals, and compared the values to the 5-min HRV. For statistical analysis, null hypothesis testing, Cohen’s d statistics, Pearson’s correlation coefficient, and Bland-Altman analysis were used, with a statistical significance level of P < 0.05. The feasibility of ultra-short-term HRV and the minimum time required for analysis showed differences in each condition and for each analysis method. If the strict criteria satisfying all the statistical methods were followed, the ultra-short-term HRV could be derived from a from 30 to 240-s length of ECG. However, at least 120 s was required in the post-exercise recovery or exercise conditions, and even ultra-short-term HRV was not measurable in some variables. In contrast, according to the lenient criteria needed to satisfy only one of the statistical criteria, the minimum time required for ultra-short-term HRV analysis was 10–60 s in the resting condition, 10–180 s in the exercise condition, and 10–120 s in the post-exercise recovery condition. In conclusion, the results of this study showed that a longer measurement time was required for ultra-short-term HRV analysis in dynamic conditions. This suggests that the existing ultra-short-term HRV research results derived from the static condition cannot applied to the non-static conditions of daily life and that a criterion specific to the non-static conditions are necessary.

Abdominothoracic Postural Tone Influences the Sensations Induced by Meal Ingestion

Postprandial objective abdominal distention is frequently associated with a subjective sensation of abdominal bloating, but the relation between both complaints is unknown. While the bloating sensation has a visceral origin, abdominal distention is a behavioral somatic response, involving contraction and descent of the diaphragm with protrusion of the anterior abdominal wall. Our aim was to determine whether abdominal distention influences digestive sensations. In 16 healthy women we investigated the effect of intentional abdominal distention on experimentally induced bloating sensation (by a meal overload). Participants were first taught to produce diaphragmatic contraction and visible abdominal distention. After a meal overload, sensations of bloating (0 to 10) and digestive well-being (-5 to + 5) were measured during 30-s. maneuvers alternating diaphragmatic contraction and diaphragmatic relaxation. Compared to diaphragmatic relaxation, diaphragmatic contraction was associated with diaphragmatic descent (by 21 + 3 mm; p < 0.001), objective abdominal distension (32 + 5 mm girth increase; p = 0.001), more intense sensation of bloating (7.3 + 0.4 vs. 8.0 + 0.4 score; p = 0.010) and lower digestive well-being (-0.9 + 0.5 vs. -1.9 + 0.5 score; p = 0.028). These results indicate that somatic postural tone underlying abdominal distention worsens the perception of visceral sensations (ClinicalTrials.gov ID: NCT04691882).

Classification of Drivers' Workload Using Physiological Signals in Conditional Automation

The use of automation in cars is increasing. In future vehicles, drivers will no longer be in charge of the main driving task and may be allowed to perform a secondary task. However, they might be requested to regain control of the car if a hazardous situation occurs (i.e., conditionally automated driving). Performing a secondary task might increase drivers' mental workload and consequently decrease the takeover performance if the workload level exceeds a certain threshold. Knowledge about the driver's mental state might hence be useful for increasing safety in conditionally automated vehicles. Measuring drivers' workload continuously is essential to support the driver and hence limit the number of accidents in takeover situations. This goal can be achieved using machine learning techniques to evaluate and classify the drivers' workload in real-time. To evaluate the usefulness of physiological data as an indicator for workload in conditionally automated driving, three physiological signals from 90 subjects were collected during 25 min of automated driving in a fixed-base simulator. Half of the participants performed a verbal cognitive task to induce mental workload while the other half only had to monitor the environment of the car. Three classifiers, sensor fusion and levels of data segmentation were compared. Results show that the best model was able to successfully classify the condition of the driver with an accuracy of 95%. In some cases, the model benefited from sensors' fusion. Increasing the segmentation level (e.g., size of the time window to compute physiological indicators) increased the performance of the model for windows smaller than 4 min, but decreased for windows larger than 4 min. In conclusion, the study showed that a high level of drivers' mental workload can be accurately detected while driving in conditional automation based on 4-min recordings of respiration and skin conductance.

Contactless analysis of heart rate variability during cold pressor test using radar interferometry and bidirectional LSTM networks

Contactless measurement of heart rate variability (HRV), which reflects changes of the autonomic nervous system (ANS) and provides crucial information on the health status of a person, would provide great benefits for both patients and doctors during prevention and aftercare. However, gold standard devices to record the HRV, such as the electrocardiograph, have the common disadvantage that they need permanent skin contact with the patient. Being connected to a monitoring device by cable reduces the mobility, comfort, and compliance by patients. Here, we present a contactless approach using a 24 GHz Six-Port-based radar system and an LSTM network for radar heart sound segmentation. The best scores are obtained using a two-layer bidirectional LSTM architecture. To verify the performance of the proposed system not only in a static measurement scenario but also during a dynamic change of HRV parameters, a stimulation of the ANS through a cold pressor test is integrated in the study design. A total of 638 minutes of data is gathered from 25 test subjects and is analysed extensively. High F-scores of over 95% are achieved for heartbeat detection. HRV indices such as HF norm are extracted with relative errors around 5%. Our proposed approach is capable to perform contactless and convenient HRV monitoring and is therefore suitable for long-term recordings in clinical environments and home-care scenarios.

Association of Psychosocial Factors With Short-Term Resting Heart Rate Variability: The Atherosclerosis Risk in Communities Study

Background Psychosocial factors predict heart disease risk, but our understanding of underlying mechanisms is limited. We sought to evaluate the physiologic correlates of psychosocial factors by measuring their relationships with heart rate variability (HRV), a measure of autonomic health, in the ARIC (Atherosclerosis Risk in Communities) study. We hypothesize that increased psychosocial stress associates with lower HRV. Methods and Results We studied 9331 participants in ARIC with short-term HRV data at visits 2 and 4. The mean (SD) age was 54.4 (5.7) years, 55% were women, and 25% were Black. Psychosocial factors included: (1) vital exhaustion (VE), (2) anger proneness, a personality trait, and (3) perceived social support. Linear models adjusted for sociodemographic and cardiovascular risk factors. Low frequency HRV (ln ms2) was significantly lower in the highest versus lowest quartiles of VE (B=-0.14, 95% CI, -0.24 to -0.05). When comparing this effect to age (B=-0.04, 95% CI, -0.05 to -0.04), the difference was equivalent to 3.8 years of accelerated aging. Perceived social support associated with lower time-domain HRV. High VE (versus low VE) also associated with greater decreases in low frequency over time, and both anger and VE associated with greater increases in resting heart rate over time. Survival analyses were performed with Cox models, and no evidence was found that HRV explains the excess risk found with high VE and low perceived social support. Conclusions Vital exhaustion, and to a lesser extent anger and social support, were associated with worse autonomic function and greater adverse changes over time.

A Critical Review of Ultra-Short-Term Heart Rate Variability Norms Research

Heart rate variability (HRV) is the fluctuation in time between successive heartbeats and is defined by interbeat intervals. Researchers have shown that short-term (∼5-min) and long-term (≥24-h) HRV measurements are associated with adaptability, health, mobilization, and use of limited regulatory resources, and performance. Long-term HRV recordings predict health outcomes heart attack, stroke, and all-cause mortality. Despite the prognostic value of long-term HRV assessment, it has not been broadly integrated into mainstream medical care or personal health monitoring. Although short-term HRV measurement does not require ambulatory monitoring and the cost of long-term assessment, it is underutilized in medical care. Among the diverse reasons for the slow adoption of short-term HRV measurement is its prohibitive time cost (∼5 min). Researchers have addressed this issue by investigating the criterion validity of ultra-short-term (UST) HRV measurements of less than 5-min duration compared with short-term recordings. The criterion validity of a method indicates that a novel measurement procedure produces comparable results to a currently validated measurement tool. We evaluated 28 studies that reported UST HRV features with a minimum of 20 participants; of these 17 did not investigate criterion validity and 8 primarily used correlational and/or group difference criteria. The correlational and group difference criteria were insufficient because they did not control for measurement bias. Only three studies used a limits of agreement (LOA) criterion that specified a priori an acceptable difference between novel and validated values in absolute units. Whereas the selection of rigorous criterion validity methods is essential, researchers also need to address such issues as acceptable measurement bias and control of artifacts. UST measurements are proxies of proxies. They seek to replace short-term values which, in turn, attempt to estimate long-term metrics. Further adoption of UST HRV measurements requires compelling evidence that these metrics can forecast real-world health or performance outcomes. Furthermore, a single false heartbeat can dramatically alter HRV metrics. UST measurement solutions must automatically edit artifactual interbeat interval values otherwise HRV measurements will be invalid. These are the formidable challenges that must be addressed before HRV monitoring can be accepted for widespread use in medicine and personal health care.

Physiological Signal Analysis and Classification of Stress from Virtual Reality Video Game

Stress can affect a person's performance and health positively and negatively. A lot of the relaxation methods have been suggested to reduce the amount of stress. This study used virtual reality (VR) video games to alleviate stress. Physiological signals captured from Electrocardiogram (ECG), galvanic skin response (GSR), and respiration (RESP) were used to determine if the subject was stressed or relaxed. Time and frequency domain features were then extracted to evaluate stress levels. Frequency domain methods such as low-frequency (LF), high-frequency (HF), LF-HF ratio (LF/HF) are considered the most effective for HRV analysis, Poincare plots are moré discerning visually and shares a 81% correlation with LF/HF ratio. GSR is associated with EDA activity, which only increases due to stress. Stress and relax were classified using Linear Discriminant Analysis (LDA), Decision Tree, Support Vector machine (SVM), Gradient Boost (GB), and Naive Bayes. GB performed the best with an accuracy of 85% after 5 fold cross validation with 100 iterations, which is admirable from a small dataset with 50 samples.

A Novel Smartphone App for the Measurement of Ultra-Short-Term and Short-Term Heart Rate Variability: Validity and Reliability Study

Background

Smartphone apps for heart rate variability (HRV) measurement have been extensively developed in the last decade. However, ultra–short-term HRV recordings taken by wearable devices have not been examined.

Objective

The aims of this study were the following: (1) to compare the validity and reliability of ultra–short-term and short-term HRV time-domain and frequency-domain variables in a novel smartphone app, Pulse Express Pro (PEP), and (2) to determine the agreement of HRV assessments between an electrocardiogram (ECG) and PEP.

Methods

In total, 60 healthy adults were recruited to participate in this study (mean age 22.3 years [SD 3.0 years], mean height 168.4 cm [SD 8.0 cm], mean body weight 64.2 kg [SD 11.5 kg]). A 5-minute resting HRV measurement was recorded via ECG and PEP in a sitting position. Standard deviation of normal R-R interval (SDNN), root mean square of successive R-R interval (RMSSD), proportion of NN50 divided by the total number of RR intervals (pNN50), normalized very-low–frequency power (nVLF), normalized low-frequency power (nLF), and normalized high-frequency power (nHF) were analyzed within 9 time segments of HRV recordings: 0-1 minute, 1-2 minutes, 2-3 minutes, 3-4 minutes, 4-5 minutes, 0-2 minutes, 0-3 minutes, 0-4 minutes, and 0-5 minutes (standard). Standardized differences (ES), intraclass correlation coefficients (ICC), and the Spearman product-moment correlation were used to compare the validity and reliability of each time segment to the standard measurement (0-5 minutes). Limits of agreement were assessed by using Bland-Altman plot analysis.

Results

Compared to standard measures in both ECG and PEP, pNN50, SDNN, and RMSSD variables showed trivial ES (<0.2) and very large to nearly perfect ICC and Spearman correlation coefficient values in all time segments (>0.8). The nVLF, nLF, and nHF demonstrated a variation of ES (from trivial to small effects, 0.01-0.40), ICC (from moderate to nearly perfect, 0.39-0.96), and Spearman correlation coefficient values (from moderate to nearly perfect, 0.40-0.96). Furthermore, the Bland-Altman plots showed relatively narrow values of mean difference between the ECG and PEP after consecutive 1-minute recordings for SDNN, RMSSD, and pNN50. Acceptable limits of agreement were found after consecutive 3-minute recordings for nLF and nHF.

Conclusions

Using the PEP app to facilitate a 1-minute ultra–short-term recording is suggested for time-domain HRV indices (SDNN, RMSSD, and pNN50) to interpret autonomic functions during stabilization. When using frequency-domain HRV indices (nLF and nHF) via the PEP app, a recording of at least 3 minutes is needed for accurate measurement.

Recurrent probabilistic neural network-based short-term prediction for acute hypotension and ventricular fibrillation

In this paper, we propose a novel method for predicting acute clinical deterioration triggered by hypotension, ventricular fibrillation, and an undiagnosed multiple disease condition using biological signals, such as heart rate, RR interval, and blood pressure. Efforts trying to predict such acute clinical deterioration events have received much attention from researchers lately, but most of them are targeted to a single symptom. The distinctive feature of the proposed method is that the occurrence of the event is manifested as a probability by applying a recurrent probabilistic neural network, which is embedded with a hidden Markov model and a Gaussian mixture model. Additionally, its machine learning scheme allows it to learn from the sample data and apply it to a wide range of symptoms. The performance of the proposed method was tested using a dataset provided by Physionet and the University of Tokyo Hospital. The results show that the proposed method has a prediction accuracy of 92.5% for patients with acute hypotension and can predict the occurrence of ventricular fibrillation 5 min before it occurs with an accuracy of 82.5%. In addition, a multiple disease condition can be predicted 7 min before they occur, with an accuracy of over 90%.