Search Results (615)

The development of atrial fibrillation involves the synergistic effects of electrical remodeling, structural remodeling and neural remodeling, among which remodeling of the autonomic nervous system (ANS) plays a pivotal role in disease initiation and progression. Heart rate variability (HRV), as an important tool for assessing autonomic function, has been widely applied in cardiovascular research. In particular, ultra-short-term heart rate variability (usHRV) analysis has demonstrated significant value in the early prediction of atrial fibrillation. Full article

Heart rate variability (HRV) is an established biomarker of autonomic nervous system activity, yet its profile in miniature cattle remains poorly understood despite their growing importance in sustainable farming. This study compared HRV parameters between miniature and Holstein cows and assessed the influence of age on these profiles. Eighty clinically healthy female cattle (40 miniature, 40 Holstein), aged 2 to 8 years, were evaluated under field conditions using a Polar H10 heart rate monitor. RR intervals were analyzed using Kubios HRV software to obtain time- and frequency-domain indices. Miniature cows exhibited significantly lower heart rates and higher time-domain measures (RMSSD and SDNN) compared to Holsteins, while frequency-domain analysis revealed significant differences in LF, HF, and LF/HF ratio, suggesting group-associated differences in proportional autonomic balance. Age-stratified analysis revealed that these physiological distinctions were more pronounced in older cows (6–8 years). However, given the observational cross-sectional design of this study, confounding factors—specifically the different farm environments, management systems, and the active lactation status of the Holstein group—preclude attributing these differences solely to breed or body size. Therefore, these results suggest an associative physiological pattern rather than a definitive autonomic adaptation. Despite these limitations, portable HRV monitoring proved feasible under farm conditions, providing valuable preliminary baseline data that can inform future controlled studies on bovine cardiovascular welfare. Full article

Objectives: This study investigated the effects of exercise selection and rest interval duration on post-exercise cardiac autonomic modulation following resistance exercise (RE). Methods: Eleven (4 females) resistance-trained individuals performed a single RE session consisting of either a multi-joint exercise (back squat) or a single-joint exercise (leg extension), using rest intervals of 1 or 2 min between sets. Heart rate variability (HRV) was assessed at baseline (pre-exercise) and 30 min following the RE session. RR intervals were recorded for 15 min with participants resting in the supine position on an examination bed in a quiet environment. For HRV analysis, a 5-min artifact-free segment of RR intervals was selected and processed using Kubios HRV software, version 4.3.0 (Kubios Oy, Kuopio, Finland). The HRV metrics analyzed included the root mean square of successive differences (RMSSD), low-frequency normalized (LF), the low-frequency/high-frequency (LF/HF) ratio, and the standard deviation of transverse dispersion (SD1). Results: A significant main effect of time was observed for RMSSD, LF, and the LF/HF ratio. The back squat exercise elicited a significant reduction (p < 0.05) in vagal-related indices (RMSSD and SD1) regardless of interval duration. Longer rest intervals were associated with increased (p < 0.05) sympathetic modulation, as reflected by higher LF and LF/HF values 30 min post-exercise. No significant time × group interactions were observed for most HRV variables. Conclusions: Exercise selection and rest interval duration differentially influence post-exercise cardiac autonomic responses following RE. Multi-joint exercises induce greater vagal withdrawal, whereas longer rest intervals favor sympathetic predominance during recovery. These findings highlight the importance of manipulating RE variables to manage autonomic stress and recovery. Full article

Precise respiration assessment is crucial for heart rate variability (HRV) interpretation as respiratory components—particularly respiratory sinus arrhythmia (RSA)—provide essential information on vagally mediated regulation. Conventional single-lead electrocardiogram-derived respiration (EDR) methods measure the amplitude modulation of the QRS-waveform caused by respiratory chest movements. This causes a displacement of the electrical heart axis in relation to the ECG lead axis, typically within the 2D frontal plane of the Einthoven electrode montage. Another approach is based on heartbeat acceleration and deceleration during respective inspiration and expiration causing RR interval modulation. However, interval-based methods depend on the complexity of sympathovagal factors that affect RSA. The present feasibility study accounts for the 3D rotational movement of the electrical heart axis during the respiratory cycle and avoids non-respiratory neuromodulatory confounds. The beat-to-beat cardiac rotation was extracted from Frank-XYZ coordinates reconstructed via a four-electrode EASI device. In a pilot study with data from 19 healthy adults performing acoustically paced breathing (6–18 bpm), three surrogates (${\mathrm{RR-Interval}}_{\mathrm{EDR}}$, ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$) were compared using a unified Python 3.11.13 pipeline (3D VCG R-peak detection, multivariate Mahalanobis artifact correction, wavelet-based analysis) against a synthetic reference derived from the instructed breathing schedule. The results demonstrated a consistently lower estimation error and higher reference-based signal-to-noise ratio (refSNR), measuring spectral alignment with the paced-breathing trajectory for ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ and achieving a mean refSNR of 6.01 dB (vs. 4.62 dB for ${\mathrm{RR-Interval}}_{\mathrm{EDR}}$ and 3.20 dB for ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$) and a mean absolute estimation error of 0.016 Hz (vs. 0.050 Hz and 0.032 Hz, respectively). Notably, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ and ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$ performance slightly improved at higher heart rates, consistent with the interpretation that higher cardiac sampling density benefits spectral resolution for chest movement-based methods, whereas ${\mathrm{RR-Interval}}_{\mathrm{EDR}}$ showed no significant heart rate dependence. Furthermore, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ was compared with the EDR results obtained by applying the Kubios-HRV Premium software (version 3.5.0). Kubios-EDR yielded higher precision at elevated breathing frequencies, whereas ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ outperformed Kubios-EDR at breathing rates below 10 bpm—a range that is particularly relevant for vagally activating slow breathing protocols or treatments. Future work should validate this method using a direct respiration measurement under spontaneous natural breathing conditions. Full article

Background: Sleep-disordered breathing (SDB) is frequently accompanied by autonomic nervous system (ANS) dysfunction, which is closely associated with an increased incidence of cardiovascular diseases and elevated mortality risk. Heart rate variability (HRV) serves as a classic metric for evaluating sympathovagal balance; however, the specific impacts of four distinct types of respiratory events—obstructive apnea (OA), central apnea (CA), mixed apnea (MA), and hypopnea (HYP)—on HRV remain underinvestigated. Utilizing ultra-short-term HRV analysis, this study aimed to evaluate the immediate effects of different respiratory events on ANS function, while further exploring the modulatory roles of arousal, Apnea–Hypopnea Index (AHI) severity and sleep stages (non-rapid eye movement [NREM] vs. rapid eye movement [REM]). Methods: A total of 108 patients with SDB undergoing overnight polysomnography (PSG) were included. A total of 19,862 respiratory events, including obstructive apnea (OA), central apnea (CA), mixed apnea (MA), and hypopnea (HYP), were analyzed using 15 s ECG segments. Linear mixed-effects models (LMMs) and estimated marginal means (EMMs) with Sidak-adjusted pairwise comparisons were constructed to evaluate differences in ECG-derived features and to analyze differences between event types. Results: Central apnea (CA) was associated with significantly reduced HRV and heart rate indices, including Standard Deviation of Successive Differences (SDSD), Root Mean Square of the Successive (RMSSD), Standard Deviation 1 (SD1), and heart rate (HR), compared with other respiratory event types (all p < 0.05). Across all event types, HRV metrics exhibited consistent dynamic changes before, during, and after respiratory events (all p < 0.001), characterized by a decrease during the event followed by post-event recovery. In the interaction effect of sleep stage, SDSD was significantly lower in CA compared with both OA (estimate = −11.67, 95% CI −18.78 to −4.59, p < 0.001) and HYP (estimate = −11.38, 95% CI −18.55 to −4.20, p < 0.001) during NREM sleep. No significant differences in HRV parameters, heart rate, or QRS duration were observed between OA and HYP (all p > 0.05). Conclusions: This study is the first to elucidate the differential impacts of four distinct types of sleep respiratory events on ultra-short-term HRV, confirming that CA events exert the most profound effects on autonomic function. These findings suggest that the proportion of CA occurrences could serve as a more precise biomarker for identifying individuals at high risk for cardiovascular diseases within the SDB population. Full article

Background: This study analyzed spectral alterations of heart rate variability (HRV) in Williams syndrome (WS) during sleep, taking into account the multi-fractal properties of RR-interval spectra, including effects of aging and sleep structure. Methods: Using ECG recordings of 20 subjects with WS and matched typically developing (TD) controls, fractal and oscillatory spectral components of RR-intervals were computed. The fractal component was parametrized with a piecewise-linear function, allowing a breakpoint and separate slope and intercept values in the lower- and higher-frequency domains. The dominant peak frequency and prominence were extracted from the LF (0.04–0.15 Hz) and HF (0.15–0.4 Hz) bands. Results: Strong WS/TD group differences were found in the breakpoint frequency, high domain slope, intercept and HF peak prominence. The LF peak frequency showed a slight age-dependent decrease only in TD, and reduced values in WS independent of age. Principal component analysis identified a main fractal component describing typical alterations in the spectrum in WS, which exhibited sleep-structure associations. Conclusions: The broken power-law model successfully characterized the fractal component of RR-interval spectra, capturing altered cardiac regulation in WS, while suggesting the fractal parameters as possible biomarkers of the degree of general autonomic deregulation. Full article

This study aims to determine the validity of the short-term scaling exponent of detrended fluctuation analysis (DFA-a1), respiration rate, and the ratio between respiration rate and the DFA-a1 (RRa1) in the estimation of the load associated with the first and second ventilatory thresholds (VT1, VT2) during running in a sample of adolescents. Twenty-two adolescents (11 males and 11 females) performed an incremental graded exercise test, monitored through the Polar H10 chest strap, synchronized with the Garmin Forerunner 965 equipped with the alphaHRV app. In the estimation of the load associated with the VT1, there was no significant HRV indices effect (F_(2,36) = 1.528; p = 0.231), nor a HRV indices and sex interaction effect (F_(2,36) = 0.319; p = 0.729). In the estimation of the VT2, there was a significant HRV indices effect (F_(2,36) = 20.3; p ≤ 0.001). Specifically, DFA-a1 displayed a moderate overestimation of the speed associated with the VT2 (0.41 [0.14 to 0.68] km/h); meanwhile, the respiration rate (−0.56 [−1.03 to −0.09] km/h) and RRa1 (−0.87 [−1.38 to −0.36] km/h) displayed a small and large underestimation, respectively. No significant HRV indices and sex interaction effect was observed (F_(2,36) = 1.626; p = 0.211). In conclusion, DFA-a1, respiration rate and RRa1 are valid HRV indices to estimate the load associated with the VT1. However, DFA-a1 displayed a moderate overestimation of the load associated with the VT2, while the respiration rate and RRa1 displayed a small and large underestimation, respectively. Full article

Background: This study aimed to assess skin blood flow (SkBF) and choice reaction time (RT) after breathing through an increased respiratory dead space volume, following warm-up and prior to intense exercise. Methods: A group of 24 cyclists completed two exercise tests on a cycle ergometer, each at a workload of 110% of their maximal power (110%Pmax) determined during a graded test. A 15 min warm-up and an 8 min passive recovery period preceded both tests. During the recovery period before one of the tests, participants breathed through an increased respiratory dead space volume (ARDSv) of 1000 mL, while no breathing modification (non-ARDSv) was used before the other test. During the tests, measurements included skin blood flow (SkBF), body surface temperature (T), heart rate variability (HRV) parameters, and choice reaction time (RT). In both experimental protocols, main and mixed effects were detected across five repeated SkBF measurements (taken during the warm-up, the first half of recovery, the second half of recovery, during the 110%Pmax test, and in post-test recovery). Results: The analysis revealed higher HR and lower SDNN values (p < 0.05) during the post-warm-up rest period in the ARDSv protocol compared to the non-ARDSv protocol. The Friedman analysis of variance showed statistically significant effects of repeated measurements of SkBF in the non-ARDSv test (χ² = 52.37; df = 4; p = 0.00; W = 0.55) and in the ARDSv test (χ² = 64.1; df = 4; p = 0.00; W = 0.67). Similar effects were obtained in the T analysis. Post hoc tests showed that SkBF and T at restitution after the 110% Pmax test were statistically significantly higher than SkBF and T during the 110% Pmax test only in the ARDSv protocol. Analysis of variance revealed a repeated-measures effect for mean RT (ƞ² = 0.21; df = 1; p = 0.00; F = 11.97) and covariance analysis showed that baseline mean RT was a strong predictor of outcome mean RT, while the study protocol was a weak predictor of post-exercise mean RT. Conclusions: Higher HR and lower SDNN during the period between warm-up and the 3 min test suggest increased physiological strain associated with the ARDSv procedure. Furthermore, only weak and inconclusive effects were observed for skin blood flow and choice reaction time responses following ARDSv application. Full article

Cognitive stress detection based on electrocardiogram (ECG) is challenged by the high dimensionality of multichannel analysis, redundancy between heart rate variability (HRV) and morphological descriptors, and variability in classifier performance. We developed and evaluated a cognitive stress classification framework based on a standardized ECG acquisition protocol, the integration of HRV and morphological descriptors extracted from 12 leads, and an adaptive feature selection strategy using a binary genetic algorithm with explicit penalization of dimensionality. Seventy healthy students aged 18–25 years participated, and cognitive stress was induced using a task based on PMA-R Factor R. The initial dataset included 27 descriptors per lead, and the proposed dimensionality reduction method was compared with two reference schemes: no dimensionality reduction and conventional principal component analysis (PCA) with a 99% cumulative explained variance threshold. Performance was assessed over 30 data splits using five classifiers: logistic regression, linear support vector machine (SVM), radial basis function SVM (SVM-RBF), k-nearest neighbors (KNN), and decision tree. The best trade-off between parsimony and predictive performance was achieved with $\lambda =0.05$, yielding a compact subset of 11 features on average and a mean AUC of 0.830. In the final comparison, the adaptive strategy achieved the best overall performance with SVM-RBF (AUC $=0.830\pm 0.047$; specificity $=0.814\pm 0.115$), outperforming both the full feature set and PCA. These findings indicate that penalized genetic selection validated across multiple classifiers is an effective strategy for identifying compact, discriminative, and robust feature subsets for ECG-based cognitive stress classification. Full article

Objective: This pilot study aimed to explore how a receptive music intervention, based on musical genres reflecting maternal listening preferences during pregnancy, affects distress levels in Italian preterm infants. Specifically, it investigated the effects of soft pop/rock music, compared with classical music, on infants’ LF/HF ratio (derived from heart rate variability [HRV]) and peripheral oxygen saturation (SpO₂), which were used as physiological markers of distress. Method: This retrospective observational pilot study analyzed clinical data routinely collected between May 2014 and January 2015 from 27 preterm infants (gestational age 23–32 weeks; birth weight < 1500 g) who received receptive music therapy as part of standard family-centered care in the NICU. Maternal listening preferences during pregnancy were assessed in 30 mothers via an ad hoc questionnaire; a content analysis identified, at the group level, the three most frequently reported artists (i.e., Jovanotti, Vasco Rossi, and W. A. Mozart), which were used to create three standardized playlists. According to the internal clinical procedure, each infant underwent four sessions on consecutive days: a no-music condition on Day 1, followed by the three music conditions on Days 2–4 in randomized order. The LF/HF ratio and SpO₂ were measured at five time points per session (one pre-test, three intra-session time points, and one post-test). Wilcoxon signed-rank tests were used to compare conditions and time points, with effect sizes and a Benjamini–Hochberg (FDR) correction for multiple comparisons. Results: The LF/HF ratio did not differ significantly across music conditions or relative to the no-music condition. SpO₂ was higher during the Mozart condition than during the no-music condition at three of the five time points; this association remained significant after FDR correction, with medium-to-large effect sizes. No effect was observed for the soft pop/rock conditions on physiological indexes. Conclusions: Receptive music therapy based on maternal listening during pregnancy was not associated with changes in the LF/HF ratio. The Mozart condition was associated with higher SpO₂ than the no-music condition. Given the small sample, the single-center setting, and the retrospective observational design, these findings are preliminary and require confirmation in larger, adequately powered prospective trials. Future studies should also examine the specific musical features (e.g., tempo, harmonic structure, voice timbre) that may drive these physiological responses. Full article

Considerable efforts have been devoted to accurately monitoring the depth of anesthesia to ensure patient safety during surgery. Traditional approaches typically rely on electroencephalogram (EEG)-based indices, such as the Bispectral Index (BIS), which require specialized equipment. In contrast, electrocardiogram (ECG) signals are widely available in clinical settings and can be conveniently acquired via wearable devices, while also exhibiting strong responsiveness to anesthetic agents. Inspired by biomimetic physiological regulation mechanisms, this study proposes a wearable-compatible ECG-based framework for depth-of-anesthesia detection that leverages autonomic nervous system characteristics and a knowledge graph-enhanced graph convolutional network (GCN). ECG recordings from 110 patients were preprocessed, and 20 anesthesia-related features were extracted, spanning morphological, statistical, spectral, heart rate variability (HRV), and entropy-based descriptors; feature selection methods identified 13 discriminative features. A patient-level knowledge graph was first constructed using the 88 training patients (1760 nodes), and test patient nodes were incorporated only after training was complete for inductive inference. Experimental results demonstrate that the proposed deep knowledge GCN achieves a test accuracy of 98.18% in distinguishing between awake and deep sleep anesthesia states, indicating that biomimetic, wearable-compatible ECG analysis combined with knowledge graph learning holds strong potential as a cost-effective alternative to traditional EEG-based anesthesia monitoring systems. Full article

Background: Fatigued driving is a key contributing factor to major traffic accidents. Existing detection technologies suffer from issues such as delayed identification, high error rates, and a lack of quantified causal relationships between physiological and behavioral indicators. This study aims to clarify the intrinsic relationship between electrophysiological and driving behavior data during the progression of driving fatigue. Methods: Four categories of driving behavior data and electrocardiographic (ECG) heart rate variability (HRV) indicators were selected as the study subjects. Based on a four-stage standardized simulated driving experiment ranging from wakefulness to severe fatigue, the correlations between indicators were quantified using Pearson correlation analysis, and a four-layer physiological–behavioral fusion fatigue assessment model was constructed. Results: Autonomic dysregulation is the intrinsic cause of abnormal driving behavior. The two exhibit a highly synchronized, stepwise progressive evolution pattern, with |r| ≥ 0.75 among core indicators. The accuracy of the constructed model exceeded 90% for all fatigue stages, reaching 97.8% for severe fatigue detection, with a response time of ≤0.5 s. Conclusions: This model effectively addresses the limitations of single-monitoring technologies and provides theoretical support and technical guidance for multimodal identification and graded early warning of driving fatigue. Full article

Robust decoding of latent cognitive states from non-stationary physiological time series is a challenging high-dimensional signal processing problem. Traditional unimodal frameworks based only on electroencephalography often show covariate shift and weak cross-task generalization. This study presents the Neuro-Cardiac Symbiotic Engine, a multimodal fusion architecture that combines high-frequency cortical EEG dynamics with low-frequency autonomic regulation derived from heart rate variability within a unified discriminative feature space. The pipeline integrates spectral decomposition and autonomic quadratic descriptors through a memory-optimized high-performance computing workflow on the CEDIA supercomputer. To reduce domain discrepancy between memory and piloting tasks, we design a few-shot calibration strategy based on affine manifold alignment and probabilistic ensemble inference. Validation on 29 subjects reaches a mean classification accuracy of 99.13 percent, far above the zero-shot baseline near 38 percent. Topological analysis also indicates phase-space contraction under high workload, where fused vagal and frontal-parietal biomarkers concentrate system dynamics into a low-entropy attractor. The results establish a mathematically grounded framework for passive brain–computer interfaces and show that orthogonal neuro-visceral integration is critical for reliable cognitive state estimation. Full article

Background: Hypertension is increasingly recognized as a complex psychophysiological condition in which psychological factors interact with autonomic regulation and cardiac electrical stability. This study aimed to investigate the associations of spiritual orientation, perceived stress, and self-efficacy with heart rate variability (HRV) and electrocardiographic (ECG) repolarization parameters in individuals with hypertension. Methods: A total of 200 participants were included, comprising 100 hypertensive patients and 100 age- and sex-matched healthy controls. HRV was assessed using time-domain indices (SDNN and RMSSD), while ECG parameters included heart rate, QRS duration, QT interval, Tp-e interval, and Tp-e/QT ratio. Psychosocial variables were evaluated using validated scales. Group comparisons, correlation analyses, and multivariate regression models were performed. Results: Compared with controls, hypertensive patients exhibited significantly lower SDNN (68.73 ± 10.74 vs. 82.85 ± 10.74 ms, p < 0.001, Cohen’s d = 1.52) and RMSSD (35.55 ± 8.36 vs. 44.17 ± 8.36 ms, p < 0.001, d = 1.18), along with higher heart rate (74.73 ± 9.12 vs. 68.72 ± 8.85 bpm, p < 0.001, d = 1.11) and increased repolarization parameters, including QT interval (407 ± 18.3 vs. 397.58 ± 17.9 ms, p < 0.001, d = −0.69), Tp-e interval (97.95 ± 10.2 vs. 90.94 ± 9.8 ms, p < 0.001, d = 0.89), and Tp-e/QT ratio (0.24 ± 0.02 vs. 0.23 ± 0.02, p < 0.001). Spiritual orientation was positively correlated with SDNN (r = 0.274, p < 0.001) and RMSSD (r = 0.242, p < 0.001) and negatively correlated with heart rate (r = −0.277, p < 0.001), Tp-e (r = −0.256, p < 0.001), and Tp-e/QT ratio (r = −0.258, p < 0.001). Perceived stress showed inverse correlations with HRV indices and positive associations with repolarization parameters. In multivariate analysis, spiritual orientation remained an independent predictor of higher HRV indices, whereas perceived stress independently predicted a longer Tp-e interval and lower HRV. Conclusions: Spiritual orientation and stress-related factors are significantly associated with both autonomic function and cardiac repolarization in hypertension. These findings support a psychophysiological model in which psychosocial resources and stress responses jointly influence cardiovascular regulation. Integrating psychosocial assessment into hypertension management may provide additional insights beyond traditional risk factors. Full article

People spend the majority of their lives within environments shaped by multiple interacting exposures, including thermal conditions, acoustic noise, lighting, and air quality, yet remain largely unaware of how these settings affect their comfort. Existing comfort research treats domains in isolation under controlled laboratory conditions, leaving real-world multi-domain effects on personal comfort underexplored. This paper proposes a unified Comfort Framework that fuses three practical data layers: macro-environmental conditions retrieved via location-based APIs, kinematic and micro-environmental context captured through smartphone sensors, and physiological responses recorded by a chest-worn ECG sensor. Binary comfort states are labelled in real time using a minimal-disruption lap-button protocol on a consumer smartwatch. We validate the pipeline through a single-subject pilot of 18 free-living sessions. Random Forest classification across 10 valid leave-one-session-out folds achieved an F1 macro of 0.456 ± 0.151, indicating that consumer wearable comfort prediction in unconstrained free-living conditions is more challenging than controlled chamber studies suggest. Descriptive statistics showed dataset-level differences between comfort states in wrist skin temperature (31.9 vs. 33.3 °C), heart rate (70.7 vs. 77.1 bpm), and RMSSD (42.1 vs. 34.3 ms), with overlap between classes consistent with the modest classification performance. SHAP analysis identified acoustic features, HRV features, and wrist temperature as the strongest comfort signals. The framework is architecturally designed to address all four IEQ domains, though this pilot empirically validated only thermal and acoustic signals. Full article