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Wu D, Ono R, Wang S, Kobayashi Y, Sughimoto K, Liu H. Pulse wave signal-driven machine learning for identifying left ventricular enlargement in heart failure patients. Biomed Eng Online 2024; 23:60. [PMID: 38909231 PMCID: PMC11193305 DOI: 10.1186/s12938-024-01257-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Accepted: 06/12/2024] [Indexed: 06/24/2024] Open
Abstract
BACKGROUND Left ventricular enlargement (LVE) is a common manifestation of cardiac remodeling that is closely associated with cardiac dysfunction, heart failure (HF), and arrhythmias. This study aimed to propose a machine learning (ML)-based strategy to identify LVE in HF patients by means of pulse wave signals. METHOD We constructed two high-quality pulse wave datasets comprising a non-LVE group and an LVE group based on the 264 HF patients. Fourier series calculations were employed to determine if significant frequency differences existed between the two datasets, thereby ensuring their validity. Then, the ML-based identification was undertaken by means of classification and regression models: a weighted random forest model was employed for binary classification of the datasets, and a densely connected convolutional network was utilized to directly estimate the left ventricular diastolic diameter index (LVDdI) through regression. Finally, the accuracy of the two models was validated by comparing their results with clinical measurements, using accuracy and the area under the receiver operating characteristic curve (AUC-ROC) to assess their capability for identifying LVE patients. RESULTS The classification model exhibited superior performance with an accuracy of 0.91 and an AUC-ROC of 0.93. The regression model achieved an accuracy of 0.88 and an AUC-ROC of 0.89, indicating that both models can quickly and accurately identify LVE in HF patients. CONCLUSION The proposed ML methods are verified to achieve effective classification and regression with good performance for identifying LVE in HF patients based on pulse wave signals. This study thus demonstrates the feasibility and potential of the ML-based strategy for clinical practice while offering an effective and robust tool for diagnosing and intervening ventricular remodeling.
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Affiliation(s)
- Dandan Wu
- Graduate School of Science and Engineering, Chiba University, Chiba, Japan
| | - Ryohei Ono
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Sirui Wang
- Graduate School of Science and Engineering, Chiba University, Chiba, Japan
| | - Yoshio Kobayashi
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Koichi Sughimoto
- Graduate School of Science and Engineering, Chiba University, Chiba, Japan
| | - Hao Liu
- Graduate School of Science and Engineering, Chiba University, Chiba, Japan.
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2
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Xing X, Hong J, Alastruey J, Long X, Liu H, Dong WF. Robust arterial compliance estimation with Katz's fractal dimension of photoplethysmography. Front Physiol 2024; 15:1398904. [PMID: 38915780 PMCID: PMC11194390 DOI: 10.3389/fphys.2024.1398904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 05/21/2024] [Indexed: 06/26/2024] Open
Abstract
Arterial compliance (AC) plays a crucial role in vascular aging and cardiovascular disease. The ability to continuously estimate aortic AC or its surrogate, pulse pressure (PP), through wearable devices is highly desirable, given its strong association with daily activities. While the single-site photoplethysmography (PPG)-derived arterial stiffness indices show reasonable correlations with AC, they are susceptible to noise interference, limiting their practical use. To overcome this challenge, our study introduces a noise-resistant indicator of AC: Katz's fractal dimension (KFD) of PPG signals. We showed that KFD integrated the signal complexity arising from compliance changes across a cardiac cycle and vascular structural complexity, thereby decreasing its dependence on individual characteristic points. To assess its capability in measuring AC, we conducted a comprehensive evaluation using both in silico studies with 4374 virtual human data and real-world measurements. In the virtual human studies, KFD demonstrated a strong correlation with AC (r = 0.75), which only experienced a slight decrease to 0.66 at a signal-to-noise ratio of 15dB, surpassing the best PPG-morphology-derived AC measure (r = 0.41) under the same noise condition. In addition, we observed that KFD's sensitivity to AC varied based on the individual's hemodynamic status, which may further enhance the accuracy of AC estimations. These in silico findings were supported by real-world measurements encompassing diverse health conditions. In conclusion, our study suggests that PPG-derived KFD has the potential to continuously and reliably monitor arterial compliance, enabling unobtrusive and wearable assessment of cardiovascular health.
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Affiliation(s)
- Xiaoman Xing
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Jingyuan Hong
- Division of Imaging Sciences and Biomedical Engineering, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Jordi Alastruey
- Division of Imaging Sciences and Biomedical Engineering, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Xi Long
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Haipeng Liu
- Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom
| | - Wen-Fei Dong
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
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3
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Yang J, Lü J, Qiu Z, Zhang M, Yan H. Risk prediction of pulse wave for hypertensive target organ damage based on frequency-domain feature map. Med Eng Phys 2024; 126:104161. [PMID: 38621841 DOI: 10.1016/j.medengphy.2024.104161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 02/29/2024] [Accepted: 03/27/2024] [Indexed: 04/17/2024]
Abstract
The application of deep learning to the classification of pulse waves in Traditional Chinese Medicine (TCM) related to hypertensive target organ damage (TOD) is hindered by challenges such as low classification accuracy and inadequate generalization performance. To address these challenges, we introduce a lightweight transfer learning model named MobileNetV2SCP. This model transforms time-domain pulse waves into 36-dimensional frequency-domain waveform feature maps and establishes a dedicated pre-training network based on these maps to enhance the learning capability for small samples. To improve global feature correlation, we incorporate a novel fusion attention mechanism (SAS) into the inverted residual structure, along with the utilization of 3 × 3 convolutional layers and BatchNorm layers to mitigate model overfitting. The proposed model is evaluated using cross-validation results from 805 cases of pulse waves associated with hypertensive TOD. The assessment metrics, including Accuracy (92.74 %), F1-score (91.47 %), and Area Under Curve (AUC) (97.12 %), demonstrate superior classification accuracy and generalization performance compared to various state-of-the-art models. Furthermore, this study investigates the correlations between time-domain and frequency-domain features in pulse waves and their classification in hypertensive TOD. It analyzes key factors influencing pulse wave classification, providing valuable insights for the clinical diagnosis of TOD.
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Affiliation(s)
- Jingdong Yang
- Autonomous Robot Lab, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Jiangtao Lü
- Autonomous Robot Lab, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Zehao Qiu
- Autonomous Robot Lab, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Mengchu Zhang
- Shanghai Key Laboratory of Health Identification and Assessment, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Haixia Yan
- Shanghai Key Laboratory of Health Identification and Assessment, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
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4
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Wang S, Ono R, Wu D, Aoki K, Kato H, Iwahana T, Okada S, Kobayashi Y, Liu H. Pulse wave-based evaluation of the blood-supply capability of patients with heart failure via machine learning. Biomed Eng Online 2024; 23:7. [PMID: 38243221 PMCID: PMC10797936 DOI: 10.1186/s12938-024-01201-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 01/04/2024] [Indexed: 01/21/2024] Open
Abstract
Pulse wave, as a message carrier in the cardiovascular system (CVS), enables inferring CVS conditions while diagnosing cardiovascular diseases (CVDs). Heart failure (HF) is a major CVD, typically requiring expensive and time-consuming treatments for health monitoring and disease deterioration; it would be an effective and patient-friendly tool to facilitate rapid and precise non-invasive evaluation of the heart's blood-supply capability by means of powerful feature-abstraction capability of machine learning (ML) based on pulse wave, which remains untouched yet. Here we present an ML-based methodology, which is verified to accurately evaluate the blood-supply capability of patients with HF based on clinical data of 237 patients, enabling fast prediction of five representative cardiovascular function parameters comprising left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVDd), left ventricular end-systolic diameter (LVDs), left atrial dimension (LAD), and peripheral oxygen saturation (SpO2). Two ML networks were employed and optimized based on high-quality pulse wave datasets, and they were validated consistently through statistical analysis based on the summary independent-samples t-test (p > 0.05), the Bland-Altman analysis with clinical measurements, and the error-function analysis. It is proven that evaluation of the SpO2, LAD, and LVDd performance can be achieved with the maximum error < 15%. While our findings thus demonstrate the potential of pulse wave-based, non-invasive evaluation of the blood-supply capability of patients with HF, they also set the stage for further refinements in health monitoring and deterioration prevention applications.
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Affiliation(s)
- Sirui Wang
- Graduate School of Science and Engineering, Chiba University, Chiba, Japan
| | - Ryohei Ono
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Dandan Wu
- Graduate School of Science and Engineering, Chiba University, Chiba, Japan
| | - Kaoruko Aoki
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Hirotoshi Kato
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Togo Iwahana
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Sho Okada
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Yoshio Kobayashi
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Hao Liu
- Graduate School of Science and Engineering, Chiba University, Chiba, Japan.
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Shimada T, Matsubara K, Koyama D, Matsukawa M, Ohsaki M, Kobayashi Y, Saito K, Yamagami H. Development of evaluation system for cerebral artery occlusion in emergency medical services: noninvasive measurement and utilization of pulse waves. Sci Rep 2023; 13:3339. [PMID: 36849592 PMCID: PMC9971203 DOI: 10.1038/s41598-023-30229-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 02/19/2023] [Indexed: 03/01/2023] Open
Abstract
Rapid reperfusion therapy can reduce disability and death in patients with large vessel occlusion strokes (LVOS). It is crucial for emergency medical services to identify LVOS and transport patients directly to a comprehensive stroke center. Our ultimate goal is to develop a non-invasive, accurate, portable, inexpensive, and legally employable in vivo screening system for cerebral artery occlusion. As a first step towards this goal, we propose a method for detecting carotid artery occlusion using pulse wave measurements at the left and right carotid arteries, feature extraction from the pulse waves, and occlusion inference using these features. To meet all of these requirements, we use a piezoelectric sensor. We hypothesize that the difference in the left and right pulse waves caused by reflection is informative, as LVOS is typically caused by unilateral artery occlusion. Therefore, we extracted three features that only represented the physical effects of occlusion based on the difference. For inference, we considered that the logistic regression, a machine learning technique with no complex feature conversion, is a reasonable method for clarifying the contribution of each feature. We tested our hypothesis and conducted an experiment to evaluate the effectiveness and performance of the proposed method. The method achieved a diagnostic accuracy of 0.65, which is higher than the chance level of 0.43. The results indicate that the proposed method has potential for identifying carotid artery occlusions.
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Affiliation(s)
- Takuma Shimada
- grid.255178.c0000 0001 2185 2753Faculty of Science and Engineering, Doshisha University, Kyoto, Japan
| | - Kazumasa Matsubara
- grid.255178.c0000 0001 2185 2753Faculty of Science and Engineering, Doshisha University, Kyoto, Japan
| | - Daisuke Koyama
- grid.255178.c0000 0001 2185 2753Faculty of Science and Engineering, Doshisha University, Kyoto, Japan
| | - Mami Matsukawa
- Faculty of Science and Engineering, Doshisha University, Kyoto, Japan.
| | - Miho Ohsaki
- grid.255178.c0000 0001 2185 2753Faculty of Science and Engineering, Doshisha University, Kyoto, Japan
| | - Yasuyo Kobayashi
- grid.410814.80000 0004 0372 782XDepartment of Neurology, Nara Medical University, Nara, Japan
| | - Kozue Saito
- grid.410814.80000 0004 0372 782XDepartment of Neurology, Nara Medical University, Nara, Japan
| | - Hiroshi Yamagami
- grid.416803.80000 0004 0377 7966Department of Stroke Neurology, National Hospital Organization Osaka National Hospital, Osaka, Japan
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Li G, Zhu Y, Guo Y, Mabuchi T, Li D, Huang S, Wang S, Sun H, Tokumasu T. Deep Learning to Reveal the Distribution and Diffusion of Water Molecules in Fuel Cell Catalyst Layers. ACS APPLIED MATERIALS & INTERFACES 2023; 15:5099-5108. [PMID: 36652634 DOI: 10.1021/acsami.2c17198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Water management in the catalyst layers (CLs) of proton-exchange membrane fuel cells is crucial for its commercialization and popularization. However, the high experimental or computational cost in obtaining water distribution and diffusion remains a bottleneck in the existing experimental methods and simulation algorithms, and further mechanistic exploration at the nanoscale is necessary. Herein, we integrate, for the first time, molecular dynamics simulation with our customized analysis framework based on a multiattribute point cloud dataset and an advanced deep learning network. This was achieved through our workflow that generates simulated transport data of water molecules in the CLs as the training and test dataset. Deep learning framework models the multibody solid-liquid system of CLs on a molecular scale and completes the mapping from the Pt/C substrate structure and Nafion aggregates to the density distribution and diffusion coefficient of water molecules. The prediction results are comprehensively analyzed and error evaluated, which reveals the highly anisotropic interaction landscape between 50,000 pairs of interacting nanoparticles and explains the structure and water transport property relationship in the hydrated Nafion film on the molecular scale. Compared to the conventional methods, the proposed deep learning framework shows computational cost efficiency, accuracy, and good visual display. Further, it has a generality potential to model macro- and microscopic mass transport in different components of fuel cells. Our framework is expected to make real-time predictions of the distribution and diffusion of water molecules in CLs as well as establish statistical significance in the structural optimization and design of CLs and other components of fuel cells.
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Affiliation(s)
- Gaoyang Li
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
| | - Yonghong Zhu
- School of Chemical Engineering, Northwest University, Xi'an710069Shaanxi, China
| | - Yuting Guo
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
| | - Takuya Mabuchi
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
- Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi980-8577, Japan
| | - Dong Li
- School of Chemical Engineering, Northwest University, Xi'an710069Shaanxi, China
| | - Shengfeng Huang
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
| | - Sirui Wang
- Graduate School of Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba263-8522, Japan
| | - Haiyi Sun
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
| | - Takashi Tokumasu
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
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Wang S, Wu D, Li G, Zhang Z, Xiao W, Li R, Qiao A, Jin L, Liu H. Deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments. Front Physiol 2023; 13:1094743. [PMID: 36703930 PMCID: PMC9872942 DOI: 10.3389/fphys.2022.1094743] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 12/09/2022] [Indexed: 01/11/2023] Open
Abstract
Hemodynamic prediction of carotid artery stenosis (CAS) is of great clinical significance in the diagnosis, prevention, and treatment prognosis of ischemic strokes. While computational fluid dynamics (CFD) is recognized as a useful tool, it shows a crucial issue that the high computational costs are usually required for real-time simulations of complex blood flows. Given the powerful feature-extraction capabilities, the deep learning (DL) methodology has a high potential to implement the mapping of anatomic geometries and CFD-driven flow fields, which enables accomplishing fast and accurate hemodynamic prediction for clinical applications. Based on a brain/neck CT angiography database of 280 subjects, image based three-dimensional CFD models of CAS were constructed through blood vessel extraction, computational domain meshing and setting of the pulsatile flow boundary conditions; a series of CFD simulations were undertaken. A DL strategy was proposed and accomplished in terms of point cloud datasets and a DL network with dual sampling-analysis channels. This enables multimode mapping to construct the image-based geometries of CAS while predicting CFD-based hemodynamics based on training and testing datasets. The CFD simulation was validated with the mass flow rates at two outlets reasonably agreed with the published results. Comprehensive analysis and error evaluation revealed that the DL strategy enables uncovering the association between transient blood flow characteristics and artery cavity geometric information before and after surgical treatments of CAS. Compared with other methods, our DL-based model trained with more clinical data can reduce the computational cost by 7,200 times, while still demonstrating good accuracy (error<12.5%) and flow visualization in predicting the two hemodynamic parameters. In addition, the DL-based predictions were in good agreement with CFD simulations in terms of mean velocity in the stenotic region for both the preoperative and postoperative datasets. This study points to the capability and significance of the DL-based fast and accurate hemodynamic prediction of preoperative and postoperative CAS. For accomplishing real-time monitoring of surgical treatments, further improvements in the prediction accuracy and flexibility may be conducted by utilizing larger datasets with specific real surgical events such as stent intervention, adopting personalized boundary conditions, and optimizing the DL network.
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Affiliation(s)
- Sirui Wang
- Graduate School of Engineering, Chiba University, Chiba, Japan
| | - Dandan Wu
- Graduate School of Engineering, Chiba University, Chiba, Japan
| | - Gaoyang Li
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Zhiyuan Zhang
- Department of Interventional Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Weizhong Xiao
- Department of Interventional Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Ruichen Li
- Graduate School of Engineering, Chiba University, Chiba, Japan
| | - Aike Qiao
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Long Jin
- Department of Interventional Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China,*Correspondence: Hao Liu, ; Long Jin,
| | - Hao Liu
- Graduate School of Engineering, Chiba University, Chiba, Japan,*Correspondence: Hao Liu, ; Long Jin,
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Song X, Liu Y, Wang S, Zhang H, Qiao A, Wang X. Non-invasive hemodynamic diagnosis based on non-linear pulse wave theory applied to four limbs. Front Bioeng Biotechnol 2023; 11:1081447. [PMID: 36970627 PMCID: PMC10033961 DOI: 10.3389/fbioe.2023.1081447] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 02/28/2023] [Indexed: 03/29/2023] Open
Abstract
Introduction: Hemodynamic diagnosis indexes (HDIs) can comprehensively evaluate the health status of the cardiovascular system (CVS), particularly for people older than 50 years and prone to cardiovascular disease (CVDs). However, the accuracy of non-invasive detection remains unsatisfactory. We propose a non-invasive HDIs model based on the non-linear pulse wave theory (NonPWT) applied to four limbs. Methods: This algorithm establishes mathematical models, including pulse wave velocity and pressure information of the brachial and ankle arteries, pressure gradient, and blood flow. Blood flow is key to calculating HDIs. Herein, we derive blood flow equation for different times of the cardiac cycle considering the four different distributions of blood pressure and pulse wave of four limbs, then obtain the average blood flow in a cardiac cycle, and finally calculate the HDIs. Results: The results of the blood flow calculations reveal that the average blood flow in the upper extremity arteries is 10.78 ml/s (clinically: 2.5-12.67 ml/s), and the blood flow in the lower extremity arteries is higher than that in the upper extremity. To verify model accuracy, the consistency between the clinical and calculated values is verified with no statistically significant differences (p < 0.05). Model IV or higher-order fitting is the closest. To verify the model generalizability, considering the risk factors of cardiovascular diseases, the HDIs are recalculated using model IV, and thus, consistency is verified (p < 0.05 and Bland-Altman plot). Conclusion: We conclude our proposed algorithmic model based on NonPWT can facilitate the non-invasive hemodynamic diagnosis with simpler operational procedures and reduced medical costs.
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Affiliation(s)
- Xiaorui Song
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai’an, China
| | - Yi Liu
- Department of Ultrasound, Taian Maternity and Child Health Care Hospital, Tai’an, China
| | - Sirui Wang
- Graduate School of Engineering, Chiba University, Chiba, Japan
| | - Honghui Zhang
- College of Engineering, Inner Mongolia Minzu University, Tongliao, China
| | - Aike Qiao
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Xuezheng Wang
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai’an, China
- Department of Medical Image, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
- *Correspondence: Xuezheng Wang,
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