Heart Rate Variability Metrics Research Articles

Acute exercise impacts heart rate variability but not cognitive flexibility during subsequent simulated firefighter occupational tasks.

Acute exercise can transiently enhance cognitive flexibility. The cognitive demand of firefighters makes it relevant to understand if on-shift exercise could produce similar improvements in cognitive performance during subsequent occupational tasks. Metrics of heart rate variability (HRV), such as time- and frequency-domain outcomes, may shed light upon the influence exercise has on cognition, as they discern information related to cardiac autonomic (sympathetic/parasympathetic) function. We aimed to determine if acute resistance and aerobic exercise impact cognitive flexibility during occupational tasks and its relation to HRV. 32 participants completed a baseline Wisconsin Card Sorting Task (WCST) and three experimental trials: resistance exercise (RE), aerobic exercise (AE), or a rested control (CON). An occupational task assessment (OTA) including four rounds of 10 deadlifts and a 0.15-mile sandbag carry in an environmental chamber (35°C/50% humidity) was completed after each trial. The second round was followed by the WCST. Repeated measures ANOVAs were used to analyze differences by condition. For the WCST, total, perseverative, and non-perseverative errors did not differ (ps > 0.39). Time-domain HRV metrics were not different (ps > 0.05). All frequency-domain metrics, other than low-frequency power, were not different (ps > 0.24). Low-frequency power was lower based on condition (p = 0.03). Post hoc analysis showed low-frequency power was lower following AE compared to RE and CON. Results suggest an acute bout of on-shift aerobic or resistance exercise may not impact cognitive flexibility during subsequent simulated occupational tasks, despite depressed metrics of heart rate variability following aerobic exercise.

Is Greener Better? Quantifying the Impact of a Nature Walk on Stress Reduction Using HRV and Saliva Cortisol Biomarkers

The physiological impact of walking in nature was quantified via continuous heart rate variability (HRV), pre- and post-walk saliva cortisol measures, and self-reported mood and mindfulness scores for N = 17 participants who walked “The Green Road” at Walter Reed National Military Medical Center in Bethesda, Maryland. For N = 15 of the participants, HRV analysis revealed two main groups: group one individuals had a 104% increase (mean) in the root mean square standard deviation (RMSSD) and a 47% increase (mean) in the standard deviation of NN values (SDNN), indicating an overall reduction in physiological stress from walking the Green Road, and group two individuals had a decrease (mean) of 42% and 31% in these respective HRV metrics, signaling an increase in physiological stresses. Post-walk self-reported scores for vigor and mood disturbance were more robust for the Green Road than for a comparable urban road corridor and showed that a higher HRV during the walk was associated with improved overall mood. Saliva cortisol was lower after taking a walk for all participants, and it showed that walking the Green Road elicited a significantly larger reduction in cortisol of 53%, on average, when compared with 37% of walking along an urban road. It was also observed that the order in which individuals walked the Green Road and urban road also impacted their cortisol responses, with those walking the urban road before the Green Road showing a substantial reduction in cortisol, suggesting a possible attenuation effect of walking the Green Road first. These findings provide quantitative data demonstrating the stress-reducing effects of being in nature, thus supporting the health benefit value of providing access to nature more broadly in many settings.

Heart Rate Variability Metrics during Sleep in Children with Asperger’s Syndrome

Heart Rate Variability Metrics during Sleep in Children with Asperger’s Syndrome

Implication of heart rhythm complexity in predicting long-term outcomes in pulmonary hypertension

Pulmonary hypertension (PH) is a serious disease, however simple tools to predict outcomes are lacking. In our previous investigation, we found that heart rate variability (HRV) and heart rhythm complexity (HRC) were associated with the detection and severity of PH, however their association with PH mortality remains unclear.The aim of this study was to investigate these metrics as a tool for determining long-term outcomes in PH patients. We enrolled 74 Asian PH patients with WHO PH group 1 or 4 at a single hospital in Taiwan between March 2012 and June 2018. After a median follow-up duration of 58 months (to January 2023), 22 patients had died. The patients who died had a significantly lower lean body mass index (BMI), impaired renal function, higher N-terminal pro B-type natriuretic peptide (NT-proBNP) level, lower very low-frequency (VLF), lower short-term detrended fluctuation analysis α1 (DFAα1), and lower multiscale entropy scale 5 value. In multivariable analysis, BMI, VLF and multiscale entropy scale 5 were significantly associated with survival. The best cut-off VLF and scale 5 values were 115.13 and 0.738, respectively. We then categorized the study population into three groups: both elevated VLF/scale 5 (group 1), either depressed VLF or depressed scale 5 (group 2), and both depressed VLF/scale 5 (group 3). The results showed that group 1 had the best outcomes, whereas group 3 had the worst survival (P < 0.001).Combining HRV and HRC metrics appears to be a good non-invasive tool to predict the long-term outcomes of patients with PH.

Heart Rate Variability and Cerebral Autoregulation in Patients with Traumatic Brain Injury with Paroxysmal Sympathetic Hyperactivity Syndrome.

Severe traumatic brain injury (TBI) can lead to transient changes in autonomic nervous system (ANS) functioning and development of paroxysmal sympathetic hyperactivity (PSH) syndrome. Clinical manifestation of ANS disorders may be obscured by therapeutic interventions in TBI. This study aims to analyze ANS metrics and cerebral autoregulation in patients with PSH syndrome to determine their significance in early prognostication. This single-center retrospective study investigated the relationship between changes in ANS metrics, cerebral autoregulation, and PSH syndrome. Arterial blood pressure and intracranial pressure signals were monitored for 5 days post TBI. ANS metrics included time and frequency domain heart rate variability (HRV) metrics. Cerebral autoregulation was assessed using the pressure reactivity index. Sixty-six patients with severe TBI (median age 33 [interquartile range 26-50] years) were analyzed, and PSH was confirmed in nine cases. Impairment of cerebral autoregulation was observed in 67% of patients with PSH and 72% without the syndrome. Patients with PSH had higher HRV in the low-frequency range (LF; 253 ± 178 vs. 176 ± 227 ms2; p = 0.035) and lower heart rates (HRs; 70 ± 7 vs. 78 ± 19 bpm; p = 0.027) compared to those without PSH. A receiver operating characteristic curve analysis indicated that HR (area under the curve(AUC) = 0.73, p = 0.006) and HRV in the LF (AUC = 0.70, p = 0.009) are moderate predictors of PSH. In the multiple logistic regression model for PSH, diffuse axonal trauma (odds ratio(OR) = 10.82, 95% confidence interval(CI) = 1.70-68.98, p = 0.012) and HR (OR =0.91, 95% CI 0.84-0.98, p = 0.021) were significant factors. Elevated HRV in the LF and decreased HR may serve as early predictors of PSH syndrome development, particularly in patients with diffuse axonal trauma. Further research is needed to investigate the utility of the cerebral autoregulation-ANS relationship in PSH prognostication.

Fusing YOLOv5s-MediaPipe-HRV to classify engagement in E-learning: From the perspective of external observations and internal factors

The rapid advancements in computer vision technology present significant potential for the automatic recognition of learner engagement in E-learning. We conducted a two-stage experiment to assess learner engagement based on behavioural (external observations) and physiological (internal factors) cues. Using computer vision technology and wearable sensors, we extracted three feature sets: action, head posture and heart rate variability (HRV). Subsequently, we integrated our constructed YOLOv5s–MediaPipe behaviour detection model with a physiological detection model based on HRV to comprehensively evaluate learners’ behavioural, affective and cognitive engagement. Additionally, we developed a method and criteria for assessing distraction based on behaviour, ultimately creating a comprehensive, efficient, low-cost and easy-to-use system for the automatic recognition of learner engagement. Experimental results showed that our improved YOLOv5s model achieved a mean average precision of 92.2 %, while halving both the number of parameters and model size. Unlike other deep learning-based methods, using MediaPipe–OpenCV for head posture analysis offers advantages in real-time performance, making it lightweight and easy to deploy. Our proposed long short-term memory classifier, based on sensitive HRV metrics and their normalisation, demonstrated satisfactory performance on the test set, with an accuracy = 80 %, precision = 81 %, recall = 80 % and an F1 score = 80 %.

The Effects of a Single Vagus Nerve's Neurodynamics on Heart Rate Variability in Chronic Stress: A Randomized Controlled Trial.

The modulation of the autonomic nervous system's activity, particularly increasing its parasympathetic tone, is of significant interest in clinical physiotherapy due to its potential benefits for stress-related conditions and recovery processes. This study evaluated the effectiveness of the addition of neurodynamics in enhancing parasympathetic activation in subjects with chronic stress. A clinical trial randomly assigned participants to a group with neurodynamics (6 bpm breathing protocol + manual therapy + neurodynamic technique) or a group without neurodynamics (6 bpm breathing protocol + manual therapy only). Metrics of heart rate variability (HRV), including the Mean Heart Rate (Mean HR), standard deviation of intervals between consecutive heartbeats (SDNN), Heart Rate Difference (Diff. HR), Root Mean Square of Successive Differences (RMSSD), number of intervals differing by more than 50 ms (NN50), percentage of consecutive NN intervals that differed by more than 50 ms (pNN50), and the high-frequency component measured in standardized units (HF), were assessed before, during, and after the intervention. During the intervention, the group with neurodynamics showed significant changes in all variables except in the pNN50 and HF while the group without neurodynamics only showed improvements in the Mean HR, SDNN, and RMSSD. In the post-intervention phase, the group with neurodynamics maintained an increase in HRV while the group without neurodynamics experienced a decrease, suggesting an increase in sympathetic activity. Vagal nerve neurodynamics appear to represent an effective method for enhancing parasympathetic activation in patients with chronic stress. The results highlight the importance of a more comprehensive analysis of HRV variables in order to obtain a correct picture of the impact of interventions on the complex and multifaceted functioning of the autonomic nervous system.

The consequences of cardiac autonomic nervous system modulation during pulmonary vein isolation: A review.

Pulmonary vein isolation (PVI) is a well-established treatment modality for atrial fibrillation (AF). Apart from the desired effect regarding the arrhythmic substrate within the left atrium, PVI commonly leads to modulation of the intrinsic cardiac autonomic nervous system (ICANS). Using the available literature, this article presents the anatomy of ICANS and describes methods of assessing its function, mainly focusing on heart rate (HR) variability metrics. Then, we summarize the modern pathophysiological outlooks on the onset and recurrence of AF and explain how the arrhythmia and the activation of ICANS are intertwined. Further, the article discusses the extent, dynamics and persistence of ICANS modulation during PVI, accounting for various modalities and procedural strategies. Both the potential benefits and pitfalls of such modulation are explored, considering AF recurrence, HR and HR variability changes, as well as the unclear effect on ventricular arrhythmias and nerve remodeling. Finally, the article aims to outline further directions of research necessary to improve our understanding of ICANS and its modulation.

Hardware-accelerated neural network model for early prediction of sudden cardiac arrest based on heart rate variability metrics

AbstractSudden Cardiac Arrest (SCA) constitutes a dire medical condition, marked by the abrupt cessation of effective blood circulation due to the heart's failure to contract properly. This leads to acute circulatory collapse, often culminating in loss of consciousness within an hour and potentially resulting in fatality within minutes if left unattended. Heart rate variability (HRV) serves as a critical biometric, derived from electrocardiogram (ECG) signals with ventricular depolarization waves to further calculate the R-R Intervals (RRIs). These intervals provide the basis for extracting various characteristics of cardiac rhythm, encompassing time-domain, frequency-domain, and nonlinear features. This study presents a neural network-based classification algorithm that leverages HRV metrics to categorize patients into SCA and Normal Sinus Rhythm (NSR) cohorts. The proposed neural network (NN) model showcased impressive results, achieving an accuracy of 96.23%, a sensitivity of 94.35%, and a specificity of 98.12% in detecting SCA, as evaluated through leave-one-subject-out analysis. In order to harness the benefits of hardware acceleration, the algorithm is implemented on a Field-Programmable Gate Array (FPGA). Its computational efficiency is subsequently benchmarked against traditional software-based methodologies. The hardware-level implementation is made possible in Verilog hardware description language (HDL) and was verified successfully with expected performance by register-transfer level (RTL) simulation via Vivado 2020.2.

Hypertension and its correlation with autonomic nervous system dysfunction, heart rate variability and chronic inflammation

Objective This study investigates the relationship between hypertension, dysregulation of the autonomic nervous system, heart rate variability (HRV), and chronic inflammation. Methods We analysed a cohort of 50 hypertensive patients treated at the affiliated Hospital of Jianghan University. The average systolic and diastolic blood pressures (BPs) in this group were 155.26 and 95.32 mmHg, respectively. A control group of 50 healthy volunteers, undergoing routine physical examinations at the same hospital, was also analysed. Results The average systolic BP of the control group was 115.64 ± 10.27 mmHg, and the average diastolic BP was 75.33 ± 8.25 mmHg. In contrast, the experimental group exhibited an average systolic BP of 155.26 ± 20.13 mmHg and an average diastolic BP of 95.32 ± 12.16 mmHg. Both systolic and diastolic BPs were significantly higher in the hypertensive group (p < 0.05). The experimental group also demonstrated reduced HRV and skin conductance response, alongside increased BP variability (BPV), urinary epinephrine levels and prolonged pupillary light reaction time compared to controls (p < 0.05). Notably, Standard Deviation of Normal to Normal Intervals (SDNN) and Root Mean Square of Successive Differences (RMSSD) values were significantly lower in the experimental group (p < 0.05). Furthermore, levels of inflammatory markers such as CRP, TNF-α, IL-6 and IL-1β were markedly elevated in hypertensive patients (p < 0.05). Negative correlations were observed between systolic and diastolic BP with HRV metrics, while positive correlations were found between BP and BPV as well as urinary adrenaline levels. Conclusions The findings indicate that hypertension is closely associated with autonomic nervous system dysfunction, reduced HRV and increased chronic inflammation. A comprehensive approach to hypertension management should integrate these interrelated physiological and pathological mechanisms, with potential therapeutic interventions targeting autonomic function and inflammatory states.

Heart rate variability metrics and myocardial recovery in heart failure with reduced ejection fraction

Heart rate variability metrics and myocardial recovery in heart failure with reduced ejection fraction

Orienteering Is More than Just Running! Acute Effect of Competitive Pressure on Autonomic Cardiac Activity among Elite Orienteering Athletes.

Background and Objectives: Orienteering is a sport characterized by high physical exertion and intense mental demands, which increase susceptibility to errors. Understanding the impact of such errors on psychophysiological responses, particularly on heart rate variability (HRV), is essential. This study aimed to investigate the relationship between psychophysiological indicators and checkpoint errors made by elite orienteers during official competition. Materials and Methods: Fifty-three orienteers participated in this study, and their performance was continuously monitored and recorded by using a global positioning system (GPS) and HRV data. Errors made during the orienteering events were identified and analyzed. HRV data were examined in three temporal segments: before, during, and after the identified and standardized errors. Results: The analyses indicated that errors significantly impacted HRV indices across multiple domains: the time domain, frequency domain, and nonlinear domain. Additionally, a significant effect of sex on the normalized the root mean square of successive differences (r-MSSD) before and after the error was observed. Conclusions: The findings of this study underscore the significant impact of errors made by orienteers on cardiovascular responses, as evidenced by measurable alterations in HRV metrics. Cardiovascular activity, represented by the HRV, can provide useful information for coaches and sport psychologists to adopt effective training programs for athletes.

Network analysis of the relationship between different heart rate variability metrics during sleep

AbstractHeart rate variability (HRV) refers to the physiological phenomenon of variation in heartbeat duration, which can be characterized using various metrics. Considering a complex systems approach, in this study we used network modeling to quantitatively evaluate the relationship between different HRV metrics during sleep. Polysomnography recordings were performed on 24 healthy participants and their cardiac activity was sampled from the N2, N3, and REM sleep stages. Fifty-eight HRV metrics were calculated, and the relationship between each was assessed using mutual information (MI). One network was created for each sleep stage; HRV metrics constituted its nodes, and MI values were used to establish its edges. Repeated measures ANOVA was applied to each metric to assess variation between sleep stages. It was found that all three networks had characteristics of complex networks. Several communities of shared similar metrics were found across the three sleep stages. Of these, one community had the same metrics in stages N2 and N3, but in REM sleep was divided into three communities. REM sleep exhibited significant differences compared to the other sleep stages in several metrics. These preliminary findings allow us to suggest the application of this method in other HRV research contexts, which will determine its scope and limitations.

Heart rate variability as a dynamic marker of surgeons' stress during vascular surgery.

A surgeon experiences elevated stress levels when operating. Acute stress is linked to cognitive overload, worsening surgical performance. Chronic stress poses a significant risk to a surgeon's health. Identifying intraoperative stress may allow for preventative strategies that reduce surgeons' stress and subsequently improve patient outcomes. The aim of this study was to assess the feasibility of using heart rate variability as a marker of stress during vascular surgery. A total of 11 senior surgeons were evaluated performing three different vascular surgery procedures. Heart rate variability metrics (low-frequency to high-frequency ratio and standard deviation of the normal-normal interval) were determined from single-lead ECG traces at predetermined procedural performance points. State-Trait Anxiety Inventory-6, a validated stress tool, was used to assess surgeon-reported stress. Subjective reports of procedural difficulty were also collected. One-way ANOVA compared heart rate variability at key performance points with baseline. Pearson's coefficient assessed correlation between heart rate variability and subjective stress. Data were collected for six carotid endarterectomies, six open abdominal aortic aneurysm repairs, and five lower limb bypasses. Heart rate variability metrics indicating markedly greater stress were observed at key performance points across all procedures. Peaks in stress were consistent across different surgeons performing the same procedure. A significant correlation was observed between heart rate variability metrics and subjective State-Trait Anxiety Inventory-6 stress reports (r = 0.768, P =<0.001). The most difficult procedural steps reported corresponded with heart rate variability metrics displaying the greatest stress. Heart rate variability may be a viable approach to assess intraoperative stress and cognitive load during vascular surgery and could be used to evaluate whether a theatre intervention (for example timeout) could reduce stress in areas of surgical difficulty.

The impact of controlled breathing on autonomic nervous system modulation: analysis using phase-rectified signal averaging, entropy and heart rate variability

Objective.The present study investigated how breathing stimuli affect both non-linear and linear metrics of the autonomic nervous system (ANS).Approach.The analysed dataset consisted of 70 young, healthy volunteers, in whom arterial blood pressure (ABP) was measured noninvasively during 5 min sessions of controlled breathing at three different frequencies: 6, 10 and 15 breaths min-1. CO2concentration and respiratory rate were continuously monitored throughout the controlled breathing sessions. The ANS was characterized using non-linear methods, including phase-rectified signal averaging (PRSA) for estimating heart acceleration and deceleration capacity (AC, DC), multiscale entropy, approximate entropy, sample entropy, and fuzzy entropy, as well as time and frequency-domain measures (low frequency, LF; high-frequency, HF; total power, TP) of heart rate variability (HRV).Main results.Higher breathing rates resulted in a significant decrease in end-tidal CO2concentration (p< 0.001), accompanied by increases in both ABP (p <0.001) and heart rate (HR,p <0.001). A strong, linear decline in AC and DC (p <0.001 for both) was observed with increasing breathing rate. All entropy metrics increased with breathing frequency (p <0.001). In the time-domain, HRV metrics significantly decreased with breathing frequency (p <0.01 for all). In the frequency-domain, HRV LF and HRV HF decreased (p= 0.038 andp= 0.040, respectively), although these changes were modest. There was no significant change in HRV TP with breathing frequencies.Significance.Alterations in CO2levels, a potent chemoreceptor trigger, and changes in HR most likely modulate ANS metrics. Non-linear PRSA and entropy appear to be more sensitive to breathing stimuli compared to frequency-dependent HRV metrics. Further research involving a larger cohort of healthy subjects is needed to validate our observations.

SPECIAL ISSUE: The Effect of Perceived Racial Discrimination in African Americans on HRV

Health disparities among African Americans have been reported for decades. This is especially true for cardiovascular disease (CVD) even when compared with other minority groups in America. Such disparities have been tied to perceived discrimination. A previous study found that perceived racism accounted for a substantial percentage of the variance in resting heart rate variability (HRV), an independent biomarker of cardiac health. The present study attempts to replicate such findings. This study includes 57 healthy African American individuals who met inclusion criteria. Participants were given the Perceived Ethnic Discrimination Questionnaire–Community Version (PEDQ-CV) and the Perceived Racism Scale (PRS) and had their resting HRV and blood pressure measured. After accounting for breathing rate and age, the lifetime score on the PRS uniquely accounted for up to 45% of the variability of HRV metrics. The PEDQ-CV was not found to be a statistically significant predictor of HRV. Within this sample, blood pressure was not significantly predicted by either scale. The findings suggest that lifetime perceived racism may directly affect HRV indices, which may indicate an increased risk for CVD. Further research should continue to investigate this association and potential avenues for related intervention.

Neuromodulation of Cardiovascular Risks Associated With Cardiotoxic Chemotherapy: A First-in-Human Randomized Pilot Study. Neuromodulation in Cancer Study (NCAN).

Cardiotoxic chemotherapy is used to treat malignancies such as breast cancer and lymphoma. These treatments predispose patients to cardiotoxicity that can lead to cancer treatment-related cardiac dysfunction (CTRCD). The use of high doses of anthracyclines or in combination with human epidermal growth factor receptor 2 antagonists is associated with a progressively higher risk of CTRCD. CTRCD is preceded by increased activation of the sympathetic nervous system and abnormal left ventricular mechanical deformation as measured by abnormal global longitudinal strain (GLS). Low-level tragus stimulation (LLTS) is a new, safe, noninvasive technique that offers great potential to reduce increased sympathetic activation and improve GLS. Here, we describe a study method to examine the effects of LLTS on autonomic balance and cardiac function in breast cancer or lymphoma patients treated with anthracyclines. A first-in-human pilot, randomized, double-blind feasibility study will evaluate 104 patients (age >50y) with breast cancer or lymphoma who receive anthracyclines with one additional CTRCD risk factor. Patients undergo 2 weeks of LLTS daily (1 h/d). Autonomic balance will be measured using heart rate variability metrics. Strain imaging using GLS will be performed pre and post-LLTS. Endothelial inflammation and oxidative stress measures will be performed using in vitro assays at baseline and after 2 weeks. We hypothesize that LLTS stabilizes sympathovagal imbalance and improves cardiac performance in anthracycline-treated patients with breast cancer or lymphoma.

Resting Heart Rate Variability is Independently Associated with Visceral Fat Rating Scores in Saudi Adult Males.

Visceral adipose tissue (VAT) may be a specific modifiable contributor to body composition-related autonomic impairment. To compare heart rate variability (HRV) between groups stratified by visceral fat rating (VFR) and compare associations between HRV and body composition metrics. A cross-sectional study was conducted on healthy men (n=99,age=37.8±13.4 years, body mass index [BMI]=26.9±4.6 kg/m2). HRV was derived from 5-minute electrocardiographic recordings. Body composition (body fat percentage, VFR, and muscle mass to visceral fat ratio [MMVFR]) was estimated using tetrapolar bioelectrical impedance analysis. Participants were categorized into groups according to VFR: G1 (VFR=1-8); G2(VFR=9-12); and G3(VFR>12). Age-adjusted comparisons were made between groups. Independent associations were quantified with multiple linear regressions. P <0.05 was significant. Root-mean square of successive differences (RMSSD) and standard deviation of normal RR intervals (SDNN) were higher for G1 vs. G2 and G3 (p<0.05). Low-frequency power (LF) was higher in G1 than in G2 (p<0.05). VFR and MMVFR were negatively associated with SDNN, RMSSD, LF, and HF (p<0.05). After adjusting for age, BMI, and systolic and diastolic blood pressure, VFR was significantly predictive of RMSSD, SDNN, and HF (p=0.002,-0.027), and MMVFR was significantly predictive of RMSSD and SDNN (p=0.020,-0.023). Men in the lowest VFR category had the highest HRV. VFR was more strongly associated with HRV than body fat percentage and MMVR. Time domain parameters were more sensitive to VAT than frequency domain parameters. HRV parameters could be the primary parameters of interest in tracking cardiac-autonomic status in response to interventions targeting VAT reduction.

The relationship between emotions and HRV under the stimulation of bra images

It is a stressful ordeal in general to buy bras online aimlessly. The wise approach for eCommerce platform is to use emotion recognition to effectively assess consumers’ needs through the bra images, which can quickly close their preferences and accurately recommend them suitable bras. However, the significant physiological features that can be used for emotion recognition in fashion are still in the exploratory stage. This study focuses on the relationship between cardiac activity and emotional responses (the different levels of Satisfaction (S) and Attractiveness (A) under the stimulation of bra images. Firstly, we labelled 96 bra images in the dimensions and selected 45 bra images to make three groups of emotional stimuli using k-means clustering analysis. Then, we collected Photoplethysmogram (PPG) signals from 46 subjects consisting of 11 males and 35 females simulated by three groups of the stimuli, extracted 10 metrics of heart rate variability (HRV), and then analyzed the relationships between HRV and emotions based on their subjective evaluation. The males and females have different cardiac activity patterns under the same emotion, that the parasympathetic of males are more active than females. In a non-neutral emotion, the parasympathetic nervous system would keep an activated state. However, it is dominated by the parasympathetic nervous system whether in high or low satisfaction. While high and low attractiveness are respectively dominated by the sympathetic and parasympathetic nerves. The results of this study have certain significance for understanding the physiological phenomenon of emotions and further help to explore emotion recognition technology which can be used in product development to enhance the competitiveness of enterprises.

Tracking Vigilance Fluctuations in Real-Time: A Sliding-Window HRV-based Machine-Learning Approach.

Heart rate variability (HRV)-based machine learning models hold promise for real-world vigilance evaluation, yet their real-time applicability is limited by lengthy feature extraction times and reliance on subjective benchmarks. This study aimed to improve the objectivity and efficiency of HRV-based vigilance evaluation by associating HRV and behavior metrics through a sliding-window approach. Forty-four healthy adults underwent psychomotor vigilance tasks under both well-rested and sleep-deprived conditions, with simultaneous electrocardiogram recording. A sliding-window approach (30s length, 10s step) was used for HRV feature extraction and behavior assessment. Repeated-measures ANOVA was used to examine how HRV related to objective vigilance levels. Stability selection technique was applied for feature selection, and the vigilance ground truth-high (fastest 40%), intermediate (middle 20%), and low (slowest 40%)-were determined based on each participant's range of performance. Four machine-learning classifiers-k-nearest neighbours, support vector machine (SVM), AdaBoost, and random forest-were trained and tested using cross-validation. Fluctuated vigilance performance indicated pronounced state instability, particularly after sleep deprivation. Temporary decrements in performance were associated with a decrease in heart rate and an increase in time-domain heart rate variability. SVM achieved the best performance, with a cross-validated accuracy of 89% for binary classification of high versus low vigilance epochs. Overall accuracy dropped to 72% for three-class classification in leave-one-participant-out cross-validation, but SVM maintained a precision of 84% in identifying low-vigilance epochs. Sliding-window-based HRV metrics would effectively capture the fluctuations in vigilance during task execution, enabling more timely and accurate detection of performance decrement.