Figure - uploaded by Tam Pham
Content may be subject to copyright.
Source publication
The use of heart rate variability (HRV) in research has been greatly popularized over the past decades due to the ease and affordability of HRV collection, coupled with its clinical relevance and significant relationships with psychophysiological constructs and psychopathological disorders. Despite the wide use of electrocardiograms (ECG) in resear...
Context in source publication
Similar publications
The use of heart rate variability (HRV) in research has been greatly popularized over the past decades due to the ease and affordability of HRV collection, coupled with its clinical relevance and significant relationships with psychophysiological constructs and psychopathological disorders. Despite the wide use of electrocardiogram (ECG) in researc...
Citations
... This decline in total power may be due to age-related changes in the ANS, including both sympathetic and parasympathetic branches. However, the age-related decrease in HRV total power is not always linear and can be modulated by factors such as physical activity, stress levels, and overall cardiovascular health 18,20 . Studies have also suggested that while certain HRV parameters decline, others may show a rebound increase after mid-life, underscoring the complexity of autonomic regulation as it relates to the aging process 21 . ...
Introduction. Pulmonary hypertension (PH) and right ventricular dysfunction (RVD) have a significant negative impact on the prognosis of patients with heart failure (HF), but their clinical implications on HF hospitalizations after myocardial revascularization remains insufficiently elucidated.
The objective of the study was to assess the prognostic impact of PH and RVD on the risk of HF hospitalizations during the first year after myocardial revascularization.
Material and methods. The prospective study included 275 patients with ischemic HF who underwent myocardial revascularization and were divided into two groups: HF hospitalization (+) and HF hospitalization (-). The clinical examination, echocardiography and laboratory tests were performed in all patients. The follow up period was 12 months.
Results. The HF hospitalization rate was 18.1%. In the HF hospitalization (+) group, the percentage of patients with high probability of PH (56.0% vs. 26.7%) and RVD (70.0% vs 32.4%) was higher. The most relevant prognostic factors were RVD, along with tricuspid annular plane systolic excursion (TAPSE), right ventricular (RV) fractional area change, systolic velocity of the lateral tricuspid valve annulus, RV index of myocardial performance, RV end-diastolic diameter, the ratio between TAPSE and pulmonary artery systolic pressure, high echocardiographic probability of PH, peak tricuspid regurgitation velocity. A prediction model for HF hospitalizations during the first year after myocardial revascularization was developed (AUC=0.827).
Conclusions. The echocardiographic parameters suggestive of PH and RVD proved to be relevant prognostic factors for HF hospitalizations in the first year after myocardial revascularization. In this context, a predictive model with good discriminatory performance was developed.
Keywords: pulmonary hypertension, right ventricular dysfunction, heart failure hospitalizations, prognosis, myocardial revascularization.
Ruseva BK, Vezenkov SR, Tonchev PT, Mihaylov II, Manolova VR, Radoyski UG. (2025) Comparative analysis of heart rate variability in aviator cadets and instructors during ground training. Arch Balk Med Union. 60 (1) : 21-31.
... O aumento de ativação de vias simpáticas associado ao tônus vagal baixo mostra resultados negativos relacionados aos transtornos de humor e risco aumentado de desenvolvimento de comorbidades (Pham et al.;2021). ...
... O aumento de ativação de vias simpáticas associado ao tônus vagal baixo mostra resultados negativos relacionados aos transtornos de humor e risco aumentado de desenvolvimento de comorbidades (Pham et al.;2021). ...
Introdução: A paralisia cerebral (PC) é caracterizada por um conjunto de desordens não progressivas, mas mutáveis, causadas por uma lesão no encéfalo imaturo e acompanhadas por desajustes no balanço simpático-vagal, com menor variabilidade da frequência cardíaca (VFC) quando comparada aos indivíduos com desenvolvimento típico. A termografia infravermelha (TIV) é capaz de observar a atividade simpática através da temperatura da pele de forma remota e não invasiva. A terapia por fotobiomodulação (TFBM) tem mostrado efeitos promissores nas respostas de modulação de variáveis autonômicas. Objetivo: Verificar o efeito da TFBM nas variáveis autonômicas de indivíduos com PC. Métodos: Foram incluídos no estudo oito crianças e adolescentes (8,75 ± 1,67 anos de idade) com PC e randomizados entre os grupos TFBM (GFBM [n = 5]) e placebo (GP [n = 3]). A intervenção consistiu em 12 sessões, duas vezes por semana, com aplicação da TFBM no GFBM em sete regiões de cada membro inferior (cluster 850 nm, 3.276 J, 3 J/cm²) e simulação de aplicação no GP. O aparelho Polar (RS800CX) foi utilizado para registro dos intervalos R-R e a câmera termográfica FLIR E8 WI-FI (FLIR® Systems, Inc.), com resolução 320 x 240 pixels, foi utilizada para a TIV. A análise estatística foi feita por meio do teste ANOVA de medidas repetidas com post-hoc de Bonferroni e o nível de significância adotado foi de p < 0,05. Resultados: Foi observada diferença significativa na condição pós-intervenção para iRR (p = 0,035), pNN50 (p = 0,047) e frequência cardíaca (p = 0,018). Os dados de iRR e pNN50 são marcadores parassimpáticos e apresentaram valores aumentados para momento pós no GFBM. Para a frequência cardíaca, indicador do comportamento simpático, houve diminuição no GFBM. Apesar da TIV não ter apresentado diferença estatística significativa, houve aumento da temperatura facial e diminuição da temperatura periférica no GFBM, sugerindo redução da condição de estresse. Conclusão: A TFBM apontou resultados promissores de maior influência do sistema nervoso parassimpático e modulação do sistema nervoso simpático, podendo promover melhores condições de saúde.
... SDNN Index (SDNNI): SDNNI represents the mean standard deviation of NN intervals within 5-minute segments throughout a 24-hour HRV recording 40 . Thus, this measure estimates the variability influenced by factors affecting HRV within a 5-min period. ...
... To compute SDNNI, the 24-h data is divided into 288 segments of 5 min each, with the standard deviation calculated for the NN intervals within each segment, and then averaged across all segments 3,7 . SDNNI is suggested to be a measure of the short-term components modulating HR signals 40 . ...
... However, RMSSD provides a better estimate of vagal activity, and most researchers prefer it to pNN50 7,34 . The RMSSD, NN50, pNN50, and HR Max − HR Min are derived from the difference between successive NN intervals; hence, they can be termed difference-based indices 40 . ...
Heart rate variability (HRV), the variation in the time interval between successive heartbeats, has emerged as a method to evaluate autonomic function and is increasingly accepted as a biomarker reflecting the balance between the sympathetic and parasympathetic nervous system (SNS and PNS, respectively) branches. Since 1996, most HRV measurements have been performed according to the Task Force standards of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. However, despite the established guidelines and growing body of research on HRV, this technique has not been fully incorporated into routine clinical practice. This review provides a comprehensive overview of the different aspects of HRV measurement, highlighting the fundamental principles, available methods, and physiological basis of HRV assessment to elucidate its role in understanding autonomic function in normal and abnormal health conditions.
... Research into heart rate variability has intensified, employing both statistical and nonlinear dynamics approaches. Most of these studies are descriptive of cardiac dynamics and are associated with the study of various pathologies [41]. Heart rate variability has been used as a predictive factor for coronary events, strokes, and sudden cardiac death [42]. ...
Background/Objectives: Probability theory and dynamic systems have enabled the development of diagnostic support tools that simplify Holter evaluation. Method: A study was conducted on 80 Holter tests over 21 h with patients over 21 years old. Four prototypes were selected based on normal, chronic, acute, and pacemaker diagnoses. An induction was created using the heart rate ranges of the prototypes, from 55 to 105, as the general probability space. Probability theory was applied to the frequency repetition ranges of 1000 to 2000 and 2001 to 3000. A blinded study was conducted with the remaining Holter tests, applying the same methodology used for the prototypes. A physical/mathematical induction was performed for the prototypes, and the other Holter tests were analyzed in a blinded study. Results: The results were compared to the predictions of the prototypes, and sensitivity, specificity, and the kappa coefficient were calculated. In the 1000–2000 range, the repetition counts for normal dynamics were 14 to 11, for chronic cases 31 to 21, for acute cases 11 to 9, and for pacemaker dynamics 5 to 4. In the 2001–3000 range, the repetitions for normal dynamics were 3 to 0, for chronic cases 14 to 10, for acute cases 6 to 3, and for pacemaker dynamics 2. The cumulative probabilities loaded for the 1000–2000 range were as follows: normal dynamics, 0.46 to 0.35; chronic dynamics, 0.48 to 0.35; acute cases, 0.6 to 0.5; and pacemaker dynamics, 0.6 to 0.5. In the 2001–3000 range, the cumulative probabilities loaded for normal dynamics were 1 to 0; for chronic cases, 0.7 to 0.54; for acute cases, 0.75 to 0.46; and for pacemaker dynamics, 1. The frequencies observed in the repetition ranges for 1000–2000 were normal, 95 to 55; chronic, 105 to 65; acute, 100 to 75; and pacemaker, 75 to 60. For the 2001–3000 range, the frequencies were normal, 95 to 65; chronic, 85 to 65; acute, 100 to 80; and pacemaker, 65 to 60. The probabilities were less than 0.3 for normal dynamics and greater than 0.3 for chronic, acute, and pacemaker dynamics across different frequency ranges, differentiating the dynamics. Conclusions: The epidemiological study results for sensitivity, specificity, and kappa coefficient were all 1. To conclude, a diagnostic support tool was developed for cardiac dynamics with clinical applications based on the appearance of frequency ranges and probability theory, enabling differentiation of normal, chronic, acute, and pacemaker dynamics.
... The ANS is instrumental in regulating cardiovascular, respiratory, and metabolic functions, making HRV an effective proxy for physiological states [8]. Studies have shown that a higher HRV is associated with better cardiovascular health, recovery, and stress resilience, while low HRV is often linked to increased risk of heart disease, stress, and overtraining in athletes [9,10], which has proven to be a valuable tool in stress management, mental health monitoring, and chronic disease prevention, underscoring its versatility as a physiological marker. ...
Heart rate variability (HRV) is a widely used biomarker for assessing physiological stress, recovery, and training load in sports science. During exercise, the mechanical changes in various parts of the body, such as muscle contraction and relaxation, joint movement, and the dynamic response of the cardiovascular system, are closely related to HRV. However, traditional analysis methods face significant challenges in handling HRV’s nonlinear dynamics and noise sensitivity. These limitations reduce their effectiveness in complex sport scenarios. To address these limitations, this study proposes an innovative HRV feature extraction framework that integrates reinforcement learning (RL) with an attention-based Long Short-Term Memory (LSTM) network. The framework dynamically optimizes feature selection and weighting through RL. The integration of an attention mechanism enables the model to prioritize critical temporal segments, improving its ability to capture and interpret key physiological patterns. Additionally, the model combines time-domain, frequency-domain, biomechanical factors, and nonlinear features, providing a comprehensive and robust representation of HRV signals. The framework was validated on four publicly available datasets covering resting, exercise, stress, and recovery states. It achieved an average accuracy of 95.0% and an F1-score of 90.8%, outperforming state-of-the-art baselines by 2.7% to 3.4%. These results demonstrate the proposed method’s superior performance in stress detection, training load prediction, and recovery assessment, establishing it as a scalable and adaptive tool for HRV-based sports training monitoring and health management. The framework’s innovative design offers significant advancements in the analysis of complex HRV data, paving the way for intelligent and personalized applications in sports science and healthcare.
... In the context of affect dynamics, Markov chains can be employed to analyze and predict emotional state transitions over time, utilizing physiological measures like RMSSD to understand the probabilistic nature of emotional fluctuations (40). RMSSD is one of the most suitable indexes for heart rate variability in short term: it aligns perfectly with the properties of Markovian chains, a stochastic model of transition, which considers pairs of transitions one at a time, disregarding the path preceding that transition (60,61). ...
Background
Affect dynamics, or variations in emotional experiences over time, are linked to psychological health and well-being, with moderate emotional variations indicating good psychophysical health. Given the impact of emotional state on cardiac variability, our objective was to develop a quantitative method to measure affect dynamics for better understanding emotion temporal management in Anorexia Nervosa (AN).
Methods
The study proposed an experimental and methodological approach to evaluate physiological affect dynamics in clinical settings. It tested affective transitions and temporal changes using emotional images from the International Affective Picture System (IAPS), examining physiological characteristics of a patient with AN. The methodology involved calculating a heart rate variability index, e.g., RMSSD, and using it in a Discrete Time and Discrete Space Markov chain to define, quantify, and predict emotional fluctuations over time.
Results
The patient with Anorexia Nervosa showed a high likelihood of transitioning from positive to negative emotional states, particularly at lower arousal levels. The steady state matrix indicated a tendency to remain in highly activated pleasant states, reflecting difficulties in maintaining emotional balance.
Conclusions
Employing Markov chains provided a quantitative and insightful approach for examining affect dynamics in a patient with AN. This methodology accurately measures emotional transitions and provides a clear and interpretable framework for clinicians and patients. By leveraging Markovian indexes, mental health professionals may gain a comprehensive understanding of emotional fluctuations’ patterns. Moreover, graphical representations of emotional transitions may enhance the clinician-patient dialogue, facilitating a clearer emotional and physiological profile for the implementation of personalized treatment procedures.
... The significance of these parameters has been comprehensively reviewed in recent literature. 26,27 Among the time-domain HRV parameters, we measured the mean RR interval and standard deviation of NN intervals (SDNN) for each time window. The SDNN is posited to be more indicative of PNS activity when assessed over a short-term resting condition. ...
Objective Executive function correlates with the parasympathetic nervous system (PNS) based on static heart rate variability (HRV) measurements. Our study advances this understanding by employing dynamic assessments of the PNS to explore and quantify its relationship with inhibitory control (IC).Methods We recruited 31 men aged 20–35 years. We monitored their electrocardiogram (ECG) signals during the administration of the Conners’ Continuous Performance Test-II (CCPT-II) on a weekly basis over 2 weeks. HRV analysis was performed on ECG-derived RR intervals using 5-minute windows, each overlapping for the next 4 minutes to establish 1-minute intervals. For each time window, the HRV metrics extracted were: mean RR intervals, standard deviation of NN intervals (SDNN), low-frequency power with logarithm (lnLF), and high-frequency power with logarithm (lnHF). Each value was correlated with detectability and compared to the corresponding baseline value at t0.Results Compared with the baseline level, SDNN and lnLF showed marked decreases during CCPT-II. The mean values of HRV showed significant correlation with d’, including mean SDNN (R=0.474, p=0.012), mean lnLF (R=0.390, p=0.045), and mean lnHF (R=0.400, p=0.032). In the 14th time window, the significant correlations included SDNN (R=0.578, p=0.002), lnLF (R=0.493, p=0.012), and lnHF (R=0.432, p=0.031). Significant correlation between d’ and HRV parameters emerged only during the initial CCPT-II.Conclusion A significant correlation between PNS and IC was observed in the first session alone. The IC in the repeated CCPT-II needs to consider the broader neural network.
... Pupil dilation is directly related to arousal (Beerendonk et al., 2024;Nobukawa et al., 2021;Unsworth & Robison, 2018;van der Wel & van Steenbergen, 2018). In addition, HR mean and variance are also variously related to individual internal states (Pham, Lau, Chen, & Makowski, 2021;Redondo, Vera, Luque-Casado, García-Ramos, & Jiménez, 2019;Siennicka et al., 2019;Gazzellini et al., 2016;Shaffer, McCraty, & Zerr, 2014). ...
Understanding individuals' internal cognitive states during group interactions is crucial for enhancing group dynamics and communication. This study investigated internal states by analyzing physiological data—EEG, electrocardiography, and pupil size—collected from high school students during group discussions. Using a data-driven clustering method, we identified four distinct internal states, each corresponding to the different power distributions in the four frequency bands of EEG activity. These states were associated with specific behaviors such as gazing at faces, speaking, and specific body language, as well as physiological metrics such as heart rate variability and pupil size. We also examined the influence of environmental factors on internal states, including the presence of a facilitator and the group size. The presence of a facilitator significantly increased the probability of participants remaining in the high alpha-power state, possibly reflecting a relaxed or moderately aroused state. This study provides insights into the physiological underpinnings of group interactions, which can be leveraged to improve educational settings and other group-based activities.
... Table 1. An overview of HRV metrics categorized by their analysis domains [44]. Figure 6. Heart Rate (HR) and its mean value extraction from PPG signal: The time interval (period) between consecutive systolic peaks (left), The time interval of bpm to measure the Heart Rate (HR) in beats per minute (bpm) (right). ...
... An overview of HRV metrics categorized by their analysis domains[44]. ...
Diabetes requires effective monitoring of the blood glucose level (BGL), traditionally achieved through invasive methods. This study addresses the non-invasive estimation of BGL by utilizing heart rate variability (HRV) features extracted from photoplethysmography (PPG) signals. A systematic feature selection methodology was developed employing advanced metaheuristic algorithms, specifically the Improved Dragonfly Algorithm (IDA), Binary Grey Wolf Optimizer (bGWO), Binary Harris Hawks Optimizer (BHHO), and Genetic Algorithm (GA). These algorithms were integrated with machine learning (ML) models, including Random Forest (RF), Extra Trees Regressor (ETR), and Light Gradient Boosting Machine (LightGBM), to enhance predictive accuracy and optimize feature selection. The IDA-LightGBM combination exhibited superior performance, achieving a mean absolute error (MAE) of 13.17 mg/dL, a root mean square error (RMSE) of 15.36 mg/dL, and 94.74% of predictions falling within the clinically acceptable Clarke error grid (CEG) zone A, with none in dangerous zones. This research underscores the efficiency of utilizing HRV and PPG for non-invasive glucose monitoring, demonstrating the effectiveness of integrating metaheuristic and ML approaches for enhanced diabetes monitoring.
... R-R interval data outside the interquartile range (IQR) were filtered. Heart rate variability, defined as the variability of R-R intervals between consecutive heartbeats, was calculated using key metrics [40]. ...
Filamin C (FLNC) is a structural protein of muscle fibers. Mutations in the FLNC gene are known to cause myopathies and cardiomyopathies in humans. Here we report the generation by a CRISPR/Cas9 editing system injected into zygote pronuclei of two mouse strains carrying filamin C mutations—one of them (AGA) has a deletion of three nucleotides at position c.7418_7420, causing E>>D substitution and N deletion at positions 2472 and 2473, respectively. The other strain carries a deletion of GA nucleotides at position c.7419_7420, leading to a frameshift and a premature stop codon. Homozygous animals (FlncAGA/AGA and FlncGA/GA) were embryonically lethal. We determined that FlncGA/GA embryos died prior to the E12.5 stage and illustrated delayed development after the E9.5 stage. We performed histological analysis of heart tissue and skeletal muscles of heterozygous strains carrying mutations in different combinations (FlncGA/wt, FlncAGA/wt, and FlncGA/AGA). By performing physiological tests (grip strength and endurance tests), we have shown that heterozygous animals of both strains (FlncGA/wt, FlncAGA/wt) are functionally indistinguishable from wild-type animals. Interestingly, compound heterozygous mice (FlncGA/AGA) are viable, develop normally, reach puberty and it was verified by ECG and Eco-CG that their cardiac muscle is functionally normal. Intriguingly, FlncGA/AGA mice demonstrated better results in the grip strength physiological test in comparison to WT animals. We also propose a structural model that explains the complementary interaction of two mutant variants of filamin C.
