Heart rate variability monitoring for emotion and disorders of emotion

Approaches to studying emotion using physiological responses to spoken narratives: A scoping review

Narratives are effective tools for evoking emotions, and physiological measurements provide a means of objectively assessing emotional reactions - making them a potentially powerful pair of tools for studying emotional processes. However, extent research combining emotional narratives and physiological measurement varies widely in design and application, making it challenging to identify previous work, consolidate findings, and design effective experiments. Our scoping review explores the use of auditory emotional narratives and physiological measures in research, examining paradigms, study populations, and represented emotions. Following the PRISMA-ScR Checklist, we searched five databases for peer-reviewed experimental studies that used spoken narratives to induce emotion and reported autonomic physiological measures. Among 3466 titles screened and 653 articles reviewed, 110 studies were included. Our exploration revealed a variety of applications and experimental paradigms; emotional narratives paired with physiological measures have been used to study diverse topics and populations, including neurotypical and clinical groups. Although incomparable designs and sometimes contradictory results precluded general recommendations as regards which physiological measures to use when designing new studies, as a whole, the body of work suggests that these tools can be valuable to study emotions. Our review offers an overview of research employing narratives and physiological measures for emotion study, and highlights weaknesses in reporting practices and gaps in our knowledge concerning the robustness and specificity of physiological measures as indices of emotion. We discuss study design considerations and transparent reporting, to facilitate future using emotional narratives and physiological measures in studying emotions.

Abnormal brain-heart electrophysiology in mild and severe orthostatic hypotension

INTRODUCTION

This study investigated the changes in cardiocerebral electrophysiology in patients with mild orthostatic hypotension (MOH) and severe orthostatic hypotension (SOH) and their relationship with the severity of orthostatic hypotension, psychiatric symptoms, and cognitive dysfunction.

METHODS

This study included 72 nonorthostatic hypotension (NOH), 17 with MOH, and 11 with SOH. Seated resting-state heart rate variability (HRV) and quantitative electroencephalogram parameters were synchronized and recorded. HRV measures in the time and frequency domains were analyzed, along with the peak frequency and power of the brain waves.

RESULTS

Abnormal neuronal activity was found in FP1 in patients with MOH, whereas it was more widespread in FP1, FP2, and O2 in patients with SOH (P < 0.05). Cardiac and cerebral electrophysiological abnormalities were significantly associated with orthostatic hypotension severity, psychiatric symptoms, and cognitive dysfunction.

CONCLUSION

Abnormal EEG activity in patients are mainly manifested in the prefrontal and occipital lobes, especially in patients with SOH. These results may help patients to better understand the mechanisms underlying orthostatic hypotension severity and psychiatric and cognitive impairment in orthostatic hypotension.

What Can Physiology Tell Us about State of Interest?

The state of interest as a positive emotion is associated with the ability to comprehend new information and/or to better consolidate already perceived information, to increase the attention level to the object, to increase informational processing, and also to influence such processes as learning and motivation. The aim of this study was to reveal oculomotor correlates that can predict the locus of interest in cases of people perceiving educational information from different areas of knowledge presented as text or multimedia content. Sixty (60) volunteers participated in the study (50% males, mean age 22.20 ± 0.51). The stimuli consisted of 16 texts covering a wide range of topics, each accompanied by a comprehension question and an interest assessment questionnaire. It was found that the multimedia content type triggered more visual attention and gave an advantage in the early stages of information processing. The first fixation duration metric for the multimedia stimuli allowed u to characterize the subjective interest assessment. Overall, the results suggest the potential role of eye-tracking in evaluating educational content and it emphasizes the importance of developing solutions based on this method to enhance the effectiveness of the educational process.

Heart-Brain Axis: A Narrative Review of the Interaction between Depression and Arrhythmia

Arrhythmias and depression are recognized as diseases of the heart and brain, respectively, and both are major health threats that often co-occur with a bidirectional causal relationship. The autonomic nervous system (ANS) serves as a crucial component of the heart-brain axis (HBA) and the pathway of interoception. Cardiac activity can influence emotional states through ascending interoceptive pathways, while psychological stress can precipitate arrhythmias via the ANS. However, the HBA and interoception frameworks are often considered overly broad, and the precise mechanisms underlying the bidirectional relationship between depression and arrhythmias remain unclear. This narrative review aims to synthesize the existing literature, focusing on the pathological mechanisms of the ANS in depression and arrhythmia while integrating other potential mechanisms to detail heart-brain interactions. In the bidirectional communication between the heart and brain, we emphasize considering various internal factors such as genes, personality traits, stress, the endocrine system, inflammation, 5-hydroxytryptamine, and behavioral factors. Current research employs multidisciplinary knowledge to elucidate heart-brain relationships, and a deeper understanding of these interactions can help optimize clinical treatment strategies. From a broader perspective, this study emphasizes the importance of considering the body as a complex, interconnected system rather than treating organs in isolation. Investigating heart-brain interactions enhance our understanding of disease pathogenesis and advances medical science, ultimately improving human quality of life.

Assessment of parameters reflecting the reactivity of the autonomic nervous system of Polish firefighters on the basis of a test in a smoke chamber

Objective

Measurement and analysis of heart rate variability in a population of professional firefighters based on heart rate (RR) recording. Assessment based on a smoke chamber test in correlation with age, length of service, body mass index.

Materials and methods

The smoke chamber test for the officers of the State Fire Service (SFS) is aimed at improving the skills and techniques of working in special clothing and in a respiratory protection set (RPS) under high psychophysical burden. The study was divided into 3 stages: 1. measurement of parameters at rest - sitting position for 5 min, 2. measurement of parameters during the firefighter's activity, effort related to the training path and the test in the smoke chamber, indefinite time (different for each firefighter), 3. measurement of parameters at rest after exercise - sitting position for 5 min. Each firefighter included in the study had fitted onto his chest a Polar H10 band with a sensor (size XXL) that measures parameters HR, HRV (sensor connected via Bluetooth to an application on the phone of a person controlling the test).

Results

The study involved 96 firefighters aged 19-45 (Mean 27.9; SD 7.4), with 1-19 years of service (Mean 5.2; SD 4.6). The study included 75 firefighters who completed the entire activity and their results were recorded completely in a way that allowed for analysis and interpretation. Results of 17 firefighters were selected (parameters describing HRV changes was carried out, which are important from the authors' experience: RMSSD, HF ms2, DFA α1).

Conclusion

The presence of excessive body weight did not affect HR parameters, which may be related to the limited possibilities of using the BMI index among people with high muscle mass. Longer work experience has a health-promoting effect on heart rate values through increased adaptation of the circulatory system to increased effort and stress. HRV parameter and ANS activity have a wide range of clinical applications, in addition to monitoring health status in the course of diseases, ANS activity can be analyzed in correlation with occupational risk factors.

Prospects for Augmenting Team Interactions with Real-Time Coordination-Based Measures in Human-Autonomy Teams

Complex work in teams requires coordination across team members and their technology as well as the ability to change and adapt over time to achieve effective performance. To support such complex interactions, recent efforts have worked toward the design of adaptive human-autonomy teaming systems that can provide feedback in or near real time to achieve the desired individual or team results. However, while significant advancements have been made to better model and understand the dynamics of team interaction and its relationship with task performance, appropriate measures of team coordination and computational methods to detect changes in coordination have not yet been widely investigated. Having the capacity to measure coordination in real time is quite promising as it provides the opportunity to provide adaptive feedback that may influence and regulate teams' coordination patterns and, ultimately, drive effective team performance. A critical requirement to reach this potential is having the theoretical and empirical foundation from which to do so. Therefore, the first goal of the paper is to review approaches to coordination dynamics, identify current research gaps, and draw insights from other areas, such as social interaction, relationship science, and psychotherapy. The second goal is to collate extant work on feedback and advance ideas for adaptive feedback systems that have potential to influence coordination in a way that can enhance the effectiveness of team interactions. In addressing these two goals, this work lays the foundation as well as plans for the future of human-autonomy teams that augment team interactions using coordination-based measures.

A Pilot Feasibility Evaluation of a Heart Rate Variability Biofeedback App to Improve Self-Care in COVID-19 Healthcare Workers

COVID-19 exacerbated burnout and mental health concerns among the healthcare workforce. Due to high work stress, demanding schedules made attuned eating behaviors a particularly challenging aspect of self-care for healthcare workers. This study aimed to examine the feasibility and acceptability of a heart rate variability biofeedback (HRVB) mobile app for improving well-being among healthcare workers reporting elevated disordered eating during COVID-19. We conducted a mixed methods pre-mid-post single-arm pilot feasibility trial (ClinicalTrials.gov NCT04921228). Deductive content analysis of participants' commentary generated qualitative themes. Linear mixed models were used to examine changes in pre- mid- to post-assessment scores on well-being outcomes. We consented 28 healthcare workers (25/89% female; 23/82% Non-Hispanic White; 22/79% nurses) to use and evaluate an HRVB mobile app. Of these, 25/89% fully enrolled by attending the app and device training; 23/82% were engaged in all elements of the protocol. Thirteen (52%) completed at least 10 min of HRVB on two-thirds or more study days. Most participants (18/75%) reported being likely or extremely likely to continue HRVB. Common barriers to engagement were busy schedules, fatigue, and technology difficulties. However, participants felt that HRVB helped them relax and connect better to their body's signals and experiences. Results suggested preliminary evidence of efficacy for improving interoceptive sensibility, mindful self-care, body appreciation, intuitive eating, stress, resilience, and disordered eating. HRVB has potential as a low-cost adjunct tool for enhancing well-being in healthcare workers through positively connecting to the body, especially during times of increased stress when attuned eating behavior becomes difficult to uphold.

Heart Rate Measurement Using the Built-In Triaxial Accelerometer from a Commercial Digital Writing Device

Currently, wearable technology is an emerging trend that offers remarkable access to our data through smart devices like smartphones, watches, fitness trackers and textiles. As such, wearable devices can enable health monitoring without disrupting our daily routines. In clinical settings, electrocardiograms (ECGs) and photoplethysmographies (PPGs) are used to monitor heart and respiratory behaviors. In more practical settings, accelerometers can be used to estimate the heart rate when they are attached to the chest. They can also help filter out some noise in ECG signals from movement. In this work, we compare the heart rate data extracted from the built-in accelerometer of a commercial smart pen equipped with sensors (STABILO's DigiPen) to standard ECG monitor readouts. We demonstrate that it is possible to accurately predict the heart rate from the smart pencil. The data collection is carried out with eight volunteers writing the alphabet continuously for five minutes. The signal is processed with a Butterworth filter to cut off noise. We achieve a mean-squared error (MSE) better than 6.685 × 10-3 comparing the DigiPen's computed Δt (time between pulses) with the reference ECG data. The peaks' timestamps for both signals all maintain a correlation higher than 0.99. All computed heart rates (HR =60Δt) from the pen accurately correlate with the reference ECG signals.

Modeling and mobile home monitoring of behavioral and psychological symptoms of dementia (BPSD)

With the increasing global aging population, dementia care has rapidly become a major social problem. Current diagnosis of Behavior and Psychological Symptoms of Dementia (BPSD) relies on clinical interviews, and behavioral rating scales based on a period of behavior observation, but these methods are not suitable for identification of occurrence of BPSD in the daily living, which is necessary for providing appropriate interventions for dementia, though, has been studied by few research groups in the literature. To address these issues, in this study developed a BPSD monitoring system consisting of a Psycho-Cognitive (PsyCo) BPSD model, a Behavior-Physio-Environment (BePhyEn) BPSD model, and an implementation platform. The PsyCo BPSD model provides BPSD assessment support to caregivers and care providers, while the BePhyEn BPSD model provides instantaneous alerts for BPSD enabled by a 24-hour home monitoring platform for early intervention, and thereby alleviation of burden to patients and caregivers. Data for acquiring the models were generated through extensive literature review and regularity determined. A mobile robot was utilized as the implementation platform for improving sensitivity of sensors for home monitoring, and elderly individual following algorithms were investigated. Experiments in a virtual home environment showed that, a virtual BPSD elderly individual can be followed safely by the robot, and BPSD occurrence could be identified accurately, demonstrating the possibility of modeling and identification of BPSD in home environment.

The relationship between emotional disorders and heart rate variability: A Mendelian randomization study

OBJECTIVE

Previous studies have shown that emotional disorders are negatively associated with heart rate variability (HRV), but the potential causal relationship between genetic susceptibility to emotional disorders and HRV remains unclear. We aimed to perform a Mendelian randomization (MR) study to investigate the potential association between emotional disorders and HRV.

METHODS

The data used for this study were obtained from publicly available genome-wide association study datasets. Five models, including the inverse variance weighted model (IVW), the weighted median estimation model (WME), the weighted model-based method (WM), the simple model (SM) and the MR-Egger regression model (MER), were utilized for MR. The leave-one-out sensitivity test, MR pleiotropy residual sum and outlier test (MR-PRESSO) and Cochran's Q test were used to confirm heterogeneity and pleiotropy.

RESULTS

MR analysis revealed that genetic susceptibility to broad depression was negatively correlated with HRV (pvRSA/HF) (OR = 0.380, 95% CI 0.146-0.992; p = 0.048). However, genetic susceptibility to irritability was positively correlated with HRV (pvRSA/HF, SDNN) (OR = 2.017, 95% CI 1.152-3.534, p = 0.008) (OR = 1.154, 95% CI 1.000-1.331, p = 0.044). Genetic susceptibility to anxiety was positively correlated with HRV (RMSSD) (OR = 2.106, 95% CI 1.032-4.299; p = 0.041). No significant directional pleiotropy or heterogeneity was detected. The accuracy and robustness of these findings were confirmed through a sensitivity analysis.

CONCLUSIONS

Our MR study provides genetic support for the causal effects of broad depression, irritable mood, and anxiety on HRV.

Acute effects of different Tai Chi practice protocols on cardiac autonomic modulation

Tai Chi serves as an effective exercise modality for enhancing autonomic regulation. However, a majority of existing studies have employed the single routine (SR) protocol as the basis for health interventions. The extent to which the gong routine application (GRA) protocol achieves similar levels of exercise load stimulation as traditional single practice routines remains uncertain. Therefore, this study the distinct characteristics of autonomic load stimulation in these different protocols, thus providing a biological foundation to support the development of Tai Chi health promotion intervention programs. we recruited a cohort of forty-five university students to participate in the 15 min GRA protocol and SR protocol. We collected heart rate and heart rate variability indicators during periods of rest, GRA protocol, and SR protocol utilizing the Polar Scale. Additionally, we assessed the mental state of the participants using the BFS State of Mind Scale. In summary, the autonomic load is lower in the GRA protocol compared to the SR protocol, with lower sympathetic activity but higher parasympathetic activity in the former. Results are specific to college students, additional research is necessary to extend support for frail older adults. It is advised to incorporate GRA protocol alongside SR protocol in Tai Chi instruction. This approach is likely to enhance Tai Chi skills and yield greater health benefits.

Research on driver's anger recognition method based on multimodal data fusion

OBJECTIVES

This paper aims to address the challenge of low accuracy in single-modal driver anger recognition by introducing a multimodal driver anger recognition model. The primary objective is to develop a multimodal fusion recognition method for identifying driver anger, focusing on electrocardiographic (ECG) signals and driving behavior signals.

METHODS

Emotion-inducing experiments were performed employing a driving simulator to capture both ECG signals and driving behavioral signals from drivers experiencing both angry and calm moods. An analysis of characteristic relationships and feature extraction was conducted on ECG signals and driving behavior signals related to driving anger. Seventeen effective feature indicators for recognizing driving anger were chosen to construct a dataset for driver anger. A binary classification model for recognizing driving anger was developed utilizing the Support Vector Machine (SVM) algorithm.

RESULTS

Multimodal fusion demonstrated significant advantages over single-modal approaches in emotion recognition. The SVM-DS model using decision-level fusion had the highest accuracy of 84.75%. Compared with the driver anger emotion recognition model based on unimodal ECG features, unimodal driving behavior features, and multimodal feature layer fusion, the accuracy increased by 9.10%, 4.15%, and 0.8%, respectively.

CONCLUSIONS

The proposed multimodal recognition model, incorporating ECG and driving behavior signals, effectively identifies driving anger. The research results provide theoretical and technical support for the establishment of a driver anger system.

Human Digital Twin in Industry 5.0: A Holistic Approach to Worker Safety and Well-Being through Advanced AI and Emotional Analytics

This research introduces a conceptual framework designed to enhance worker safety and well-being in industrial environments, such as oil and gas construction plants, by leveraging Human Digital Twin (HDT) cutting-edge technologies and advanced artificial intelligence (AI) techniques. At its core, this study is in the developmental phase, aiming to create an integrated system that could enable real-time monitoring and analysis of the physical, mental, and emotional states of workers. It provides valuable insights into the impact of Digital Twins (DT) technology and its role in Industry 5.0. With the development of a chatbot trained as an empathic evaluator that analyses emotions expressed in written conversations using natural language processing (NLP); video logs capable of extracting emotions through facial expressions and speech analysis; and personality tests, this research intends to obtain a deeper understanding of workers' psychological characteristics and stress levels. This innovative approach might enable the identification of stress, anxiety, or other emotional factors that may affect worker safety. Whilst this study does not encompass a case study or an application in a real-world setting, it lays the groundwork for the future implementation of these technologies. The insights derived from this research are intended to inform the development of practical applications aimed at creating safer work environments.

Heart rate variability status at rest in adult depressed patients: a systematic review and meta-analysis

Purposes

A meta-analysis was conducted to examine the differences in heart rate variability (HRV) between depressed patients and healthy individuals, with the purpose of providing a theoretical basis for the diagnosis of depression and the prevention of cardiovascular diseases.

Methods

To search China National Knowledge Infrastructure (CNKI), WanFang, VIP, PubMed, Web of Science, Science Direct, and Cochrane Library databases to collect case-control studies on HRV in depressed patients, the retrieval date is from the establishment of the database to December 2022. Effective Public Health Practice Project (EPHPP) scale was used to evaluate literature quality, and Stata14.0 software was used for meta-analysis.

Results

This study comprised of 43 papers, 22 written in Chinese and 21 in English, that included 2,359 subjects in the depression group and 3,547 in the healthy control group. Meta-analysis results showed that compared with the healthy control group, patients with depression had lower SDNN [Hedges' g = -0.87, 95% CI (-1.14, -0.60), Z = -6.254, p < 0.01], RMSSD [Hedges' g = -0.51, 95% CI (-0.69,-0.33), Z = -5.525, p < 0.01], PNN50 [Hedges' g = -0.43, 95% CI (-0.59, -0.27), Z = -5.245, p < 0.01], LF [Hedges' g = -0.34, 95% CI (-0.55, - 0.13), Z = -3.104, p < 0.01], and HF [Hedges' g = -0.51, 95% CI (-0.69, -0.33), Z = -5.669 p < 0.01], and LF/HF [Hedges' g = -0.05, 95% CI (-0.27, 0.18), Z = -0.410, p = 0.682] showed no significant difference.

Conclusion

This research revealed that HRV measures of depressed individuals were lower than those of the healthy population, except for LF/HF, suggesting that people with depression may be more at risk of cardiovascular diseases than the healthy population.

Severity identification for internet gaming disorder using heart rate variability reactivity for gaming cues: a deep learning approach

Background

The diminished executive control along with cue-reactivity has been suggested to play an important role in addiction. Hear rate variability (HRV), which is related to the autonomic nervous system, is a useful biomarker that can reflect cognitive-emotional responses to stimuli. In this study, Internet gaming disorder (IGD) subjects' autonomic response to gaming-related cues was evaluated by measuring HRV changes in exposure to gaming situation. We investigated whether this HRV reactivity can significantly classify the categorical classification according to the severity of IGD.

Methods

The present study included 70 subjects and classified them into 4 classes (normal, mild, moderate and severe) according to their IGD severity. We measured HRV for 5 min after the start of their preferred Internet game to reflect the autonomic response upon exposure to gaming. The neural parameters of deep learning model were trained using time-frequency parameters of HRV. Using the Class Activation Mapping (CAM) algorithm, we analyzed whether the deep learning model could predict the severity classification of IGD and which areas of the time-frequency series were mainly involved.

Results

The trained deep learning model showed an accuracy of 95.10% and F-1 scores of 0.995 (normal), 0.994 (mild), 0.995 (moderate), and 0.999 (severe) for the four classes of IGD severity classification. As a result of checking the input of the deep learning model using the CAM algorithm, the high frequency (HF)-HRV was related to the severity classification of IGD. In the case of severe IGD, low frequency (LF)-HRV as well as HF-HRV were identified as regions of interest in the deep learning model.

Conclusion

In a deep learning model using the time-frequency HRV data, a significant predictor of IGD severity classification was parasympathetic tone reactivity when exposed to gaming situations. The reactivity of the sympathetic tone for the gaming situation could predict only the severe group of IGD. This study suggests that the autonomic response to the game-related cues can reflect the addiction status to the game.

ECG Multi-Emotion Recognition Based on Heart Rate Variability Signal Features Mining

Heart rate variability (HRV) serves as a significant physiological measure that mirrors the regulatory capacity of the cardiac autonomic nervous system. It not only indicates the extent of the autonomic nervous system's influence on heart function but also unveils the connection between emotions and psychological disorders. Currently, in the field of emotion recognition using HRV, most methods focus on feature extraction through the comprehensive analysis of signal characteristics; however, these methods lack in-depth analysis of the local features in the HRV signal and cannot fully utilize the information of the HRV signal. Therefore, we propose the HRV Emotion Recognition (HER) method, utilizing the amplitude level quantization (ALQ) technique for feature extraction. First, we employ the emotion quantification analysis (EQA) technique to impartially assess the semantic resemblance of emotions within the domain of emotional arousal. Then, we use the ALQ method to extract rich local information features by analyzing the local information in each frequency range of the HRV signal. Finally, the extracted features are classified using a logistic regression (LR) classification algorithm, which can achieve efficient and accurate emotion recognition. According to the experiment findings, the approach surpasses existing techniques in emotion recognition accuracy, achieving an average accuracy rate of 84.3%. Therefore, the HER method proposed in this paper can effectively utilize the local features in HRV signals to achieve efficient and accurate emotion recognition. This will provide strong support for emotion research in psychology, medicine, and other fields.

ECG-Free Heartbeat Detection in Seismocardiography and Gyrocardiography Signals Provides Acceptable Heart Rate Variability Indices in Healthy and Pathological Subjects

Cardio-mechanical monitoring techniques, such as Seismocardiography (SCG) and Gyrocardiography (GCG), have received an ever-growing interest in recent years as potential alternatives to Electrocardiography (ECG) for heart rate monitoring. Wearable SCG and GCG devices based on lightweight accelerometers and gyroscopes are particularly appealing for continuous, long-term monitoring of heart rate and its variability (HRV). Heartbeat detection in cardio-mechanical signals is usually performed with the support of a concurrent ECG lead, which, however, limits their applicability in standalone cardio-mechanical monitoring applications. The complex and variable morphology of SCG and GCG signals makes the ECG-free heartbeat detection task quite challenging; therefore, only a few methods have been proposed. Very recently, a template matching method based on normalized cross-correlation (NCC) has been demonstrated to provide very accurate detection of heartbeats and estimation of inter-beat intervals in SCG and GCG signals of pathological subjects. In this study, the accuracy of HRV indices obtained with this template matching method is evaluated by comparison with ECG. Tests were performed on two public datasets of SCG and GCG signals from healthy and pathological subjects. Linear regression, correlation, and Bland-Altman analyses were carried out to evaluate the agreement of 24 HRV indices obtained from SCG and GCG signals with those obtained from ECG signals, simultaneously acquired from the same subjects. The results of this study show that the NCC-based template matching method allowed estimating HRV indices from SCG and GCG signals of healthy subjects with acceptable accuracy. On healthy subjects, the relative errors on time-domain indices ranged from 0.25% to 15%, on frequency-domain indices ranged from 10% to 20%, and on non-linear indices were within 8%. The estimates obtained on signals from pathological subjects were affected by larger errors. Overall, GCG provided slightly better performances as compared to SCG, both on healthy and pathological subjects. These findings provide, for the first time, clear evidence that monitoring HRV via SCG and GCG sensors without concurrent ECG is feasible with the NCC-based template matching method for heartbeat detection.

Decoding auditory-evoked response in affective states using wearable around-ear EEG system

Objective.In this paper, an around-ear EEG system is investigated as an alternative methodology to conventional scalp-EEG-based systems in classifying human affective states in the arousal-valence domain evoked in response to auditory stimuli.Approach.EEG recorded from around the ears is compared to EEG collected according to the international 10-20 system in terms of efficacy in an affective state classification task. A wearable device with eight dry EEG channels is designed for ear-EEG acquisition in this study. Twenty-one subjects participated in an experiment consisting of six sessions over three days using both ear and scalp-EEG acquisition methods. Experimental tasks consisted of listening to an auditory stimulus and self-reporting the elicited emotion in response to the said stimulus. Various features were used in tandem with asymmetry methods to evaluate binary classification performances of arousal and valence states using ear-EEG signals in comparison to scalp-EEG.Main results.We achieve an average accuracy of 67.09% ± 6.14 for arousal and 66.61% ± 6.14 for valence after training a multi-layer extreme learning machine with ear-EEG signals in a subject-dependent context in comparison to scalp-EEG approach which achieves an average accuracy of 68.59% ± 6.26 for arousal and 67.10% ± 4.99 for valence. In a subject-independent context, the ear-EEG approach achieves 63.74% ± 3.84 for arousal and 64.32% ± 6.38 for valence while the scalp-EEG approach achieves 64.67% ± 6.91 for arousal and 64.86% ± 5.95 for valence. The best results show no significant differences between ear-EEG and scalp-EEG signals for classifications of affective states.Significance.To the best of our knowledge, this paper is the first work to explore the use of around-ear EEG signals in emotion monitoring. Our results demonstrate the potential use of around-ear EEG systems for the development of emotional monitoring setups that are more suitable for use in daily affective life log systems compared to conventional scalp-EEG setups.

Game Fun Prediction Based on Frequency Domain Physiological Signals: Observational Study

Traditionally, the subjective questionnaire collected from game players is regarded as a primary tool to evaluate a video game. However, the subjective evaluation result may vary due to individual differences, and it is not easy to provide real-time feedback to optimize the user experience. This paper aims to develop an objective game fun prediction system. In this system, the wearables with photoplethysmography (PPG) sensors continuously measure the heartbeat signals of game players, and the frequency domain heart rate variability (HRV) parameters can be derived from the inter-beat interval (IBI) sequence. Frequency domain HRV parameters, such as low frequency(LF), high frequency(HF), and LF/HF ratio, highly correlate with the human's emotion and mental status. Most existing works on emotion measurement during a game adopt time domain physiological signals such as heart rate and facial electromyography (EMG). Time domain signals can be easily interfered with by noises and environmental effects. The main contributions of this paper include (1) regarding the curve transition and standard deviation of LF/HF ratio as the objective game fun indicators and (2) proposing a linear model using objective indicators for game fun score prediction. The self-built dataset in this study involves ten healthy participants, comprising 36 samples. According to the analytical results, the linear model's mean absolute error (MAE) was 4.16%, and the root mean square error (RMSE) was 5.07%. While integrating this prediction model with wearable-based HRV measurements, the proposed system can provide a solution to improve the user experience of video games.

EEG and peripheral markers of viewer ratings: a study of short films

Introduction

Cinema is an important part of modern culture, influencing millions of viewers. Research suggested many models for the prediction of film success, one of them being the use of neuroscientific tools. The aim of our study was to find physiological markers of viewer perception and correlate them to short film ratings given by our subjects. Short films are used as a test case for directors and screenwriters and can be created to raise funding for future projects; however, they have not been studied properly with physiological methods.

Methods

We recorded electroencephalography (18 sensors), facial electromyography (corrugator supercilii and zygomaticus major), photoplethysmography, and skin conductance in 21 participants while watching and evaluating 8 short films (4 dramas and 4 comedies). Also, we used machine learning (CatBoost, SVR) to predict the exact rating of each film (from 1 to 10), based on all physiological indicators. In addition, we classified each film as low or high rated by our subjects (with Logistic Regression, KNN, decision tree, CatBoost, and SVC).

Results

The results showed that ratings did not differ between genres. Corrugator supercilii activity ("frowning" muscle) was larger when watching dramas; whereas zygomaticus major ("smiling" muscle) activity was larger during the watching of comedies. Of all somatic and vegetative markers, only zygomaticus major activity, PNN50, SD1/SD2 (heart rate variability parameters) positively correlated to the film ratings. The EEG engagement indices, beta/(alpha+theta) and beta/alpha correlated positively with the film ratings in the majority of sensors. Arousal (beta_F3 + beta_F4)/(alpha_F3 + alpha_F4), and valence (alpha_F4/beta_F4) - (alpha_F3/beta_F3) indices also correlated positively to film ratings. When we attempted to predict exact ratings, MAPE was 0.55. As for the binary classification, logistic regression yielded the best values (area under the ROC curve = 0.62) than other methods (0.51-0.60).

Discussion

Overall, we revealed EEG and peripheral markers, which reflect viewer ratings and can predict them to a certain extent. In general, high film ratings can reflect a fusion of high arousal and different valence, positive valence being more important. These findings broaden our knowledge about the physiological basis of viewer perception and can be potentially used at the stage of film production.

ECG-Free Heartbeat Detection in Seismocardiography Signals via Template Matching

Cardiac monitoring can be performed by means of an accelerometer attached to a subject's chest, which produces the Seismocardiography (SCG) signal. Detection of SCG heartbeats is commonly carried out by taking advantage of a simultaneous electrocardiogram (ECG). SCG-based long-term monitoring would certainly be less obtrusive and easier to implement without an ECG. Few studies have addressed this issue using a variety of complex approaches. This study proposes a novel approach to ECG-free heartbeat detection in SCG signals via template matching, based on normalized cross-correlation as heartbeats similarity measure. The algorithm was tested on the SCG signals acquired from 77 patients with valvular heart diseases, available from a public database. The performance of the proposed approach was assessed in terms of sensitivity and positive predictive value (PPV) of the heartbeat detection and accuracy of inter-beat intervals measurement. Sensitivity and PPV of 96% and 97%, respectively, were obtained by considering templates that included both systolic and diastolic complexes. Regression, correlation, and Bland-Altman analyses carried out on inter-beat intervals reported slope and intercept of 0.997 and 2.8 ms (R² > 0.999), as well as non-significant bias and limits of agreement of ±7.8 ms. The results are comparable or superior to those achieved by far more complex algorithms, also based on artificial intelligence. The low computational burden of the proposed approach makes it suitable for direct implementation in wearable devices.

The Evaluation of Emotional Intelligence by the Analysis of Heart Rate Variability

Emotional intelligence (EI) is a critical social intelligence skill that refers to an individual's ability to assess their own emotions and those of others. While EI has been shown to predict an individual's productivity, personal success, and ability to maintain positive relationships, its assessment has primarily relied on subjective reports, which are vulnerable to response distortion and limit the validity of the assessment. To address this limitation, we propose a novel method for assessing EI based on physiological responses-specifically heart rate variability (HRV) and dynamics. We conducted four experiments to develop this method. First, we designed, analyzed, and selected photos to evaluate the ability to recognize emotions. Second, we produced and selected facial expression stimuli (i.e., avatars) that were standardized based on a two-dimensional model. Third, we obtained physiological response data (HRV and dynamics) from participants as they viewed the photos and avatars. Finally, we analyzed HRV measures to produce an evaluation criterion for assessing EI. Results showed that participants' low and high EI could be discriminated based on the number of HRV indices that were statistically different between the two groups. Specifically, 14 HRV indices, including HF (high-frequency power), lnHF (the natural logarithm of HF), and RSA (respiratory sinus arrhythmia), were significant markers for discerning between low and high EI groups. Our method has implications for improving the validity of EI assessment by providing objective and quantifiable measures that are less vulnerable to response distortion.

The physiology of intraoperative error: using electrokardiograms to understand operator performance during robot-assisted surgery simulations

BACKGROUND

No platform for objective, synchronous and on-line evaluation of both intraoperative error and surgeon physiology yet exists. Electrokardiogram (EKG) metrics have been associated with cognitive and affective features that are known to impact surgical performance but have not yet been analyzed in conjunction with real-time error signals using objective, real-time methods.

METHODS

EKGs and operating console point-of-views (POVs) for fifteen general surgery residents and five non-medically trained participants were captured during three simulated robotic-assisted surgery (RAS) procedures. Time and frequency-domain EKG statistics were extracted from recorded EKGs. Intraoperative errors were detected from operating console POV videos. EKG statistics were synchronized with intraoperative error signals.

RESULTS

Relative to personalized baselines, IBI, SDNN and RMSSD decreased 0.15% (S.E. 3.603e-04; P = 3.25e-05), 3.08% (S.E. 1.603e-03; P < 2e-16) and 1.19% (S.E. 2.631e-03; P = 5.66e-06), respectively, during error. Relative LF RMS power decreased 1.44% (S.E. 2.337e-03; P = 8.38e-10), and relative HF RMS power increased 5.51% (S.E. 1.945e-03; P < 2e-16).

CONCLUSIONS

Use of a novel, on-line biometric and operating room data capture and analysis platform enabled detection of distinct operator physiological changes during intraoperative errors. Monitoring operator EKG metrics during surgery may help improve patient outcomes through real-time assessments of intraoperative surgical proficiency and perceived difficulty as well as inform personalized surgical skills development.

The efficacy of heart rate variability biofeedback in patients with acute ischemic stroke: A protocol for systematic review and meta-analysis

BACKGROUND

Stroke is the most common serious neurological disorder, and in high-income countries, it is the fourth-leading cause of death, long-term disability, and reduced quality of life among adults. Heart rate variability (HRV) can improve autonomic dysfunction, cognitive impairment, and psychological distress in other patient populations, but its effect in patients with acute ischemic stroke is still unclear. We conducted a protocol for systematic review and meta-analysis to evaluate the efficacy of HRV biofeedback in patients with acute ischemic stroke.

METHODS

A computerized literature search will be performed in the following electronic databases from their inceptions to October 2022: PubMed, EMBASE, MEDLINE, Cochrane Central Register of Controlled Clinical Trials, China Knowledge Resource Integrated Database, Wanfang Data Information, and Weipu Database for Chinese Technical Periodicals. The risk of bias in the included articles is assessed according to the Risk of Bias Assessment Tool in Cochrane Handbook of Systematic Reviews. Data are analyzed with the Review Manager Version 5.3 software.

RESULTS

This paper will provide high-quality synthesis to assess the efficacy of HRV biofeedback in patients with acute ischemic stroke.

CONCLUSION

HRV biofeedback may be a promising intervention for improving autonomic function, cognitive impairment, and psychological distress in patients with acute ischemic stroke.

A neural circuit from the dorsal CA3 to the dorsomedial hypothalamus mediates balance between risk exploration and defense

An appropriate balance between explorative and defensive behavior is essential for the survival and reproduction of prey animals in risky environments. However, the neural circuit and mechanism that allow for such a balance remains poorly understood. Here, we use a semi-naturalistic predator threat test (PTT) to observe and quantify the defense-exploration balance, especially risk exploration behavior in mice. During the PTT, the activity of the putative dorsal CA3 glutamatergic neurons (dCA3^Glu) is suppressed by predatory threat and risk exploration, whereas the neurons are activated during contextual exploration. Moreover, optogenetic excitation of these neurons induces a significant increase in risk exploration. A circuit, comprising the dorsal CA3, dorsal lateral septal, and dorsomedial hypothalamic (dCA3^Glu-dLS^GABA-DMH) areas, may be involved. Moreover, activation of the dCA3^Glu-dLS^GABA-DMH circuit promotes the switch from defense to risk exploration and suppresses threat-induced increase in arousal.

Are changes in pain associated with changes in heart rate variability in patients treated for recurrent or persistent neck pain?

BACKGROUND

Persistent or recurrent neck pain is associated with perturbations in the autonomic nervous system balance, and nociceptive stimulation has been seen to influence this balance. However, very few prospective studies have addressed the extent to which changes in pain associate with changes in autonomic cardiac regulation. Therefore, we investigated if changes in pain vary with changes in heart rate variability in a cohort of patients treated for persistent or recurrent neck pain.

METHOD

This analysis is based on data from a randomized controlled trial in which participants were given home stretching exercises with or without spinal manipulative therapy for two weeks. As the effectiveness of the intervention (home stretching exercises and spinal manipulative therapy) was found to be equal to the control (home stretching exercises alone), all 127 participants were studied as one cohort in this analysis. During the intervention, pain levels were recorded using daily text messages, and heart rate variability was measured in the clinics three times over two weeks. Two approaches were used to classify patients based on changes in pain intensity: 1) Clinically important changes in pain were categorized as either "improved" or "not improved" and, 2) Pain development was measured using pain trajectories, constructed in a data driven approach. The association of pain categories and trajectories with changes in heart rate variability indices over time were then analysed using linear mixed models.

RESULTS

Heart rate variability did not differ significantly between improved and not-improved patients, nor were there any associations with the different pain trajectories.

CONCLUSIONS

In conclusion, changes in pain after home stretching exercises with or without spinal manipulative therapy over two weeks were not significantly associated with changes in heart rate variability for patients with persistent or recurrent neck pain. Future studies should rely on more frequent measurements of HRV during longer treatment periods.

TRIAL REGISTRATION

The trial was registered at ClinicalTrials.gov, registration number: NCT03576846.

Effect of Exercise Intervention on Internet Addiction and Autonomic Nervous Function in College Students

Objective

To investigate the effects of 12-week physical exercise (jogging, basketball, and outdoor training) on sleep quality, harmful mood, and heart rate variability (HRV) in college students with Internet addiction.

Methods

46 college students with Internet addiction were chosen and then randomly assigned to the Internet addiction group (IA, n = 23) and the Internet addiction exercise group (IA+EX, n = 23). The subjects in the IA+EX group underwent physical exercise for 12 weeks (three times per week), and the IA group did not perform regular physical exercise during the experiment. Then, the degree of Internet addiction, depression, and sleep quality were evaluated by using Young's Internet Addiction Test (IAT) scale, Center for Epidemiologic Studies Depression (CES-D) scale, and Pittsburgh sleep quality index (PSQI); HRV were measured by using Polar Team 2 before and after physical exercise intervention.

Results

(1) After the 12-week exercise, compared to preexercise intervention, the scores of IAT, CES-D, and PSQI significantly decreased (t = 12.183, 9.238, 5.660; P < 0.01) in the IA+EX group; compared with the IA group, the scores of IAT, CES-D, and PSQI significantly decreased (t = 2.449, 3.175, 4.487; P < 0.05, P <0.01) in IA+EX group college students with Internet addiction. (2) After the 12-week exercise, compared to preexercise intervention, LFn and the ratio of LF/HF significantly decreased (t = 5.650, 3.493; P < 0.01) and HFn significantly increased (t = −2.491, P < 0.05) in the IA+EX group; there were no significant differences in the above indexes before and after the experiment in the IA group (P > 0.05). Compared with the IA group, HFn significantly increased (t = 3.616, P < 0.01) and the ratio of LF/HF significantly decreased (t = 2.099, P < 0.01) in IA+EX group college students with Internet addiction; there was no significant difference in LFn between the two groups.

Conclusion

Long-term physical exercise could significantly reduce the degree of Internet addiction and depression, improve sleep quality, and balance sympathetic parasympathetic function of college students with Internet addiction, indicating that exercise-based intervention might be an effective way to alleviate or even eliminate Internet addiction.

Complexity Modulation in functional Brain-Heart Interplay series driven by Emotional Stimuli: an early study using Fuzzy Entropy

Increasing attention has recently been devoted to the multidisciplinary investigation of functional brain-heart interplay (BHI), which has provided meaningful insights in neuroscience and clinical domains including cardiology, neurology, clinical psychology, and psychiatry. While neural (brain) and heartbeat series show high nonlinear and complex dynamics, a complexity analysis on BHI series has not been performed yet. To this end, in this preliminary study, we investigate BHI complexity modulation in 17 healthy subjects undergoing a 3-minute resting state and emotional elicitation through standardized image slideshow. Electroencephalographic and heart rate variability series were the inputs of an adhoc BHI model, which provides directional (from-heart-to-brain and from-brain-to-heart) estimates at different frequency bands. A Fuzzy entropy analysis was performed channel-wise on the model output for the two experimental conditions. Results suggest that BHI complexity increases in the emotional elicitation phase with respect to a resting state, especially in the functional direction from the heart to the brain. We conclude that BHI complexity may be a viable computational tool to characterize neurophysiological and pathological states under different experimental conditions.

Depressed Mood Prediction of Elderly People with a Wearable Band

Depression in the elderly is an important social issue considering the population aging of the world. In particular, elderly living alone who has narrowed social relationship due to bereavement and retirement are more prone to be depressed. Long-term depressed mood can be a precursor to eventual depression as a disease. Our goal is how to predict the depressed mood of single household elderly from unobtrusive monitoring of their daily life. We have selected a wearable band with multiple sensors for monitoring elderly people. Depression questionnaire has been surveyed periodically to be used as the labels. Instead of working with depression patients, we recruited 14 single household elderly people from a nearby community. The wearable band provided daily activity and biometric data for 71 days. From the data, we generate a depressed mood prediction model. Multiple features from the collected sensor data are exploited for model generation. One general model is generated to be used as the baseline for the initial model deployment. Personal models are also generated for model refinement. The general model has a high recall of 80% in an MLP model. Individual models achieved an average recall of 82.7%. In this study, we have demonstrated that we can generate depressed mood prediction models with data collected from real daily living. Our work has shown the feasibility of using a wearable band as an unobtrusive depression monitoring sensor even for elderly people.

Association between perceived life stress and subjective well-being among Chinese perimenopausal women: a moderated mediation analysis

BACKGROUND

The impact of perceived life stress on subjective well-being has been well-established; while few studies have explored the mediating and moderating mechanisms of the association between perceived life stress and subjective well-being among perimenopausal women. This study is aimed at exploring the mediating effect of depressive symptoms and the role of interests/hobbies as a moderator in the association between perceived life stress and subjective well-being among perimenopausal women.

METHODS

The participants were 1,104 perimenopausal women at the age of 40 to 60, who were asked to complete a paper-based questionnaire. A single item was used to measure self-perceived life stress and interests/hobbies. The Zung Self-rating Depression Scale (SDS) and Subjective Well-being Scale for Chinese Citizens (SWBS-CC) were applied to assess both depressive symptoms and subjective well-being. Multiple linear regression analysis and the PROCESS macro were adopted to analyse not only the mediating effect of depressive symptoms but also the moderating role of interests/hobbies.

RESULTS

Perceived life stress was negatively associated with subjective well-being (B =  - 1.424, β =  - 0.101, P < 0.001). The impact of perceived life stress on subjective well-being was partially mediated by depressive symptoms (mediation effect = -0.760, 95% confidence intervals (CI) [-1.129, -0.415]). In addition, the interaction term between depressive symptoms and interests/hobbies was significantly related to subjective well-being (β =  - 0.060, P < 0.05), indicating moderating effect. Moderated mediation had a significant index (Index = -0.220, SE = 0.099, 95% CI [-0.460, -0.060]).

CONCLUSIONS

Perceived life stress was negatively related to subjective well-being. The impact of perceived life stress on subjective well-being was mediated by depressive symptoms. Besides, interests/hobbies moderated the indirect effect of depressive symptoms on the relationship between perceived life stress and subjective well-being.

MATLAB based Realtime Data Acquisition Tool for Multimodal Biofeedback and Arduino based Instruments

AfDaq is an open-source, plug and play, MATLAB based tool that offers the capabilities of multi-channel real-time data acquisition, visualization, manipulation, and local saving of data for offline analysis. The MATLAB Arduino package suffers from serious timing jitter during real-time data acquisition. This timing jitter associated with four main commands (Analog Read, Digital Read, Digital Write and PWM Set) available in MATLAB Arduino package is statistically analyzed and a simple post-hoc timing jitter correction mechanism is proposed to acquire data points with high timing accuracy. The benchmark of the final program is conducted at various sampling rates for multichannel acquisition with 10 Hz comes as the maximum sampling rate for 5 channel recording. In the end, a use case of the developed tool for physiological data acquisition in multimodal biofeedback is presented. The software tool, data, and analysis scripts that support the findings of this study are released as an open-source project to support the replicability and reproducibility of the research.

Enhanced detection of abnormalities in heart rate variability and dynamics by 7-day continuous ECG monitoring

Background

The analysis of heart rate variability (HRV) and heart rate (HR) dynamics by Holter ECG has been standardized to 24 hs, but longer‐term continuous ECG monitoring has become available in clinical practice. We investigated the effects of long‐term ECG on the assessment of HRV and HR dynamics.

Methods

Intraweek variations in HRV and HR dynamics were analyzed in 107 outpatients with sinus rhythm. ECG was recorded continuously for 7 days with a flexible, codeless, waterproof sensor attached on the upper chest wall. Data were divided into seven 24‐h segments, and standard time‐ and frequency‐domain HRV and nonlinear HR dynamics indices were computed for each segment.

Results

The intraweek coefficients of variance of HRV and HR dynamics indices ranged from 2.9% to 26.0% and were smaller for frequency‐domain than for time‐domain indices, and for indices reflecting slower HR fluctuations than faster fluctuations. The indices with large variance often showed transient abnormalities from day to day over 7 days, reducing the positive predictive accuracy of the 24‐h ECG for detecting persistent abnormalities over 7 days. Conversely, 7‐day ECG provided 2.3‐ to 6.5‐fold increase in sensitivity to detect persistent plus transient abnormalities compared with 24‐h ECG. It detected an average of 1.74 to 2.91 times as many abnormal indices as 24‐h ECG.

Conclusions

Long‐term ECG monitoring increases the accuracy and sensitivity of detecting persistent and transient abnormalities in HRV and HR dynamics and allows discrimination between the two types of abnormalities. Whether this discrimination improves risk stratification deserves further studies.

Assessment of Cognitive Student Engagement Using Heart Rate Data in Distance Learning during COVID-19

Student engagement allows educational institutions to make better decisions regarding teaching methodologies, methods for evaluating the quality of education, and ways to provide timely feedback. Due to the COVID-19 pandemic, identifying cognitive student engagement in distance learning has been a challenge in higher education institutions. In this study, we implemented a non-self-report method assessing students’ heart rate data to identify the cognitive engagement during active learning activities. Additionally, as a supplementary tool, we applied a previously validated self-report method. This study was performed in distance learning lessons on a group of university students in Bogota, Colombia. After data analysis, we validated five hypotheses and compared the results from both methods. The results confirmed that the heart rate assessment had a statistically significant difference with respect to the baseline during active learning activities, and this variance could be positive or negative. In addition, the results show that if students are previously advised that they will have to develop an a new task after a passive learning activity (such as a video projection), their heart rate will tend to increase and consequently, their cognitive engagement will also increase. We expect this study to provide input for future research assessing student cognitive engagement using physiological parameters as a tool.

Immediate effects of the high-velocity low-amplitude thrust on the heart rate autonomic modulation of judo athletes

INTRODUCTION

There is controversy about the repercussions of high speed-low amplitude thrust (HVLAT) manipulation in the thoracic region on the autonomic nervous system.

OBJECTIVE

To evaluate the immediate effects of the HVLAT in the high thoracic region on the heart rate autonomic modulation of judo athletes.

METHODS

In the experimental study, thirty-eight healthy men divided into 2 groups (Judo athletes and non-athletes) having heart rate variability (HRV) collected beat-to-beat using a cardio-pacemater during all stages of the manipulation: i) rest, ii) time 1 (participant positioning), iii) time 2 (positioning of the participant together with the therapist), iv) HVLAT manipulation, v) post 5min, vi) post 10min and vii) post 15min HVLAT. Systolic blood pressure (SBP), diastolic blood pressure (DBP), breath frequency (BF), and HRV were also analyzed.

RESULTS

A higher sympathetic modulation was observed with an increase in the standard deviation of successive normal R-R intervals (SDNN) and SD2 indices representing the total variability, however, there was no significant statistical difference in the root mean square of the mean squared differences (RMSSD), percentual of interval differences of successive NN intervals greater than 50 ms (pNN50), and SD1 variables, which represent the parasympathetic nervous system.

CONCLUSION

HVLAT manipulation was able to decrease HRV during manipulation, reflecting sympathetic hyperactivity. However, the return of the HRV indices to the baseline conditions in the first minutes of recovery in Judo athletes and non-athletes reflected the safety of the application of the manipulation in these conditions studied.

Machine Learning Methods for Fear Classification Based on Physiological Features

This paper focuses on the binary classification of the emotion of fear, based on the physiological data and subjective responses stored in the DEAP dataset. We performed a mapping between the discrete and dimensional emotional information considering the participants’ ratings and extracted a substantial set of 40 types of features from the physiological data, which represented the input to various machine learning algorithms—Decision Trees, k-Nearest Neighbors, Support Vector Machine and artificial networks—accompanied by dimensionality reduction, feature selection and the tuning of the most relevant hyperparameters, boosting classification accuracy. The methodology we approached included tackling different situations, such as resolving the problem of having an imbalanced dataset through data augmentation, reducing overfitting, computing various metrics in order to obtain the most reliable classification scores and applying the Local Interpretable Model-Agnostic Explanations method for interpretation and for explaining predictions in a human-understandable manner. The results show that fear can be predicted very well (accuracies ranging from 91.7% using Gradient Boosting Trees to 93.5% using dimensionality reduction and Support Vector Machine) by extracting the most relevant features from the physiological data and by searching for the best parameters which maximize the machine learning algorithms’ classification scores.

Internet of things issues related to psychiatry

Background

Internet of Things (IoT) devices for remote monitoring, diagnosis, and treatment are widely viewed as an important future direction for medicine, including for bipolar disorder and other mental illness. The number of smart, connected devices is expanding rapidly. IoT devices are being introduced in all aspects of everyday life, including devices in the home and wearables on the body. IoT devices are increasingly used in psychiatric research, and in the future may help to detect emotional reactions, mood states, stress, and cognitive abilities. This narrative review discusses some of the important fundamental issues related to the rapid growth of IoT devices.

Main body

Articles were searched between December 2019 and February 2020. Topics discussed include background on the growth of IoT, the security, safety and privacy issues related to IoT devices, and the new roles in the IoT economy for manufacturers, patients, and healthcare organizations.

Conclusions

The use of IoT devices will increase throughout psychiatry. The scale, complexity and passive nature of data collection with IoT devices presents unique challenges related to security, privacy and personal safety. While the IoT offers many potential benefits, there are risks associated with IoT devices, and from the connectivity between patients, healthcare providers, and device makers. Security, privacy and personal safety issues related to IoT devices are changing the roles of manufacturers, patients, physicians and healthcare IT organizations. Effective and safe use of IoT devices in psychiatry requires an understanding of these changes.

The interactive effects of heart rate variability and mindfulness on indicators of well-being in healthcare professionals' daily working life

OBJECTIVE

Heart rate variability (HRV) and mindfulness have been described as correlates of self-regulation and well-being. The goal of the present study was to investigate their interactions from a within-person perspective in the context of work.

METHODS

Applying an ambulatory assessment approach, we studied 89 healthcare professionals across two to four work shifts. Self-reports of momentary job demands, mindfulness, and well-being (as indicated by emotional exhaustion, relaxation, and contentment) were provided three to four times a day via smartphone questionnaires. Electrocardiogram and activity sensors continuously recorded data from the beginning to the end of the shifts. Multilevel models based on 937 measurements were built for emotional exhaustion, relaxation, and contentment.

RESULTS

After controlling for covariates, including bodily movement, shift, and job demands, short-term HRV was marginally significantly related to decreased emotional exhaustion and significantly related to increased relaxation. State mindfulness was significantly related to decreased emotional exhaustion, and increased relaxation and contentment. Furthermore, HRV and mindfulness significantly interacted such that emotional exhaustion was lowest and relaxation was highest when both HRV and mindfulness were high.

CONCLUSIONS

Together, the findings provide insights into the use of HRV and mindfulness as indexes of psychophysiological regulatory resources that seemingly intensify their respective beneficial effects on the daily well-being of employees.

Heart Rate Information-Based Machine Learning Prediction of Emotions Among Pregnant Women

In this study, the extent to which different emotions of pregnant women can be predicted based on heart rate-relevant information as indicators of autonomic nervous system functioning was explored using various machine learning algorithms. Nine heart rate-relevant autonomic system indicators, including the coefficient of variation R-R interval (CVRR), standard deviation of all NN intervals (SDNN), and square root of the mean squared differences of successive NN intervals (RMSSD), were measured using a heart rate monitor (MyBeat) and four different emotions including "happy," as a positive emotion and "anxiety," "sad," "frustrated," as negative emotions were self-recorded on a smartphone application, during 1 week starting from 23rd to 32nd weeks of pregnancy from 85 pregnant women. The k-nearest neighbor (k-NN), support vector machine (SVM), logistic regression (LR), random forest (RF), naïve bayes (NB), decision tree (DT), gradient boosting trees (GBT), stochastic gradient descent (SGD), extreme gradient boosting (XGBoost), and artificial neural network (ANN) machine learning methods were applied to predict the four different emotions based on the heart rate-relevant information. To predict four different emotions, RF also showed a modest area under the receiver operating characteristic curve (AUC-ROC) of 0.70. CVRR, RMSSD, SDNN, high frequency (HF), and low frequency (LF) mostly contributed to the predictions. GBT displayed the second highest AUC (0.69). Comprehensive analyses revealed the benefits of the prediction accuracy of the RF and GBT methods and were beneficial to establish models to predict emotions based on autonomic nervous system indicators. The results implicated SDNN, RMSSD, CVRR, LF, and HF as important parameters for the predictions.

Análise da variabilidade da frequência cardíaca em crianças submetidas a jogos eletrônicos

Objetivo: avaliar a função autonômica do coração de crianças sadias em situações de jogos eletrônicos, mediante a análise de Variabilidade da Frequência cardíaca.Métodos: participaram deste estudo 60 crianças sadias, que foram monitoradas por um monitor de frequência cardíaca digital e submetidas ao experimento com o jogo eletrônico. A análise da Variabilidade da Frequência cardíaca foi calculadacom emprego da transformada Wavelet Contínua.Resultados: pode-se observar um aumento na intensidade dos valores de baixa frequência/alta frequência, sugerindo influência das fases do protocolo, de modo que houve uma elevação nos valores da fase de Repouso para a fase de Jogo, mas não foi encontrado um valor significativo. Entre as fases de Repouso (1,52±0,97 ms²) e Recuperação (1,89±1,04 ms²) houve um aumento significativo obtendo um valor de p=0,003. Comparando os valores de baixa frequência/alta frequência entre as fases Jogo 2,37±1,20 ms² e Recuperação 1,89±1,04 ms², verificou-se uma redução significativa da relação (p = 0,016).Conclusão: conclui-se que Jogos eletrônicos podem provocar um aumento da atividade simpática, diminuindo a Variabilidade da Frequência cardíaca das crianças estudadas, sugerindo uma situação estressante.

Pattern Recognition of Cognitive Load Using EEG and ECG Signals

The matching of cognitive load and working memory is the key for effective learning, and cognitive effort in the learning process has nervous responses which can be quantified in various physiological parameters. Therefore, it is meaningful to explore automatic cognitive load pattern recognition by using physiological measures. Firstly, this work extracted 33 commonly used physiological features to quantify autonomic and central nervous activities. Secondly, we selected a critical feature subset for cognitive load recognition by sequential backward selection and particle swarm optimization algorithms. Finally, pattern recognition models of cognitive load conditions were constructed by a performance comparison of several classifiers. We grouped the samples in an open dataset to form two binary classification problems: (1) cognitive load state vs. baseline state; (2) cognitive load mismatching state vs. cognitive load matching state. The decision tree classifier obtained 96.3% accuracy for the cognitive load vs. baseline classification, and the support vector machine obtained 97.2% accuracy for the cognitive load mismatching vs. cognitive load matching classification. The cognitive load and baseline states are distinguishable in the level of active state of mind and three activity features of the autonomic nervous system. The cognitive load mismatching and matching states are distinguishable in the level of active state of mind and two activity features of the autonomic nervous system.

Respiratory Rhythm, Autonomic Modulation, and the Spectrum of Emotions: The Future of Emotion Recognition and Modulation

Pulmonary ventilation and respiration are considered to be primarily involved in oxygenation of blood for oxygen delivery to cells throughout the body for metabolic purposes. Other pulmonary physiological observations, such as respiratory sinus arrhythmia, Hering Brewer reflex, cardiorespiratory synchronization, and the heart rate variability (HRV) relationship with breathing rhythm, lack complete explanations of physiological/functional significance. The spectrum of waveforms of breathing activity correlate to anxiety, depression, anger, stress, and other positive and negative emotions. Respiratory pattern has been thought not only to be influenced by emotion but to itself influence emotion in a bi-directional relationship between the body and the mind. In order to show how filling in gaps in understanding could lead to certain future developments in mind-body medicine, biofeedback, and personal health monitoring, we review and discuss empirical work and tracings to express the vital role of bodily rhythms in influencing emotion, autonomic nervous system activity, and even general neural activity. Future developments in measurement and psychophysiological understanding of the pattern of breathing in combination with other parameters such as HRV, cardiorespiratory synchronization, and skin conductivity may allow for biometric monitoring systems to one day accurately predict affective state and even affective disorders such as anxiety. Better affective prediction based on recent research when incorporated into personal health monitoring devices could greatly improve public mental health by providing at-home biofeedback for greater understanding of one's mental state and for mind-body affective treatments such as breathing exercises.

Subjective Evaluation of Performance in a Collaborative Task Is Better Predicted From Autonomic Response Than From True Achievements

Whereas the fundamental role of the body in social cognition seems to be generally accepted, elucidating the bodily mechanisms associated with non-verbal communication and cooperation between two or more persons is still a challenging endeavor. In this article we propose a fresh approach for investigating the function of the autonomic nervous system that is reflected in parameters of heart rate variability, respiration, and electrodermal activity in a social setting. We analyzed autonomic parameters of dyads solving a target-tracking task together with the partner or individually. A machine classifier was trained to predict the subjects' rating of performance and collaboration either from tracking error data or from the set of autonomic parameters. When subjects collaborated, this classifier could predict the subjective performance ratings better from the autonomic response than from the objective performance of the subjects. However, when they solved the task individually, predictability from autonomic parameters dropped to the level of objective performance, indicating that subjects were more rational in rating their performance in this condition. Moreover, the model captured general knowledge about the population that allows it to predict the performance ratings of an unseen subject significantly better than chance. Our results suggest that, in particular in situations that require collaboration with others, evaluation of performance is shaped by the bodily processes that are quantified by autonomic parameters. Therefore, subjective performance assessments appear to be modulated not only by the output of a rational or discriminative system that tracks the objective performance but to a significant extent also by interoceptive processes.

Human Emotion Recognition: Review of Sensors and Methods

Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or human-robot interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems.