Physiological measurement最新文献

{"title":"EEG-ECG/HRV associations under multi-stage cognitive stress: characterization and mechanisms.","authors":"Yanyu 艳羽 Lei 雷, Hongli Yu, Ranlin Zhao, Jiawei Wang, Wenxuan Nie, Tongtong Kan","doi":"10.1088/1361-6579/aeb501","DOIUrl":"https://doi.org/10.1088/1361-6579/aeb501","url":null,"abstract":"<p><strong>Objective: </strong>Mental stress affects cognitive, emotional, and cardiovascular function, but subjective stress scales are susceptible to individual bias. Brain-heart coupling offers a potential approach to stress assessment. This study examined stage-dependent EEG-ECG/HRV associations during mental arithmetic and Stroop tasks.</p><p><strong>Approach: </strong>Sixty healthy adults were recruited, EEG and ECG were recorded during baseline rest, four mental-arithmetic stages, inter-task rest, and four Stroop stages. Stage-specific Spearman correlations characterized between-participant associations between five EEG band-power measures and seven ECG/HRV indices. Mixed-effects models separating within- and between-participant effects tested whether within-participant associations varied across difficulty stages and tasks.</p><p><strong>Main results: </strong>Significant EEG-HRV correlations decreased in number after task onset and partially recovered during inter-task rest. Mental arithmetic showed more pronounced negative high-frequency EEG-vagal HRV correlations, whereas Stroop showed Theta/Beta-HR associations at onset and sustained Alpha-RMSSD associations during middle-to-late stages. Neither task showed FDR-significant stage-averaged within-participant associations. Within-participant EEG × difficulty-stage interactions were FDR-significant for 12 of 35 whole-frontal associations during mental arithmetic and none during Stroop. Combined models identified five significant within-participant EEG × difficulty × task interactions. No linear difficulty interactions survived FDR correction in sensitivity analyses.</p><p><strong>Significance: </strong>These findings support stage-dependent EEG-ECG/HRV associations in this fixed-order cognitive-stress paradigm. They may inform physiologically interpretable candidate measures for objective mental stress assessment and wearable monitoring.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing entropy-based heart rate variability metrics: fixed tolerance improves congestive heart failure classification.","authors":"Anton Petaja, Teemu Pukkila, Matias Kanniainen, Esa Räsänen","doi":"10.1088/1361-6579/aeb500","DOIUrl":"https://doi.org/10.1088/1361-6579/aeb500","url":null,"abstract":"<p><strong>Objective: </strong>&#xD; Entropy-based heart rate variability (HRV) metrics are widely used to characterize heartbeat dynamics and distinguish physiological from pathological states, but their classification performance is highly sensitive to parameter selection. This study investigated a fixed-tolerance approach derived from a healthy reference population as an alternative to conventional subject-specific tolerance normalization and assessed how entropy-parameter selection influences classification performance.&#xD;&#xD;Approach:&#xD; Approximate entropy (ApEn), sample entropy (SampEn), and multiscale entropy (MSE) were evaluated using both conventional and fixed-tolerance formulations in 24-hour Holter recordings from 274 healthy subjects and 946 patients with congestive heart failure (CHF). Classification performance was quantified using the area under the receiver operating characteristic curve (AUC). Parameter sensitivity was assessed through systematic sweeps of the embedding dimension, tolerance coefficient, and MSE scale range.&#xD;&#xD;Main results:&#xD; Classification performance varied substantially across the parameter space, and the embedding-dimension and tolerance values commonly used in the literature were generally suboptimal. The fixed-tolerance implementation consistently outperformed the conventional subject-specific approach, achieving AUC values of 0.78 (+0.03) for ApEn, 0.77 (+0.09) for SampEn, and 0.74 (+0.14) for MSE. For MSE, tolerance selection had a substantially larger influence on classification performance than scale-dependent parameterization.&#xD;&#xD;Significance:&#xD; The results suggest that a substantial portion of the classification potential of entropy-based HRV analysis remains unexploited under conventional tolerance normalization and parameter selection. More generally, entropy-based HRV metrics appear to be more sensitive to methodological choices than is commonly assumed. The fixed-tolerance approach provides a simple and computationally inexpensive strategy for improving entropy-based CHF classification, although validation in independent datasets is needed to establish its broader applicability.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Per-Class Conformal Risk Control for Multi-Label ECG Classification: Achieving Clinically-Justified False Negative Rate Guarantees.","authors":"Oussama El Allam, Mohamed Hamlich","doi":"10.1088/1361-6579/aeb3c6","DOIUrl":"https://doi.org/10.1088/1361-6579/aeb3c6","url":null,"abstract":"<p><strong>Objective: </strong>Automated electrocardiogram (ECG) classifiers require uncertainty procedures that address class-specific clinical risk in intrinsically multi-label data.&#xD;Approach: We propose per-class risk-controlling prediction sets for multi-label ECG classification. For each diagnostic class, binary false-negative-rate (FNR) loss is calibrated with a one-sided exact-binomial upper confidence bound. The nine disease-specific targets are controlled simultaneously at 95% family-wise confidence using Bonferroni allocation. The final operating threshold is the more inclusive of the exact-binomial threshold and a conservative reference threshold derived from a concentration bound; this safeguard uses calibration data only and cannot weaken the exact guarantee.&#xD;Main results: We evaluated the method on PTB-XL (21,388 retained recordings) and Chapman-Shaoxing (10,199 retained recordings), using a patient-disjoint PTB-XL split and fitting-partition-only normalisation. The proposed method met all 9/9 prespecified disease-specific FNR targets on held-out test sets and produced non-vacuous simultaneous exact-binomial guarantees for all nine targets; uniform standard conformal prediction met 5/9. Test FNR was 0.0% for myocardial infarction (exact 95% upper bound 0.35%, at 80.3% FPR), 1.0% for ST/T changes, and 0.3% for atrial fibrillation; the high-sensitivity operating point is purchased with substantially reduced specificity. Under PTB-XL-to-Chapman shift, macro class coverage fell from 94.3% to 45.5%, demonstrating that the guarantees do not transport without local recalibration.&#xD;Significance: Exact-binomial per-class risk control provides a transparent high-sensitivity operating point for ECG triage while making its specificity, prediction-set-size, calibration-support, and exchangeability limitations explicit.&#xD.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149183718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Directional discordance between a deployed commercial 12-lead ECG interpretation algorithm and expert cardiologists, and its distribution by sex and age: a post-deployment audit using PTB-XL.","authors":"Kaihui Du, Xiao Luo, Qibao Zhou","doi":"10.1088/1361-6579/aeb3c7","DOIUrl":"https://doi.org/10.1088/1361-6579/aeb3c7","url":null,"abstract":"<p><strong>Background: </strong>Automated 12-lead ECG algorithms are widely used, yet their disagreement with cardiologists across patient subgroups is rarely audited on public data. We characterized the directional discordance of the GE Marquette 12SL algorithm against expert labels.&#xD;Methods: In this retrospective audit of 14,885 ECGs from 13,480 patients (PTB-XL/PTB-XL+ dataset), we compared 12SL (v23) statements with standardized cardiologist labels for myocardial infarction (MI), conduction disturbance (CD), and left ventricular hypertrophy (LVH). We calculated observed agreement and paired marginal differences with 95% confidence intervals. Discordance was modeled using generalized estimating equations incorporating age, sex, and their interaction.&#xD;Results: Observed agreement was high (0.842-0.896) but directionally divergent. 12SL produced fewer positive statements than cardiologists for CD (paired marginal difference 0.092, 95% CI 0.085-0.098) and more for LVH (-0.070, 95% CI -0.075 to -0.064); MI showed no net difference. The false-negative proportion for CD decreased with age, while false-positive proportions rose with age for CD and MI. For LVH false positives, the age association was strongly modified by sex (interaction 1.60, 95% CI 1.47-1.73), being absent in women but steep in men. The LVH direction was specific to primary 12SL statements and reversed if supportive statements were included.&#xD;Conclusion: A widely deployed commercial ECG algorithm exhibits diagnosis-specific, directional discordance with expert reading that varies by age and sex, underscoring the value of reproducible post-deployment auditing. Because no clinical gold standard was available, these findings describe agreement with experts rather than absolute diagnostic accuracy.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149183782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving sleep monitoring in athletes: a comparative evaluation of four automated sleep scoring algorithms on 204 polysomnography recordings.","authors":"Flynn Crosbie, Umaer Hanif, Anis Aloulou, Maxime Chauvineau, Mounir Chennaoui, Mathieu Nedelec, Damien Léger","doi":"10.1088/1361-6579/aea9ea","DOIUrl":"10.1088/1361-6579/aea9ea","url":null,"abstract":"<p><p><i>Objective.</i>Automated sleep scoring offers the potential for fast, precise, and cost-effective analysis. This study evaluated the accuracy, robustness, and clinical agreement of four algorithms against manual polysomnography (PSG) in athletes, and assessed how signal degradation and individual physiological traits affect automated scoring in ambulatory settings.<i>Approach.</i>Data from 75 healthy athletes (91.1% male, mean age 26.3 ± 5.4 years, range [18-41], 204 PSG recordings) were retrospectively collected from studies conducted in our laboratory. Manual scoring was compared to four automated sleep staging algorithms: Luna, U-Sleep, Yet Another Spindle Algorithm (YASA), and Greifswald Sleep Stage Classifier (GSSC). Performance was evaluated on accuracy (per stage, macro and weighted<i>F</i>1), sensitivity (confusion matrices), robustness (signal quality:<i>R</i>²; individual differences: beta coefficients) and clinical relevance (prediction of total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO) and sleep efficiency (SE)).<i>Main results.</i>All algorithms demonstrated strong agreement with manual scoring, with macro<i>F</i>1 scores of 0.76 (GSSC), 0.71 (U-Sleep), 0.70 (YASA), and 0.69 (Luna). GSSC was the most accurate and robust across sport, physiological, behavioral, and anthropometric variables. It showed minimal bias and narrow limits of agreement for TST, WASO and SE. GSSC was also the most resilient to signal degradation (marginal<i>R</i>²= 0.05), compared to YASA and Luna (0.06) and U-Sleep (0.24).<i>Significance.</i>Automated sleep staging algorithms demonstrated accuracy comparable to typical inter-rater agreement, supporting broader use in athletes. These algorithms could make PSG more accessible for both monitoring and research, providing deeper insights into sleep and its influence on athletic performance.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149002371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultra-short-term HRV analysis in a dynamic scenario using the traditional and functional approach.","authors":"Daniela Kuruczova, Filip Zlámal, Veronika Eclerova, Peter Lenart, Michal Vít, Jan Novák, Jan Koláček, Julie Bienertova-Vasku","doi":"10.1088/1361-6579/aea2ed","DOIUrl":"10.1088/1361-6579/aea2ed","url":null,"abstract":"<p><p><i>Objective</i>. Heart rate variability (HRV) is increasingly used to study short, dynamic, stimulus-aligned physiological responses, but recordings shorter than one minute are vulnerable to artefacts, non-stationarity and loss of information when reduced to single-number metrics. The objective of this study was to evaluate whether selected ultra-short-term (UST) HRV metrics and functional data analysis (FDA) can be used to characterize cardiovascular arousal during a brief dynamic simulated interpersonal threat/self-defence scenario.<i>Approach</i>. We first reviewed HRV metrics that are theoretically suitable for UST recordings and then examined their sensitivity to physiologically plausible trend components using simulated 30 s RR-interval series. We subsequently applied both traditional HRV metrics and FDA to a real-world dataset from a controlled self-defence experiment. For the real-world analysis, 20 s windows were aligned to the externally annotated onset of physical contact, covering 10 s before and 10 s after the event. RR-interval series were cleaned, converted to heart-rate time series, represented as smoothed B-spline functions, and compared using mixed-effects models and functional ANOVA. First-derivative functions were analysed to separate absolute HR level from the rate of HR change.<i>Main results</i>. In the simulation, mean and median HR primarily reflected vertical shifts in the HR trajectory, whereas SDNN, the HRV triangular index and SD2 were sensitive to the rate of increase. RMSSD and SD1 were the least affected by the imposed trend, consistent with their mathematical relationship and their focus on short-term beat-to-beat variability. In the real-world dataset, both approaches detected group- and scenario-related differences. The FDA approach additionally localized these differences across the event-aligned time window and distinguished differences in absolute HR from differences in the rate of HR increase.<i>Significance</i>. FDA should not be interpreted as a real-time classifier or as a replacement for established HRV metrics. Rather, it provides a complementary framework for analysing short, non-stationary, event-aligned recordings in dynamic settings, especially when the timing and shape of the physiological response are scientifically relevant.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An intelligent evaluation model for spirometry quality using wavelet and deep time-series methods.","authors":"Haibo Yang, Xuanfeng Li, Ruibin Liu, Zhufeng Wang, Tao Qian, Jinping Zheng, Chitin Hon","doi":"10.1088/1361-6579/ae7c54","DOIUrl":"10.1088/1361-6579/ae7c54","url":null,"abstract":"<p><p><i>Objective:</i>Quality control of spirometry is a critical yet challenging prerequisite for accurate pulmonary function tests, especially in primary care settings where skilled personnel are often unavailable. This study aims to develop an intelligent evaluation model to automatically identify anomaly types in spirometry reports, ensuring the reliability of diagnostic data.<i>Approach:</i>We proposed a hybrid model, which integrates an artificial neural networks with wavelet analysis and integration of convolutional neural network and long short-term memory models. The model was trained and validated using over 3000 curves collected from the First Affiliated Hospital of Guangzhou Medical University.<i>Main results:</i>The proposed model achieved an average accuracy of 96.73% on the validation set and 95.84% agreement on the test set. Comparative analysis shows that our method outperforms existing benchmarks in Precision (0.965), Recall (0.971), and F1 score (0.968). The model successfully identifies a wide range of anomalies, including cough, glottic closure, and initial leakage, with high consistency.<i>Significance:</i>This approach provides a high-precision, near real-time diagnostic tool for spirometry quality assessment. By simplifying data management and standardizing evaluation processes, the model supports primary healthcare institutions in enhancing testing quality and diagnostic reliability, particularly in resource-limited environments.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148227378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-scale temporal feature fusion and lead-graph attention for multi-label classification of 12-lead ECGs.","authors":"Feiyan Zhou, Mingyang Li","doi":"10.1088/1361-6579/aeb225","DOIUrl":"https://doi.org/10.1088/1361-6579/aeb225","url":null,"abstract":"<p><strong>Objective: </strong>Automatic multi-label classification of 12-lead electrocardiograms remains challenging because of heterogeneous waveform morphology, long-range temporal dependencies, diagnosis-dependent inter-lead relationships, and severe label imbalance. This study develops an adaptive framework that jointly models temporal features and diagnosis-specific lead interactions.&#xD;&#xD;Approach. We propose a multi-scale residual-temporal network incorporating a diagnosis-conditioned, record-specific sparse lead graph. A shared temporal encoder independently extracts dynamic node features from each lead waveform. Learnable diagnosis prototypes participate in edge-score computation before message passing, generating a distinct graph for every recording and diagnostic category. For each target lead, only the top-k relationships are retained, while established lead connections are incorporated as a physiological soft prior with learnable strength. Prototype-guided graph predictions are integrated with global semantic predictions through class-wise gated fusion. Weighted asymmetric loss and validation-based class-specific thresholds address label imbalance.&#xD;&#xD;Main results. The model was evaluated on CPSC2018, G12EC, and PTB-XL across five multi-label settings. It achieved Macro-F1 scores of 0.8327 on nine-class CPSC2018, 0.5857 ± 0.0142 on 30-class G12EC, and 0.7782, 0.6421, and 0.4910 on the 5-, 12-, and 23-class PTB-XL tasks, respectively. The corresponding absolute gains over the strongest listed Macro-F1 comparators were 0.0147, 0.1428, 0.0226, 0.1335, and 0.0102. Macro-AUPRC was highest in all five settings, although Hamming Loss on the 23-class PTB-XL task was 0.0025 higher than that of ECG-STAR. Ablation and paired-bootstrap analyses supported the contribution of diagnosis-conditioned graph modelling.&#xD;&#xD;Significance. Conditioning sparse lead relationships jointly on each recording and diagnostic category extends fixed or class-agnostic lead graphs and provides an adaptive representation of disease-dependent multi-lead evidence. Clinical generalisability requires validation on independent hospital cohorts.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149138012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Progressive hierarchical attention and frequency-domain decoupled physiological loss for unsupervised remote photoplethysmography.","authors":"Dangguo Shao, Yiting Peng, Sanli Yi, Hongbin Wang, Shengxiang Gao, Lei Ma","doi":"10.1088/1361-6579/aeac9a","DOIUrl":"10.1088/1361-6579/aeac9a","url":null,"abstract":"<p><p><i>Objective.</i>Remote photoplethysmography (rPPG) enables contactless heart rate (HR) estimation from facial video. Unsupervised methods still face two coupled limitations: fixed backbones lack progressive hierarchical feature refinement, and contrastive objectives mainly regulate inter-sample spectral relations rather than the single-waveform morphology used for bandpass filtering and dominant-peak HR readout at test time.<i>Approach.</i>We propose progressive hierarchical attention (PHA) and a frequency-domain decoupled physiological (FDP) loss on a single-branch three-dimensional CNN trained with Contrast-Phys-style power-spectral-density contrastive learning. PHA cascades spatial, temporal, and channel attention with learnable stage-wise fusion, as a physiologically motivated design. FDP anchors each waveform at a dynamic fundamental frequency and applies out-of-band suppression with confidence-gated harmonic constraints.<i>Main results.</i>On UBFC-rPPG, PURE, and COHFACE under in-domain and cross-dataset protocols, PHA and FDP yield complementary gains and remain competitive with contrastive and structure-decoupling baselines, while retaining a single-branch inference pipeline without background region of interest.<i>Significance.</i>By coupling feature-level hierarchical refinement with loss-level spectral anchoring, the framework aligns unsupervised training with practical frequency-domain HR readout and offers an interpretable, deployment-friendly paradigm for video-based physiological measurement.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149063504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Photoplethysmographic signal analysis for vascular ageing assessment.","authors":"Sara Lombardi, Parmis Karimpour, Leonardo Bocchi, James May, Panicos Kyriacou","doi":"10.1088/1361-6579/aeab64","DOIUrl":"10.1088/1361-6579/aeab64","url":null,"abstract":"<p><p><i>Objective</i>. Vascular ageing (VA) is an age-related process characterised by vessel stiffening and altered endothelial function. Recognised as a major predictor of cardiovascular events, VA remains under-assessed in clinical practice due to limitations of current measurement systems. Photoplethysmography (PPG) emerges as a promising non-invasive, cost-effective tool, but its application to VA is hindered by signal complexity and lack of standardisation. This study investigated the relationship between VA and PPG waveform morphology through combined<i>in vitro</i>and<i>in vivo</i>analyses.<i>Approach</i>.<i>In vitro</i>, PPG signals were acquired from vascular phantoms with 3 stiffness levels.<i>in vivo</i>, PPG signals from 31 healthy subjects in two age groups (years andyears) were analysed, assuming increased stiffness with age. A novel skew-Gaussian decomposition model was developed to reconstruct pulses and extract morphological features.<i>In vitro</i>feature differences were assessed using the Kruskal-Wallis test;<i>in vivo</i>data were analysed with an XGBoost classifier to discriminate age groups.<i>Main results</i>. The skew-Gaussian model exhibited excellent waveform reconstruction ().<i>In vitro</i>, 23 of 60 features differed significantly across stiffness levels, particularly between the stiffest phantom and the others. Amplitude and energy parameters correlated negatively with stiffness, indicating reduced vascular compliance. Temporal features related to wave reflection correlated positively, suggesting altered signal dynamics.<i>in vivo</i>, the XGBoost classifier achieved 87% mean accuracy, with 83% sensitivity and 92% specificity in differentiating age groups.<i>Significance</i>. Feature relevance analysis confirmed the physiological significance of the extracted parameters, supporting the potential of PPG as a non-invasive and scalable technology for monitoring VA.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149025381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}