Heart Rate Variability Features Research Articles

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals.

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human physiology and improve health outcomes. However, the multimodal analysis of these signals is challenging due to the lack of guidelines, standardized signal processing and statistical tools, graphical user interfaces (GUIs), and automation for processing large datasets or increasing reproducibility. A further void exists in standardized EEG and heart-rate variability (HRV) feature extraction methods, undermining clinical diagnostics or the robustness of machine learning (ML) models. In response to these limitations, we introduce the BrainBeats toolbox. Implemented as an open-source EEGLAB plugin, BrainBeats integrates three main protocols: 1) Heartbeat-evoked potentials (HEP) and oscillations (HEO) for assessing time-locked brain-heart interplay at the millisecond accuracy; 2) EEG and HRV feature extraction for examining associations/differences between various brain and heart metrics or for building robust feature-based ML models; 3) Automated extraction of heart artifacts from EEG signals to remove any potential cardiovascular contamination while conducting EEG analysis. We provide a step-by-step tutorial for applying these three methods to an open-source dataset containing simultaneous 64-channel EEG, ECG, and PPG signals. Users can easily fine-tune parameters to tailor their unique research needs using the graphical user interface (GUI) or the command line. BrainBeats should make brain-heart interplay research more accessible and reproducible.

Remote Short Sessions of Heart Rate Variability Biofeedback Monitored With Wearable Technology: Open-Label Prospective Feasibility Study.

Heart rate variability (HRV) biofeedback is often performed with structured education, laboratory-based assessments, and practice sessions. It has been shown to improve psychological and physiological function across populations. However, a means to remotely use and monitor this approach would allow for wider use of this technique. Advancements in wearable and digital technology present an opportunity for the widespread application of this approach. The primary aim of the study was to determine the feasibility of fully remote, self-administered short sessions of HRV-directed biofeedback in a diverse population of health care workers (HCWs). The secondary aim was to determine whether a fully remote, HRV-directed biofeedback intervention significantly alters longitudinal HRV over the intervention period, as monitored by wearable devices. The tertiary aim was to estimate the impact of this intervention on metrics of psychological well-being. To determine whether remotely implemented short sessions of HRV biofeedback can improve autonomic metrics and psychological well-being, we enrolled HCWs across 7 hospitals in New York City in the United States. They downloaded our study app, watched brief educational videos about HRV biofeedback, and used a well-studied HRV biofeedback program remotely through their smartphone. HRV biofeedback sessions were used for 5 minutes per day for 5 weeks. HCWs were then followed for 12 weeks after the intervention period. Psychological measures were obtained over the study period, and they wore an Apple Watch for at least 7 weeks to monitor the circadian features of HRV. In total, 127 HCWs were enrolled in the study. Overall, only 21 (16.5%) were at least 50% compliant with the HRV biofeedback intervention, representing a small portion of the total sample. This demonstrates that this study design does not feasibly result in adequate rates of compliance with the intervention. Numerical improvement in psychological metrics was observed over the 17-week study period, although it did not reach statistical significance (all P>.05). Using a mixed effect cosinor model, the mean midline-estimating statistic of rhythm (MESOR) of the circadian pattern of the SD of the interbeat interval of normal sinus beats (SDNN), an HRV metric, was observed to increase over the first 4 weeks of the biofeedback intervention in HCWs who were at least 50% compliant. In conclusion, we found that using brief remote HRV biofeedback sessions and monitoring its physiological effect using wearable devices, in the manner that the study was conducted, was not feasible. This is considering the low compliance rates with the study intervention. We found that remote short sessions of HRV biofeedback demonstrate potential promise in improving autonomic nervous function and warrant further study. Wearable devices can monitor the physiological effects of psychological interventions.

Changes in Maternal Heart Rate Variability and Photoplethysmography Morphology after Corticosteroid Administration: A Prospective, Observational Study.

Background: Owing to the association between dysfunctional maternal autonomic regulation and pregnancy complications, assessing non-invasive features reflecting autonomic activity-e.g., heart rate variability (HRV) and the morphology of the photoplethysmography (PPG) pulse wave-may aid in tracking maternal health. However, women with early pregnancy complications typically receive medication, such as corticosteroids, and the effect of corticosteroids on maternal HRV and PPG pulse wave morphology is not well-researched. Methods: We performed a prospective, observational study assessing the effect of betamethasone (a commonly used corticosteroid) on non-invasively assessed features of autonomic regulation. Sixty-one women with an indication for betamethasone were enrolled and wore a wrist-worn PPG device for at least four days, from which five-minute measurements were selected for analysis. A baseline measurement was selected either before betamethasone administration or sufficiently thereafter (i.e., three days after the last injection). Furthermore, measurements were selected 24, 48, and 72 h after betamethasone administration. HRV features in the time domain and frequency domain and describing heart rate (HR) complexity were calculated, along with PPG morphology features. These features were compared between the different days. Results: Maternal HR was significantly higher and HRV features linked to parasympathetic activity were significantly lower 24 h after betamethasone administration. Features linked to sympathetic activity remained stable. Furthermore, based on the PPG morphology features, betamethasone appears to have a vasoconstrictive effect. Conclusions: Our results suggest that administering betamethasone affects maternal autonomic regulation and cardiovasculature. Researchers assessing maternal HRV in complicated pregnancies should schedule measurements before or sufficiently after corticosteroid administration.

Exploring a multimodal approach for utilizing digital biomarkers for childhood mental health screening.

Depression and anxiety are prevalent mental health concerns among children and adolescents. The application of conventional assessment methods, such as survey questionnaires to children, may lead to self-reporting issues. Digital biomarkers provide extensive data, reducing bias in mental health self-reporting, and significantly influence patient screening. Our primary objectives were to accurately assess children's mental health and to investigate the feasibility of using various digital biomarkers. This study included a total of 54 boys and girls aged between 7 to 11 years. Each participant's mental state was assessed using the Depression, Anxiety, and Stress Scale. Subsequently, the subjects participated in digital biomarker collection tasks. Heart rate variability (HRV) data were collected using a camera sensor. Eye-tracking data were collected through tasks displaying emotion-face stimuli. Voice data were obtained by recording the participants' voices while they engaged in free speech and description tasks. Depressive symptoms were positively correlated with low frequency (LF, 0.04-0.15 Hz of HRV) in HRV and negatively associated with eye-tracking variables. Anxiety symptoms had a negative correlation with high frequency (HF, 0.15-0.40 Hz of HRV) in HRV and a positive association with LF/HF. Regarding stress, eye-tracking variables indicated a positive correlation, while pNN50, which represents the proportion of NN50 (the number of pairs of successive R-R intervals differing by more than 50 milliseconds) divided by the total number of NN (R-R) intervals, exhibited a negative association. Variables identified for childhood depression included LF and the total time spent looking at a sad face. Those variables recognized for anxiety were LF/HF, heart rate (HR), and pNN50. For childhood stress, HF, LF, and Jitter showed different correlation patterns between the two grade groups. We examined the potential of multimodal biomarkers in children, identifying features linked to childhood depression, particularly LF and the Sad.TF:time. Anxiety was most effectively explained by HRV features. To explore reasons for non-replication of previous studies, we categorized participants by elementary school grades into lower grades (1st, 2nd, 3rd) and upper grades (4th, 5th, 6th). This study confirmed the potential use of multimodal digital biomarkers for children's mental health screening, serving as foundational research.

An artificial intelligence-enabled Holter algorithm to identify patients with ventricular tachycardia by analysing their electrocardiogram during sinus rhythm.

Ventricular tachycardia (VT) is a dangerous cardiac arrhythmia that can lead to sudden cardiac death. Early detection and management of VT is thus of high clinical importance. We hypothesize that it is possible to identify patients with VT during sinus rhythm by leveraging a continuous 24 h Holter electrocardiogram and artificial intelligence. We analysed a retrospective Holter data set from the Rambam Health Care Campus, Haifa, Israel, which included 1773 Holter recordings from 1570 non-VT patients and 52 recordings from 49 VT patients. Morphological and heart rate variability features were engineered from the raw electrocardiogram signal and fed, together with demographical features, to a data-driven model for the task of classifying a patient as either VT or non-VT. The model obtained an area under the receiving operative curve of 0.76 ± 0.07. Feature importance suggested that the proportion of premature ventricular beats and beat-to-beat interval variability was discriminative of VT, while demographic features were not. This original study demonstrates the feasibility of VT identification from sinus rhythm in Holter.

Characterizing Heart Rate Variability Response to Maximal Exercise Testing in People with Huntington's Disease.

Huntington's disease (HD) is an autosomal dominant, neurodegenerative disease that involves dysfunction in the autonomic nervous system (ANS). Heart rate variability (HRV) is a valid and noninvasive measure for ANS dysfunction, yet no study has characterized HRV response to exercise in people with HD. Characterize HRV response to exercise in individuals with HD and explore its implications for exercise prescription and cardiac dysautonomia mechanisms. 19 participants with HD were recruited as part of a cohort of individuals enrolled in the Physical Activity and Exercise Outcomes in Huntington's Disease (PACE-HD) study at Teachers College, Columbia University (TC). 13 non-HD age- and gender-matched control participants were also recruited from TC. HRV was recorded with a Polar H10 heart rate (HR) monitor before, during, and after a ramp cycle-ergometer exercise test. Participants with HD showed reduced HR peak (p < 0.01) and HR reserve (p < 0.001) compared with controls. Participants with HD demonstrated reduced root mean square of successive differences between normal-to-normal intervals (RMSSD) and successive differences of normal-to-normal intervals (SDSD) at rest (p < 0.001). Participants with HD also showed differences for low frequency (LF) power (p < 0.01), high frequency (HF) normalized units (nu) (p < 0.05), LF (nu) (p < 0.001), and HF/LF ratio (p < 0.05) compared with controls. We found reduced aerobic exercise capacity and sympathovagal dysautonomia both at rest and during post-exercise recovery in people with HD, suggesting modified exercise prescription may be required for people with HD. Further investigations focusing on cardiac dysautonomia and underlying mechanisms of sympathovagal dysautonomia in people with HD are warranted.

Unraveling Pain Levels: A Data-Uncertainty Guided Approach for Effective Pain Assessment

Pain, a primary reason for seeking medical help, requires essential pain assessment for effective management. Studies have recognized electrodermal activity (EDA) signaling's potential for automated pain assessment, but traditional algorithms often ignore the noise and uncertainty inherent in pain data. To address this, we propose a learning framework predicated on data uncertainty, introducing two forms: a) subject-level stimulation-reaction drift; b) ambiguity in self-reporting scores. We formulate an uncertainty assessment using Heart Rate Variability (HRV) features to guide the selection of responsive pain profiles and reweight subtask importance based on the vagueness of self-reported data. These methods are integrated within an end-to-end neural network learning paradigm, focusing the detector on more accurate insights within the uncertainty domain. Extensive experimentation on both the publicly available biovid dataset and the proprietary Apon dataset demonstrates our approach's effectiveness. In the biovid dataset, we achieved a 6% enhancement over the state-of-the-art methodology, and on the Apon dataset, our method outperformed baseline approaches by over 20%.

Factor structure of the functional state of qualified wrestlers at the stage of special basic training

Changing the rules of wrestling competitions leads to an increase in tension and intensity of competitive activity. This requires wrestlers to have a high level of functional fitness. Therefore, monitoring the functional state of qualified wrestlers is necessary for correcting the training process. In article the factor structure of the functional state of qualified wrestlers at the stage of special basic training was considered. The parameters of heart rate variability, mental states and neurodynamics characteristics were studied. Purpose of work to determine the factor structure of the functional state of qualified wrestlers at the stage of specialized basic training. Methodology. The 31 qualified wrestlers, age 14-16 took part in the study. The analysis revealed a relative autonomic balance between the sympathetic and parasympathetic parts of the autonomic nervous system in the examined athletes. In addition, qualified wrestlers have an optimal mental state that corresponds to the balance of excitation and inhibition processes and optimization of decision-making processes under time constraints. It was revealed that the structure of the functional state of qualified wrestlers includes complex characteristics that support the main elements of the functional system, ensuring the adaptation of the athletes’ body to the training process. The resulting factor structure indicates two factors: “morphofunctional” and “neurodynamic”. The first factor reflects the level of functional readiness and the level of training of wrestlers. In fact, the first factor is the basis of the formation of the functional state of highly qualified wrestlers, which reflects the main factors of the functional system responsible for the formation of the functional state of wrestlers. The second factor reflects the wrestlers’ ability to optimally perceive and process information from the environment under conditions of physical and neuropsychic stress. Conclusions. Respectively, for optimization of functional state in qualified wrestlers should be considered level of functional fitness and ability of athlete to perception and information processing.

The Effect of Cardiovascular Medication on Heart Rate Variability in Patients Presenting with Early Sepsis at the Emergency Department: A Prospective Cohort Study

Our SepsiVit study showed that long-term, automatically analyzed ECG recordings can be used to determine heart rate variability (HRV) features associated with the clinical deterioration of early septic patients at the ED. This study focus on the influence of cardiovascular medication on HRV in patients with early sepsis at the ED. This study is an exploratory post-hoc analysis of our SepsiVit study. Eligible patients were connected to a mobile bedside monitor for continuously ECG measurements. The first 3 hours were analyzed for this study. Between January 2017 and December 2018, 171 patients were included with early sepsis, defined as infection and two or more systemic inflammatory response syndrome criteria. We excluded sixteen patients because of insufficient measurements. Therefore, we included 155 patients in the final analysis: 72.9% with sepsis, 2.6% with septic shock, and 24.5% classified as infection. In 9.0% of the patients, medication directly impacting cardiac contractility was administered, while 22.6% received medication with an indirect effect. A combination of both types of medication was prescribed to 17.4% of the patients. The majority of patients (51.0%) did not utilize any cardiovascular medication. Patients using both medication with direct and indirect effect were on average 10 years older than patients using no cardiovascular medication (p 0.037). No differences in vital signs or HRV parameters were found in patients using cardiovascular medication. Our results showed that HRV is not influenced by cardiovascular medication. Consequently, the correction of HRV features for the use of cardiovascular medication is unnecessary when analyzing, modelling, and interpreting these signals.

Determination of Signs of Sleep Apnea Using Machine Learning Methods in Combination with Reducing the Dimensionality of Heart Rate Variability Features

Obstructive Sleep Apnea Syndrome (OSAS) is a clinically significant disorder characterized by recurrent episodes of upper airway obstruction, manifesting as either apnea or hypopnea, predominantly occurring at the pharyngeal level. Despite the preservation of respiratory muscle function during these episodes, OSAS poses considerable health risks, including cardiovascular complications and cognitive impairment. In recent years, a growing body of literature has explored novel methodologies to discern and diagnose OSAS, with a particular focus on cardiac activity analysis through Heart Rate Variability (HRV). This study contributes to the existing literature by conducting a comprehensive HRV analysis aimed at identifying indicative patterns of sleep apnea. The analysis incorporates diverse parameters within both time and frequency domains, facilitating a nuanced understanding of the complex interplay between cardiac dynamics and respiratory disruptions during sleep. In an effort to enhance the interpretability of the data, various scaling and dimensionality reduction techniques, such as Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP), were applied. The dataset utilized in this investigation comprises records from 70 patients, sourced from the Apnea-ECG Database on the Physionet platform. To discern the optimal classification model, several machine learning algorithms were employed after the dimensionality reduction, including k-Nearest Neighbors (k-NN), logistic regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting. Intriguingly, the results demonstrate a remarkable 100% accuracy across all classifiers when utilizing the UMAP dimensionality reduction method. A distinctive feature of the proposed methodology lies in its amalgamation of machine learning techniques with HRV parameters post-dimensionality reduction. This approach not only enhances the interpretability of the complex physiological data but also underscores the potential applicability of the developed model in real-world scenarios for the detection of OSAS. The robustness of the proposed approach, as evidenced by its high accuracy rates, positions it as a promising tool for advancing diagnostic capabilities in the realm of sleep medicine. Future research endeavors may further refine and validate this methodology, paving the way for its integration into clinical practice and contributing to the broader landscape of sleep disorder diagnostics.

The Clinical Characteristics of Heart Rate Variability After Stroke: A Systematic Review.

The autonomic nervous system dysfunction has been reported in up to 76% of stroke patients 7 days after an acute stroke. Heart rate variability (HRV) is one of the important indicators reflecting the balance of sympathetic and parasympathetic nerves. Therefore, we performed a systematic literature review of existing literature on the association between heart rate variability and the different types of stroke. We included studies published in the last 32 years (1990 to 2022). The electronic databases MEDLINE and PubMed were searched. We selected the research that met the inclusion or exclusion criteria. A narrative synthesis was performed. This review aimed to summarize evidence regarding the potential mechanism of heart rate variability among patients after stroke. In addition, the association of clinical characteristics of heart rate variability and stroke has been depicted. The review further discussed the relationship between post-stroke infection and heart rate variability, which could assist in curbing clinical infection in patients with stroke. HRVas a noninvasive clinical monitoring tool can quantitatively assess the changes in autonomic nervous system activity and further predict the outcome of stroke. HRV could play an important role in guiding the clinical practice for autonomic nervous system disorder after stroke.

Intelligent fatigue detection based on hierarchical multi-scale ECG representations and HRV measures

Fatigue detection based on electrocardiogram (ECG) from wearable devices is a convenient approach during exercise. However, how to model the data variations of ECG time series and capture effective information for accurate fatigue detection is still challenging. In this study, we propose a novel fatigue assessment architecture designed to adaptively extract and integrate both the ECG and heart rate variability (HRV) features, so as to obtain rich contextual information from ECG sequences and improve exercise-induced fatigue prediction performance. Inspired by the ECG observation process of human experts, the hierarchical transformer framework is designed to model the long-range dependencies of ECG time series and obtain multi-scale ECG representations. To reduce the computation cost, we also devise a sampling-efficient multi-head attention (SMA) mechanism. Finally, a novel gated feature fusion layer is developed to adaptively learn the weights of both the ECG and HRV features for fatigue prediction. The proposed fatigue prediction system is validated with real fatigue datasets which were collected from healthy participants. The experimental results show that the proposed approach outperforms other recent state-of-the-art systems in terms of accuracy 94.0%, specificity 99.0%, recall 89.3%, F1-score 93.7%, G-mean score 91.7%, MCC 0.837, and Cohen’s Kappa 0.847, based on the ECG time series with duration 20s. This demonstrates that the proposed system can be used to accurately detect fatigue in real time during exercise, such that assisting coaches or athletes to monitor and adjust their exercise session for preventing overexertion and fatigue-induced injury.

Fast Multilevel Mental Stress Identification From Bispectrum-Based Heart Rate Variability Feature

Fast and accurate identification of mental stress is critical to reducing its risk to human health and the related consequences in industry. This study proposed a bispectrum-based feature to improve the accuracy and reduce the latency of multi-level mental stress identification from heart rate variability (HRV). The performance was evaluated by two stress induction protocols, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , Stroop color-word interference test (SCIT) and mental arithmetic calculation (MAC), and the results demonstrated that the bispectrum-based methods significantly outperformed traditional HRV features with windows as short as 15 s. The high-order components found by an established nonlinear test, and the strong feature correlations detected between short and long windows, further validated the effectiveness and the feasibility of the proposed methods on mental stress identification from super-short windows. The results had the potential to promote the applications of HRV-based mental stress management across industries with the Internet of Medical Things (IoMT) devices.

Heart Rate Variability as a Tool for Seizure Prediction: A Scoping Review

The most critical burden for People with Epilepsy (PwE) is represented by seizures, the unpredictability of which severely impacts quality of life. The design of real-time warning systems that can detect or even predict ictal events would enhance seizure management, leading to high benefits for PwE and their caregivers. In the past, various research works highlighted that seizure onset is anticipated by significant changes in autonomic cardiac control, which can be assessed through heart rate variability (HRV). This manuscript conducted a scoping review of the literature analyzing HRV-based methods for detecting or predicting ictal events. An initial search on the PubMed database returned 402 papers, 72 of which met the inclusion criteria and were included in the review. These results suggest that seizure detection is more accurate in neonatal and pediatric patients due to more significant autonomic modifications during the ictal transitions. In addition, conventional metrics are often incapable of capturing cardiac autonomic variations and should be replaced with more advanced methodologies, considering non-linear HRV features and machine learning tools for processing them. Finally, studies investigating wearable systems for heart monitoring denoted how HRV constitutes an efficient biomarker for seizure detection in patients presenting significant alterations in autonomic cardiac control during ictal events.

Resampling Strategies and their Influence on Heart Rate Variability Features in Low Sampling Rate Electrocardiogram Data

Heart rate variability (HRV) is a parameter to measure fluctuations in the interval between heartbeats. HRV provides essential insights into the cardiovascular function and autonomic nervous system. Electrocardiograms (ECG) on wearable devices are often recorded at low sampling rates, limiting temporal resolution and information. Resampling is a technique of changing the sampling rate from a high sampling rate to a lower sampling rate and vice versa. This research aims to evaluate the effect of resampling ECG data with a low sampling rate on HRV features. ECG data consists of 50 Hz and 100 Hz sampling rates. Data with a 50 Hz sampling rate is up-sampled up to 100 Hz, while 100 Hz data is down-sampled up to 50 Hz and up-sampled up to 250 Hz using the Fast Fourier Transform Interpolation Method. Upsampling from 50 Hz to 100 Hz shows unsatisfactory results, except for some HRV features such as NN20, pNN20, and CVI. Better results were found when up sampling from 100 Hz up to 250 Hz, with some HRV features showing good concordance values. However, downsampling from 100 Hz up to 50 Hz is unsuitable for HRV feature analysis. To obtain accurate HRV analysis results in all domains, it is highly recommended to use a sampling rate above 100 Hz.

Towards Accurate Search for Neonatal Heartbeat: Weighted Algorithm for Reliable ECG Analysis of Premature Infants.

Accurate identification of the QRS complex is critical to analyse heart rate variability (HRV), which is linked to various adverse outcomes in premature infants. Reliable and accurate extraction of HRV characteristics at a large scale in the neonatal context remains a challenge. In this paper, we investigate the capabilities of 15 state-of-the-art QRS complex detection implementations using two real-world preterm neonatal datasets. As an attempt to improve the accuracy and reliability, we introduce a weighted ensemble-based method as an alternative. Obtained results indicate the superiority of the proposed method over the state of the art on both datasets with an F1-score of 0.966 (95% CI 0.962-0.97) and 0.893 (95% CI 0.892-0.894). This motivates the deployment of ensemble-based methods for any HRV-based analysis to ensure robust and accurate QRS complex detection.

A transcutaneous acupoint electrical simulation glove for relieving the mental fatigue of crane drivers in real building environment

In the field of construction, the lifting environment of precast parts is more complex, which leads to the driver’s fatigue. When the tower crane driver appears driving fatigue, it will appear slow operation response, hoisting precast parts appear abnormal swing, which will endanger the safety of on-site operators. Therefore, this study developed a kind of transcutaneous acupoint electrical stimulation gloves. When the crane driver wears this kind of glove, the good contraction of the glove can make the stimulation electrode closely fit with the three points, so as to perform electrical stimulation on the Neìguān point (内关PC6), Láogóng point (劳宫PC8) and Hégŭ point (合谷L14) of the palm to relieve the driver’s driving fatigue. In this study, non-periodic transcutaneous acupoint electrical stimulation (NPTAES) was used to stimulate human acupuncture points. This is different from the traditional periodic transcutaneous acupoint electrical stimulation (PTAES) method for relieving mental fatigue. In addition, this study used hilbert marginal spectral entropy (HMSE) to calculate the heart rate variability (HRV) characteristics of the subjects, so as to detect and analyze the driving fatigue of the drivers. At the same time, the drivers’ blinking frequency and electroencephalogram (EEG) characteristics were analyzed comprehensively. The results show that: The NPTAES method used in this study is superior to the PTAES method in alleviating driving fatigue and greatly improves the efficiency of tower crane drivers. Compared to other methods, the HMSE method proposed in this study, when analyzing signals, stronger ability to characterize signal characteristics.

Стан нейродинамічних функцій та регуляторних процесів серця за показниками варіабельності серцевого ритму в осіб з недостатньою та підвищеною масою тіла

One of the most common diseases in the whole world is obesity. Every year, 18-20 thousand new cases of obesity are recorded in Ukraine. Also, the number of obese students during their studies in institutions of higher education is rapidly increasing, for whom the first year of study is the most difficult and problematic. It is known that even under normal conditions, excessive body weight requires increased energy supply and persons with insufficient weight suffer from a lack of reserves. Therefore, the purpose of our work was to study the features of the state of neurodynamic functions and regulatory processes of the heart based on indicators of heart rate variability in female students with different body weights during the first year of study at a higher education institution. This will be important for the development of individual disease prevention measures for this category of students. The study examined full-time female students (17-18 years old) of the Faculty of Natural Geography Education and Ecology of National Pedagogical Dragomanov University, who were divided into groups according to body mass index: normal, overweight, and underweight. The study was conducted on days of high mental performance under standard conditions in six stages. An electrocardiogram was registered, rhythmograms and variation pulsometry indicators were calculated, and the functional state of the central nervous system was studied using computer methods. According to the Kruskal-Wallis and ANOVA tests, the following indicators were most sensitive to the influence of body weight: moda, moda amplitude, stress index, autonomic balance index, and heart rate. According to Spearman’s non-parametric correlation analysis, at each stage of the examination, correlations between variation characteristics of heart rate variability and indicators of the state of the central nervous system were revealed, and their nature was clarified. It was established that the nature of the correlations in the studied groups was different. Thus, it was established that the deviation of body weight from the norm affects the regulatory processes of the heart and neurodynamic functions during the first year of study. Overweight and underweight female students during the first year of study had a significantly higher intension of regulatory mechanisms compared to female students with a normal body weight, so they can be classified as a group at risk of developing psychosomatic diseases.

Flow in human-robot collaboration-multimodal analysis and perceived challenge detection in industrial scenarios.

Introduction: Flow state, the optimal experience resulting from the equilibrium between perceived challenge and skill level, has been extensively studied in various domains. However, its occurrence in industrial settings has remained relatively unexplored. Notably, the literature predominantly focuses on Flow within mentally demanding tasks, which differ significantly from industrial tasks. Consequently, our understanding of emotional and physiological responses to varying challenge levels, specifically in the context of industry-like tasks, remains limited. Methods: To bridge this gap, we investigate how facial emotion estimation (valence, arousal) and Heart Rate Variability (HRV) features vary with the perceived challenge levels during industrial assembly tasks. Our study involves an assembly scenario that simulates an industrial human-robot collaboration task with three distinct challenge levels. As part of our study, we collected video, electrocardiogram (ECG), and NASA-TLX questionnaire data from 37 participants. Results: Our results demonstrate a significant difference in mean arousal and heart rate between the low-challenge (Boredom) condition and the other conditions. We also found a noticeable trend-level difference in mean heart rate between the adaptive (Flow) and high-challenge (Anxiety) conditions. Similar differences were also observed in a few other temporal HRV features like Mean NN and Triangular index. Considering the characteristics of typical industrial assembly tasks, we aim to facilitate Flow by detecting and balancing the perceived challenge levels. Leveraging our analysis results, we developed an HRV-based machine learning model for discerning perceived challenge levels, distinguishing between low and higher-challenge conditions. Discussion: This work deepens our understanding of emotional and physiological responses to perceived challenge levels in industrial contexts and provides valuable insights for the design of adaptive work environments.

Improving the Agreement Between the First Heart-Rate-Variability Threshold and the Gas-Exchange Threshold.

The first heart-rate (HR) -variability (HRV) -derived threshold based on detrended fluctuation analysis alpha 1 (DFA a1) has shown inconsistent agreement with the gas-exchange threshold (GET). This study examined whether a custom method of computing thefirst HRV threshold (HRVT1) based on individual HRV characteristics would improve agreement. Fourteen participants underwent ramp incremental testing measuring gas-exchange variables and RR intervals. Comparisons were made between the oxygen consumption (V˙O2)/HR at the GET versus the V˙O2/HR at the standard DFA a1 = 0.75 (HRVT1s) or a custom value (HRVT1c) based on the DFA a1 midway between the maximum seen during the early ramp incremental and 0.5. Mean values for GET V˙O2 versus HRVT1s V˙O2 and GETHR versus HRVT1sHR were statistically different (25.4 [3.3] vs 29.8 [6.8]mL·kg-1·min-1, P = .01, d = 0.80; 131 [11] vs 146 [22]beats·min-1, P = .005, d = 0.91). There were no statistical differences when using the HRVT1c (25.4 [3.3] vs 25.1 [5.7]mL·kg-1·min-1, P = .77, d = 0.08; 131 [11] vs 132 [17]beats·min-1, P = .65, d = 0.12). Equivalence between GET and HRVT1c V˙O2/HR was also verified. Mean maximal DFA a1 during the early ramp incremental was 1.52 (0.22) with mean HRVT1c of 1.01 (0.11). Pearson r correlation coefficients were between .67 and .70 for all GET to HRVT1 comparisons. The second HRV threshold and respiratory compensation point parameters showed agreement and correlations in line with prior studies. The HRVT1c showed stronger agreement to GET parameters than seen using the HRVT1s. It is recommended that evaluations of the HRVT1 consider this approach in determining the HR and V˙O2 at this threshold.