IEEE Transactions on Biomedical Engineering最新文献

{"title":"Prior Image Guided Ring Artifact Correction for Photon-Counting Detector Data in Hybrid Spectral CT.","authors":"Xin Lu, Xinran Yu, Yi Du, Yunsong Zhao","doi":"10.1109/TBME.2025.3573882","DOIUrl":"https://doi.org/10.1109/TBME.2025.3573882","url":null,"abstract":"<p><strong>Objective: </strong>Photon-counting detector (PCD) is an advanced and innovative X-ray detector, that offers significant advantages such as improved spatial resolution and higher dose efficiency. However, as a new X-ray detection device, PCD faces technical challenges, particularly the non-uniformity among detector units, which can lead to ring artifacts in reconstructed CT images. To address this challenge, we propose a novel image-domain post-processing method to effectively correct ring artifacts in PCD-reconstructed images.</p><p><strong>Method: </strong>The method is specifically designed for a self-developed hybrid spectral CT system equipped with both a PCD and an energy integration detector (EID). The ring artifact-free EID-reconstructed images are used as prior images to guide the correction of ring artifacts in the PCD-reconstructed images. In addition, a carefully designed weight is introduced in the optimization model to prevent the degradation of boundary details in the target images caused by blurred edges in prior images.</p><p><strong>Results: </strong>The effectiveness of the proposed method is validated on both simulated data and real data, demonstrating that the proposed method can efficiently and effectively correct ring artifacts in the reconstructed images of PCD.</p><p><strong>Significance: </strong>This confirms that the proposed method provides a practical solution for hybrid spectral CT imaging.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Joint Geometric Topological Analysis Network (JGTA-Net) for Detecting and Segmenting Intracranial Aneurysms.","authors":"Xinyue Zhang, Zonghan Lyu, Yang Wang, Bo Peng, Jingfeng Jiang","doi":"10.1109/TBME.2025.3572837","DOIUrl":"https://doi.org/10.1109/TBME.2025.3572837","url":null,"abstract":"<p><strong>Objective: </strong>The rupture of intracranial aneurysms leads to subarachnoid hemorrhage. Detecting intracranial aneurysms before rupture and stratifying their risk is critical in guiding preventive measures. Point-based aneurysm segmentation provides a plausible pathway for automatic aneurysm detection. However, challenges in existing segmentation methods motivate the proposed work.</p><p><strong>Methods: </strong>We propose a dual-branch network model (JGTANet) for accurately detecting aneurysms. JGTA-Net employs a hierarchical geometric feature learning framework to extract local contextual geometric information from the point cloud representing intracranial vessels. Building on this, we integrated a topological analysis module that leverages persistent homology to capture complex structural details of 3D objects, filtering out short-lived noise to enhance the overall topological invariance of the aneurysms. Moreover, we refined the segmentation output by quantitatively computing multi-scale topological features and introducing a topological loss function to preserve the correct topological relationships better. Finally, we designed a feature fusion module that integrates information extracted from different modalities and receptive fields, enabling effective multi-source information fusion.</p><p><strong>Results: </strong>Experiments conducted on the IntrA dataset demonstrated the superiority of the proposed network model, yielding state-of-the-art segmentation results (e.g., Dice and IOU are approximately 0.95 and 0.90, respectively). Our IntrA results were confirmed by testing on two independent datasets: One with comparable lengths to the IntrA dataset and the other with longer and more complex vessels.</p><p><strong>Conclusions: </strong>The proposed JGTA-Net model outperformed other recently published methods (> 10% in DSC and IOU), showing our model's strong generalization capabilities.</p><p><strong>Significance: </strong>The proposed work can be integrated into a large deep-learning-based system for assessing brain aneurysms in the clinical workflow.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144132048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Polarized Microwave-induced Thermoacoustic Imaging for Detection of Dilated Cardiomyopathy in vivo.","authors":"Zhiyuan Jin, Xue Li, Yu Wang, Bohan Zhang, Yichao Fu, Huan Qin","doi":"10.1109/TBME.2025.3573326","DOIUrl":"https://doi.org/10.1109/TBME.2025.3573326","url":null,"abstract":"<p><strong>Objective: </strong>Altered myocardial fiber arrangement is a hallmark feature in the early stages of dilated cardiomyopathy (DCM). However, current medical imaging modalities have limitations in resolving microstructural changes within the myocardium. In this study, we introduce a high-spatiotemporal-resolution polarized microwave-induced thermoacoustic imaging (P-MTAI) technique for in vivo detection of myocardial fiber rearrangement in DCM.</p><p><strong>Methods: </strong>Leveraging the anisotropic arrangement and orientation of myocardial fibers, the technique analyzes thermoacoustic signals generated by stimulating the myocardium with linearly polarized pulsed microwaves from four orthogonal directions, enabling the assessment of its spatial microstructure. To mitigate motion artifacts induced by cardiac contraction, the system acquires thermoacoustic images at a frame rate of 100 Hz. The end-diastolic phase, corresponding to maximal cardiac relaxation, is identified from consecutively acquired frames across multiple cardiac cycles, and frames from this phase are selected for polarization analysis. A derived parameter, the degree of microwave absorption anisotropy (DOMA), is employed to quantify the transition of myocardial fiber arrangement from an organized to a disorganized state.</p><p><strong>Results: </strong>The efficacy of P-MTAI was validated in a rabbit model of DCM. Results indicate a statistically significant reduction in myocardial DOMA values in DCM-affected rabbits compared to healthy controls.</p><p><strong>Conclusion: </strong>These results demonstrating the potential of P-MTAI for early-stage DCM detection.</p><p><strong>Significance: </strong>This study provides a novel approach for the early detection of dilated cardiomyopathy, with significant clinical translational potential and application prospects.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144132140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrical Pathways Through the Intricate Network of Skeletal Muscle Fibres: Insights From MRI-Validated Numerical Modelling.","authors":"Rok Smerc, Marko Strucic, Matej Kranjc, Igor Sersa, Damijan Miklavcic, Samo Mahnic-Kalamiza","doi":"10.1109/TBME.2025.3572353","DOIUrl":"https://doi.org/10.1109/TBME.2025.3572353","url":null,"abstract":"<p><strong>Objective: </strong>Skeletal muscles exhibit pronounced anisotropy due to their highly oriented fibre structure, a property that significantly influences the spatial distribution of tissue mechanical and electrical properties. Understanding this anisotropy is critical for advancing biomedical applications such as electrical stimulation, bioelectric impedance analysis, and novel therapeutic interventions such as pulsed field ablation (PFA).</p><p><strong>Methods: </strong>We developed a numerical model incorporating realistic skeletal muscle fibre geometry at the microscale to elucidate the origins of the experimentally observed anisotropy at the bulk tissue level. To validate the model, we evaluated the skeletal muscle anisotropy using current density imaging (CDI), a magnetic resonance-based technique.</p><p><strong>Results: </strong>The developed numerical model identifies the origins of the observed anisotropy in bulk tissue. Experimental CDI measurements validate the model, confirming that the observed current anisotropy arises from the intrinsic properties of individual muscle fibres and their organization within the tissue. Remarkably, this anisotropy persists several - even up to 48 - hours post-mortem, suggesting a structural basis that transcends the level of muscle cell membranes.</p><p><strong>Conclusion: </strong>The integration of CDI with advanced modelling provides a powerful framework for understanding and leveraging skeletal muscle anisotropy in both imaging and therapeutic applications.</p><p><strong>Significance: </strong>Our study provides an experimentally validated model of skeletal muscle that is relevant to biomedical applications involving electrical treatments. It also invites further experimentation using tissues immediately after harvesting, demonstrating potential use of ex vivo tissues as models of in vivo tissue, reducing the need for experimentation with live animals and the associated ethical burden.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144127404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-Resolution Brain Metabolic Imaging at Ultrahigh Field Using Extended Spatiospectral Encoding and Subspace Modeling.","authors":"Rong Guo, Yudu Li, Yibo Zhao, Wen Jin, Yuhui Chai, Aaron Anderson, Wael Hassaneen, Bruce Damon, Tracey Wszalek, Yao Li, Hannes M Wiesner, Xiao-Hong Zhu, Wei Chen, Bradley P Sutton, Zhi-Pei Liang","doi":"10.1109/TBME.2025.3572448","DOIUrl":"10.1109/TBME.2025.3572448","url":null,"abstract":"<p><strong>Objective: </strong>To develop a high-resolution magnetic resonance (MR) metabolic imaging method for mapping human brain metabolite distributions at ultrahigh field (7T).</p><p><strong>Methods: </strong>In data acquisition, a free-induction-decay (FID) based MR spectroscopic imaging (MRSI) sequence was implemented. To achieve high spatial resolution, the sequence used fast echo-planar spectroscopic imaging (EPSI) trajectories with echo-spacings larger than the Nyquist sampling interval. Using this sequence, 3D MRSI signals at isotropic nominal resolutions of 3.0 mm and 1.8 mm were acquired within scan times of 4.8 and 14.2 minutes, respectively. In data processing, model-based methods integrating subspace learning, spectral modeling, and generalized series modeling were developed to address key challenges, including spectral ghosting, low signal-to-noise ratio, and spectral aliasing.</p><p><strong>Results: </strong>The proposed acquisition and processing methods successfully generated high-resolution, high-quality metabolite maps of the human brain at 7T. Experimental results from phantom and in vivo scans validated the proposed method and showed its capability to capture detailed brain metabolite distributions.</p><p><strong>Conclusion: </strong>This work demonstrates the feasibility of high-resolution brain metabolic imaging at ultrahigh field using MRSI acquisition sequence and model-based processing methods.</p><p><strong>Significance: </strong>By providing high-resolution spatial mapping of brain metabolites within clinically feasible scan times, the proposed method promises to offer a powerful imaging tool for investigating brain metabolism, which is expected to be useful for various brain imaging applications.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144119386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Average Responses of Brain Displacement Under Rotational Loading for Computational Model Validation.","authors":"Ahmed A Alshareef, J Sebastian Giudice, Taotao Wu, Matthew B Panzer","doi":"10.1109/TBME.2025.3572300","DOIUrl":"https://doi.org/10.1109/TBME.2025.3572300","url":null,"abstract":"<p><strong>Objective: </strong>Computational models of the brain are typically validated using individual subjects from datasets of brain motion, but a comparison to an individual subject does not consider the biomechanical variation that naturally exists in the population. When data from multiple subjects is available, biomechanical corridors are constructed for the assessment of model biofidelity. However, a robust set of corridors for brain's biomechanical response due to applied head kinematics does not exist for model validation. The aim of this study was to create corridors based on a dataset of in situ brain displacement that included six specimens tested under a set of twelve loading conditions.</p><p><strong>Methods: </strong>There were three main factors that complicated this task, including variation in head kinematics, differences in the initial position of the sensors, and the clustering of spatially scattered data. We employed various numerical and statistical methods to account for these experimental variations, with optimization and validation of the techniques conducted using the existing in situ dataset and a computational brain model.</p><p><strong>Results: </strong>Corridors were constructed using average and standard deviation of the specimen responses in the dataset for 24 discrete locations within the brain. Peak displacement showed a variance of less than 30% for most brain sensor locations.</p><p><strong>Conclusion: </strong>The corridors will serve as a better validation tool for assessing the biofidelity of computational brain models and will help understand inter-subject variability in brain biomechanics.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144119381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-Invasive Assessment of Structural and Mechanical Microenvironment (MME) Changes during Long Bone Regeneration Using Multi-Modal and Multi-Parametric Ultrasound Imaging Techniques in a Segmental Tibial Sheep Model In Vivo.","authors":"Songyuan Tang, Peer Shajudeen, Francesca Taraballi, Candice Hasse, Fernando Cabrera, Xu Yang, Md Tauhidul Islam, Enrica De Rosa, Bradley Weiner, Matthew Becker, Ennio Tasciotti, Raffaella Righetti","doi":"10.1109/TBME.2025.3572366","DOIUrl":"https://doi.org/10.1109/TBME.2025.3572366","url":null,"abstract":"<p><strong>Objective: </strong>The underlying regeneration process of bony defects often exhibits multifaceted nature, which may not be completely characterized by imaging methods currently available to the clinic. In this paper, we present the first longitudinal study to use multi-modal and multi-parametric ultrasound (US) imaging to assess bone regeneration in situations of segmental defects. Our intention is to demonstrate the utility of 3-D US and ultra-sound elastography (USE) to monitor ongoing biological processes accompanying bone regeneration.</p><p><strong>Methods: </strong>We derived two imaging markers from the proposed multimodal US imaging technique: the new-bone bulk volume and fibrovascular connective tissue area and computed their global and local statistics in a subject-specific manner.</p><p><strong>Results: </strong>From a cohort of 5 sheep treated with baseline tissue engineered construct (TEC), the distance (mm) between surface reconstructions from multi-view 3-D US and CT was 0.30 ± 0.67 (60 days post implantation) and 0.22 ± 0.43 (120 days post implantation). From USE, we discovered a new contrast mechanism between the soft tissue and fibrovascular connective tissue in axial normal strain elastograms and corroborated it using end-point histology. From two sheep, we detected negative and positive correlations between the fibrovascular connective tissue area at 60 days post shell implantation and the area of bone mass that continued to form after 60 days post shell implantation.</p><p><strong>Conclusion: </strong>Based on our results, it is feasible to use the proposed multi-modal and multi-parametric US imaging technique to assess structural and mechanical micro-environmental changes.</p><p><strong>Significance: </strong>In the future, 3-D US and USE may become important quantitative tools for bone fracture healing diagnosis and prognosis.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144119390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive Information Decomposition as a Tool to Quantify Emergent Dynamical Behaviors In Physiological Networks.","authors":"Luca Faes, Gorana Mijatovic, Laura Sparacino, Alberto Porta","doi":"10.1109/TBME.2025.3570937","DOIUrl":"10.1109/TBME.2025.3570937","url":null,"abstract":"<p><strong>Objective: </strong>This work introduces a framework for multivariate time series analysis aimed at detecting and quantifying collective emerging behaviors in the dynamics of physiological networks.</p><p><strong>Methods: </strong>Given a network system mapped by a vector random process, we compute the predictive information (PI) between the present and past network states and dissect it into amounts quantifying the unique, redundant and synergistic information shared by the present of the network and the past of each unit. Emergence is then quantified as the prevalence of the synergistic over the redundant contribution. The framework is implemented in practice using vector autoregressive (VAR) models.</p><p><strong>Results: </strong>Validation in simulated VAR processes documents that emerging behaviors arise in networks where multiple causal interactions coexist with internal dynamics. The application to cardiovascular and respiratory networks mapping the beat-to-beat variability of heart rate, arterial pressure and respiration measured at rest and during postural stress reveals the presence of statistically significant net synergy, as well as its modulation with sympathetic nervous system activation.</p><p><strong>Conclusion: </strong>Causal emergence can be efficiently assessed decomposing the PI of network systems via VAR models applied to multivariate time series. This approach evidences the synergy/redundancy balance as a hallmark of integrated short-term autonomic control in cardiovascular and respiratory networks.</p><p><strong>Significance: </strong>Measures of causal emergence provide a practical tool to quantify the mechanisms of causal influence that determine the dynamic state of cardiovascular and neural network systems across distinct physiopathological conditions.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Temporal and Spectral Processing for Improved Digital Subtraction Angiography using Photon-Counting Detectors.","authors":"Suyu Liao, Xiaoxuan Zhang, Xiao Jiang, Matthew Tivnan, J Webster Stayman, Grace J Gang","doi":"10.1109/TBME.2025.3570925","DOIUrl":"10.1109/TBME.2025.3570925","url":null,"abstract":"<p><strong>Objective: </strong>Digital subtraction angiography (DSA) is the gold standard modality for diagnostics and guidance for interventional procedures. Spectral imaging has previously been explored for DSA, but severe noise amplification from material decomposition has impeded clinical adoption. We present a novel joint processing strategy that leverages both temporal and spectral information for material decomposition to address this issue.</p><p><strong>Methods: </strong>We develop a model-based material decomposition approach that utilizes the pre- and post-contrast images simultaneously for material estimation. Performance was evaluated on a small-vessel phantom on a test bench with a photon-counting detector. Joint processing was compared with temporal subtraction and previously proposed spectral DSA techniques including hybrid subtraction and conventional three-material decomposition. Additional simulation was performed to investigate performance with perfectly calibrated spectral response and sensitivity to patient motion.</p><p><strong>Results: </strong>The improved conditioning of the proposed method effectively reduces bias and noise in the spectral results and allows three-material decomposition with dual-energy spectral measurements. The method achieved more than an order of magnitude variance reduction compared to previously proposed spectral DSA techniques. Compared to temporal subtraction, a mean variance reduction of 23.9% was achieved in simulation and 10.8% in experimental data. The degree of reduction is object-dependent. Noise reduction achieved in physical experiments is slightly lower than that in simulation, likely due to bias from imperfect spectral calibration. The method is equally sensitive to motion compared to temporal subtraction.</p><p><strong>Conclusion: </strong>The proposed method addresses a major image quality challenge limiting previous approaches and outperforms temporal subtraction.</p><p><strong>Significance: </strong>Such improvements facilitate the clinical translation of spectral angiography.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying Chaotic Behavior in Noisy Dynamical Systems: A Study on Heartbeat Dynamics.","authors":"Martina Bianco, Andrea Scarciglia, Claudio Bonanno, Gaetano Valenza","doi":"10.1109/TBME.2025.3566470","DOIUrl":"10.1109/TBME.2025.3566470","url":null,"abstract":"<p><strong>Background: </strong>Heart rate variability (HRV) series reflects the dynamical variation of R-R intervals in time and is one of the outputs of the cardiovascular system. This system has been recognized for generating nonlinear and complex dynamics, with the latter referring to a high sensitivity to small -theoretically infinitesimal - input changes. While early research associated chaotic behavior with the cardiovascular system, evidence of stochastic inputs, i.e., a physiological noise, invalidated those conclusions.</p><p><strong>Aim: </strong>We introduce a novel methodological framework for quantifying the presence of regular or chaotic dynamics in noisy dynamical systems. We aim to perform a comprehensive characterization of the cardiovascular system dynamics, accounting for dynamical noise inputs.</p><p><strong>Methodology: </strong>The method relies on the estimation of asymptotic growth rate of noisy mean square displacement series in a two-dimensional phase space. Cardiac oscillatory components are modelled through an Inverse-Gaussian function. We validated the proposed method using synthetic series comprising well-known regular and chaotic maps. We applied the method to real HRV series from 23 healthy subjects, as well as 28 patients with atrial fibrillation and 34 congestive heart failure, gathered during unstructured long-term activity.</p><p><strong>Results: </strong>Results on synthetic data validate the correctness of the method. While cardiac pathology does not modulate chaotic behavior, atrial fibrillation induces higher sensitivity to input changes.</p><p><strong>Conclusion: </strong>The proposed methodological framework provides a quantitative means for characterizing physiological dynamics in terms of regular versus chaotic patterns. Our findings demonstrate that HRV series is the output of a non-chaotic (regular) system driven by dynamical noise.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}