Physiological measurement最新文献

{"title":"tinyHLS: a novel open source high level synthesis tool targeting hardware accelerators for artificial neural network inference.","authors":"Ingo Hoyer, Alexander Utz, Christoph Hoog Antink, Karsten Seidl","doi":"10.1088/1361-6579/ada8f0","DOIUrl":"10.1088/1361-6579/ada8f0","url":null,"abstract":"<p><p><i>Objective.</i>In recent years, wearable devices such as smartwatches and smart patches have revolutionized biosignal acquisition and analysis, particularly for monitoring electrocardiography (ECG). However, the limited power supply of these devices often precludes real-time data analysis on the patch itself.<i>Approach.</i>This paper introduces a novel Python package, tinyHLS (High Level Synthesis), designed to address these challenges by converting Python-based AI models into platform-independent hardware description language code accelerators. Specifically designed for convolutional neural networks, tinyHLS seamlessly integrates into the AI developer's workflow in Python TensorFlow Keras. Our methodology leverages a template-based hardware compiler that ensures flexibility, efficiency, and ease of use. In this work, tinyHLS is first-published featuring templates for several layers of neural networks, such as dense, convolution, max and global average pooling. In the first version, rectified linear unit is supported as activation. It targets one-dimensional data, with a particular focus on time series data.<i>Main results.</i>The generated accelerators are validated in detecting atrial fibrillation on ECG data, demonstrating significant improvements in processing speed (62-fold) and energy efficiency (4.5-fold). Quality of code and synthesizability are ensured by validating the outputs with commercial ASIC design tools.<i>Significance.</i>Importantly, tinyHLS is open-source and does not rely on commercial tools, making it a versatile solution for both academic and commercial applications. The paper also discusses the integration with an open-source RISC-V and potential for future enhancements of tinyHLS, including its application in edge servers and cloud computing. The source code is available on GitHub:https://github.com/Fraunhofer-IMS/tinyHLS.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142966300","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 systematic review of automated prediction of sudden cardiac death using ECG signals.","authors":"Preeti P Ghasad, Jagath V S Vegivada, Vipin M Kamble, Ankit A Bhurane, Nikhil Santosh, Manish Sharma, Ru-San Tan, U Rajendra Acharya","doi":"10.1088/1361-6579/ad9ce5","DOIUrl":"10.1088/1361-6579/ad9ce5","url":null,"abstract":"&lt;p&gt;&lt;p&gt;&lt;i&gt;Background&lt;/i&gt;. Sudden cardiac death (SCD) stands as a life-threatening cardiac event capable of swiftly claiming lives. It ranks prominently among the leading causes of global mortality, contributing to approximately 10% of deaths worldwide. The timely anticipation of SCD holds the promise of immediate life-saving interventions, such as cardiopulmonary resuscitation. However, recent strides in the realms of deep learning (DL), machine learning (ML), and artificial intelligence have ushered in fresh opportunities for the automation of SCD prediction using physiological signals. Researchers have devised numerous models to automatically predict SCD using a combination of diverse feature extraction techniques and classifiers. Methods: We conducted a thorough review of research publications ranging from 2011 to 2023, with a specific focus on the automated prediction of SCD. Traditionally, specialists utilize molecular biomarkers, symptoms, and 12-lead ECG recordings for SCD prediction. However, continuous patient monitoring by experts is impractical, and only a fraction of patients seeks help after experiencing symptoms. However, over the past two decades, ML techniques have emerged and evolved for this purpose. Importantly, since 2021, the studies we have scrutinized delve into a diverse array of ML and DL algorithms, encompassing K-nearest neighbors, support vector machines, decision trees, random forest, Naive Bayes, and convolutional neural networks as classifiers.&lt;i&gt;Results&lt;/i&gt;. This literature review presents a comprehensive analysis of ML and DL models employed in predicting SCD. The analysis provided valuable information on the fundamental structure of cardiac fatalities, extracting relevant characteristics from electrocardiogram (ECG) and heart rate variability (HRV) signals, using databases, and evaluating classifier performance. The review offers a succinct yet thorough examination of automated SCD prediction methodologies, emphasizing current constraints and underscoring the necessity for further advancements. It serves as a valuable resource, providing valuable insights and outlining potential research directions for aspiring scholars in the domain of SCD prediction.&lt;i&gt;Conclusions&lt;/i&gt;. In recent years, researchers have made substantial strides in the prediction of SCD by leveraging openly accessible databases such as the MIT-BIH SCD Holter and Normal Sinus Rhythm, which contains extensive 24 h recordings of SCD patients. These sophisticated methodologies have previously demonstrated the potential to achieve remarkable accuracy, reaching levels as high as 97%, and can forecast SCD events with a lead time of 30-70 min. Despite these promising outcomes, the quest for even greater accuracy and reliability persists. ML and DL methodologies have shown great promise, their performance is intrinsically linked to the volume of training data available. Most predictive models rely on small-scale databases, raising concerns about their appl","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142807683","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":"Deep learning generalization for diabetic retinopathy staging from fundus images.","authors":"Yevgeniy Men, Jonathan Fhima, Leo Anthony Celi, Lucas Zago Ribeiro, Luis Filipe Nakayama, Joachim A Behar","doi":"10.1088/1361-6579/ada86a","DOIUrl":"10.1088/1361-6579/ada86a","url":null,"abstract":"<p><p><i>Objective</i>. Diabetic retinopathy (DR) is a serious diabetes complication that can lead to vision loss, making timely identification crucial. Existing data-driven algorithms for DR staging from digital fundus images (DFIs) often struggle with generalization due to distribution shifts between training and target domains.<i>Approach</i>. To address this, DRStageNet, a deep learning model, was developed using six public and independent datasets with 91 984 DFIs from diverse demographics. Five pretrained self-supervised vision transformers (ViTs) were benchmarked, with the best further trained using a multi-source domain (MSD) fine-tuning strategy.<i>Main results</i>. DINOv2 showed a 27.4% improvement in L-Kappa versus other pretrained ViT. MSD fine-tuning improved performance in four of five target domains. The error analysis revealing 60% of errors due to incorrect labels, 77.5% of which were correctly classified by DRStageNet.<i>Significance</i>. We developed DRStageNet, a DL model for DR, designed to accurately stage the condition while addressing the challenge of generalizing performance across target domains. The model and explainability heatmaps are available atwww.aimlab-technion.com/lirot-ai.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142953090","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":"Rotating radial injection pattern for highly sensitive electrical impedance tomography of human lung anomalies.","authors":"Oumaima Bader, Najoua Essoukri Ben Amara, Oliver G Ernst, Olfa Kanoun","doi":"10.1088/1361-6579/ada9b5","DOIUrl":"https://doi.org/10.1088/1361-6579/ada9b5","url":null,"abstract":"<p><strong>Objective: </strong>Electrical Impedance Tomography (EIT) is a non-invasive technique used for lung imaging. A significant challenge in EIT is reconstructing images of deeper thoracic regions due to the low sensitivity of boundary voltages to internal conductivity variations. The current injection pattern is decisive as it influences the current path, boundary voltages, and their sensitivity to tissue changes. &#xD;Approach. This study introduces a novel current injection pattern with radially placed electrodes excited in a rotating radial pattern. The effectiveness of the proposed pattern was investigated using a 3D computational model that mimics the human thorax, replicating its geometry and tissue electrical properties. To examine the detection of lung anomalies, models representing both healthy and unhealthy states, including cancer-like anomalies in three different positions, were developed.&#xD;The new pattern was compared to common patterns-Adjacent, Skip 1, and Opposite-using Finite Element Analysis (FEA). The comparison focused on the current density within lung nodules and the sensitivity to changes in anomaly positions. &#xD;Main Results. Results showed that the new pattern achieved the maximum current density within anomalies compared to surrounding tissues, with peak values near the closest electrode pairs to the anomalies. Specifically, current density magnitudes reached 72.73 10^{-9} A.m, 145.24 10^{-9} A.m, and 26.43 10^{-9} A.m for the three different positions, respectively. Furthermore, the novel pattern's sensitivity to anomaly position changes surpassed the common patterns.</p><p><strong>Significance: </strong>These results demonstrate the efficiency of the proposed injection pattern in detecting lung anomalies compared to the common injection patterns.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142979498","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":"Automated system for diagnosing pulmonary fibrosis using crackle analysis in recorded lung sounds based on iterative envelope mean fractal dimension filter.","authors":"Ravi Pal, Anna Barney, Giacomo Sgalla, Simon L F Walsh, Nicola Sverzellati, Sophie Fletcher, Stefania Cerri, Maxime Cannesson, Luca Richeldi","doi":"10.1088/1361-6579/ada9b4","DOIUrl":"https://doi.org/10.1088/1361-6579/ada9b4","url":null,"abstract":"<p><p>Patients with pulmonary fibrosis (PF) often experience long waits before getting a correct diagnosis, and this delay in reaching specialized care is associated with increased mortality, regardless of the severity of the disease. Early diagnosis and timely treatment of PF can potentially extend life expectancy and maintain a better quality of life. Crackles present in the recorded lung sounds may be crucial for the early diagnosis of PF. This paper describes an automated system for differentiating lung sounds related to PF from other pathological lung conditions using the average number of crackles per breath cycle (NOC/BC). The system is divided into four main parts: (1) pre-processing, (2) separation of crackles from normal breath sounds using the iterative envelope mean fractal dimension (IEM-FD) filter, (3) crackle verification and counting, and (4) estimating NOC/BC. The system was tested on a dataset consisting of 48 (24 fibrotic and 24 non-fibrotic) subjects and the results were compared with an assessment by two expert respiratory physicians. The set of HRCT images, reviewed by two expert radiologists for the presence or absence of pulmonary fibrosis, was used as the ground truth for evaluating the PF and non-PF classification performance of the system. The overall performance of the automatic classifier based on receiver operating curve-derived cut-off value for average NOC/BC of 18.65 (AUC=0.845, 95 % CI 0.739-0.952, p<0.001; sensitivity=91.7 %; specificity=59.3 %) compares favorably with the averaged performance of the physicians (sensitivity=83.3 %; specificity=56.25 %). Although radiological assessment should remain the gold standard for diagnosis of fibrotic interstitial lung disease, the automatic classification system has strong potential for diagnostic support, especially in assisting general practitioners in the auscultatory assessment of lung sounds to prompt further diagnostic work up of patients with suspect of interstitial lung disease.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142979401","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":"Generative adversarial networks with fully connected layers to denoise PPG signals.","authors":"Itzel Alexia Avila Castro, Helder Oliveira, Ricardo Goncalves Correia, Barrie R Hayes-Gill, Stephen P Morgan, Serhiy Korposh, David Gomez, Tânia Pereira","doi":"10.1088/1361-6579/ada9b6","DOIUrl":"https://doi.org/10.1088/1361-6579/ada9b6","url":null,"abstract":"<p><strong>Objective: </strong>The detection of arterial pulsating signals at the skin periphery with Photoplethysmography (PPG) are easily distorted by motion artifacts. This work explores the alternatives to the aid of PPG reconstruction with movement sensors (accelerometer and/or gyroscope) which to date have demonstrated the best pulsating signal reconstruction.</p><p><strong>Approach: </strong>A generative adversarial network with fully connected layers (FC-GAN) is proposed for the reconstruction of distorted PPG signals. Artificial corruption was performed to the clean selected signals from the BIDMC Heart Rate dataset, processed from the larger MIMIC II waveform database to create the training, validation and testing sets.</p><p><strong>Main results: </strong>The heart rate of this dataset was further extracted to evaluate the performance of the model&#xD;obtaining a mean absolute error (MAE) of 1.31 BPM comparing the HR of the target and reconstructed PPG signals with HR between 70 and 115 BPM.</p><p><strong>Significance: </strong>The model architecture is effective at reconstructing noisy PPG signals regardless the length and amplitude of the corruption introduced. The performance over a range of heart rates (70-115 BPM), indicates a promising approach for real-time PPG signal reconstruction without the aid of acceleration or angular velocity inputs.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142979409","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 adaptive learning method for long-term gesture recognition based on surface electromyography.","authors":"Yurong Li, Xiaofeng Lin, Heng Lin, Nan Zheng","doi":"10.1088/1361-6579/ad9a37","DOIUrl":"10.1088/1361-6579/ad9a37","url":null,"abstract":"<p><p><i>Objective.</i>The surface electromyography (EMG) signal reflects the user's intended actions and has become the important signal source for human-computer interaction. However, classification models trained on EMG signals from the same day cannot be applied for different days due to the time-varying characteristics of the EMG signal and the influence of electrodes shift caused by device wearing for different days, which hinders the application of commercial prosthetics. This type of gesture recognition for different days is usually referred to as long-term gesture recognition.<i>Approach.</i>To address this issue, we propose a long-term gesture recognition method by optimizing feature extraction, dimensionality reduction, and classification model calibration in EMG signal recognition. Our method extracts differential common spatial patterns features and then conduct dimensionality reduction with non-negative matrix factorization, effectively reducing the influence of the non-stationarity of the EMG signals. Based on clustering and classification self-training scheme, we select samples with high confidence from unlabeled samples to adaptively updates the model before daily formal use.<i>Main results.</i>We verify the feasibility of our method on a dataset consisting of 30 d of gesture data. The proposed gesture recognition scheme achieves accuracy over 90%, similar to the performance of daily calibration with labeled data. However, our method needs only one repetition of unlabeled gestures samples to update the classification model before daily formal use.<i>Significance.</i>From the results we can conclude that the proposed method can not only ensure superior performance, but also greatly facilitate the daily use, which is especially suitable for long-term application.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142771531","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":"Correlation analysis of estimated pulse wave velocity and severe abdominal aortic calcification: based on the National Health and Nutrition Examination Survey database.","authors":"Guanghui Zhao, Zhiyu Guo, Peng Zheng","doi":"10.1088/1361-6579/ad9ce6","DOIUrl":"https://doi.org/10.1088/1361-6579/ad9ce6","url":null,"abstract":"<p><p><i>Objective.</i>To investigate how severe abdominal aortic calcification (SAAC) and estimated pulse wave velocity (ePWV) relate to each other and to all-cause and cardiovascular disease (CVD) mortalities.<i>Approach.</i>National Health and Nutrition Examination Survey 2013-2014 data were analyzed. ePWV, computed using age and mean blood pressure, served as an independent variable. Dependent variable SAAC (AAC score >6) was quantified using dual-energy x-ray absorptiometry and Kauppila grading. A weighted logistic regression model, interaction terms, and restricted cubic spline analysis examined relationship between ePWV and SAAC. Kaplan-Meier curves were drawn among SAAC people. A weighted Cox regression model was built to examine associations of ePWV with all-cause and CVD mortalities.<i>Main results.</i>2849 individuals were included. A strong positive connection (odds ratio (OR) > 1,<i>P</i>< 0.05) was seen between ePWV and SAAC risk. Interaction term<i>P</i>-value indicated that only ethnicity (<i>P</i>< 0.05) had an impact on this link but smoking, alcohol use, age, sex, body mass index, or hypertension did not. SAAC patients showed greater ePWV, all-cause and CVD mortalities (<i>P</i>< 0.05) than non-SAAC subjects. Greater ePWV (>12.00 m s^-1) was associated with higher risks of all-cause and CVD mortalities in SAAC participants (hazard ratio (HR) > 1,<i>P</i>< 0.05). Significance<i>.</i>This study, for the first time based on the NHANES database, reveals a positive correlation between ePWV and SAAC, and identifies ePWV as an independent predictor of all-cause and cardiovascular mortality risk in patients with SAAC, providing a new biomarker for the prevention and early intervention of cardiovascular diseases.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":"45 12","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142932605","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":"Developing technologies to assess vascular ageing: a roadmap from VascAgeNet.","authors":"Serena Zanelli, Davide Agnoletti, Jordi Alastruey, John Allen, Elisabetta Bianchini, Vasiliki Bikia, Pierre Boutouyrie, Rosa Maria Bruno, Rachel Climie, Djammaleddine Djeldjli, Eugenia Gkaliagkousi, Alessandro Giudici, Kristina Gopcevic, Andrea Grillo, Andrea Guala, Bernhard Hametner, Jayaraj Joseph, Parmis Karimpour, Vimarsha Kodithuwakku, Panicos A Kyriacou, Antonios Lazaridis, Mai Tone Lønnebakken, Maria Raffaella Martina, Christopher Clemens Mayer, P M Nabeel, Petras Navickas, János Nemcsik, Stefan Orter, Chloe Park, Telmo Pereira, Giacomo Pucci, Ana Belen Amado Rey, Paolo Salvi, Ana Carolina Gonçalves Seabra, Ute Seeland, Thomas van Sloten, Bart Spronck, Gerard Stansby, Indra Steens, Thomas Stieglitz, Isabella Tan, Dave Veerasingham, Siegfried Wassertheurer, Thomas Weber, Berend E Westerhof, Peter H Charlton","doi":"10.1088/1361-6579/ad548e","DOIUrl":"10.1088/1361-6579/ad548e","url":null,"abstract":"<p><p>Vascular ageing (vascular ageing) is the deterioration of arterial structure and function which occurs naturally with age, and which can be accelerated with disease. Measurements of vascular ageing are emerging as markers of cardiovascular risk, with potential applications in disease diagnosis and prognosis, and for guiding treatments. However, vascular ageing is not yet routinely assessed in clinical practice. A key step towards this is the development of technologies to assess vascular ageing. In this Roadmap, experts discuss several aspects of this process, including: measurement technologies; the development pipeline; clinical applications; and future research directions. The Roadmap summarises the state of the art, outlines the major challenges to overcome, and identifies potential future research directions to address these challenges.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11697036/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141261732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hemodynamic effects of bifurcation and stenosis geometry on carotid arteries with different degrees of stenosis.","authors":"Yuxin Guo, Jianbao Yang, Junzhen Xue, Jingxi Yang, Siyu Liu, XueLian Zhang, Yixin Yao, Anlong Quan, Yang Zhang","doi":"10.1088/1361-6579/ad9c13","DOIUrl":"10.1088/1361-6579/ad9c13","url":null,"abstract":"<p><p><i>Objective.</i>Carotid artery stenosis (CAS) is a key factor in pathological conditions, such as thrombosis, which is closely linked to hemodynamic parameters. Existing research often focuses on analyzing the influence of geometric characteristics at the stenosis site, making it difficult to predict the effects of overall vascular geometry on hemodynamic parameters. The objective of this study is to comprehensively examine the influence of geometric morphology at different degrees of CAS and at bifurcation sites on hemodynamic parameters.<i>Approach.</i>A three-dimensional model is established using computed tomography angiography images, and eight geometric parameters of each patient are measured by MIMICS. Then, computational fluid dynamics is utilized to investigate 60 patients with varying degrees of stenosis (10%-95%). Time and grid tests are conducted to optimize settings, and results are validated through comparison with reference calculations. Subsequently, correlation analysis using SPSS is performed to examine the relationship between the eight geometric parameters and four hemodynamic parameters. In MATLAB, prediction models for the four hemodynamic parameters are developed using back propagation neural networks (BPNN) and multiple linear regression.<i>Main results.</i>The BPNN model significantly outperforms the multiple linear regression model, reducing mean absolute error, mean squared error, and root mean squared error by 91.7%, 93.9%, and 75.5%, respectively, and increasing<i>R</i>²from 19.0% to 88.0%. This greatly improves fitting accuracy and reduces errors. This study elucidates the correlation and patterns of geometric parameters of vascular stenosis and bifurcation in evaluating hemodynamic parameters of CAS.<i>Significance.</i>This study opens up new avenues for improving the diagnosis, treatment, and clinical management strategies of CAS.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142802007","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}