Physiological measurement最新文献

{"title":"Augmenting single-lead ECG interpretation through QRS waveform decomposition and rotation.","authors":"W Brian Chiu, Faisal Amlani, Derek G Rinderknecht","doi":"10.1088/1361-6579/ae85b3","DOIUrl":"10.1088/1361-6579/ae85b3","url":null,"abstract":"<p><p><i>Objective</i>. The single-lead electrocardiogram (ECG) is widely used in watches and other wearable devices, with proven efficacy in cardiac rhythm interpretation. However, for morphological ECG interpretation, multiple viewpoints (leads) are generally necessary.<i>Approach</i>. This contribution introduces a novel methodology to rotate the QRS waveform of a single-lead ECG for estimating QRSs of different viewpoints that may be more favorable for morphological interpretation. This rotation can be achieved by recognizing that the three-dimensional QRS loop is approximately planar, and that the shape of any one-dimensional projection of a planar loop can be determined entirely by its component on the QRS plane. In particular, this methodology models a single-lead QRS as a composition of an 'intrinsic component' specific to the individual and an 'angular component' represented as a simple sinusoid defined by the 'phase' of the QRS along the continuum between monophasic and biphasic waveforms. Indeed, for any single-lead QRS of an arbitrary viewpoint on the QRS plane, its phase can be computed based on its isoelectric zero-crossing(s). Rotation of a QRS to a different (target) viewpoint can then be achieved by substitution of the angular component of the QRS with the angular component of the target phase.<i>Main results</i>. The performance of this methodology is assessed in this work with the large PhysioNet PTB-XL database, as a proof-of-concept application, where single-lead QRS complexes and their respective monophasic rotations are evaluated for single-lead prediction of human-annotated 12-lead ECG interpretation of left bundle branch block. Results forsingle-lead predictions of left bundle branch block via incorporating QRS rotation demonstrate again in positive predictive value and again in1 score for corresponding sensitivities of at least.<i>Significance</i>. Towards facilitating morphological interpretation, the QRS waveform of a single-lead ECG can be augmented to provide more favorable viewpoints of its QRS complex.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148376648","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":"Inter-patient multi-label ECG classification via low-rank adaptation fine-tuned large language models with dynamic graph convolutional network.","authors":"Yuhao Cheng, Lirong Wang","doi":"10.1088/1361-6579/ae85b6","DOIUrl":"10.1088/1361-6579/ae85b6","url":null,"abstract":"<p><p><i>Objective.</i>Electrocardiogram analysis is vital for cardiovascular diagnosis, yet traditional methods struggle with inter-patient variability and complex multi label correlations. Existing large language model (LLM) integrations often freeze internal parameters, which limits adaptation to idiosyncratic signal patterns across diverse subjects.<i>Approach.</i>To overcome these challenges, this study proposes a framework that integrates LLMs fine-tuned via low rank adaptation (LoRA) with a multi-label dynamic graph convolutional network. The application of LoRA allows the model to encode specific signal features and mitigate inter-patient variability while retaining essential clinical semantic knowledge. Furthermore, the dynamic graph convolutional module is designed to capture the complex correlations between diverse cardiac diseases, thereby enhancing the modeling of multi-label dependencies. The final inference strategy leverages refined semantic embeddings to achieve accurate classification of diagnostic categories without requiring extensive labeled training data for every new patient population.<i>Main results.</i>Experimental results on the PTB-XL dataset yield<i>F</i>1-scores of 0.781 for the five class task and 0.370 for the twenty class task, representing improvements of 12.4% and 12.9% respectively compared to state of the art methods. Evaluation on the CPSC2018 dataset further confirms robust cross-dataset generalization with an<i>F</i>1-score of 0.606, which corresponds to an 18.8% relative improvement over existing approaches.<i>Significance.</i>By effectively resolving inter patient variability and modeling disease correlations, the proposed framework provides a scalable and accurate solution for diagnosing cardiac conditions in complex clinical environments where annotated data is limited.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148376653","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":"Respiratory event type and duration modulate PPG waveforms in OSA.","authors":"Jutta Nurminen, Salla Hietakoste, Timothy Howarth, Brett Duce, Timo Leppänen, Teemu Laitinen, Antti Kulkas, Samu Kainulainen","doi":"10.1088/1361-6579/ae7feb","DOIUrl":"10.1088/1361-6579/ae7feb","url":null,"abstract":"<p><p><i>Objective.</i>Obstructive sleep apnea (OSA) increases circulatory system load and risk of cardiovascular diseases (CVDs). Despite being biomarkers of vascular health, photoplethysmography (PPG) signal waveforms have not been systematically investigated in patients with OSA. Thus, we aimed to study the immediate effects of respiratory event characteristics on PPG waveforms and to examine potential patterns that could be associated with cardiovascular health.<i>Approach.</i>We retrospectively analyzed 520 clinical polysomnographies from suspected OSA patients, focusing on three PPG signal segments: 10 s before, during, and 10 s after respiratory events (<i>n</i>= 14442 in each segment). We determined PPG waveform parameters (full width at half maximum (FWHM) as well as differences in absolute and relative time (Δ<i>t</i>, Δ<i>t</i>_rel) and amplitude (Δ<i>A</i>, and Δ<i>A</i>_rel) between systolic peak and dicrotic notch) within each segment and tested the effect of respiratory event type (apnea vs hypopnea) and duration (10-20 s, 20-30 s, ⩾30 s).<i>Main results.</i>Before and during respiratory events, longer respiratory events significantly (<i>p</i>< 0.001) increased Δ<i>A</i>_reland FWHM, and decreased Δ<i>A</i>and Δ<i>t</i>_rel. These findings were significantly (<i>p</i>< 0.001) more pronounced during apneas than hypopneas. After the events, all PPG waveform parameters fluctuated strongly. Moreover, the type and duration of the respiratory event, pre-event values of PPG parameters, and heart rate were significantly (<i>p</i>< 0.001) associated with changes in the parameter values during the event.<i>Significance.</i>By analyzing PPG characteristics, we could enhance the evaluation of physiological stress caused by respiratory events in patients with OSA and therefore provide tools for the risk assessment of CVDs.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148284056","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":"Detection of bronchopulmonary dysplasia in infants and prediction of school-age lung function from tidal breathing data using recurrent neural networks.","authors":"Abdessamad Falhi, Malena Gwerder, Céline Rüttimann, Urs Frey, Edgar Delgado-Eckert","doi":"10.1088/1361-6579/ae85b1","DOIUrl":"https://doi.org/10.1088/1361-6579/ae85b1","url":null,"abstract":"<p><p><i>Objective.</i>To test whether machine learning (ML) models trained on tidal breathing flow time series can discriminate between individuals with and without respiratory disease and predict lung function indices obtained from conventional pulmonary function testing.<i>Background.</i>accurate assessment of respiratory function in infants and young children is challenging because conventional pulmonary function testing requires sophisticated equipment and/or active patient cooperation. Tidal breathing measurements, in contrast, can be obtained non-invasively with little or no patient cooperation and at low cost, yet their clinical utility has been limited. We hypothesized that sufficiently long tidal breathing flow time series contain clinically relevant information that can be extracted using a recurrent neural network known as a long short-term memory (LSTM) network.<i>Approach.</i>We evaluated LSTM models in two scenarios within the Basel-Bern Infant Lung Development cohort. First, we assessed the ability of a model trained on flow and derived volume time series to detect bronchopulmonary dysplasia (BPD) in 329 infants. Second, we examined whether a model trained on tidal breathing flow alone could predict forced expiratory volume in one second (FEV₁) in 135 school-age children. Signals were filtered and normalized prior to model training, and performance was evaluated on held-out test datasets.<i>Main results.</i>For BPD detection, the model achieved 97.0% accuracy, 100% specificity, 91.7% sensitivity, 100% precision, and an F1-score of 95.7%. For FEV₁prediction, Bland-Altman analysis showed a mean bias of -0.009 l (95% CI -0.091-0.074), with limits of agreement of -0.416 l and 0.399 l. The mean relative prediction error was 13.7%.<i>Significance.</i>These findings demonstrate that temporal patterns in tidal breathing flow signals contain diagnostically and functionally relevant information. ML applied to tidal breathing measurements may provide a low-burden, minimal-cooperation approach for early respiratory disease detection and functional assessment across early life stages.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":"47 7","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148456441","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":"Small changes in hand height alter absorbance, but not pulsation, in the finger pulse plethysmograph.","authors":"Onur Howie, Jamie R K Marland, Ralph Shackleton, Gordon Drummond","doi":"10.1088/1361-6579/ae7f7a","DOIUrl":"10.1088/1361-6579/ae7f7a","url":null,"abstract":"<p><p><i>Objective.</i>Previous studies of finger pulse plethysmography have used large changes in finger height to assess how pressure influences light transmission. Light transmission is affected by pulsation transmitted from the arterial pressure, and also by the volume of blood held in the finger vasculature. Smaller changes of hand height, in relation to the neck, could elucidate more precisely the pressures and mechanisms associated with vascular volume changes.<i>Approach.</i>We studied 18 (15 male, 3 female) healthy young volunteers in warm quiet surroundings, with one arm supported horizontally at the level of the shoulder. Comparisons were made with the hand placed 5 cm above and 5 cm below the horizontal, in random sequence. Red and infrared transmission were recorded from a standard pulse oximeter finger probe. Sections of recording were chosen where short periods of vasoconstriction were absent. Total absorbance values at peak and trough, and absorbance pulse amplitude, were calculated. We compared measures in the two hand positions, and before and after inflation of a cuff at the wrist, within each subject.<i>Main results.</i>In the dependent position, infrared light absorbances were greater: peak, 0.054 (0.043, 0.070) and trough (0.053 (0.045, 0.069) (median, quartiles) (<i>P</i>< 0.0001). Pulse amplitude was unaffected. Cuff inflation had no effect on the peak and trough values, but pulse amplitude decreased: the difference between paired values for infrared was 0.0049 (0.0075, 0.0015) and red, 0.0030 (0.0043, 0.0010) (median paired difference, 95% confidence limits). (<i>P</i>< 0.005).<i>Significance.</i>The minimal effect of cuff venous obstruction supports previous work reporting flow limitation in venous drainage of the arm. Our observations suggest that an increase in filling pressure caused by lowering the hand by 10 cm increases overall blood volume in the finger, suggesting that the plethysmograph waveform may result from pressure changes much less than arterial.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148278359","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":"Continuous tracking of aortic aneurysm diameter with peripheral pulse waves: a computational framework combining sequential Markov chain Monte Carlo with Kalman filtering.","authors":"Kiran Bhattacharyya","doi":"10.1088/1361-6579/ae85b5","DOIUrl":"10.1088/1361-6579/ae85b5","url":null,"abstract":"<p><p><i>Objective.</i>Abdominal aortic aneurysms (AAA) affect more than 1% of adults over 50 and carry significant mortality risk. Current surveillance relies on intermittent imaging (ultrasound or MRI) at 6-24 month intervals, which may miss rapid growth acceleration between visits. We investigate the feasibility of continuous aneurysm diameter tracking using peripheral pulse waves, like those detected by photoplethysmography (PPG) devices.<i>Approach.</i>We use a simplified one-dimensional (1D) hemodynamic model that simulates pulse wave propagation from the heart to the pedal digital artery. We first demonstrate diameter estimation when the hemodynamic model parameters defining systemic circulation are known within bounds for an individual, aggregating thousands of observations over hours or days. We then address the more challenging scenario where systemic parameters are only known to be within wider population-level bounds, using a sequential Monte Carlo approach that combines an ensemble Markov chain Monte Carlo (MCMC) sampler with Kalman filtering to marginalise over unknown parameters while tracking the aneurysm diameter. Both approaches are evaluated through 12 month tracking simulations of virtual patients with constant and accelerating aneurysm growth.<i>Main results.</i>While single-observation diameter estimation is fundamentally limited by noise and confounding variables, aggregating 1 600 measurements under baseline noise conditions reduces diameter uncertainty to 0.8 mm when patient-specific hemodynamic parameters are known within bounds. In this setting, tracking simulations across eight virtual patients achieve average root-mean-square error (RMSE) of0.3 mm. When systemic parameters are known only within population-level bounds, joint Bayesian estimation over the full parameter space achieves a median RMSE of 0.65 mm (1.40.3 mm, meanstandard error) across 50 virtual patients, remaining within clinically relevant ranges despite the underlying parameters being only partially identifiable.<i>Significance.</i>These computational results suggest that peripheral pulse wave monitoring through PPG sensors could complement traditional imaging for AAA, potentially enabling earlier detection of growth and more timely intervention.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148376732","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":"A comprehensive inference-time augmentation framework in physiological signals: application to PPG-based AF detection.","authors":"Davood Fattahi, Runze Yan, Saurabh Kataria, Zhaoliang Chen, Xiao Hu","doi":"10.1088/1361-6579/ae7d82","DOIUrl":"10.1088/1361-6579/ae7d82","url":null,"abstract":"<p><p><i>Objective.</i>Accurate classification of physiological signals in real-world deployments is challenged by sensor noise, motion artifacts, and distribution shifts between training and deployment data. Inference-time augmentation (ITA), which applies augmentations during inference rather than retraining, offers a simple, model-agnostic mechanism to improve robustness. However, ITA application to physiological signals has remained narrow in scope, relying on limited augmentation methods with fixed, unoptimized parameters. This work proposes a unified ITA framework to address that gap.<i>Approach.</i>The framework incorporates 13 augmentation methods spanning time-domain, amplitude-domain, frequency-domain, and artifact-injection transformations, with hyperparameters systematically optimized via Bayesian optimization. We evaluate the framework on atrial fibrillation (AF) detection from 30 s photoplethysmography (PPG) signals using two deep learning architectures, generative pre-trained transformer (GPT)-PPG (in two sizes) and ResNet, across five datasets comprising more than 400 patients and ∼9800 h of PPG recording. Two evaluation strategies are assessed: standard ITA applied to all inputs, and selective ITA applied to initially positive predictions.<i>Main results.</i>Standard ITA consistently improved the area under the receiver operating characteristic curve (AUROC, up to 8.5% for GPT-PPG and 0.7% for ResNet) and the area under the precision-recall curve (AUPRC, up to 10.6% for GPT-PPG and 0.8% for ResNet) across all model-dataset combinations. Selective ITA further reduced average FPR by up to 4.4% (GPT-PPG) and 1.3% (ResNet) on non-AF PPG datasets.<i>Significance.</i>These findings establish ITA as a practical, model-agnostic approach for improving the reliability of PPG-based AF classification in deployment settings where retraining is not feasible, with broader applicability to physiological signal analysis.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148259236","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":"Quantification of pendelluft in electrical impedance tomography data: opening pandora's box? a literature review of analytical methods.","authors":"Annemijn H Jonkman, Juliette E Francovich, Anne Lotte Kuiper, Diederik Gommers, Gaetano Scaramuzzo, Peter Somhorst","doi":"10.1088/1361-6579/ae7d81","DOIUrl":"10.1088/1361-6579/ae7d81","url":null,"abstract":"<p><p><i>Introduction.</i>Pendelluft, i.e. the movement of air between different lung regions, has been associated with lung injury and ventilation inefficiencies in mechanically ventilated patients. Electrical impedance tomography (EIT) is the only bedside technology for real-time imaging of pendelluft. However, no universal method exists for measuring and quantifying pendelluft on EIT data. This review aims to evaluate and summarize the methods used to quantify pendelluft, with a focus on EIT analysis techniques.<i>Approach</i>. A search was conducted in three databases: Embase, Web of Science and Medline, using predefined search criteria. Steps for preprocessing and computation of pendelluft were identified. Calculation steps were standardized to enable comparison across studies, and the methods were applied to EIT data to visualize the differences between them.<i>Main results</i>. 19 studies were included, with six unique methods for quantifying pendelluft on EIT data. The methods have some similarities, but mostly quantify pendelluft differently. Preprocessing methods vary greatly among the methods. These methodological differences are reflected in the variability of results when applying the methods to patient data.<i>Significance</i>. Different methods for quantifying pendelluft with EIT exist, which differ considerably in pendelluft quantification and in EIT preprocessing. Further development and consensus on quantification techniques and standardized reporting are necessary for advancing research into pendelluft and its impact, and improving clinical applicability. This highlights a promising research avenue for EIT as bedside modality for monitoring the impact of spontaneous breathing on regional lung mechanics.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148259265","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":"Electrodermal activity measurements: a review of artifacts.","authors":"Dindar S Bari, Haval Y Yacoob Aldosky, Ørjan G Martinsen","doi":"10.1088/1361-6579/ae7bae","DOIUrl":"10.1088/1361-6579/ae7bae","url":null,"abstract":"<p><p>Electrodermal activity (EDA) is a general term used to indicate changes in the electrical parameters of the skin reflecting sympathetic nervous system activity. EDA has been widely used in various fields; however, the reliability of high-quality EDA measurements is frequently compromised by artifacts and environmental factors, particularly for wearable instruments. This review provides a comprehensive examination of the major sources of artifacts in EDA measurements and discusses approaches to mitigate their effects. To the best of our knowledge, this is the first review to address all sources of artifacts in EDA recordings alongside potential strategies for their removal. Physiological artifacts (respiration, movement, and speech), technical artifacts (electrode and operating systems), and environmental influences (temperature, humidity, sound noise, and air pressure and altitude) are the main sources of EDA signal changes. Various approaches and tools are recommended to detect and mitigate the effects of these artifacts. Some of them work for laboratory-controlled measurements, but are difficult to meet with ambulatory measurements. Therefore, the reliability of EDA measurements obtained from wearable devices continues to be challenged by artifacts. Ongoing developments in data processing, filtering, and instrumentation should address these concerns in EDA measurements.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148219228","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":"Dissecting the integrated information of cardiovascular and cardiorespiratory systems at rest and during physiological stress.","authors":"Irene Franzone, Roberta Saputo, Yuri Antonacci, Michal Javorka, Riccardo Pernice, Luca Faes","doi":"10.1088/1361-6579/ae7fec","DOIUrl":"10.1088/1361-6579/ae7fec","url":null,"abstract":"<p><p><i>Objective.</i>In the framework of integrated information theory, different measures have been proposed to quantify the information integrated in a dynamic system composed by interconnected units, each capturing a different aspect of the system connections. While integrated information measures have been traditionally applied to brain dynamics, in this study we propose their implementation for the investigation of the short-term regulation of cardiovascular (CV) and cardiorespiratory (CR) systems reflected by the spontaneous variability of heart period (HP), systolic arterial pressure (SAP) and respiration (RESP).<i>Approach.</i>We selected two integrated information measures related to well-known dynamic properties of CV and CR systems: whole-minus-sum (WMS) quantifying the information stored in the whole system above and beyond the information stored in its units, and causal density (CD) that handles integration in terms of the information transferred between the system units. WMS and CD measures were computed from the parameters of vector autoregressive models fitted respectively on the bivariate time series of{HP, SAP}and{HP, RESP}, obtained from healthy young subjects at rest, during head-up tilt and during a mental arithmetic task.<i>Main results.</i>We found that CV and CR systems dynamically integrate significant amounts of information, which undergo modulations in response to the tasks performed, especially during tilt, proving that orthostatic stress significantly reduces the degree of cooperation between the parts of the systems. A different response to stress was observed for CD and WMS measures, related to the changes in the information storage and transfer induced by the orthostatic and mental challenges and their physiological interpretation.<i>Significance.</i>The possibility offered by the integrated information measures, in combination with their constituent contributions, allowed us to disentangle in a comprehensive way the statistical relationships underlying the CV and CR dynamics, and to investigate for the first time the concept of integration in the CV and CR systems.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148283990","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}