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Aghilinejad A, Tamborini A, Gharib M. A new methodology for determining the central pressure waveform from peripheral measurement using Fourier-based machine learning. Artif Intell Med 2024; 154:102918. [PMID: 38924863 DOI: 10.1016/j.artmed.2024.102918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 04/02/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024]
Abstract
Radial applanation tonometry is a well-established technique for hemodynamic monitoring and is becoming popular in affordable non-invasive wearable healthcare electronics. To assess the central aortic pressure using radial-based measurements, there is an essential need to develop mathematical approaches to estimate the central pressure waveform. In this study, we propose a new Fourier-based machine learning (F-ML) methodology to transfer non-invasive radial pressure measurements to the central pressure waveform. To test the method, collection of tonometry recordings of the radial and carotid pressure measurements are used from the Framingham Heart Study (2640 individuals, 55 % women) with mean (range) age of 66 (40-91) years. Method-derived estimates are significantly correlated with the measured ones for three major features of the pressure waveform (systolic blood pressure, r=0.97, p < 0.001; diastolic blood pressure, r=0.99, p < 0.001; and mean blood pressure, r=0.99, p < 0.001). In all cases, the Bland-Altman analysis shows negligible bias in the estimations and error is bounded to 5.4 mmHg. Findings also suggest that the F-ML approach reconstructs the shape of the central pressure waveform accurately with the average normalized root mean square error of 5.5 % in the testing population which is blinded to all stages of machine learning development. The results show that the F-ML transfer function outperforms the conventional generalized transfer function, particularly in terms of reconstructing the shape of the central pressure waveform morphology. The proposed F-ML transfer function can provide accurate estimates for the central pressure waveform, and ultimately expand the usage of non-invasive devices for central hemodynamic assessment.
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Affiliation(s)
- Arian Aghilinejad
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, United States.
| | - Alessio Tamborini
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, United States
| | - Morteza Gharib
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, United States
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2
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Iscan M, Yesildirek A. An intelligent aortic valve model for complete cardiac cycle. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2024:e3838. [PMID: 38888136 DOI: 10.1002/cnm.3838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 05/15/2024] [Accepted: 05/18/2024] [Indexed: 06/20/2024]
Abstract
The aortic valve (AV) is crucial for cardiovascular (CV) hemodynamic, impacting cardiac output (CO) and left ventricular volumetric flow rate (LVQ). Its nonlinear behavior challenges standard LVQ prediction methods as well as CO one. This study presents a novel approach for modeling the AV in the CV system, offering an improved method for estimating crucial parameters like LVQ across various AV conditions, including aortic stenosis (AS). The model, based on AV channel length during the entire cardiac phase, introduces a time-varying AV resistance (TV-AVR) parameterized by the pressure ratio across the AV and LVQ, enabling the simulation of both healthy and AS-related conditions. To validate this model, in vitro measurements are compared using a hybrid mock circulatory loop device. An unconventional use of a convolutional neural network (CNN) corrects the model's estimates, eliminating the need for labeled datasets. This approach, incorporating real-time learning and transforming 1-D CV signals into 2-D tensors, significantly improves the accuracy of LVQ measurements, achieving an error rate of less than 3.41 ± 4.84% for CO in healthy conditions and 2.83 ± 1.35% in AS cases-a 33.13% enhancement over linear diode models. These results underscore the potential of this approach for enhancing the diagnosis, prediction, and treatment of AV diseases. The key contributions of the proposed method encompass nonlinear TV-AVR estimation, investigation of transient CV responses, prediction of instantaneous CO, development of a flexible framework for noninvasive measurements integration, and the introduction of an adjustable resistance model using an extended Kalman filter (EKF) and CNN combination, all without requiring labeled data.
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Affiliation(s)
- Mehmet Iscan
- Mechatronics Engineering Department, Yildiz Technical University, Istanbul, Turkey
| | - Aydin Yesildirek
- Mechatronics Engineering Department, Yildiz Technical University, Istanbul, Turkey
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3
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Wołos K, Pstras L, Debowska M, Dabrowski W, Siwicka-Gieroba D, Poleszczuk J. Non-invasive assessment of stroke volume and cardiovascular parameters based on peripheral pressure waveform. PLoS Comput Biol 2024; 20:e1012013. [PMID: 38635856 PMCID: PMC11060565 DOI: 10.1371/journal.pcbi.1012013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 04/30/2024] [Accepted: 03/19/2024] [Indexed: 04/20/2024] Open
Abstract
Cardiovascular diseases are the leading cause of death globally, making the development of non-invasive and simple-to-use tools that bring insights into the state of the cardiovascular system of utmost importance. We investigated the possibility of using peripheral pulse wave recordings to estimate stroke volume (SV) and subject-specific parameters describing the selected properties of the cardiovascular system. Peripheral pressure waveforms were recorded in the radial artery using applanation tonometry (SphygmoCor) in 35 hemodialysis (HD) patients and 14 healthy subjects. The pressure waveforms were then used to estimate subject-specific parameters of a mathematical model of pulse wave propagation coupled with the elastance-based model of the left ventricle. Bioimpedance cardiography measurements (PhysioFlow) were performed to validate the model-estimated SV. Mean absolute percentage error between the simulated and measured pressure waveforms was 4.0% and 2.8% for the HD and control group, respectively. We obtained a moderate correlation between the model-estimated and bioimpedance-based SV (r = 0.57, p<0.05, and r = 0.58, p<0.001, for the control group and HD patients, respectively). We also observed a correlation between the estimated end-systolic elastance of the left ventricle and the peripheral systolic pressure in both HD patients (r = 0.84, p<0.001) and the control group (r = 0.70, p<0.01). These preliminary results suggest that, after additional validation and possibly further refinement to increase accuracy, the proposed methodology could support non-invasive assessment of stroke volume and selected heart function parameters and vascular properties. Importantly, the proposed method could be potentially implemented in the existing devices measuring peripheral pressure waveforms.
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Affiliation(s)
- Kamil Wołos
- Laboratory of Mathematical Modeling of Physiological Processes, Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland
| | - Leszek Pstras
- Laboratory of Mathematical Modeling of Physiological Processes, Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland
| | - Malgorzata Debowska
- Laboratory of Mathematical Modeling of Physiological Processes, Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland
| | - Wojciech Dabrowski
- Department of Anesthesiology and Intensive Therapy, Medical University of Lublin, Lublin, Poland
| | - Dorota Siwicka-Gieroba
- Department of Anesthesiology and Intensive Therapy, Medical University of Lublin, Lublin, Poland
| | - Jan Poleszczuk
- Laboratory of Mathematical Modeling of Physiological Processes, Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland
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4
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Zhang Q, Zhang YH, Hao LL, Xu XH, Wu GF, Lin L, Xu XL, Qi L, Tian S. A numerical study on the siphonic effect of enhanced external counterpulsation at lower extremities with a coupled 0D-1D closed-loop personalized hemodynamics model. J Biomech 2024; 166:112057. [PMID: 38520934 DOI: 10.1016/j.jbiomech.2024.112057] [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: 10/26/2023] [Revised: 03/05/2024] [Accepted: 03/18/2024] [Indexed: 03/25/2024]
Abstract
Enhanced external counterpulsation (EECP) is a treatment and rehabilitation approach for ischemic diseases, including coronary artery disease. Its therapeutic benefits are primarily attributed to the improved blood circulation achieved through sequential mechanical compression of the lower extremities. However, despite the crucial role that hemodynamic effects in the lower extremity arteries play in determining the effectiveness of EECP treatment, most studies have focused on the diastole phase and ignored the systolic phase. In the present study, a novel siphon model (SM) was developed to investigate the interdependence of several hemodynamic parameters, including pulse wave velocity, femoral flow rate, the operation pressure of cuffs, and the mean blood flow changes in the femoral artery throughout EECP therapy. To verify the accuracy of the SM, we coupled the predicted afterload in the lower extremity arteries during deflation using SM with the 0D-1D patient-specific model. Finally, the simulation results were compared with clinical measurements obtained during EECP therapy to verify the applicability and accuracy of the SM, as well as the coupling method. The precision and reliability of the previously developed personalized approach were further affirmed in this study. The average waveform similarity coefficient between the simulation results and the clinical measurements during the rest state exceeded 90%. This work has the potential to enhance our understanding of the hemodynamic mechanisms involved in EECP treatment and provide valuable insights for clinical decision-making.
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Affiliation(s)
- Qi Zhang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China
| | - Ya-Hui Zhang
- Department of Cardiology, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong 518033, China
| | - Li-Ling Hao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China.
| | - Xuan-Hao Xu
- Department of Cardiology, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong 518033, China
| | - Gui-Fu Wu
- Department of Cardiology, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong 518033, China
| | - Ling Lin
- Department of Radiology, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong 518033, China
| | - Xiu-Li Xu
- Department of Cardiology, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong 518033, China.
| | - Lin Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China.
| | - Shuai Tian
- Department of Cardiology, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong 518033, China.
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5
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Bikia V, Rovas G, Anagnostopoulos S, Stergiopulos N. On the similarity between aortic and carotid pressure diastolic decay: a mathematical modelling study. Sci Rep 2023; 13:10775. [PMID: 37402771 DOI: 10.1038/s41598-023-37622-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 06/24/2023] [Indexed: 07/06/2023] Open
Abstract
Aortic diastolic pressure decay (DPD) has been shown to have considerable pathophysiological relevance in the assessment of vascular health, as it is significantly affected by arterial stiffening. Nonetheless, the aortic pressure waveform is rarely available and hence the utility of the aortic DPD is limited. On the other hand, carotid blood pressure is often used as a surrogate of central (aortic) blood pressure in cardiovascular monitoring. Although the two waveforms are inherently different, it is unknown whether the aortic DPD shares a common pattern with the carotid DPD. In this study, we compared the DPD time constant of the aorta (aortic RC) and the DPD time constant of the carotid artery (carotid RC) using an in-silico-generated healthy population from a previously validated one-dimensional numerical model of the arterial tree. Our results demonstrated that there is near-absolute agreement between the aortic RC and the carotid RC. In particular, a correlation of ~ 1 was reported for a distribution of aortic/carotid RC values equal to 1.76 ± 0.94 s/1.74 ± 0.87 s. To the best of our knowledge, this is the first study to compare the DPD of the aortic and the carotid pressure waveform. The findings indicate a strong correlation between carotid DPD and aortic DPD, supported by the examination of curve shape and the diastolic decay time constant across a wide range of simulated cardiovascular conditions. Additional investigation is required to validate these results in human subjects and assess their applicability in vivo.
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Affiliation(s)
- Vasiliki Bikia
- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland.
| | - Georgios Rovas
- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland
| | - Sokratis Anagnostopoulos
- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland
| | - Nikolaos Stergiopulos
- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland
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Penninkangas RM, Choudhary MK, Mangani C, Maleta K, Teivaanmäki T, Niemelä O, Ashorn P, Ashorn U, Pörsti I. Low length-for-age Z-score within 1 month after birth predicts hyperdynamic circulation at the age of 21 years in rural Malawi. Sci Rep 2023; 13:10283. [PMID: 37355681 PMCID: PMC10290681 DOI: 10.1038/s41598-023-37269-9] [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: 12/02/2022] [Accepted: 06/19/2023] [Indexed: 06/26/2023] Open
Abstract
Low birth weight predisposes to the development of hypertension in middle- and high-income countries. We examined the relation of early life length-for-age score (Z-score) on cardiovascular function in young adults in Malawi, a low-income country. Capture of supine, seated, and standing brachial pulse waveforms (Mobil-O-Graph) were performed in 223 females and 152 males (mean age 21 years), and analyzed according to the length-for-age Z-score tertiles during the first month of life. Plasma LDL cholesterol in young adulthood was slightly lower in the lowest versus highest tertile. Otherwise, blood hemoglobin and plasma chemistry were similar in all tertiles. Irrespective of posture, blood pressure, forward and backward wave amplitudes, and pulse wave velocity were corresponding in all tertiles. In the three postures, the lowest tertile presented with 4.5% lower systemic vascular resistance than the highest tertile (p = 0.005), and 4.4% and 5.5% higher cardiac output than the middle and highest tertiles, respectively (p < 0.01). Left cardiac work was 6.8% and 6.9% higher in the lowest tertile than in the middle and highest tertiles, respectively (p < 0.01). To conclude, in a low-income environment, low length-for-age Z-score after birth predicted hyperdynamic circulation at 21 years of age without changes in blood pressure and metabolic variables.
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Affiliation(s)
| | - Manoj Kumar Choudhary
- Faculty of Medicine and Health Technology, Tampere University, 33014, Tampere, Finland
| | - Charles Mangani
- School of Global and Public Health, Kamuzu University of Health Sciences, Chichiri Blantyre, Malawi
| | - Kenneth Maleta
- School of Global and Public Health, Kamuzu University of Health Sciences, Chichiri Blantyre, Malawi
| | - Tiina Teivaanmäki
- Department of Pediatrics, Helsinki University Hospital, Helsinki, Finland
| | - Onni Niemelä
- Department of Laboratory Medicine and Medical Research Unit, Seinäjoki Central Hospital, Seinäjoki, Finland
| | - Per Ashorn
- Faculty of Medicine and Health Technology, Tampere University, 33014, Tampere, Finland
- Department of Pediatrics, Tampere University Hospital, Tampere, Finland
| | - Ulla Ashorn
- Faculty of Medicine and Health Technology, Tampere University, 33014, Tampere, Finland
| | - Ilkka Pörsti
- Faculty of Medicine and Health Technology, Tampere University, 33014, Tampere, Finland.
- Department of Internal Medicine, Tampere University Hospital, Tampere, Finland.
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7
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Çelikbudak Orhon C, Stergiopulos N, Noble S, Giannakopoulos G, Müller H, Adamopoulos D. The Impact of Left Ventricular Performance and Afterload on the Evaluation of Aortic Valve Stenosis: A 1D Mathematical Modeling Approach. Bioengineering (Basel) 2023; 10:bioengineering10040425. [PMID: 37106613 PMCID: PMC10136235 DOI: 10.3390/bioengineering10040425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/22/2023] [Accepted: 03/24/2023] [Indexed: 03/30/2023] Open
Abstract
The transaortic valvular pressure gradient (TPG) plays a central role in decision-making for patients suffering from severe aortic stenosis. However, the flow-dependence nature of the TPG makes the diagnosis of aortic stenosis challenging since the markers of cardiac performance and afterload present high physiological interdependence and thus, isolated effects cannot be measured directly in vivo. We used a validated 1D mathematical model of the cardiovascular system, coupled with a model of aortic stenosis, to assess and quantify the independent effect of the main left ventricular performance parameters (end-systolic (Ees) and end-diastolic (Eed) elastance) and principal afterload indices (total vascular resistance (TVR) and total arterial compliance (TAC)) on the TPG for different levels of aortic stenosis. In patients with critical aortic stenosis (aortic valve area (AVA) ≤ 0.6 cm2), a 10% increase of Eed from the baseline value was associated with the most important effect on the TPG (−5.6 ± 0.5 mmHg, p < 0.001), followed by a similar increase of Ees (3.4 ± 0.1 mmHg, p < 0.001), in TAC (1.3 ±0.2 mmHg, p < 0.001) and TVR (−0.7 ± 0.04 mmHg, p < 0.001). The interdependence of the TPG left ventricular performance and afterload indices become stronger with increased aortic stenosis severity. Disregarding their effects may lead to an underestimation of stenosis severity and a potential delay in therapeutic intervention. Therefore, a comprehensive evaluation of left ventricular function and afterload should be performed, especially in cases of diagnostic challenge, since it may offer the pathophysiological mechanism that explains the mismatch between aortic severity and the TPG.
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Du S, Yao Y, Sun G, Wang L, Alastruey J, Avolio AP, Xu L. Personalized aortic pressure waveform estimation from brachial pressure waveform using an adaptive transfer function. Comput Biol Med 2023; 155:106654. [PMID: 36791548 DOI: 10.1016/j.compbiomed.2023.106654] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 01/16/2023] [Accepted: 02/08/2023] [Indexed: 02/12/2023]
Abstract
BACKGROUND AND OBJECTIVE The aortic pressure waveform (APW) provides reliable information for the diagnosis of cardiovascular disease. APW is often measured using a generalized transfer function (GTF) applied to the peripheral pressure waveform acquired noninvasively, to avoid the significant risks of invasive APW acquisition. However, the GTF ignores various physiological conditions, which affects the accuracy of the estimated APW. To solve this problem, this study utilized an adaptive transfer function (ATF) combined with a tube-load model to achieve personalized and accurate estimation of APW from the brachial pressure waveform (BPW). METHODS The proposed method was validated using APWs and BPWs from 34 patients. The ATF was defined using a tube-load model in which pulse transit time and reflection coefficients were determined from, respectively, the diastolic-exponential-pressure-decay of the APW and a piece-wise constant approximation. The root-mean-square-error of overall morphology, mean absolute errors of common hemodynamic indices (systolic blood pressure, diastolic blood pressure and pulse pressure) were used to evaluate the ATF. RESULTS The proposed ATF performed better in estimating diastolic blood pressure and pulse pressure (1.63 versus 1.94 mmHg, and 2.37 versus 3.10 mmHg, respectively, both P < 0.10), and produced similar errors in overall morphology and systolic blood pressure (3.91 versus 4.24 mmHg, and 2.83 versus 2.91 mmHg, respectively, both P > 0.10) compared to GTF. CONCLUSION Unlike the GTF which uses fixed parameters trained on existing clinical datasets, the proposed method can achieve personalized estimation of APW. Hence, it provides accurate pulsatile hemodynamic measures for the evaluation of cardiovascular function.
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Affiliation(s)
- Shuo Du
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, Liaoning, China
| | - Yang Yao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, Liaoning, China
| | - Guozhe Sun
- Department of Cardiology, The First Hospital of China Medical University, Shenyang, 110122, Liaoning, China
| | - Lu Wang
- School of Computer Science and Engineering, Northeastern University, Shenyang, 110169, China
| | - Jordi Alastruey
- Department of Biomedical Engineering, King's College, London, SE1 7EH, United Kingdom
| | - Alberto P Avolio
- Macquarie School of Medicine, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
| | - Lisheng Xu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, Liaoning, China; Neusoft Research of Intelligent Healthcare Technology, Co. Ltd, Shenyang, 110169, Liaoning, China.
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9
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Zhang Q, Zhang Y, Hao L, Zhong Y, Wu K, Wang Z, Tian S, Lin Q, Wu G. A personalized 0D-1D model of cardiovascular system for the hemodynamic simulation of enhanced external counterpulsation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 227:107224. [PMID: 36379202 DOI: 10.1016/j.cmpb.2022.107224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/21/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Enhanced external counterpulsation (EECP) is a non-invasive treatment modality capable of treating a variety of ischemic diseases. Currently, no effective methods of predicting the patient-specific hemodynamic effects of EECP are available. In this study, a personalized 0D-1D model of the cardiovascular system was developed for hemodynamic simulation to simulate the changes in blood flow in the EECP state and develop the best treatment protocol for each individual. METHODS A 0D-1D closed-loop model of the cardiovascular system was developed for hemodynamic simulation, consisting of a 1D wave propagation model for arteries, a 0D model for veins and capillaries, and a one-fiber model for the heart. Additionally, a simulation model coupling EECP with a 1D model was established. Physiological data, including the blood flow in different arteries, were clinically collected from 22 volunteers at rest and in the EECP state. Sensitivity analysis and a simulated annealing algorithm were used to build personalized 0D-1D models using the clinical data in the rest state as optimization objectives. Then, the clinical data on EECP were used to verify the applicability and accuracy of the personalized models. RESULTS The simulation results and clinical data were found to be in agreement for all 22 subjects, with waveform similarity coefficients (r) exceeding 90% for most arteries at rest and 80% for most arteries during EECP. CONCLUSIONS The 0D-1D closed-loop model and the optimized method can facilitate personalized modeling of the cardiovascular system using the data in the rest state and effectively predict the hemodynamic changes in the EECP state, which is significant for the numerical simulation of personalized hemodynamics. The model can also potentially be used to make decisions regarding patient-specific treatment.
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Affiliation(s)
- Qi Zhang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China
| | - Yahui Zhang
- Department of Cardiology, The Eighth Affiliated Hospital Sun Yat-sen University, Shenzhen, Guangdong, 518033, China; School of Rehabilitation Sciences and Engineering, University of Health and Rehabilitation Sciences, Qingdao, Shandong, 266071, China
| | - Liling Hao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China.
| | - Yujia Zhong
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China
| | - Kunlin Wu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China
| | - Zhuo Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China
| | - Shuai Tian
- Department of Cardiology, The Eighth Affiliated Hospital Sun Yat-sen University, Shenzhen, Guangdong, 518033, China
| | - Qi Lin
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China
| | - Guifu Wu
- Department of Cardiology, The Eighth Affiliated Hospital Sun Yat-sen University, Shenzhen, Guangdong, 518033, China.
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Tossas-Betancourt C, Li NY, Shavik SM, Afton K, Beckman B, Whiteside W, Olive MK, Lim HM, Lu JC, Phelps CM, Gajarski RJ, Lee S, Nordsletten DA, Grifka RG, Dorfman AL, Baek S, Lee LC, Figueroa CA. Data-driven computational models of ventricular-arterial hemodynamics in pediatric pulmonary arterial hypertension. Front Physiol 2022; 13:958734. [PMID: 36160862 PMCID: PMC9490558 DOI: 10.3389/fphys.2022.958734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Pulmonary arterial hypertension (PAH) is a complex disease involving increased resistance in the pulmonary arteries and subsequent right ventricular (RV) remodeling. Ventricular-arterial interactions are fundamental to PAH pathophysiology but are rarely captured in computational models. It is important to identify metrics that capture and quantify these interactions to inform our understanding of this disease as well as potentially facilitate patient stratification. Towards this end, we developed and calibrated two multi-scale high-resolution closed-loop computational models using open-source software: a high-resolution arterial model implemented using CRIMSON, and a high-resolution ventricular model implemented using FEniCS. Models were constructed with clinical data including non-invasive imaging and invasive hemodynamic measurements from a cohort of pediatric PAH patients. A contribution of this work is the discussion of inconsistencies in anatomical and hemodynamic data routinely acquired in PAH patients. We proposed and implemented strategies to mitigate these inconsistencies, and subsequently use this data to inform and calibrate computational models of the ventricles and large arteries. Computational models based on adjusted clinical data were calibrated until the simulated results for the high-resolution arterial models matched within 10% of adjusted data consisting of pressure and flow, whereas the high-resolution ventricular models were calibrated until simulation results matched adjusted data of volume and pressure waveforms within 10%. A statistical analysis was performed to correlate numerous data-derived and model-derived metrics with clinically assessed disease severity. Several model-derived metrics were strongly correlated with clinically assessed disease severity, suggesting that computational models may aid in assessing PAH severity.
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Affiliation(s)
| | - Nathan Y. Li
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, United States
| | - Sheikh M. Shavik
- Department of Mechanical Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - Katherine Afton
- Department of Pediatrics, Division of Pediatric Cardiology, University of Michigan, Ann Arbor, MI, United States
| | - Brian Beckman
- Department of Pediatrics, Nationwide Children’s Hospital, Columbus, OH, United States
| | - Wendy Whiteside
- Department of Pediatrics, Division of Pediatric Cardiology, University of Michigan, Ann Arbor, MI, United States
| | - Mary K. Olive
- Department of Pediatrics, Division of Pediatric Cardiology, University of Michigan, Ann Arbor, MI, United States
| | - Heang M. Lim
- Department of Pediatrics, Division of Pediatric Cardiology, University of Michigan, Ann Arbor, MI, United States
| | - Jimmy C. Lu
- Department of Pediatrics, Division of Pediatric Cardiology, University of Michigan, Ann Arbor, MI, United States
| | - Christina M. Phelps
- Department of Pediatrics, Nationwide Children’s Hospital, Columbus, OH, United States
| | - Robert J. Gajarski
- Department of Pediatrics, Nationwide Children’s Hospital, Columbus, OH, United States
| | - Simon Lee
- Department of Pediatrics, Nationwide Children’s Hospital, Columbus, OH, United States
| | - David A. Nordsletten
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
- Department of Surgery, University of Michigan, Ann Arbor, MI, United States
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Ronald G. Grifka
- Department of Pediatrics, Division of Pediatric Cardiology, University of Michigan, Ann Arbor, MI, United States
| | - Adam L. Dorfman
- Department of Pediatrics, Division of Pediatric Cardiology, University of Michigan, Ann Arbor, MI, United States
| | - Seungik Baek
- Department of Mechanical Engineering, Michigan State University, East Lansing, MI, United States
| | - Lik Chuan Lee
- Department of Mechanical Engineering, Michigan State University, East Lansing, MI, United States
| | - C. Alberto Figueroa
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
- Department of Surgery, University of Michigan, Ann Arbor, MI, United States
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Choudhary MK, Penninkangas RM, Eräranta A, Niemelä O, Mangani C, Maleta K, Ashorn P, Ashorn U, Pörsti I. Posture-Related Differences in Cardiovascular Function Between Young Men and Women: Study of Noninvasive Hemodynamics in Rural Malawi. J Am Heart Assoc 2022; 11:e022979. [PMID: 35195013 PMCID: PMC9075090 DOI: 10.1161/jaha.121.022979] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Background Cardiovascular risk is higher in men than in women, but little information exists about sex‐related differences in cardiovascular function from low‐income countries. We compared hemodynamics between sexes in rural Malawi in a cohort followed up since their birth. Methods and Results Supine, seated, and standing hemodynamics were recorded from 251 women and 168 men (mean age, 21 years; body mass index, 21 kg/m2) using oscillometric brachial waveform analyses (Mobil‐O‐Graph). The results were adjusted for estimated glomerular filtration rate, and plasma potassium, lipids, and glucose. Men had higher brachial and aortic systolic blood pressure and stroke index regardless of posture (P<0.001), and higher upright but similar supine diastolic blood pressure than women. Regardless of posture, heart rate was lower in men (P<0.001), whereas cardiac index did not differ between sexes. Women presented with lower supine and standing systemic vascular resistance index (P<0.001), whereas supine‐to‐standing increase in vascular resistance (P=0.012) and decrease in cardiac index (P=0.010) were higher in women. Supine left cardiac work index was similar in both sexes, whereas standing and seated left cardiac work index was higher in men than in women (P<0.001). Conclusions In young Malawian adults, men had higher systolic blood pressure, systemic vascular resistance, and upright cardiac workload, whereas women presented with higher posture‐related changes in systemic vascular resistance and cardiac output. These findings show systematic sex‐related differences in cardiovascular function in a cohort from a low‐income country with high exposure to prenatal and postnatal malnutrition and infectious diseases.
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Affiliation(s)
| | | | - Arttu Eräranta
- Faculty of Medicine and Health Technology Tampere University Tampere Finland
| | - Onni Niemelä
- Faculty of Medicine and Health Technology Tampere University Tampere Finland.,Department of Laboratory Medicine and Medical Research Unit Seinäjoki Central Hospital Seinäjoki Finland
| | - Charles Mangani
- School of Public Health and Family Medicine College of Medicine University of Malawi Blantyre Malawi
| | - Kenneth Maleta
- School of Public Health and Family Medicine College of Medicine University of Malawi Blantyre Malawi
| | - Per Ashorn
- Faculty of Medicine and Health Technology Tampere University Tampere Finland.,Department of Pediatrics Tampere University Hospital Tampere Finland
| | - Ulla Ashorn
- Faculty of Medicine and Health Technology Tampere University Tampere Finland
| | - Ilkka Pörsti
- Faculty of Medicine and Health Technology Tampere University Tampere Finland.,Department of Internal Medicine Tampere University Hospital Tampere Finland
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12
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Bikia V, McEniery CM, Roussel EM, Rovas G, Pagoulatou S, Wilkinson IB, Stergiopulos N. Validation of a Non-invasive Inverse Problem-Solving Method for Stroke Volume. Front Physiol 2022; 12:798510. [PMID: 35153811 PMCID: PMC8826540 DOI: 10.3389/fphys.2021.798510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 12/22/2021] [Indexed: 11/13/2022] Open
Abstract
Stroke volume (SV) is a major biomarker of cardiac function, reflecting ventricular-vascular coupling. Despite this, hemodynamic monitoring and management seldomly includes assessments of SV and remains predominantly guided by brachial cuff blood pressure (BP). Recently, we proposed a mathematical inverse-problem solving method for acquiring non-invasive estimates of mean aortic flow and SV using age, weight, height and measurements of brachial BP and carotid-femoral pulse wave velocity (cfPWV). This approach relies on the adjustment of a validated one-dimensional model of the systemic circulation and applies an optimization process for deriving a quasi-personalized profile of an individual’s arterial hemodynamics. Following the promising results of our initial validation, our first aim was to validate our method against measurements of SV derived from magnetic resonance imaging (MRI) in healthy individuals covering a wide range of ages (n = 144; age range 18–85 years). Our second aim was to investigate whether the performance of the inverse problem-solving method for estimating SV is superior to traditional statistical approaches using multilinear regression models. We showed that the inverse method yielded higher agreement between estimated and reference data (r = 0.83, P < 0.001) in comparison to the agreement achieved using a traditional regression model (r = 0.74, P < 0.001) across a wide range of age decades. Our findings further verify the utility of the inverse method in the clinical setting and highlight the importance of physics-based mathematical modeling in improving predictive tools for hemodynamic monitoring.
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Affiliation(s)
- Vasiliki Bikia
- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland
- *Correspondence: Vasiliki Bikia,
| | - Carmel M. McEniery
- Division of Experimental Medicine and Immunotherapeutics, University of Cambridge, Cambridge, United Kingdom
| | - Emma Marie Roussel
- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland
| | - Georgios Rovas
- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland
| | - Stamatia Pagoulatou
- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland
| | - Ian B. Wilkinson
- Division of Experimental Medicine and Immunotherapeutics, University of Cambridge, Cambridge, United Kingdom
| | - Nikolaos Stergiopulos
- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland
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13
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Zhou Y, He Y, Wu J, Cui C, Chen M, Sun B. A method of parameter estimation for cardiovascular hemodynamics based on deep learning and its application to personalize a reduced-order model. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3533. [PMID: 34585523 DOI: 10.1002/cnm.3533] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 09/26/2021] [Indexed: 06/13/2023]
Abstract
Precise model personalization is a key step towards the application of cardiovascular physical models. In this manuscript, we propose to use deep learning (DL) to solve the parameter estimation problem in cardiovascular hemodynamics. Based on the convolutional neural network (CNN) and fully connected neural network (FCNN), a multi-input deep neural network (DNN) model is developed to map the nonlinear relationship between measurements and the parameters to be estimated. In this model, two separate network structures are designed to extract the features of two types of measurement data, including pressure waveforms and a vector composed of heart rate (HR) and pulse transit time (PTT), and a shared structure is used to extract their combined dependencies on the parameters. Besides, we try to use the transfer learning (TL) technology to further strengthen the personalized characteristics of a trained-well network. For assessing the proposed method, we conducted the parameter estimation using synthetic data and in vitro data respectively, and in the test with synthetic data, we evaluated the performance of the TL algorithm through two individuals with different characteristics. A series of estimation results show that the estimated parameters are in good agreement with the true values. Furthermore, it is also found that the estimation accuracy can be significantly improved by a multicycle combination strategy. Therefore, we think that the proposed method has the potential to be used for parameter estimation in cardiovascular hemodynamics, which can provide an immediate, accurate, and sustainable personalization process, and deserves more attention in the future.
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Affiliation(s)
- Yang Zhou
- School of Mechanical Engineering, Southeast University, Nanjing, China
| | - Yuan He
- Internal Medicine-Cardiovascular Department, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jianwei Wu
- School of Mechanical Engineering, Southeast University, Nanjing, China
| | - Chang Cui
- Internal Medicine-Cardiovascular Department, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Minglong Chen
- Internal Medicine-Cardiovascular Department, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Beibei Sun
- School of Mechanical Engineering, Southeast University, Nanjing, China
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14
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Bikia V, Lazaroska M, Scherrer Ma D, Zhao M, Rovas G, Pagoulatou S, Stergiopulos N. Estimation of Left Ventricular End-Systolic Elastance From Brachial Pressure Waveform via Deep Learning. Front Bioeng Biotechnol 2021; 9:754003. [PMID: 34778228 PMCID: PMC8578926 DOI: 10.3389/fbioe.2021.754003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 10/07/2021] [Indexed: 11/13/2022] Open
Abstract
Determination of left ventricular (LV) end-systolic elastance (E es ) is of utmost importance for assessing the cardiac systolic function and hemodynamical state in humans. Yet, the clinical use of E es is not established due to the invasive nature and high costs of the existing measuring techniques. The objective of this study is to introduce a method to assess cardiac contractility, using as a sole measurement an arterial blood pressure (BP) waveform. Particularly, we aim to provide evidence on the potential in using the morphology of the brachial BP waveform and its time derivative for predicting LV E es via convolution neural networks (CNNs). The requirement of a broad training dataset is addressed by the use of an in silico dataset (n = 3,748) which is generated by a validated one-dimensional mathematical model of the cardiovasculature. We evaluated two CNN configurations: 1) a one-channel CNN (CNN1) with only the raw brachial BP signal as an input, and 2) a two-channel CNN (CNN2) using as inputs both the brachial BP wave and its time derivative. Accurate predictions were yielded using both CNN configurations. For CNN1, Pearson's correlation coefficient (r) and RMSE were equal to 0.86 and 0.27 mmHg/ml, respectively. The performance was found to be greatly improved for CNN2 (r = 0.97 and RMSE = 0.13 mmHg/ml). Moreover, all absolute errors from CNN2 were found to be less than 0.5 mmHg/ml. Importantly, the brachial BP wave appeared to be a promising source of information for estimating E es . Predictions were found to be in good agreement with the reference E es values over an extensive range of LV contractility values and loading conditions. Therefore, the proposed methodology could be easily transferred to the bedside and potentially facilitate the clinical use of E es for monitoring the contractile state of the heart in the real-life setting.
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Affiliation(s)
- Vasiliki Bikia
- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland
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15
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Bikia V, Rovas G, Pagoulatou S, Stergiopulos N. Determination of Aortic Characteristic Impedance and Total Arterial Compliance From Regional Pulse Wave Velocities Using Machine Learning: An in-silico Study. Front Bioeng Biotechnol 2021; 9:649866. [PMID: 34055758 PMCID: PMC8155726 DOI: 10.3389/fbioe.2021.649866] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 04/08/2021] [Indexed: 01/04/2023] Open
Abstract
In-vivo assessment of aortic characteristic impedance (Z ao ) and total arterial compliance (C T ) has been hampered by the need for either invasive or inconvenient and expensive methods to access simultaneous recordings of aortic pressure and flow, wall thickness, and cross-sectional area. In contrast, regional pulse wave velocity (PWV) measurements are non-invasive and clinically available. In this study, we present a non-invasive method for estimating Z ao and C T using cuff pressure, carotid-femoral PWV (cfPWV), and carotid-radial PWV (crPWV). Regression analysis is employed for both Z ao and C T . The regressors are trained and tested using a pool of virtual subjects (n = 3,818) generated from a previously validated in-silico model. Predictions achieved an accuracy of 7.40%, r = 0.90, and 6.26%, r = 0.95, for Z ao , and C T , respectively. The proposed approach constitutes a step forward to non-invasive screening of elastic vascular properties in humans by exploiting easily obtained measurements. This study could introduce a valuable tool for assessing arterial stiffness reducing the cost and the complexity of the required measuring techniques. Further clinical studies are required to validate the method in-vivo.
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Affiliation(s)
- Vasiliki Bikia
- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland
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16
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Bikia V, Adamopoulos D, Pagoulatou S, Rovas G, Stergiopulos N. AI-Based Estimation of End-Systolic Elastance From Arm-Pressure and Systolic Time Intervals. Front Artif Intell 2021; 4:579541. [PMID: 33937742 PMCID: PMC8079739 DOI: 10.3389/frai.2021.579541] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 02/03/2021] [Indexed: 12/15/2022] Open
Abstract
Left ventricular end-systolic elastance (Ees) is a major determinant of cardiac systolic function and ventricular-arterial interaction. Previous methods for the Ees estimation require the use of the echocardiographic ejection fraction (EF). However, given that EF expresses the stroke volume as a fraction of end-diastolic volume (EDV), accurate interpretation of EF is attainable only with the additional measurement of EDV. Hence, there is still need for a simple, reliable, noninvasive method to estimate Ees. This study proposes a novel artificial intelligence—based approach to estimate Ees using the information embedded in clinically relevant systolic time intervals, namely the pre-ejection period (PEP) and ejection time (ET). We developed a training/testing scheme using virtual subjects (n = 4,645) from a previously validated in-silico model. Extreme Gradient Boosting regressor was employed to model Ees using as inputs arm cuff pressure, PEP, and ET. Results showed that Ees can be predicted with high accuracy achieving a normalized RMSE equal to 9.15% (r = 0.92) for a wide range of Ees values from 1.2 to 4.5 mmHg/ml. The proposed model was found to be less sensitive to measurement errors (±10–30% of the actual value) in blood pressure, presenting low test errors for the different levels of noise (RMSE did not exceed 0.32 mmHg/ml). In contrast, a high sensitivity was reported for measurements errors in the systolic timing features. It was demonstrated that Ees can be reliably estimated from the traditional arm-pressure and echocardiographic PEP and ET. This approach constitutes a step towards the development of an easy and clinically applicable method for assessing left ventricular systolic function.
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Affiliation(s)
- Vasiliki Bikia
- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland
| | | | - Stamatia Pagoulatou
- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland
| | - Georgios Rovas
- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland
| | - Nikolaos Stergiopulos
- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland
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17
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Buraioli I, Lena D, Sanginario A, Leone D, Mingrone G, Milan A, Demarchi D. A New Noninvasive System for Clinical Pulse Wave Velocity Assessment: The Athos Device. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:133-142. [PMID: 33560991 DOI: 10.1109/tbcas.2021.3058010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
This paper presents a low cost, noninvasive, clinical-grade Pulse Wave Velocity evaluation device. The proposed system relies on a simultaneous acquisition of femoral and carotid pulse waves to improve estimation accuracy and correctness. The sensors used are two high precision MEMS force sensors, encapsulated in two ergonomic probes, and connected to the main unit. Data are then wirelessly transmitted to a standard laptop, where a dedicated graphical user interface (GUI) runs for analysis and recording. Besides the interface, the Athos system provides a Matlab algorithm to process the signals quickly and achieve a reliable PWV assessment. To better compare the results at the end of each analysis, a detailed report is generated, including all the relevant examination information (subject data, mean PTT, and obtained PWV). A pre-clinical study was conducted to validate the system by realizing several Pulse Wave Velocity measurements on ten heterogeneous healthy subjects of different ages. The collected results were then compared with those measured by a well-established and largely more expensive clinical device (SphygmoCor).
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18
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Noninvasive estimation of aortic hemodynamics and cardiac contractility using machine learning. Sci Rep 2020; 10:15015. [PMID: 32929108 PMCID: PMC7490416 DOI: 10.1038/s41598-020-72147-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 08/13/2020] [Indexed: 02/07/2023] Open
Abstract
Cardiac and aortic characteristics are crucial for cardiovascular disease detection. However, noninvasive estimation of aortic hemodynamics and cardiac contractility is still challenging. This paper investigated the potential of estimating aortic systolic pressure (aSBP), cardiac output (CO), and end-systolic elastance (Ees) from cuff-pressure and pulse wave velocity (PWV) using regression analysis. The importance of incorporating ejection fraction (EF) as additional input for estimating Ees was also assessed. The models, including Random Forest, Support Vector Regressor, Ridge, Gradient Boosting, were trained/validated using synthetic data (n = 4,018) from an in-silico model. When cuff-pressure and PWV were used as inputs, the normalized-RMSEs/correlations for aSBP, CO, and Ees (best-performing models) were 3.36 ± 0.74%/0.99, 7.60 ± 0.68%/0.96, and 16.96 ± 0.64%/0.37, respectively. Using EF as additional input for estimating Ees significantly improved the predictions (7.00 ± 0.78%/0.92). Results showed that the use of noninvasive pressure measurements allows estimating aSBP and CO with acceptable accuracy. In contrast, Ees cannot be predicted from pressure signals alone. Addition of the EF information greatly improves the estimated Ees. Accuracy of the model-derived aSBP compared to in-vivo aSBP (n = 783) was very satisfactory (5.26 ± 2.30%/0.97). Future in-vivo evaluation of CO and Ees estimations remains to be conducted. This novel methodology has potential to improve the noninvasive monitoring of aortic hemodynamics and cardiac contractility.
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