Search Results (2,990)

Heart rate variability (HRV) is a key physiological marker for autonomic nervous system function and cardiovascular health. Photoplethysmography (PPG) is commonly used to derive HRV metrics in wearable and low-cost monitoring systems. This study presents a comparative assessment of basic HRV metrics obtained from a MAX30102 optical sensor and a custom analog circuitry with an ADS1115 analog-to-digital converter (ADC). Both measurement pathways were carefully aligned using analog high-pass and low-pass filters and a consistent digital filtering pipeline, ensuring that the frequency bands relevant to HRV were preserved. PPG signals were recorded simultaneously, and inter-beat intervals were extracted to calculate the Standard Deviation of NN intervals (SDNN), Root Mean Square of Successive Differences (RMSSD), and Percentage of successive NN intervals >50 ms (pNN50) across multiple 30-s windows. Bland–Altman analysis was employed to evaluate agreement between the two methods. Results indicate that the analog circuit with an ADS1115 achieves comparable HRV basic metrics to the MAX30102 sensor, with improved Signal-to-Noise Ratio (SNR) due to high-resolution ADC and low-noise analog amplification. These findings demonstrate that a carefully designed analog acquisition system can reliably reproduce HRV basic parameters from PPG signals, providing an alternative approach for low-cost, flexible biosensing platforms. Full article

Heart rate variability (HRV) has emerged as a key biomarker for assessing autonomic nervous system activity, stress, fatigue, and emotional states. With the rapid development of wearable sensor technologies, HRV analysis has expanded from clinical environments to real-world, continuous monitoring. This review summarizes current applications of HRV metrics in wearable devices, including fitness tracking, mental stress assessment, sleep quality evaluation, and early detection of physiological or psychological disorders. Recent advances in photoplethysmography (PPG)-based HRV estimation have enabled noninvasive and user-friendly measurement, though challenges remain in accuracy under motion and variable environmental conditions. We also discuss methodological considerations, such as artifact correction, data segmentation, and the integration of HRV with other biosignals for multimodal analysis. Emerging research suggests that combining HRV with metrics such as respiration rate, skin conductance, and accelerometry can enhance robustness and interpretability in dynamic settings. Finally, future directions are proposed toward personalized health analytics, emotion-aware computing, and real-time adaptive feedback systems. This review highlights the growing potential of wearable HRV analysis as a foundation for preventive healthcare and human–machine symbiosis. Full article

This study examined the within-session (same-day) test–retest reliability of heart rate (HR) and parasympathetic modulation, assessed using the root mean square of successive differences (RMSSD), across exercise and recovery phases in trained soccer players. Twenty-seven male soccer players (age: 24.9 ± 3.7 years) completed a standardized soccer training session. HR and RMSSD were recorded using an ECG-based chest-strap monitor at rest, pre-exercise, and at ~10–20 min, 1 h, and 3 h post-exercise. At each time point, two consecutive 5 min seated recordings were obtained under identical conditions. Test–retest reliability was evaluated using intraclass correlation coefficients (ICC_(3,1)), standard error of measurement (SEM), coefficient of variation (CV%), minimal detectable change (MDC₉₅), paired-samples t-tests, and Hedges’ g effect sizes. HR demonstrated excellent reliability across all time points (ICC = 0.980–0.994; SEM = 0.87–1.25 bpm; CV% = 1.33–3.70%). RMSSD showed excellent reliability at rest (ICC = 0.944) and pre-exercise (ICC = 0.918), moderate reliability during early recovery (~10–20 min; ICC = 0.551), and good reliability at 1 h (ICC = 0.826) and 3 h post-exercise (ICC = 0.873). No significant systematic differences were observed between test and retest measurements (all p > 0.05), and effect sizes were trivial. These findings indicate that within-session reliability of HR remains consistently high across exercise and recovery phases, whereas RMSSD reliability varies according to measurement timing, particularly during early recovery. Full article

The electrocardiogram (ECG) is a central tool in cardiovascular diagnostics, yet interpretation requires expertise and remains subject to variability. Multimodal large language models (MLLMs) have shown emerging capabilities in medical image analysis, but their performance in ECG interpretation remains insufficiently characterized. This study evaluated the diagnostic accuracy and inter-run reliability of five MLLMs across ECG interpretation tasks. Thirteen standard 12-lead ECGs were presented to five models (ChatGPT-5.3, Gemini 3.1 Pro, Claude Opus 4.6, Grok 4.1, and ERNIE 5.0) across five independent runs per case, yielding 2275 task-level assessments. Six categorical interpretation tasks (rhythm, electrical axis, PR/P-wave morphology, QRS duration, ST/T-wave morphology, and QTc interval) were compared with expert-consensus ground truth, while heart rate estimation was evaluated using mean absolute error (MAE). Overall categorical accuracy ranged from 52.3% to 64.9%. QRS duration classification achieved the highest accuracy (66.2–90.8%), whereas ST/T-wave assessment showed the lowest performance (20.0–41.5%). Heart rate MAE ranged from 14.8 to 46.7 bpm. A dissociation between diagnostic accuracy and inter-run reliability was observed across models. These findings indicate that current MLLMs do not achieve clinically reliable ECG interpretation performance and highlight the importance of assessing diagnostic accuracy and inter-run reliability when evaluating artificial intelligence systems in biomedical diagnostics. Full article

Background: Metabolic syndrome (MetS) leads to alterations in cardiac autonomic control that can be detected from electrocardiogram (ECG)-derived markers, particularly when the cardiovascular system is challenged during an oral glucose tolerance test (OGTT). Methods: In this paper, we present an automated framework for MetS identification using RR intervals and heart rate variability (HRV) features extracted from 12-lead ECG recordings acquired during the five OGTT stages in 40 male participants (15 with MetS, 10 controls, and 15 endurance-trained marathon runners). RR intervals were first derived using a multilead Pan-Tompkins approach with fusion-based validation. From these RR series, HRV descriptors were computed from time-domain statistics (RR mean, SDNN, rMSSD, pNN50), spectral indices (VLF, LF, HF, LF/HF), and nonlinear measures (SD1, SD2, SampEn, DFA-${\alpha }_1$). Conventional HRV analysis revealed pronounced physiological differences between groups: MetS subjects exhibited reduced parasympathetic activity, reflected by lower rMSSD and SD1, lower HF power, and higher LF/HF ratios, whereas marathoners showed greater vagal modulation, higher HF power, and increased signal complexity. Healthy controls showed an intermediate autonomic profile. Using RR sequences and HRV descriptors (256 samples per stage), we trained three multimodal classifiers: a CNN-MLP model with a softmax output, a CNN-MLP model with an SVM head, and a CNN + LSTM-MLP + SVM architecture. Results: All models achieved strong discriminative performance, with accuracies ranging from 0.92 to 0.95, F1-macro values from 0.92 to 0.95, and macro-AUC values from 0.96 to 0.97. The CNN-MLP model achieved the best overall performance, whereas the CNN + LSTM-MLP + SVM model showed strong class discrimination, particularly for endurance athletes, while maintaining competitive recall for MetS. Conclusions: These findings support the feasibility of ECG-based autonomic assessment as a complementary non-invasive approach for early metabolic risk detection in clinical and preventive cardiometabolic screening settings. Full article

(1) Background: Recent studies indicated the prevalence of stress among students. The increased level of stress is concerning due to its association with cardiovascular diseases. This study examined stress within the academic setting and its effects on heart rate patterns, addressing a gap in analysis methods beyond heart rate variability. (2) Methods: The data were collected from 125 students at a large university in Texas who were highly likely to experience stress disorders. Students were asked to wear a smartwatch for the duration of an academic semester to report their stress events. (3) Results: A total of 1513 stress events were reported. The highest frequency of stress events was reported at the beginning of the week, particularly on Tuesdays, and mostly between 10 am and 6 pm. Results also showed significant increases in the number of significant lags, the number of peaks in autocorrelation plots, and the scaling exponent in DFA plots. This indicates persistent correlations in the heart rate data and less regular, less predictable heart rate patterns and rhythms than during non-stress moments. (4) Conclusions: Findings underscore the importance of using time series analysis to understand the complexities in heart rate rhythm associated with stress, with the potential to inform future stress monitoring capabilities. Full article

Background: Normal-weight obesity (NWO) is a nutritional status in which individuals have a normal body mass index (BMI) with a high percentage of body fat (%BF). However, the impact of elevated %BF on cardiometabolic risk remains unclear. This study aimed to evaluate whether NWO is associated with worse cardiometabolic risk markers and scores. Methods: We conducted a cross-sectional study using a convenience sample of employees from a public hospital. Participants aged ≥18 years with a BMI between 18.5–24.9 kg/m² were included in the study. %BF was categorized according to sex and age (InBody720). Normal weight and normal %BF (NWNB) and NWO were defined using cutoff points. Body composition, serum biochemical and inflammatory markers, hemodynamics, and autonomic function were considered cardiometabolic risk markers. The visceral fat area (VFA), atherogenic coefficient (AC), atherogenic index of plasma (AIP), body shape index (ABSI), and Framingham Risk (FR) score were considered cardiometabolic risk scores. Statistical significance was set at p < 0.05. Results: Of the 228 eligible participants, 52 met the inclusion criteria (NWNB, N = 29 and NWO, N = 23). Participants with NWO presented worse values of lipid profiles, anthropometric measurements, hemodynamic parameters, and autonomic function indices. After adjustment for age and sex, NWO remained associated with selected cardiometabolic markers, particularly LDL-c, triglycerides, and autonomic indices, whereas body composition findings should be interpreted as confirmatory of the phenotype. Conclusions: In this cross-sectional secondary analysis, NWO was associated with worse cardiometabolic markers and selected risk scores compared with NWNB. These findings support an unfavorable cardiometabolic profile in individuals with NWO, but do not allow inferences about future cardiometabolic events or causal relationships. Longitudinal studies are needed to clarify its prognostic significance. Full article

Background: Small-sided games (SSG) and running-based high-intensity interval training (HIIT) are commonly used in soccer conditioning to improve aerobic fitness and performance. Although both modalities induce high cardiovascular stress, their acute neuromuscular, perceptual, and cognitive responses remain incompletely understood when examined within the same cohort. This study compared the acute physical, psychophysiological, and cognitive responses to SSG and Tabata-type HIIT in amateur male soccer players. Methods: Thirty-two male amateur players (n = 32; age: 20.53 ± 1.65 years) completed a counterbalanced within-subject crossover design. Participants performed a 4v4 SSG protocol and a running-based Tabata-HIIT protocol (8 × 20 s, 10 s recovery) on separate days (48 h apart). Countermovement jump (CMJ), squat jump (SJ), 20-m sprint, agility t-test, heart rate, perceived exertion (Borg CR-10), mental effort, and cognitive performance (d2 test) were assessed pre- and post-exercise. Parametric variables were analyzed using 2 × 2 repeated-measures ANOVA (time × protocol; η²p), and non-parametric data were analyzed using Friedman and Wilcoxon tests (r) (p < 0.05). Results: Both protocols elicited similar cardiovascular responses (~90% HR_max). A significant protocol × time interaction was observed for CMJ (p < 0.001), showing a decline after Tabata-HIIT, whereas performance was maintained after SSG. No inter-protocol differences were found for SJ, sprint, or agility. Perceived exertion and mental effort during recovery were higher following Tabata-HIIT (p < 0.05). Cognitive performance improved after both protocols (p < 0.001), with no between-protocol differences. Conclusions: Despite comparable cardiovascular load, Tabata-HIIT was associated with greater acute neuromuscular and perceptual strain, whereas SSG preserved neuromuscular performance. Perceptual and mental responses may therefore differ despite similar physiological intensity, which may inform soccer training prescription. Full article

Chronic stress is a time-dependent condition characterized by sustained dysregulation across neural, autonomic, and endocrine systems, with important consequences for both health and socioeconomic outcomes. Unlike acute stress, which is typically characterized by short-lived physiological activation, chronic stress reflects an accumulated allostatic load and a longer-term recalibration of stress response systems. Recent advances in physiological sensing and artificial intelligence (AI) have supported the development of computational approaches for chronic stress detection using electroencephalography (EEG), heart rate variability (HRV), photoplethysmography (PPG), electrodermal activity (EDA), and wearable multimodal platforms. This narrative review examines current AI-based studies through three main inferential paradigms: resting baseline dysregulation, longitudinal physiological monitoring, and reactivity-based inference. Across modalities, classical machine learning (ML) methods, particularly support vector machines (SVMs) and tree-based ensembles, remain the most commonly used approaches, largely because available datasets are small and most pipelines still depend on engineered features. Deep learning (DL) methods are beginning to emerge, but their use remains constrained by the lack of large, standardized, longitudinal datasets specifically designed for chronic stress research. Major challenges include ambiguity in stress labeling, limited longitudinal validation, circadian confounding, inter-individual variability, and small cohort sizes. Future progress will depend on standardized datasets, biologically grounded multimodal integration, hybrid baseline-reactivity modeling, adaptive personalization, and more interpretable AI systems. Greater emphasis is also needed on clinical relevance and generalizability if AI-based chronic stress monitoring is to move beyond experimental settings. Full article

Background: Conventional analyses of physiological recovery often rely on discrete metrics that assume independence across time points, thereby ignoring intrinsic temporal continuity and masking substantial interindividual heterogeneity. This proof-of-concept study assesses the efficacy of Functional Data Analysis (FDA) as a promising framework for characterizing individual response dynamics following a functional threshold power (FTP) test. Methods: Physiological time-series data (including blood lactate, heart rate, blood pressure, and glucose levels) collected from 21 trained cyclists (10 professionals, 11 amateurs) were represented as functional objects using FDataGrid on the original sampling grid (0, 3, 5, 10, 20 min), without basis expansion or smoothing. We conducted unsupervised functional clustering (K-means; Fuzzy K-means) and supervised classification (Maximum Depth with Modified Band Depth, K-Nearest Neighbors, Nearest Centroid, functional QDA with parametric Gaussian covariance). Model performance was estimated via Repeated Stratified 5-Fold Cross-Validation with 10 repetitions (50 folds), reporting accuracy, balanced accuracy (mean ± SD), 95% CIs, permutation p-values, and sensitivity/specificity from aggregated confusion matrices. Results: Lactate (CL) and diastolic blood pressure (DBP) provided useful and statistically significant discrimination across several classifiers (e.g., KNN, Nearest Centroid, functional QDA), whereas heart rate showed modest discriminative value and glucose intermediate performance. Unsupervised analyses revealed distinct lactate recovery profiles and graded membership for hemodynamic/metabolic variables, supporting the value of FDA for resolving heterogeneity beyond group-average trends. Conclusions: FDA offers a feasible and informative approach for classifying recovery phenotypes while preserving temporal structure. Findings are promising but should be interpreted with caution due to the small sample size, sparse time points, and the need for external validation in larger, independent cohorts before translation into routine decision-making. Full article

Background/Objectives: Stress echocardiography (SE) had been recommended by professional societies for assessing patients with suspected angina. SE protocols are variable across hospitals and countries in the recommendation of the cessation of rate-controlling medication (RCMx) prior to SE. Some expert opinion papers recommend the cessation of beta receptor blockers (BBs) and rate-controlling calcium channel blockers 48 h prior to SE to improve the diagnostic accuracy of the test. There is no evidence that the continuation of RCMx can affect the outcome of SE and short-term major adverse cardiovascular events (MACEs). To investigate the efficacy of Dobutamine SE in a cohort of patients where the cessation of rate-controlling medication has not been mandated, we reviewed our data over a one-year period in patients investigated for suspected coronary artery disease (CAD). Methods: A retrospective data analysis was performed on 227 consecutive patients who underwent Dobutamine SE between January 2022 and January 2023 in a single centre. In addition to dobutamine, the protocol allowed the administration of intravenous atropine (maximum dose of 1.2 mg) and a “top up” handgrip exercise at the discretion of the performing cardiologist. We assessed the Dobutamine SE outcome (positive vs. negative), target heart rate (THR, 85% of maximum age predicted), and the achieved peak HR in the two groups with RCMx and without RCMx. We analysed the patients’ characteristics and 12-month outcomes of a combined MACE of death, non-fatal MI, stroke, admission with angina, and unplanned revascularisation. Results: Of the 227 patients, 61% were on No-RCMx (male 40%). Ninety-three percent of the patients on RCMx were on BB and 7% on other rate-controlling medications. The THR was achieved in 74% of the patients with-RCMx and 90% in the without-RCMx groups p = 0.0018. Positive Dobutamine SE was observed in 48% (43/89) of patients on RCMx vs. 28% (39/138) on No-RCMx (p = 0.0022). Patients who did not reach THR 43% (16/37) had positive Dobutamine SE compared to 35% (66/190) who reached THR (p = 0.626). There was no difference between groups in the peak WMSI. Logistic regression analysis showed that being on RCMx was independently associated with positive Dobutamine SE (OR 2.03, 95% CI 1.06–3.91, and p = 0.034). The MACE rate was higher in patients where the THR was not achieved (9/37, 24.0%) vs. where THR was achieved (9/190, 4.7%), p < 0.001, in both the with-RCMx (7/30, 23% vs. 6/66, 9.1%, p = 0.013) and without-RCMx (2/14, 14% vs. 3/124, 2.4%; p = 0.025) groups, respectively. RCMx was independently associated with MACE (OR 3.68, 95% CI 1.227–11.046, and p = 0.020). Conclusions: The use of RCMx proved to be a predictor of both SE and MACE outcomes irrespective of the achieved THR. Our data supports the practice that patients referred for Dobutamine SE on RCMx can continue taking them without impact on the test accuracy. Full article

Photoplethysmography (PPG) has emerged as an attractive modality for non-invasive cardiovascular monitoring due to its low cost, unobtrusive nature, and ubiquity in consumer wearable devices. Despite its potential, existing PPG-based arrhythmia detection systems remain limited in scope: (i) most target only atrial fibrillation, (ii) temporal localization of abnormal segments is rarely provided, and (iii) deep learning models lack explainability, hindering adoption in clinical workflows. We present a comprehensive and fully integrated framework for multi-class arrhythmia detection, segmentation, and explainability based on PPG waveforms, Heart Rate Variability (HRV), and structured clinical metadata. The proposed system introduces a CLIP-style contrastive learning module aligning PPG waveforms with clinical variables and rhythm-state textual descriptions using BioBERT; a multitask U-Net architecture performing 4-class classification and 1D segmentation; a Retrieval-Augmented Generation (RAG) pipeline leveraging Gemini Flash large language models to produce guideline-grounded diagnostic reports; and a real-time Streamlit-based web platform supporting inference, visualization, and database storage. The system significantly improves classification accuracy (from 86.27% to 91.19%) and segmentation Dice (from 0.5815 to 0.7167). These results demonstrate the feasibility of a robust, multimodal, and explainable PPG-based arrhythmia monitoring system for real-world applications. Full article

Chronic inflammation contributes to bladder cancer (BC) development and progression through dysregulated cytokine signaling and tumor–immune interactions. This case–control study investigated associations between IL6 rs1800795, TNF rs1800629, CCL2 rs1024611, and VEGFA rs699947 polymorphisms, circulating cytokine levels, clinicopathological characteristics, and autonomic nervous system balance assessed by heart rate variability (HRV) in 73 BC patients and 88 controls. Genotyping was performed using PCR–RFLP, serum cytokine levels were measured by ELISA, and associations were evaluated using logistic, linear regression, and survival analyses. No significant associations with BC risk were observed for IL6, TNF, or VEGFA variants. However, the CCL2 rs1024611 GG genotype was associated with increased BC risk (recessive model: OR = 5.82, p = 0.026). Stratified analyses showed a lower frequency of the IL6 rs1800795 C allele and TNF rs1800629 GA genotype in high-grade and muscle-invasive tumors, suggesting potential associations with reduced tumor aggressiveness. No polymorphism was associated with serum cytokine levels or disease-free survival. In BC patients, the TNF rs1800629 A allele was associated with higher parasympathetic-related HRV indices and lower sympathetic parameters, whereas no such associations were observed in controls. These findings indicate that genetic variation within inflammatory pathways may contribute to BC susceptibility and tumor phenotype and may also modulate neuroimmune interactions. Full article

Amidst global urbanization and rising psychological stress, urban green spaces are increasingly recognized as critical infrastructure for sustainable urban development and public health. However, the mechanisms by which summer vegetation structure mediates both physiological and psychological restoration, and the interplay between these two dimensions, remain poorly understood. Understanding these mechanisms is essential for designing sustainable, health-promoting urban environments that can support growing urban populations in a warming climate. This study employed a controlled field experiment in Xi’an during summer to examine the effects of five vegetation structure types (Single-Layer Grassland, single-layer woodland, tree–shrub–grass composite woodland, tree–grass composite woodland, and a non-vegetated square) on university students’ physiological (heart rate variability) and psychological (perceived restorativeness and affective states) restoration. Following stress induction, 300 participants engaged with the green spaces through both quiet sitting and walking. The results revealed three key findings: (1) the tree–shrub–grass composite woodland consistently showed the most favorable trends other vegetation types across all psychological restoration dimensions, while also showing favorable trends in physiological recovery, underscoring the importance of structural complexity for restorative quality; (2) walking significantly enhanced physiological recovery compared to seated observation across all settings, confirming the role of physical activity as a critical activator of green space benefits; (3) correlation analysis identified a specific cross-system association: the R-R interval recovery value showed a weak but significant correlation with positive affect (PA) scores, suggesting that physiological calmness and positive emotional experience are linked, yet their weak coupling under short-term exposure indicates they may operate as parallel processes with distinct temporal dynamics. These findings indicate that the restorative potential of summer green spaces emerges from an integrated framework combining vegetation complexity and activity support. We propose that future sustainable landscape design should prioritize multi-layered vegetation structures as nature-based solutions that simultaneously enhance human well-being and urban resilience. These findings provide empirical evidence for integrating health-promoting green infrastructure into sustainable urban planning frameworks, supporting multiple Sustainable Development Goals (SDGs), including SDG 3 (Good Health and Well-being), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). Full article

Understanding and monitoring driver mental workload is essential for improving road safety. This study proposes a multimodal machine learning framework to classify drivers’ mental workload using eye movement metrics, physiological signals, and driving behavior features. A driving simulator experiment was conducted with 26 participants under two workload levels induced by a secondary auditory task. Seven feature combinations and six classification algorithms were evaluated. The results showed that eye metrics were the most informative modality, and that feature selection had a greater impact on classification performance than algorithm choice. A support vector machine with optimized features was selected as the final model based on performance and stability, achieving an accuracy of 87.8% and an AUC of 0.95. To improve model transparency, SHapley Additive exPlanations (SHAP) was applied, highlighting key predictors such as blink rate and heart rate, and uncovering synergistic effects between visual and physiological variables. The model was further validated in a tunnel entrance scenario, where it identified increased workload associated with steeper longitudinal slopes. These findings emphasize the importance of multimodal data integration—particularly eye movements—for assessing mental workload. Future applications should prioritize feature diversity over algorithm complexity to enhance real-world implementation in workload monitoring systems. Full article