Physiological measurement最新文献

{"title":"Synthesis of multi-channel fetal ECG using generative modeling.","authors":"Swedha Sankaranarayanan, Marta Regis, Judith Oeh van Laar, M Beatrijs van der Hout-van der Jagt, Rik Vullings","doi":"10.1088/1361-6579/ae98e1","DOIUrl":"10.1088/1361-6579/ae98e1","url":null,"abstract":"<p><p><i>Objective</i>. Fetal electrocardiography (fECG) provides beat-to-beat cardiac information, offering the potential to improve the assessment of fetal response to uterine activity. However, the accuracy of fECG-derived cardiotocograms depends on signal quality. Scalp electrode recordings offer reliable fECG but are invasive and measurable only during labor. In contrast, non-invasive fECG measurements suffer from a low signal-to-noise ratio and maternal interference. To advance research on the use of non-invasive fECG, there is a need for large datasets with non-invasive multi-channel fECG recorded simultaneously with a clear reference fECG, such as from a scalp electrode.<i>Approach</i>. We propose a method to address this data scarcity by synthesizing multi-channel fECG signals by modifying inter-beat intervals based on a target fetal heart rate (fHR). The proposed method adjusts the duration of the TP segments between consecutive fECG complexes to effectively match the inter-beat intervals of the target fHR. The adjusted multi-channel TP segments are generated by a generative model that has learned the structure of the TP segments in our dataset. The morphology of the fetal QRS complexes remains unchanged. The fidelity of the synthesized signals is evaluated using signal quality indices (SQIs) and fHR estimates.<i>Main results</i>. Most SQIs computed on the input and synthesized signals exhibited strong correlation (), with low mean absolute differences. The error in the standard deviation of the estimated fHR and that of the target fHR wasBPM. Morphology-based SQIs exhibited strong correlations (), validating the preservation of fetal QRS complexes.<i>Significance</i>. Our approach generates realistic multi-channel fECG signals that match the target fHR patterns, supporting data augmentation and algorithm development for fetal monitoring. It addresses the scarcity of multi-channel fECG measurements with a clean reference available for algorithm development. It could enhance the robustness of data-driven and source separation methods.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148721480","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":"RETRACTION: 'Automatic identifying OSAHS patients and simple snorers based on Gaussian mixture models' (2023<i>Physiol. Meas.</i> 44 045003).","authors":"Xiaoran Sun, Li Ding, Yujun Song, Jianxin Peng, Lijuan Song, Xiaowen Zhang","doi":"10.1088/1361-6579/ae9697","DOIUrl":"https://doi.org/10.1088/1361-6579/ae9697","url":null,"abstract":"","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":"47 8","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148831428","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":"Direct measurements of alveolar gas in mechanically ventilated humans<sup />.","authors":"Gerardo Tusman, Matías Nicolás, Alejandro Carmona, Fernando Suarez Sipmann, Stephan H Böhm","doi":"10.1088/1361-6579/ae9036","DOIUrl":"10.1088/1361-6579/ae9036","url":null,"abstract":"<p><p><i>Objective.</i>The composition of alveolar gases is based on theoretical assumptions. The role of expired samples at the airway opening in representing the true value of mean alveolar gas remains a subject of debate. We hypothesized that phase III of oxygrams and capnograms obtained at the airway opening must be qualitative similar to the one found at the lung acini. The aim of this study is to test this hypothesis, analyzing the phase III of breath-by-breath samples of airway and alveolar gases from mechanically ventilated patients.<i>Approach.</i>The study compared expired gases at the airway opening and direct gas samples taken from peripheral lung parenchyma in ventilated patients undergoing thoracic surgery. We used video-assisted lobectomies as a model to get direct alveolar gas samples from the lobe that was removed by surgery.<i>Main results.</i>In ten anesthetized patients before lobectomy, the corresponding lung lobe was punctured by a 20 G angiocath at ∼5 mm of depth and then connected via a sampling line to sidestream and mainstream capnographs. An oscillating pattern in synchrony with the mechanical breath cycles was observed in the alveolar CO₂and O₂samples. The alveolar capnograms and oxygrams revealed an absence of phase I, very short phase II, and a similar phase III. The slope of phase III of the expired curves at the airway opening [median 2.20 (inter-quartile range 1.28) mmHg s^-1] were similar to the one of the alveolar mainstream capnograms [median 2.18 (inter-quartile range 1.24) mmHg s^-1;<i>p</i>= 0.76].<i>Significance.</i>Our findings suggest that alveolar gas can be reliably sampled at the airway opening. The similarity in the slopes of phase III between airway and alveolar samples highlights the significance of continuous gas diffusion through the alveolar-capillary membrane as well as the presence of convection- and diffusion-dependent inhomogeneities in their origin.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148584583","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":"Conformal reliability assessment of PPG-based ICU arrhythmia classification under class imbalance and patient heterogeneity.","authors":"Ismail Hakki Kinalioglu","doi":"10.1088/1361-6579/ae92e8","DOIUrl":"10.1088/1361-6579/ae92e8","url":null,"abstract":"<p><p><i>Objective.</i>Deep learning models for photoplethysmography (PPG)-based arrhythmia detection in intensive care are often evaluated by average accuracy, while conformal prediction (CP) is used to add marginal coverage guarantees. We ask whether aggregate accuracy and marginal conformal coverage are sufficient evidence of clinical reliability under intensive care unit (ICU) class imbalance and patient heterogeneity.<i>Approach.</i>Using the MIMIC-III-Ext-PPG benchmark with 826 critically ill patients and over 1.1 million 30 s PPG segments, we conduct a conformal reliability audit of PPG-only arrhythmia classification. We deliberately use a stress-case classifier with superficially reassuring aggregate metrics but weak rare-class behavior, and test whether accuracy and marginal CP conceal per-class and per-patient failures. We evaluate two predictors, three global conformal score constructions (LAC, adaptive prediction sets (APS), and Sorted Adaptive Prediction Sets (SAPS)), class-conditional calibration, an empirical RC3P-style class-aware sensitivity analysis, prevalence reweighting, and signal-quality-aware variants, with uncertainty quantified by patient-clustered resampling.<i>Main results.</i>The stress-case classifier attains 65.0% accuracy but only 0.388 balanced accuracy, revealing severe rare-class failure despite superficially reassuring aggregate performance. Under global CP, marginal coverage reaches 90.0%, yet critical-rhythm coverage is only 7.5%, with 267 of 268 critical-rhythm segments not assigned to the correct top class. This collapse persists across predictors and global scores (APS: 96.0% overall but 23.1% critical; SAPS: 90.2% overall but 7.8% critical) and survives full-benchmark prevalence reweighting. Class-conditional calibration raises critical-rhythm coverage to 0.825, but the patient-clustered interval [0.578, 0.985] shows that target-level reliability remains uncertified for the rarest class. More than a third of patients fall below target even among those with many segments. Signal quality is not the dominant failure axis: low-quality segments are not under-covered, and quality-weighted calibration offers no advantage over simple baselines.<i>Significance.</i>In ICU PPG arrhythmia monitoring, marginal coverage is not clinical reliability. Systems should be evaluated by per-class and per-patient coverage with patient-clustered uncertainty before claims of trustworthiness.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148631372","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":"Practical optimization of deep learning models for cuffless blood pressure estimation from photoplethysmography.","authors":"Vikas Khurana, Aejaz Mohammad Pamidi, Bharat Bhushan Joshi, Ganesh Belde, Rajesh Reddy Goutha, Ratan Bajpai, Subodha Kumar, Akram Khan","doi":"10.1088/1361-6579/ae944f","DOIUrl":"10.1088/1361-6579/ae944f","url":null,"abstract":"<p><p><i>Objective.</i>To improve deep learning-based cuffless blood pressure (BP) estimation from photoplethysmography (PPG) for continuous, non-invasive monitoring when cuff measurements are impractical, by assessing sampling frequency, systolic BP (SBP) and diastolic BP (DBP) derivation, respiratory rate (RR) effects, and errors at BP extremes.<i>Approach.</i>We analysed 205 850 ten-second paired PPG-arterial BP segments from the medical information mart for intensive care waveform database. A convolutional model with a multiresolution U-Net translated PPG to arterial BP waveforms. Inputs at 125, 62.5, and 31.25 Hz were compared. SBP and DBP were derived by minimum-maximum extraction or by averaging the top 4 systolic peaks and bottom 4 diastolic troughs per segment. Performance was summarized using mean absolute error (MAE).<i>Main results.</i>Inputs at 62.5 Hz reduced MAE versus 125 Hz (SBP 5.24 vs 6.34 mmHg; DBP 2.87 vs 3.27 mmHg), whereas 31.25 Hz increased error (SBP 16.09 mmHg; DBP 9.10 mmHg). Multi-peak averaging outperformed minimum-maximum extraction. MAE varied across RRs, with generally lower errors observed at moderate RRs, and higher errors observed at lower and higher SBP ranges.<i>Significance.</i>Sampling frequency, SBP/DBP derivation, and RR shape error patterns in PPG-only cuffless BP estimation, with higher errors at low and high SBP. These results inform device design for bedside and ambulatory monitoring. However, further validation using subject-level datasets is required to assess clinical applicability and compliance with established validation standards.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148670004","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":"Pulmonary function estimation and reliability assessment using hybrid-optimized ensemble learning.","authors":"Jiangli Zhu, Dandan Yan, Chenlei Sun, Chen Pan, Dan Shen, Feng Liu, Wenlong Xu","doi":"10.1088/1361-6579/ae968d","DOIUrl":"10.1088/1361-6579/ae968d","url":null,"abstract":"<p><p><i>Objective.</i>Pulmonary function tests are central to assessing ventilatory impairment but are often limited by accessibility, repeatability, and patient compliance, particularly in community and longitudinal monitoring settings. This study investigates whether acoustic features of voluntary coughs can serve as low burden surrogate signals reflecting trends in spirometry related indices of ventilatory impairment.<i>Approach.</i>We propose a dual output ensemble learning framework that integrates deterministic regression and probabilistic interval estimation to jointly model spirometry related parameters from cough acoustics and basic demographic information. Fifty eight cough derived acoustic features and four biometric variables were analysed using an optimized extreme gradient boosting (XGBoost) model for point estimation, coupled with natural gradient boosting (NGBoost) for uncertainty quantification. A hybrid Bayesian-Whale Optimization strategy was employed for robust hyperparameter tuning.<i>Main results.</i>In a cohort of 278 subjects with heterogeneous respiratory conditions, the proposed framework achieved root mean square errors of 0.20 l for forced vital capacity (FVC) and 0.27 l for forced expiratory volume in one second (FEV1), with coefficients of determination up to 0.94 and 0.87, respectively. Interval estimates provided uncertainty bounds consistent with model reliability, with the actual prediction interval coverage probabilities reaching 84.0% for FVC and 77.8% for FEV1. Performance for the composite FEV1/FVC ratio was comparatively lower, reflecting the increased variability of ratio based metrics.<i>Significance.</i>These findings suggest that cough acoustics encode physiologically relevant information associated with ventilatory impairment and can support proxy trend modelling of spirometry related indices, rather than direct spirometry replacement. The proposed framework demonstrates the feasibility of cough based physiological assessment for low cost respiratory screening and longitudinal monitoring, particularly where conventional spirometry is impractical. Larger multi centre studies are warranted to further evaluate generalizability and clinical integration.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148685524","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":"Slide-DML: sliding-window-based estimation of heterogeneous treatment effects.","authors":"Zhizhong Fu, Zheng Gong, Zhan Shen, Shuaiting Yao, Xiaorong Ding, Yifan Chen","doi":"10.1088/1361-6579/ae9713","DOIUrl":"10.1088/1361-6579/ae9713","url":null,"abstract":"<p><p><i>Objective.</i>Heterogeneous treatment effect (HTE) estimation is essential for understanding individual differences in physiological responses and supporting personalized healthcare. However, existing non-parametric HTE estimation methods often rely on complex partition strategies and may have limited interpretability when applied to physiological measurements with continuous variations and non-uniform data distributions. This study aims to develop an adaptive and interpretable framework for HTE estimation in physiological measurement systems.<i>Approach.</i>We propose a sliding-window-based double machine learning framework (Slide-DML) for non-parametric HTE estimation. Slide-DML adaptively constructs quasi-homogeneous local windows based on treatment effect variation and estimates local linear HTE within each selected window. The local estimates are subsequently aggregated using adaptive weighting to obtain a smooth global HTE function while preserving interpretability.<i>Main results.</i>The performance of Slide-DML was evaluated using synthetic data, semi-synthetic clinical data, and real physiological measurements. In synthetic experiments, Slide-DML achieved a mean squared error (MSE) of 0.006, outperforming existing machine learning-based HTE estimation methods. In semi-synthetic experiments, Slide-DML achieved a root MSE of 3.526, demonstrating superior performance compared with both machine learning-based and deep learning-based approaches. Experiments on real photoplethysmogram and electrocardiogram data further showed that the estimated treatment effect curves were consistent with established cardiovascular knowledge and provided improved interpretability.<i>Significance.</i>Slide-DML provides an effective and interpretable approach for estimating HTE in physiological measurement systems. By capturing continuous variations in physiological states, the proposed framework may facilitate individualized analysis of cardiovascular responses and support personalized healthcare applications, such as cuffless blood pressure monitoring using wearable physiological devices.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148689717","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":"Leipzig heart center ECG-database: arrhythmias in children and patients with congenital heart disease.","authors":"Sophia Klehs, Daniel Franke, Bayhas Alhamad, Roman Gebauer, Linus Teich, Ingo Dähnert, Tobias Teich, Christian Paech","doi":"10.1088/1361-6579/ae9033","DOIUrl":"10.1088/1361-6579/ae9033","url":null,"abstract":"<p><p><i>Objective</i>. Electrocardiogramms (ECG) interpretation is increasingly supported by algorithms trained on large-scale datasets. Here we present a first manually annotated open-source ECG-database of children and adults with congenital heart defect (CHD) with arrhythmia annotations.<i>Approach</i>. Signal data (ECG and intracardiac electrograms) were extracted from children and patients with CHD undergoing electrophysiological studies (EPS). The dataset is provided in the wave form database format. Annotations were manually annotated using the LightWAVE® software.<i>Main results</i>. The dataset comprises 39 ECGs from 39 patients (duration ranging from 00:01:17 h to 02:30:31 h). A total of 113 924 beats were annotated. A large part of beats (33%) are normal sinus rhythm. However, the database also includes sinus rhythm beats with preexcitation (12%) or complete right bundle branch block (11%). Supraventricular tachycardias (23%) are further categorized into atrioventricular reentrant tachycardias (AVRT; 15.5%) and atrioventricular nodal reentrant tachycardias (AVNRT; 7.5%). Also, ventricular tachycardias (1.7%) and very rare conditions as aberrant conducted AVRT or AVNRT or AVNRT with 2nd degree heart block are represented. Additionally paced beats are included (12%), thereof 7% atrial paced beats and 5% ventricular paced beats. Premature beats (premature ventricular or atrial beats) make up 7% of the dataset.<i>Significance</i>. This open-source database () containing many pathologies in children and patients with CHD may provide a foundation for ECG algorithms aimed at detecting ECG pathologies in this special patient population.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148584649","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":"Evaluation of HRV-PRV agreement with autoencoder-based signal quality assessment in resting and dynamic conditions.","authors":"Bartosz Olszewski, Agnieszka Uryga, Cyprian Mataczyński, Magdalena Kasprowicz","doi":"10.1088/1361-6579/ae95ae","DOIUrl":"10.1088/1361-6579/ae95ae","url":null,"abstract":"<p><p><i>Objective.</i>Pulse-to-pulse intervals obtained from continuous non-invasive blood pressure (CNBP) signals can be used to derive pulse rate variability (PRV). CNBP signals are susceptible to noise and motion artifacts, which can reduce the reliability of PRV and its agreement with heart rate variability (HRV) derived from electrocardiography (ECG). In this study, we applied a pulse-wise, unsupervised quality assessment method for CNBP signals based on a convolutional autoencoder integrating waveform morphology with pulse-level metadata. The aim was to improve the agreement between PRV and HRV metrics.<i>Approach.</i>Two datasets of CNBP and ECG recordings were analyzed: long baseline measurements (15 volunteers, average 40 min) and short recordings with postural changes (53 volunteers; 5 min squat-stand). A convolutional autoencoder was used for pulse-wise quality assessment. HRV metrics were derived from ECG, and PRV metrics were derived from CNBP before and after quality-based pulse selection (PRV). The analysis included standard time-domain indices (SDNN, RMSSD), frequency-domain measures, and nonlinear Poincaré plot descriptors (SD1, SD2). The agreement between HRV and PRV or PRVwas evaluated by Bland-Altman analysis.<i>Main results.</i>PRV systematically overestimated variability compared to HRV. During resting conditions, RMSSD and SDNN were lower for HRV (47.720.6 ms and 71.427.4 ms) than for PRV (61.726.7 ms,= 0.005 and 77.830.0 ms,= 0.003, respectively). After applying quality-based pulse selection, bias decreased fromtoms for RMSSD and fromtoms for SDNN, improving PRV-HRV agreement. During postural changes, PRV also overestimated HRV, but the effect of quality-based pulse selection was less pronounced.<i>Significance.</i>The proposed approach enhances CNBP signal quality without requiring manual labeling, making it suitable for real-world applications. Improved agreement between PRV and HRV may have important clinical implications, particularly when ECG acquisition is impractical or unavailable.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148679549","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":"Quantitative analysis of thermal recovery using dynamic infrared thermography: a methodological proof-of-concept with multi-time-constant modeling.","authors":"José-Marco Balleza Ordaz, Moisés Padilla, César Bravo-Alvarado, Svetlana Kashina, Gonzalo Páez, Manuel Servín","doi":"10.1088/1361-6579/ae95af","DOIUrl":"10.1088/1361-6579/ae95af","url":null,"abstract":"<p><p><i>Objective.</i>To evaluate thermal recovery after controlled cooling using dynamic infrared thermography and to determine whether the recovery process can be described using distinct temporal components associated with different heat transfer dynamics.<i>Approach.</i>A controlled cooling protocol was applied to the plantar region of the foot, followed by thermal recovery monitoring using a radiometric infrared camera. Temperature evolution was analyzed using a two-time-constant exponential model, where<i>τ</i>₁and<i>τ</i>₂represent the slow and fast recovery components, respectively. A frame-by-frame radiometric calibration procedure was implemented to ensure measurement stability. The distributions of<i>τ</i>₁and<i>τ</i>₂were evaluated under different cooling durations and recovery windows using non-parametric statistics.<i>Main results.</i>Thermal recovery was consistently described by two-time constants that capture distinct temporal regimes. The fast component (<i>τ</i>₂) exhibited relatively compact distributions, particularly in the short recovery window, indicating stable and repeatable behavior. In contrast, the slow component (<i>τ</i>₁) showed larger variability, especially for longer recovery windows. This variability suggests that the slow component (<i>τ</i>₁) is more sensitive to extended thermal dynamics and inter-subject variability than to measurement noise. The results also showed that longer cooling durations increased the relative contribution of the slow recovery component (<i>τ</i>₁), revealing multi-scale thermal recovery behavior.<i>Significance.</i>These findings demonstrate that thermal recovery is not a single-scale process but a multi-time dynamic phenomenon in which slow and fast recovery components provide complementary information. By explicitly separating these temporal components, the proposed framework enables a more detailed characterization of thermal dynamics than single-parameter approaches. As a methodological proof-of-concept, this work supports the use of dynamic infrared thermography as a quantitative tool for physiological measurement and establishes a reproducible basis for future clinical studies.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148679585","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}