Differences of Heart Rate Variability Between Happiness and Sadness Emotion States: A Pilot Study

Autonomic Responses Associated with Olfactory Preferences of Fragrance Consumers: Skin Conductance, Respiration, and Heart Rate

Assessing the olfactory preferences of consumers is an important aspect of fragrance product development and marketing. With the advancement of wearable device technologies, physiological signals hold great potential for evaluating olfactory preferences. However, there is currently a lack of relevant studies and specific explanatory procedures for preference assessment methods that are based on physiological signals. In response to this gap, a synchronous data acquisition system was established using the ErgoLAB multi-channel physiology instrument and olfactory experience tester. Thirty-three participants were recruited for the olfactory preference experiments, and three types of autonomic response data (skin conductance, respiration, and heart rate) were collected. The results of both individual and overall analyses indicated that olfactory preferences can lead to changes in skin conductance (SC), respiration (RESP), and heart rate (HR). The trends of change in both RESP and HR showed significant differences (with the HR being more easily distinguishable), while the SC did not exhibit significant differences across different olfactory perception preferences. Additionally, gender differences did not result in significant variations. Therefore, HR is more suitable for evaluating olfactory perception preferences, followed by RESP, while SC shows the least effect. Moreover, a logistic regression model with a high accuracy (84.1%) in predicting olfactory perception preferences was developed using the changes in the RESP and HR features. This study has significant implications for advancing the assessment of consumer olfactory preferences.

Affective Implications of Human-Animal Relationship on Pig Welfare: Integrating Non-Linear Heart Rate Variability Measures

The human-animal relationship is crucial for animal welfare. Gentle handling enhances pigs' comfort while rough handling causes fear and stress. This study examined how different human-animal relationship qualities affect the behavior and heart rate variability (linear and non-linear parameters) of 36 nursery pigs. Over six weeks, pigs experienced positive (n = 12), minimal (n = 12), or negative (n = 12) human handling. Their responses to handlers were then assessed in an experimental arena with four phases: habituation, exposure to the handler standing and sitting, and forced interaction. Pigs subjected to negative handling exhibited increased fear-related behaviors, spending less time in contact with the handler. They also exhibited heightened stress responses, with greater LF/HF ratio and Lmean values compared with positively handled pigs. Conversely, gently handled pigs displayed affiliative behaviors, accepting more strokes, and higher parasympathetic activation, indicated by greater RMSSD/SDNN and SampEn values, suggesting a more positive affective state. Minimally handled pigs exhibited some behavioral similarities to gently handled pigs, although physiological data indicated that the interaction was likely more rewarding for the gently handled pigs. These results emphasize the impact of human-animal relationships on pig welfare and highlight the value of incorporating non-linear heart rate variability parameters in such evaluations.

Vagal predominance correlates with mood state changes of winter-over expeditioners during prolonged Antarctic residence

OBJECTIVE

Winter-over expeditioners in Antarctica are challenged by various environmental and psycho-social stress factors, which may induce psychophysiological changes. The autonomic nervous system (ANS) plays a crucial role in the adaptation process under stress. However, the relationship between ANS activity and the mood states of expeditioners remains largely unexplored. This study aims to uncover the pattern of ANS adjustment under extreme Antarctic environments and provide new insights into the correlations between ANS activity and mood state changes, which may provide scientific data for medical interventions.

METHODS

Fourteen expeditioners at Zhongshan Station participated in this study. The study was conducted during four representative periods: pre-Antarctica, Antarctica-1 (pre-winter), Antarctica-2 (winter), and Antarctica-3 (summer). The heart rate variability (HRV) of the expeditioners was continuously measured for 24 hours to evaluate ANS activity. Plasma levels of catecholamines were tested by ELISA. Mood states were assessed by the Profile of Mood States (POMS) scale.

RESULTS

HRV analysis showed a disturbance of ANS during winter and summer periods. For frequency domain parameters, very low frequency (VLF), low frequency (LF), high frequency (HF), and total power (TP) significantly increased during the second half of the mission. Especially, LF/HF ratio decreased during summer, indicating the predominance of vagal tone. Results of the time domain analysis showed increased heart rate variability during the austral winter and summer. Plasma epinephrine (E) significantly increased during residence in Antarctica. Compared with pre-Antarctica, the vigor, depression, and anger scores of the expeditioners decreased significantly during the austral summer. Notably, the depression score showed a moderate positive correlation with LF/HF, while weak negative correlations with other HRV indicators, including TP, VLF, and LF. Anger score showed a moderate positive correlation with LF/HF and weak negative correlations with the average normal-to-normal (NN) interval, and the root mean square of differences between adjacent RR intervals (RMSSD). Plasma E level weakly correlated with the average NN interval.

CONCLUSION

Prolonged residence in Antarctica increased the ANS activities and shifted the cardiac autonomic modulation towards vagal predominance. The alteration of HRV correlated with mood states and plasma epinephrine levels.

Your blush gives you away: detecting hidden mental states with remote photoplethysmography and thermal imaging

Multimodal emotion recognition techniques are increasingly essential for assessing mental states. Image-based methods, however, tend to focus predominantly on overt visual cues and often overlook subtler mental state changes. Psychophysiological research has demonstrated that heart rate (HR) and skin temperature are effective in detecting autonomic nervous system (ANS) activities, thereby revealing these subtle changes. However, traditional HR tools are generally more costly and less portable, while skin temperature analysis usually necessitates extensive manual processing. Advances in remote photoplethysmography (r-PPG) and automatic thermal region of interest (ROI) detection algorithms have been developed to address these issues, yet their accuracy in practical applications remains limited. This study aims to bridge this gap by integrating r-PPG with thermal imaging to enhance prediction performance. Ninety participants completed a 20-min questionnaire to induce cognitive stress, followed by watching a film aimed at eliciting moral elevation. The results demonstrate that the combination of r-PPG and thermal imaging effectively detects emotional shifts. Using r-PPG alone, the prediction accuracy was 77% for cognitive stress and 61% for moral elevation, as determined by a support vector machine (SVM). Thermal imaging alone achieved 79% accuracy for cognitive stress and 78% for moral elevation, utilizing a random forest (RF) algorithm. An early fusion strategy of these modalities significantly improved accuracies, achieving 87% for cognitive stress and 83% for moral elevation using RF. Further analysis, which utilized statistical metrics and explainable machine learning methods including SHapley Additive exPlanations (SHAP), highlighted key features and clarified the relationship between cardiac responses and facial temperature variations. Notably, it was observed that cardiovascular features derived from r-PPG models had a more pronounced influence in data fusion, despite thermal imaging's higher predictive accuracy in unimodal analysis.

Heart rate variability (HRV) as a way to understand associations between the autonomic nervous system (ANS) and affective states: A critical review of the literature

Evidence suggests affective disorders such as depression and bipolar disorder are characterised by dysregulated autonomic nervous system (ANS) activity. These findings suggest ANS dysregulation may be involved in the pathogenesis of affective disorders. Different affective states are characterised by different ANS activity patterns (i.e., an increase or decrease in sympathetic or parasympathetic activity). To understand how ANS abnormalities are involved in the development of affective disorders, it is important to understand how affective states correlate with ANS activity before their onset. Using heart rate variability (HRV) as a tool to measure ANS activity, this review aimed to look at associations between affective states and HRV in non-clinical populations (i.e., in those without medical and psychiatric disorders). Searches on PubMed and Google Scholar were completed using the following search terms: heart rate variability, autonomic nervous system, sympathetic nervous system, parasympathetic nervous system, affective state, mood and emotion in all possible combinations. All but one of the studies examined (N = 13), demonstrated significant associations between affect and HRV. Findings suggest negative affect, encompassing both diffused longer-term experiences (i.e., mood) as well as more focused short-term experiences (i.e., emotions), may be associated with a reduction in parasympathetic activity as measured through HRV parameters known to quantify parasympathetic activity (e.g., high frequency (HF)-HRV). HRV measures typically linked to reduction in parasympathetic activity appear to be linked to negative affective states in non-clinical populations. However, given the complex and possibly non-linear relationship between HRV and parasympathetic activity, further studies need to clarify specificity of these findings. Future studies should investigate the potential utility of HRV measures as biomarkers for monitoring changes in affective states and for early detection of onset and relapse of depression in patients with affective disorders.

Quantifying time perception during virtual reality gameplay using a multimodal biosensor-instrumented headset: a feasibility study

We have all experienced the sense of time slowing down when we are bored or speeding up when we are focused, engaged, or excited about a task. In virtual reality (VR), perception of time can be a key aspect related to flow, immersion, engagement, and ultimately, to overall quality of experience. While several studies have explored changes in time perception using questionnaires, limited studies have attempted to characterize them objectively. In this paper, we propose the use of a multimodal biosensor-embedded VR headset capable of measuring electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and head movement data while the user is immersed in a virtual environment. Eight gamers were recruited to play a commercial action game comprised of puzzle-solving tasks and first-person shooting and combat. After gameplay, ratings were given across multiple dimensions, including (1) the perception of time flowing differently than usual and (2) the gamers losing sense of time. Several features were extracted from the biosignals, ranked based on a two-step feature selection procedure, and then mapped to a predicted time perception rating using a Gaussian process regressor. Top features were found to come from the four signal modalities and the two regressors, one for each time perception scale, were shown to achieve results significantly better than chance. An in-depth analysis of the top features is presented with the hope that the insights can be used to inform the design of more engaging and immersive VR experiences.

Physiological Signal Analysis and Stress Classification from VR Simulations Using Decision Tree Methods

Stress is induced in response to any mental, physical or emotional change associated with our daily experiences. While short term stress can be quite beneficial, prolonged stress is detrimental to the heart, muscle tissues and immune system. In order to be proactive against these symptoms, it is important to assess the impact of stress due to various activities, which is initially determined through the change in the sympathetic (SNS) and parasympathetic (PNS) nervous systems. After acquiring physiological data wirelessly through captive electrocardiogram (ECG), galvanic skin response (GSR) and respiration (RESP) sensors, 21 time, frequency, nonlinear, GSR and respiration features were manually extracted from 15 subjects ensuing a baseline phase, virtual reality (VR) roller coaster simulation, color Stroop task and VR Bubble Bloom game. This paper presents a comprehensive physiological analysis of stress from an experiment involving a VR video game Bubble Bloom to manage stress levels. A personalized classification and regression tree (CART) model was developed using a novel Gini index algorithm in order to effectively classify binary classes of stress. A novel K-means feature was derived from 11 other features and used as an input in the Decision Tree (DT) algorithm, strong learners Ensemble Gradient Boosting (EGB) and Extreme Gradient Boosting (XGBoost (XGB)) embedded in a pipeline to classify 5 classes of stress. Results obtained indicate that heart rate (HR), approximate entropy (ApEN), low frequency and high frequency ratio (LF/HF), low frequency (LF), standard deviation (SD1), GSR and RESP all reduced and high frequency (HF) increased following the VR Bubble Bloom game phase. The personalized CART model was able to classify binary stress with 87.75% accuracy. It proved to be more effective than other related studies. EGB was able to classify binary stress with 100% accuracy, which outperformed every other related study. XGBoost and DT were able to classify five classes of stress with 72.22% using the novel K-means feature. This feature produced less error and better model performance in comparison to using all the features. Results substantiate that our proposed methods were more effective for stress classification than most related studies.

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.

Designing a neuroclinical assessment of empathy deficits in psychopathy based on the Zipper Model of Empathy

Ho, M.H., Kemp, B.T., Eisenbarth, H. & Rijnders, R.J.P. Designing a neuroclinical assessment of empathy deficits in psychopathy based on the Zipper Model of Empathy. NEUROSCI BIOBEHAV REV YY(Y) XXX-XXX, 2023. The heterogeneity of the literature on empathy highlights its multidimensional and dynamic nature and affects unclear descriptions of empathy in the context of psychopathology. The Zipper Model of Empathy integrates current theories of empathy and proposes that empathy maturity is dependent on whether contextual and personal factors push affective and cognitive processes together or apart. This concept paper therefore proposes a comprehensive battery of physiological and behavioral measures to empirically assess empathy processing according to this model with an application for psychopathic personality. We propose using the following measures to assess each component of this model: (1) facial electromyography; (2) the Emotion Recognition Task; (3) the Empathy Accuracy task and physiological measures (e.g., heart rate); (4) a selection of Theory of Mind tasks and an adapted Dot Perspective Task, and; (5) an adjusted Charity Task. Ultimately, we hope this paper serves as a starting point for discussion and debate on defining and assessing empathy processing, to encourage research to falsify and update this model to improve our understanding of empathy.

Heart Rate Variability's Association with Positive and Negative Affect in Daily Life: An Experience Sampling Study with Continuous Daytime Electrocardiography over Seven Days

Heart rate variability has been found to be related to emotional processing and emotional responses. Studies that investigated these relationships were mostly lab-based or cross-sectional. Only limited research used intensive longitudinal data, in particular investigating within-individual processes in real-life settings. This study addresses the applicability of ambulatory-assessed electrocardiograms in combination with the experience sampling methodology by investigating the associations of various HRV measures with affective states on within- and between-individual levels. A total of 26 participants aged 18-29 years (23 females) wore electrocardiograms continuously for seven days. The participants received seven prompts per day and answered questions about their affective wellbeing. The heart rate and heart rate variability measures differed between body positions and activity classes. The heart rate and ratio of low-to-high-frequency heart rate variability were consistently associated with positive affect on a within-individual (state-like) level. These associations were mainly driven by the items of feeling "enthusiastic" and "happy". No associations were found with negative affect. Overall, we found evidence that the dominance of the sympathetic nervous system over the parasympathetic nervous system was associated with higher levels of positive affect on a within-individual (state-like) level. Suggestions for the application of ambulatory electrocardiogram assessment in the study of the association between autonomous nervous system activity and ecological momentary assessment-based variables are discussed.

A novel physiological feature selection method for emotional stress assessment based on emotional state transition

The connection between emotional states and physical health has attracted widespread attention. The emotional stress assessment can help healthcare professionals figure out the patient's engagement toward the diagnostic plan and optimize the rehabilitation program as feedback. It is of great significance to study the changes of physiological features in the process of emotional change and find out subset of one or several physiological features that can best represent the changes of psychological state in a statistical sense. Previous studies had used the differences in physiological features between discrete emotional states to select feature subsets. However, the emotional state of the human body is continuously changing. The conventional feature selection methods ignored the dynamic process of an individual's emotional stress in real life. Therefore, a dedicated experimental was conducted while three peripheral physiological signals, i.e., ElectroCardioGram (ECG), Galvanic Skin Resistance (GSR), and Blood Volume Pulse (BVP), were continuously acquired. This paper reported a novel feature selection method based on emotional state transition, the experimental results show that the number of physiological features selected by the proposed method in this paper is 13, including 5 features of ECG, 4 features of PPG and 4 features of GSR, respectively, which are superior to PCA method and conventional feature selection method based on discrete emotional states in terms of dimension reduction. The classification results show that the accuracy of the proposed method in emotion recognition based on ECG and PPG is higher than the other two methods. These results suggest that the proposed method can serve as a viable alternative to conventional feature selection methods, and emotional state transition deserves more attention to promote the development of stress assessment.

Individual variation in the neurophysiological representation of negative emotions in virtual reality is shaped by sociability

Negative emotions play a dominant role in daily human life, and mentalizing and empathy are also basic sociability in social life. However, little is known regards the neurophysiological pattern of negative experiences in immersive environments and how people with different sociabilities respond to the negative emotional stimuli at behavioral and neural levels. The present study investigated the neurophysiological representation of negative affective experiences and whether such variations are associated with one's sociability. To address this question, we examined four types of negative emotions that frequently occurred in real life: angry, anxious, fearful, and helpless. We combined naturalistic neuroimaging under virtual reality, multimodal neurophysiological recording, and behavioral measures. Inter-subject representational similarity analysis was conducted to capture the individual differences in the neurophysiological representations of negative emotional experiences. The behavioral and neurophysiological indices revealed that although the emotion ratings were uniquely different, a similar electroencephalography response pattern across these negative emotions was found over the parieto-occipital electrodes. Furthermore, the neurophysiological representations indeed reflected interpersonal variations regarding mentalizing and empathic abilities. Our findings yielded a common pattern of neurophysiological responses toward different negative affective experiences in VR. Moreover, the current results indicate the potential of taking a sociability perspective for understanding the interpersonal variations in the neurophysiological representation of emotion.

Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach

Extensive possibilities of applications have rendered emotion recognition ineluctable and challenging in the fields of computer science as well as in human-machine interaction and affective computing. Fields that, in turn, are increasingly requiring real-time applications or interactions in everyday life scenarios. However, while extremely desirable, an accurate and automated emotion classification approach remains a challenging issue. To this end, this study presents an automated emotion recognition model based on easily accessible physiological signals and deep learning (DL) approaches. As a DL algorithm, a Feedforward Neural Network was employed in this study. The network outcome was further compared with canonical machine learning algorithms such as random forest (RF). The developed DL model relied on the combined use of wearables and contactless technologies, such as thermal infrared imaging. Such a model is able to classify the emotional state into four classes, derived from the linear combination of valence and arousal (referring to the circumplex model of affect’s four-quadrant structure) with an overall accuracy of 70% outperforming the 66% accuracy reached by the RF model. Considering the ecological and agile nature of the technique used the proposed model could lead to innovative applications in the affective computing field.

A psychophysiological investigation of mourning: There are two sides to the story

Mourning constitutes an important human emotion, which might cause—among other things—major depressive symptoms when lasting for too long. To date, no study investigated whether mourning is related to specific psychophysiological activation patterns. Therefore, we examined physiological reactions induced by iconographic mourning-related stimuli in comparison to neutral and attachment stimuli in healthy adults (N = 77, mean age: 21.9). We evaluated pupillometric and eye-tracking parameters as well as heart rate variability (HRV) and skin conductance (EDA). Eye-tracking revealed a stronger dilated pupil during mourning in comparison to the neutral, but not to the attachment condition; furthermore, fixation patterns revealed less fixations on mourning stimuli. While HF HRV was reduced during mourning and attachment, we found no differences concerning EDA parameters between conditions. Results suggest specific eye-movement and pupil adaptations during representations of mourning, which might point toward inward cognition or avoidance, but no specific physiological pattern concerning HRV and EDA.

Trends in Heart-Rate Variability Signal Analysis

Heart rate variability (HRV) is the rate of variability between each heartbeat with respect to time. It is used to analyse the Autonomic Nervous System (ANS), a control system used to modulate the body's unconscious action such as cardiac function, respiration, digestion, blood pressure, urination, and dilation/constriction of the pupil. This review article presents a summary and analysis of various research works that analyzed HRV associated with morbidity, pain, drowsiness, stress and exercise through signal processing and machine learning methods. The points of emphasis with regards to HRV research as well as the gaps associated with processes which can be improved to enhance the quality of the research have been discussed meticulously. Restricting the physiological signals to Electrocardiogram (ECG), Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) analysis resulted in 25 articles which examined the cause and effect of increased/reduced HRV. Reduced HRV was generally associated with increased morbidity and stress. High HRV normally indicated good health, and in some instances, it could signify clinical events of interest such as drowsiness. Effective analysis of HRV during ambulatory and motion situations such as exercise, video gaming, and driving could have a significant impact toward improving social well-being. Detection of HRV in motion is far from perfect, situations involving exercise or driving reported accuracy as high as 85% and as low as 59%. HRV detection in motion can be improved further by harnessing the advancements in machine learning techniques.

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.

fNIRS-based functional connectivity estimation using semi-metric analysis to study decision making by nursing students and registered nurses

This study aims to investigate the generalizability of the semi-metric analysis of the functional connectivity (FC) for functional near-infrared spectroscopy (fNIRS) by applying it to detect the dichotomy in differential FC under affective and neutral emotional states in nursing students and registered nurses during decision making. The proposed method employs wavelet transform coherence to construct FC networks and explores semi-metric analysis to extract network redundancy features, which has not been considered in conventional fNIRS-based FC analyses. The trials of the proposed method were performed on 19 nursing students and 19 registered nurses via a decision-making task under different emotional states induced by affective and neutral emotional stimuli. The cognitive activities were recorded using fNIRS, and the emotional stimuli were adopted from the International Affective Digitized Sound System (IADS). The induction of emotional effects was validated by heart rate variability (HRV) analysis. The experimental results by the proposed method showed significant difference (FDR-adjusted p = 0.004) in the nursing students’ cognitive FC network under the two different emotional conditions, and the semi-metric percentage (SMP) of the right prefrontal cortex (PFC) was found to be significantly higher than the left PFC (FDR-adjusted p = 0.036). The benchmark method (a typical weighted graph theory analysis) gave no significant results. In essence, the results support that the semi-metric analysis can be generalized and extended to fNIRS-based functional connectivity estimation.

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.

Heart rate variability monitoring for emotion and disorders of emotion

BACKGROUND

Emotion is composed of cognitive processing, physiological response and behavioral reaction. Heart rate variability (HRV) refers to the fluctuations between consecutive heartbeat cycles, and is considered as a non-invasive method for evaluating cardiac autonomic function. HRV analysis plays an important role in emotional study and detection.

OBJECTIVE

In this paper, the physiological foundation of HRV is briefly described, and then the relevant literature relating to HRV-based emotion studies for the performance of HRV in different emotions, emotion recognition, the evaluation of emotional disorders, HRV biofeedback, as well as HRV-based emotion analysis and management enhanced by wearable devices, are reviewed.

SIGNIFICANCE

It is suggested that HRV is an effective tool for the measurement and regulation of emotional response, with a broad application prospect.

Measures of CNS-Autonomic Interaction and Responsiveness in Disorder of Consciousness

Neuroimaging studies have demonstrated functional interactions between autonomic (ANS) and brain (CNS) structures involved in higher brain functions, including attention and conscious processes. These interactions have been described by the Central Autonomic Network (CAN), a concept model based on the brain-heart two-way integrated interaction. Heart rate variability (HRV) measures proved reliable as non-invasive descriptors of the ANS-CNS function setup and are thought to reflect higher brain functions. Autonomic function, ANS-mediated responsiveness and the ANS-CNS interaction qualify as possible independent indicators for clinical functional assessment and prognosis in Disorders of Consciousness (DoC). HRV has proved helpful to investigate residual responsiveness in DoC and predict clinical recovery. Variability due to internal (e.g., homeostatic and circadian processes) and environmental factors remains a key independent variable and systematic research with this regard is warranted. The interest in bidirectional ANS-CNS interactions in a variety of physiopathological conditions is growing, however, these interactions have not been extensively investigated in DoC. In this brief review we illustrate the potentiality of brain-heart investigation by means of HRV analysis in assessing patients with DoC. The authors' opinion is that this easy, inexpensive and non-invasive approach may provide useful information in the clinical assessment of this challenging patient population.

Heart sound signals can be used for emotion recognition

This article studies whether heart sound signals can be used for emotion recognition. First, we built a small emotion heart sound database, and simultaneously recorded the participants’ ECG for comparative analysis. Second, according to the characteristics of the heart sound signals, two emotion evaluation indicators were proposed: HRV of heart sounds (difference between successive heartbeats) and DSV of heart sounds (the ratio of diastolic to systolic duration variability). Then, we extracted linear and nonlinear features from two emotion evaluation indicators to recognize four kinds of emotions. Moreover, we used valence dimension, arousal dimension and valence-arousal synthesis as evaluation standards. The experimental results demonstrated that heart sound signals can be used for emotion recognition. It was more effective to achieve recognition results by combining the features of HRV and DSV of heart sounds. Finally, the average accuracy of four emotion recognitions on valence dimension, arousal dimension and valence-arousal synthesis was up to 96.875%, 88.5417% and 81.25%, respectively.

Cardiorespiratory Coupling Analysis Based on Entropy and Cross-Entropy in Distinguishing Different Depression Stages

Aims

This study used entropy- and cross entropy-based methods to explore the cardiorespiratory coupling of depressive patients, and thus to assess the values of those entropy methods for identifying depression patients with different disease severities.

Methods

Electrocardiogram (ECG) and respiration signals from 69 depression patients were recorded simultaneously for 5 min. Patients were classified into three groups according to the Hamilton Depression Rating Scale (HDRS) scores: group Non-De (HDRS 0–7), Mid-De (HDRS 8–17), and Con-De (HDRS >17). Sample entropy (SEn), fuzzy measure entropy (FMEn) and high-frequency power (HF) were computed on the original RR interval time series and breath-to-breath interval time series. Cross sample entropy (CSEn) and cross fuzzy measure entropy (CFMEn) were computed on interval time series resampled at 2 Hz and 4 Hz, respectively. The difference among three patient groups and correlation between entropy values and HDRS scores were analyzed by statistical analysis. Surrogate data were also employed to confirm the validation of entropy measures in this study.

Results

A consistent increasing trend has been found among most entropy measures from Non-De, to Mid-De, and to Con-De groups, and a significant (p < 0.05) difference in FMEn of RR intervals exists between Non-De and Mid-De or Con-De groups. Significant differences have been also found in all cross entropies, between Non-De and Con-De groups and between Mid-De and Con-De groups. Furthermore, significant correlations also exist between HDRS scores and FMEn of RR intervals (R = 0.24, p < 0.05), CSEn at 4 Hz (R = 0.26, p < 0.05) or 2 Hz (R = 0.28, p < 0.05) resampling, and CFMEn at 4 Hz (R = 0.31, p < 0.01) or 2 Hz (R = 0.30, p < 0.05) resampling. A significant difference of cardiorespiratory coupling parameters between different depression stages and significant correlations between entropy measures and depression severity both indicate central autonomic dysregulation in depression patients and reflect varying degrees of vagal modulation reduction among different depression levels. Analysis based on surrogate data confirms that the non-linear properties of the physiological signals played a major role in depression recognition.

Conclusion

The current study demonstrates the potential of cardiorespiratory coupling in the auxiliary diagnosis of depression based on the entropy method.

Detection of patients with methamphetamine dependence with cue-elicited heart rate variability in a virtual social environment

In the present study, we developed a methamphetamine (METH)-related virtual social environment to elicit subjective craving and physiological reactivity. Sixty-one male patients who were abstinent from METH use and 45 age-matched healthy males (i.e., normal controls) were recruited. The physiological electrocardiogram (ECG) signals were recorded before (resting-state condition) and during viewing of a METH-cue video in the virtual environment (cue-induced condition). The cue-induced subjective craving was measured with a visual analogue scale (VAS) for patients with METH dependence. The results indicated that the cue-induced condition elicited significant differences in heart rate variability (HRV) between patients with METH dependence and normal controls. The changes of HRV indexes on time domain and non-linear domain from the resting-state condition to the cue-induced condition were positively correlated with the score on VAS of METH craving. Using a supervised machine learning algorithm with the features extracted from HRV changes, our results showed that the discriminant model provided a high predictive power for distinguishing patients with METH dependence from normal controls. Our findings support that immersing subjects with METH dependence in a METH-related virtual social environment can successfully induce physiological reactivity, and cue-induced physiological signal changes may have a potential implication in clinical practice.