Search Results (372)

Vasovagal syncope (VVS) affects 17% of children, significantly impairing quality of life. Machine learning (ML) models achieve high predictive accuracy of VVS in adults using blood pressure (BP) monitoring, but pediatric implementation remains challenging. The aim of the study was to evaluate whether ML models incorporating anthropometric data and heart rate variability (HRV) can predict VVS without BP monitoring in children with prior syncope or suspected VVS. We analyzed 87 participants (7–18 years) with VVS history. HRV indices (time-domain, frequency-domain, and nonlinear) were extracted from 5 min supine and standing ECG recordings using NeuroKit2. Multiple algorithms were tested with 10-fold cross-validation; SHAP analysis identified feature importance. AdaBoost achieved the performance of 71.0% accuracy, 76.3% sensitivity, and 63.3% specificity—78% of adult BP-dependent algorithm sensitivity. Weight, multifractal detrended fluctuation analysis during standing, and normalized low-frequency power were most influential. Alterations in symbolic dynamics and multiscale entropy indicated compromised autonomic complexity. ML models with anthropometric and HRV data show potential as an adjunctive screening tool to identify children at higher risk for syncope recurrence, requiring clinical confirmation. Full article

Electrocardiography (ECG) is widely recognized as the gold standard for measuring heart rate (HR) and heart rate variability (HRV). However, photoplethysmography (PPG) presents notable advantages in terms of wearability, affordability, and ease of integration into consumer devices, despite its susceptibility to motion artifacts and the absence of standardized processing protocols. In this study, we review current ECG and PPG signal processing methods and propose a signal quality assessment and reconstruction pipeline tailored for dynamic, in-vehicle environments. This pipeline was evaluated using data gathered from participants riding in an automated vehicle. Our findings demonstrate that while blood volume pulse (BVP) derived from PPG can provide reliable heart rate estimates and support extraction of certain HRV features, its utility in accurately capturing high-frequency HRV components remains constrained due to motion-induced noise and signal distortion. These results underscore the need for caution in interpreting PPG-derived HRV, particularly in mobile or ecologically valid contexts, and highlight the importance of establishing best practices and robust preprocessing methods to enhance the reliability of PPG sensing for field-based physiological monitoring. Full article

The purpose of this study was to develop a model capable of predicting stress levels and interpreting the underlying physiological patterns using large-scale, real-life biosignal data. To achieve this, we utilized approximately 137,000 longitudinal measurements voluntarily collected from residents of Sejong Special Self-Governing City over a two-year period (February 2023–December 2024). Based on these data, we constructed a stress prediction framework that integrates both static machine-learning models—such as Random Forest and LightGBM—and time-series deep learning models, including LSTM and Transformer architectures. Model interpretability was further enhanced through SHapley Additive exPlanations (SHAP), which quantified the contribution of key biomarkers, and through visualization of Transformer attention weights to reveal temporal interactions within the biosignal sequences. The central objective of this study was to evaluate how accurately a deep learning model can learn and reproduce stress indices generated by existing heart rate variability (HRV)-based algorithms embedded in K-FDA-approved wearable devices. Accordingly, the ground truth used in this work reflects algorithmic outputs rather than clinically validated assessments such as salivary cortisol or psychological scales. Thus, rather than identifying independent clinical stress markers, the present work focuses on determining whether a Transformer-based model can effectively approximate device-derived physiological stress levels over time, thereby providing a methodological foundation for future applications using clinically validated stress labels. Experimental results demonstrated that the Transformer model achieved approximately 98% classification accuracy across this large dataset, indicating that it successfully captures short-term biosignal fluctuations as well as long-term temporal structure. These findings collectively demonstrate the engineering feasibility of developing a large-scale, wearable-based stress monitoring system. Full article

A growing body of evidence highlights the beneficial role of physical activity in supporting cognitive functions and learning outcomes. Yet, recent studies indicate that these effects may be shaped by environmental conditions, conceptualized within the framework of the urban exposome. The present study explores the interaction between physical activity, cognitive enhancement, and environmental exposures such as air pollution, noise, sensory overstimulation, and access to green spaces. A multi-method experimental design was implemented with 60 participants randomly assigned to either an experimental or a control group. The experimental group engaged in moderate-intensity physical activity across diverse urban settings, including green parks, high-traffic streets, and indoor facilities, while the control group performed the same activity in a stable indoor environment without environmental variability. Cognitive performance was assessed before and after physical activity through standardized measures of attention, memory, and executive function. Psychological and physiological stress responses were also monitored using the Perceived Stress Scale (PSS) and heart rate variability (HRV). Results suggest that the cognitive benefits of physical activity are not exclusively attributable to internal physiological mechanisms but are significantly moderated by environmental exposures. These findings underscore the relevance of considering contextual factors when examining the links between physical activity, cognition, and academic performance. Full article

Cardiac autonomic dysfunction has been implicated in cognitive impairment, yet its potential for early detection of Alzheimer’s disease (AD) and mild cognitive impairment (MCI) remains largely unexplored. This study proposes EHANet, an electrocardiogram (ECG) and heart rate variability (HRV)-based attention network, to enable objective and noninvasive detection of cognitive impairment. Utilizing the CAUEEG dataset, we conducted comprehensive HRV analysis encompassing temporal, spectral, and nonlinear domains, revealing progressive autonomic dysregulation characterized by diminished parasympathetic activity and increased heart rate fragmentation from normal controls (NC) through MCI to AD. EHANet integrates ECG sequence representations with physiologically interpretable HRV features, achieving diagnostic accuracies of 86.91% (AD vs. NC), 76.0% (MCI vs. NC), 82.26% (AD vs. MCI), and 73.72% (three-way classification), substantially surpassing single-modality approaches. These findings demonstrate that ECG-based cardiac autonomic assessment, combined with deep learning, offers a cost-effective and practical modality for early detection and longitudinal monitoring of cognitive decline, with significant implications for clinical screening and management of neurodegenerative diseases. Full article

Norrin–Wnt signaling is essential for retinal vascular development and generation of the inner blood retinal barrier. Norrin itself is a potential therapeutic for retinal vascular repair. We explored the feasibility of producing a recombinant protein therapeutic based on human Norrin for intravitreal injection. Norrin^K86P production was tested using MBP fusion and non-tagged versions. FZD4 binding was evaluated by an ELISA, and the activation of AXIN2 gene expression in primary human retinal microvascular endothelial cells was measured by qPCR. Intravitreal injection was tested in the rat eye, evaluated by fluoresceine angiography, OCT, and ERG. MBP-tagged Norrin was resistant to HRV3C protease cleavage unless linker polypeptides were also incorporated. MBP–Norrin or cleaved MBP–Norrin also required refolding with disulfide reshuffling to generate FZD4-binding activity and to affect AXIN-2 gene expression. A production strategy based upon untagged Norrin^K86P refolded from bacterial inclusion bodies was selected. Intravitreal injection of Norrin^K86P did not affect retinal thickness nor retinal function, the latter monitored by the ERG A-wave and B-wave amplitudes. We concluded that MBP–Norrin, cleaved Norrin, and untagged Norrin from inclusion bodies display Norrin-like biological activity after refolding with disulfide reshuffling. The untagged, bacterial inclusion body process was selected for future large-scale bacterial fermentation. Norrin^K86P could be produced with Norrin-like biochemical and biological activities and was tolerated after intravitreal injection into the rat eye. Full article

This study proposes an enhanced deep learning framework for accurate detection of P-peaks in noisy photoplethysmographic (PPG) signals, utilizing a hybrid architecture that integrates wavelet-based analysis with neural network components. The P-peak detection task is formulated as a binary classification problem, where the model learns to identify the presence of a peak at each time step within fixed-length input windows. A temporal attention mechanism is incorporated to dynamically focus on the most informative regions of the signal, improving both localization and robustness. The proposed architecture combines Discrete Wavelet Transform (DWT) for multiscale signal decomposition, Convolutional Neural Networks (CNNs) for morphological feature extraction, and Long Short-Term Memory (LSTM) networks for capturing temporal dependencies. A temporal attention layer is introduced after the recurrent layers to enhance focus on time steps with the highest predictive value. An evaluation was conducted on 30 model variants, exploring different combinations of input types, decomposition levels, and activation functions. The best-performing model—Type30, which includes DWT (3 levels), CNN, LSTM, and attention—achieves an accuracy of 0.918, precision of 0.932, recall of 0.957, and F1-score of 0.923. The findings demonstrate that attention-enhanced hybrid architectures are particularly effective in handling signal variability and noise, making them highly suitable for real-world applications in wearable PPG monitoring, digital twins for Heart Rate Variability (HRV), and intelligent health systems. Full article

Photoplethysmography (PPG)-based heart rate variability (HRV) offers a cost-effective alternative to electrocardiography (ECG) for autonomic monitoring in wearable devices. We optimized signal processing on a 16-bit microcontroller by comparing 4th-order equivalent Butterworth and Elliptic IIR bandpass filters (0.8–20 Hz, zero-phase) at 1000, 500, and 250 Hz. Paired PPG–ECG recordings from 10 healthy adults were analyzed for ln HF, ln LF, and ln VLF using Lin’s concordance correlation coefficient (CCC), ±5% equivalence testing (TOST), and Passing–Bablok regression (PBR). Butterworth at 500 Hz preserved near-identity with ECG standard (CCC ≥0.94; TOST met equivalence; PBR slopes/intercepts: ln HF = 0.97x + 0.10, ln LF = 1.02x − 0.07, ln VLF = 1.01x − 0.03), while halving computational load. In contrast, Elliptic at 250 Hz degraded concordance (CCC ≈ 0.64) and failed equivalence, with greater bias from nonlinear phase and ripple-induced distortion. Elliptic performance improved at higher sampling but offered no benefit over Butterworth. These results support zero-phase Butterworth filtering at ≥500 Hz as the optimal balance of fidelity, robustness, and efficiency, enabling reliable PPG-HRV monitoring on low-power devices. As a pilot investigation (n = 10), this study establishes preliminary design parameters and optimal configurations to guide subsequent large-scale clinical validation. Full article

Understanding how training and match load influence autonomic recovery is essential for optimizing athlete monitoring. This proof-of-concept study aimed to examine the impact of training and match load on next-day heart rate variability (HRV) and to explain how different load measures influenced the internal response, using SHapley Additive Explanations (SHAP) to interpret machine learning models. Five semi-professional basketball players (23 ± 5 years; 191 ± 7 cm; 90 ± 11 kg) were monitored throughout a competitive season. HRV and load metrics were recorded daily. Differences in the natural logarithm of the root mean square of successive differences (LnRMSSD) across Non-Training, Training, and Match days were analyzed using linear mixed models. Additionally, a Gradient Boosting Machine model was developed to examine next-day HRV responses, with SHAP analysis providing both global and individual insights into feature importance. Next-morning LnRMSSD values were significantly lower on Match days compared to both Training and Non-Training days (p < 0.001). SHAP results identified rate of perceived exertion (RPE), days since last match, minutes played, and recent training load as the most influential variables associated with HRV changes. Pre-session heart rate and the root mean square of successive differences (RMSSD) values also demonstrated notable individual relevance. The ranking and magnitude of influential variables varied across players, highlighting the heterogeneity of physiological responses in team sports. While these findings are specific to this cohort, they illustrate the potential of explainable machine learning to enhance transparency and support individualized monitoring strategies. Importantly, they underscore the value of integrating both subjective and objective load measures to inform training decisions. Future research involving larger, multi-team samples is needed to validate the generalizability of these results. Full article

To support the understanding of cardiovascular monitoring and physiological signal processing, we present a portable, open-source photoplethysmography (PPG) acquisition platform developed for educational and research applications. The system is built entirely with commercial off-the-shelf components and centers around an ESP32 microcontroller, which performs high-speed analog signal acquisition at 500 samples per second, alongside real-time control, and wireless communication. A cross-platform, Python-based graphical user interface enables real-time signal visualization, peak detection, and the computation of heart rate variability (HRV) metrics, including RMSSD and SDNN, during offline analysis. All hardware and software resources are openly available to enable replication and further development. This project emphasizes accessibility, transparency, and hands-on learning in biomedical signal acquisition. System functionality is validated offline through controlled data collection from human subjects, demonstrating results consistent with established HRV benchmarks. Full article

(1) Background: There have been previous reports of autonomic modulation by aromatherapy. In this study, we recorded heart rate variability (HRV) to assess its relationship with the effects on feeling. (2) Methods: Twenty-three healthy subjects, who were blind to the aroma type, were exposed to citrus aurantium oil (CAO) or rose water (RW) aroma for 5 min using a diffuser situated in a room. Electrocardiographic data were measured continuously using a wireless device attached to the chest. R-R intervals were used to calculate HRV scores, including high-frequency (HF) variation, low-frequency (LF) variation, LF/HF ratios, the coefficient of variation in R-R (CVRR), and heart rate. A visual analog scale (VAS) was used to evaluate disfavor, fatigue, anxiety, tension, and somnolence at the end of the treatment. (3) Results: CAO significantly reduced disfavor, anxiety, and tension, while RW did not affect VAS scores. HF scores were high during the treatment with both CAO and RW, indicating parasympathetic activation. Treatment with CAO was also accompanied by an increase in LF and the CVRR, whereas treatment with RW was not. HF scores during CAO treatment were negatively correlated with somnolence. No relationships between VAS scores and HRV scores were observed in the RW treatment. (4) Conclusions: In CAO treatment, parasympathetic activation is related to feeling. RW, on the other hand, exerts its autonomic effects without changes in feeling. These results suggest that autonomic modulation by rose water may not depend on the generated feelings, suggesting the usefulness of HRV monitoring in aromatherapy. Full article

Background/Objectives: Postural orthostatic tachycardia syndrome (POTS) is a form of dysautonomia characterized by excessive tachycardia during orthostatic stress. It is frequently observed in patients with syncope, Chronic Fatigue Syndrome (CFS), and post-COVID-19 syndrome (PCS), yet the underlying mechanisms may differ across these conditions. This study aimed to assess autonomic nervous system (ANS) function in patients with syncope, CFS of insidious onset, and CFS post-COVID-19 who presented with POTS, and to compare them with age- and sex-matched patients without POTS. Methods: In this retrospective cross-sectional study, 138 patients over 18 years of age were included following head-up tilt testing (HUTT). Patients were divided into six groups: syncope with and without POTS, CFS with insidious onset with and without POTS, and CFS post-COVID-19 with and without POTS. All participants underwent HUTT, cardiovascular reflex testing (CART) by Ewing, five-minute resting ECG with short-term Heart Rate Variability (HRV) analysis, and 24 h Holter ECG monitoring. Results: The prevalence of POTS across groups ranged from 5% to 7%. Female predominance was consistent across all subgroups. In syncope with POTS, hypertensive responses during HUTT, lower rates of normal Valsalva maneuver results, and reduced HF values in short-term HRV suggested baroreceptor dysfunction with sympathetic overdrive. In both CFS subgroups with POTS, CART revealed higher rates of definite parasympathetic dysfunction, along with more frequent extreme blood pressure variation during HUTT and reduced vagally mediated HRV parameters (rMSSD, pNN50). Across groups, no significant differences were observed with regard to long-term HRV across groups. Conclusions: Distinct autonomic profiles were identified in POTS patients depending on the underlying condition. Syncope-related POTS was associated with baroreceptor dysfunction and sympathetic predominance, whereas CFS-related POTS was characterized by parasympathetic impairment and impaired short-term baroreflex regulation. Evaluating dysautonomia patterns across disease contexts may inform tailored therapeutic strategies and improve management of patients with POTS. Full article

An electrocardiogram (ECG) is a vital diagnostic tool that provides crucial insights into the heart rate, cardiac positioning, origin of electrical potentials, propagation of depolarization waves, and the identification of rhythm and conduction irregularities. Analysis of ECG is essential, especially during pregnancy, where monitoring fetal health is critical. Fetal electrocardiography (fECG) has emerged as a significant modality for evaluating the developmental status and well-being of the fetal heart throughout gestation, facilitating early detection of congenital heart diseases (CHDs) and other cardiac abnormalities. Typically, fECG signals are acquired non-invasively through electrodes placed on the maternal abdomen, which reduces risk and enhances user convenience. However, these signals are often contaminated via various sources, including maternal electrocardiogram (mECG), electromagnetic interference from power lines, baseline drift, motion artifacts, uterine contractions, and high-frequency noise. Such disturbances impair signal fidelity and threaten diagnostic accuracy. This scoping review adhering to PRISMA-ScR guidelines aims to highlight the methods for signal acquisition, existing databases for validation, and a range of algorithms proposed by researchers for improving the quality of fECG. A comprehensive examination of 157,000 uniquely identified publications from Google Scholar, PubMed, and Web of Science have resulted in the selection of 6210 records through a systematic screening of titles, abstracts, and keywords. Subsequently, 141 full-text articles were considered eligible for inclusion in this study (from 1950 to 2026). By critically evaluating established techniques in the current literature, a strategy is proposed for analyzing fECG and calculating heart rate variability (HRV) for identifying fetal heart-related abnormalities. Advances in these methodologies could significantly aid in the diagnosis of fetal heart diseases, assisting timely clinical interventions and prevention. Full article

Background/Objectives: Heart rate variability (HRV) is associated with the risk of vascular events. However, the predictive value of HRV for the presence of intracranial atherosclerosis (ICAS) is unclear. This study aimed to investigate the relationship between daytime HRV measured by 3 min ECG monitoring and ICAS identified by high-resolution magnetic resonance imaging (HR-MRI). Methods: A total of 272 adults (mean age, 63.4 ± 6.8; 43% male) were recruited from November 2022 to December 2024. A series of cardiac function parameters is automatically generated through a 3 min analysis by the electrocardiographic dispersion mapping (ECG-DM) software, including heart rate variability and myocardial ischemic metabolic impairment. HRV was assessed as the standard deviation of normal-to-normal intervals (SDNN), which was categorized into tertiles for data analysis. Myocardial micro-alteration index (MMI, %) was used as an indicator of ischemia, reflecting myocardial abnormalities at the metabolic level. Atrial and ventricular myocardial oxygenation deficits were directly visualized in a color-coded scatter plot, with different colors indicating the severity of pathological changes. On HR-MRI intracranial artery wall scanning, the prevalence of ICAS was assessed in middle cerebral arteries (MCAs), vertebral arteries (VAs), and basilar arteries (BAs), and the associated plaque characteristics (eccentricity, thickening patterns, remodeling index, and surface morphology) were evaluated. Results: Among the subjects, 209 arterial lesions caused by ICAS were detected in 152 subjects (56%), including MCAs (105/544), VAs (68/526), and BAs (36/272). Ninety-four subjects (94/272) with significant HRV deviation had ICAS (p = 0.040). Furthermore, subjects with ICAS were more likely to present with atrial hypoxia (p = 0.030) compared to those without ICAS. In multivariate analyses, lower standard deviation of normal-to-normal intervals (SDNN, odds ratio, OR = 1.55, 95% CI 1.10–2.18, p = 0.012) and atrial deviation (OR = 1.85, 95% CI 1.10–3.14, p = 0.022) were independently associated with the presence of ICAS. Conclusions: Among middle-aged or older adults in a local community, our study suggested that lower HRV and significant atrial hypoxia were independently associated with the presence of ICAS. Full article

Background/Objectives: Systemic sclerosis-associated interstitial lung disease (SSc-ILD) and chronic obstructive pulmonary disease (COPD) are linked to multi-organ vulnerability involving the lungs, heart, and kidneys. This study aimed to compare the annual changes in pulmonary, cardiac, and renal parameters in patients with SSc-ILD and COPD across three consecutive years, using both individual biomarkers and integrated composite profiles. Methods: This observational longitudinal study included repeated assessments in 2023, 2024, and 2025. Functional, laboratory, and imaging parameters were collected: 6-min walk test (6MWT), SpO₂ (pre-/post-exercise), spirometry/CT lung volumes, gas exchange (pO₂/pCO₂/lactate), echocardiography [left ventricular ejection fraction (LVEF), estimated systolic pulmonary artery pressure (sPAP)], cardiac biomarkers (NT-proBNP, MR-proANP, hsTnT), renal markers [eGFR, creatinine, albuminuria, albumin-to-creatinine ratio (ACR)], heart rate variability (HRV), and renal CT densitometry. All markers were standardized (z-scores, higher values = worse). Subprofiles were generated and aggregated into three integrated profiles (cardiac, renal, pulmonary). Within-group dynamics were analyzed using the Wilcoxon signed-rank test (year-to-year deltas), between-group comparisons with the Mann–Whitney U test, effect sizes via Cliff’s delta, and multiple testing correction with the Benjamini–Hochberg false discovery rate (FDR). Results: Exercise tolerance declined in both groups: by 2025, 6MWT distance decreased by −10 m in SSc-ILD (p = 0.006; q = 0.010) and −20 m in COPD (p = 0.002; q = 0.004); post-exercise SpO₂ fell in both cohorts (both p < 0.001; q < 0.001). MR-proANP remained consistently higher in SSc-ILD across all years (p ≤ 0.005; q ≤ 0.028). sPAP increased in both groups, reaching higher values in COPD by 2025 (p = 0.007; q = 0.033). NT-proBNP and hsTnT increased over time, while eGFR declined, and ACR rose in both cohorts (both p < 0.001; q < 0.001). HRV (HF/total power) decreased by 2025. Composite profiles showed: in 2023, the cardiac profile was worse in SSc-ILD (δ ≈ 0.27; p = 0.011; q = 0.048), but differences diminished by 2025; the renal profile was initially worse in SSc-ILD but later shifted unfavorably in COPD; the pulmonary profile showed no consistent between-group differences. Conclusions: Over three years, patients with SSc-ILD and COPD exhibited concordant deterioration in pulmonary, cardiac, and renal function. Distinct leading markers emerged: desaturation during exercise and neurohormonal activation (MR-proANP) in SSc-ILD, versus reduced 6MWT and higher sPAP in COPD. These findings support the need for integrated monitoring of the cardio–pulmo–renal continuum. Limitations include the observational design, multiple comparisons, and absence of advanced repeated-measures modeling. Full article