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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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Aghilinejad A, Gharib M. Assessing pressure wave components for aortic stiffness monitoring through spectral regression learning. EUROPEAN HEART JOURNAL OPEN 2024; 4:oeae040. [PMID: 38863521 PMCID: PMC11165314 DOI: 10.1093/ehjopen/oeae040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 05/02/2024] [Accepted: 05/10/2024] [Indexed: 06/13/2024]
Abstract
Aims The ageing process notably induces structural changes in the arterial system, primarily manifesting as increased aortic stiffness, a precursor to cardiovascular events. While wave separation analysis is a robust tool for decomposing the components of blood pressure waveform, its relationship with cardiovascular events, such as aortic stiffening, is incompletely understood. Furthermore, its applicability has been limited due to the need for concurrent measurements of pressure and flow. Our aim in this study addresses this gap by introducing a spectral regression learning method for pressure-only wave separation analysis. Methods and results Leveraging data from the Framingham Heart Study (2640 individuals, 55% women), we evaluate the accuracy of pressure-only estimates, their interchangeability with a reference method based on ultrasound-derived flow waves, and their association with carotid-femoral pulse wave velocity (PWV). Method-derived estimates are strongly correlated with the reference ones for forward wave amplitude ( R 2 = 0.91 ), backward wave amplitude ( R 2 = 0.88 ), and reflection index ( R 2 = 0.87 ) and moderately correlated with a time delay between forward and backward waves ( R 2 = 0.38 ). The proposed pressure-only method shows interchangeability with the reference method through covariate analysis. Adjusting for age, sex, body size, mean blood pressure, and heart rate, the results suggest that both pressure-only and pressure-flow evaluations of wave separation parameters yield similar model performances for predicting carotid-femoral PWV, with forward wave amplitude being the only significant factor (P < 0.001; 95% confidence interval, 0.056-0.097). Conclusion We propose an interchangeable pressure-only wave separation analysis method and demonstrate its clinical applicability in capturing aortic stiffening. The proposed method provides a valuable non-invasive tool for assessing cardiovascular health.
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Affiliation(s)
- Arian Aghilinejad
- Division of Engineering and Applied Science, California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125, USA
| | - Morteza Gharib
- Division of Engineering and Applied Science, California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125, USA
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Tamborini A, Gharib M. Validation of a Suprasystolic Cuff System for Static and Dynamic Representation of the Central Pressure Waveform. J Am Heart Assoc 2024; 13:e033290. [PMID: 38591330 DOI: 10.1161/jaha.123.033290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 03/14/2024] [Indexed: 04/10/2024]
Abstract
BACKGROUND Noninvasive pulse waveform analysis is valuable for central cardiovascular assessment, yet controversies persist over its validity in peripheral measurements. Our objective was to compare waveform features from a cuff system with suprasystolic blood pressure hold with an invasive aortic measurement. METHODS AND RESULTS This study analyzed data from 88 subjects undergoing concurrent aortic catheterization and brachial pulse waveform acquisition using a suprasystolic blood pressure cuff system. Oscillometric blood pressure (BP) was compared with invasive aortic systolic BP and diastolic BP. Association between cuff and catheter waveform features was performed on a set of 15 parameters inclusive of magnitudes, time intervals, pressure-time integrals, and slopes of the pulsations. The evaluation covered both static (subject-averaged values) and dynamic (breathing-induced fluctuations) behaviors. Peripheral BP values from the cuff device were higher than catheter values (systolic BP-residual, 6.5 mm Hg; diastolic BP-residual, 12.4 mm Hg). Physiological correction for pressure amplification in the arterial system improved systolic BP prediction (r2=0.83). Dynamic calibration generated noninvasive BP fluctuations that reflect those invasively measured (systolic BP Pearson R=0.73, P<0.001; diastolic BP Pearson R=0.53, P<0.001). Static and dynamic analyses revealed a set of parameters with strong associations between catheter and cuff (Pearson R>0.5, P<0.001), encompassing magnitudes, timings, and pressure-time integrals but not slope-based parameters. CONCLUSIONS This study demonstrated that the device and methods for peripheral waveform measurements presented here can be used for noninvasive estimation of central BP and a subset of aortic waveform features. These results serve as a benchmark for central cardiovascular assessment using suprasystolic BP cuff-based devices and contribute to preserving system dynamics in noninvasive measurements.
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Affiliation(s)
- Alessio Tamborini
- Department of Medical Engineering California Institute of Technology Pasadena CA USA
| | - Morteza Gharib
- Department of Medical Engineering California Institute of Technology Pasadena CA USA
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Alavi R, Wang Q, Gorji H, Pahlevan NM. A machine learning approach for computation of cardiovascular intrinsic frequencies. PLoS One 2023; 18:e0285228. [PMID: 37883430 PMCID: PMC10602266 DOI: 10.1371/journal.pone.0285228] [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: 01/28/2023] [Accepted: 04/17/2023] [Indexed: 10/28/2023] Open
Abstract
Analysis of cardiovascular waveforms provides valuable clinical information about the state of health and disease. The intrinsic frequency (IF) method is a recently introduced framework that uses a single arterial pressure waveform to extract physiologically relevant information about the cardiovascular system. The clinical usefulness and physiological accuracy of the IF method have been well-established via several preclinical and clinical studies. However, the computational complexity of the current L2 optimization solver for IF calculations remains a bottleneck for practical deployment of the IF method in real-time settings. In this paper, we propose a machine learning (ML)-based methodology for determination of IF parameters from a single carotid waveform. We use a sequentially-reduced Feedforward Neural Network (FNN) model for mapping carotid waveforms to the output parameters of the IF method, thereby avoiding the non-convex L2 minimization problem arising from the conventional IF approach. Our methodology also includes procedures for data pre-processing, model training, and model evaluation. In our model development, we used both clinical and synthetic waveforms. Our clinical database is composed of carotid waveforms from two different sources: the Huntington Medical Research Institutes (HMRI) iPhone Heart Study and the Framingham Heart Study (FHS). In the HMRI and FHS clinical studies, various device platforms such as piezoelectric tonometry, optical tonometry (Vivio), and an iPhone camera were used to measure arterial waveforms. Our blind clinical test shows very strong correlations between IF parameters computed from the FNN-based method and those computed from the standard L2 optimization-based method (i.e., R≥0.93 and P-value ≤0.005 for each IF parameter). Our results also demonstrate that the performance of the FNN-based IF model introduced in this work is independent of measurement apparatus and of device sampling rate.
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Affiliation(s)
- Rashid Alavi
- Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, California, United States of America
| | - Qian Wang
- Beijing Computational Science Research Center, Beijing, China
| | - Hossein Gorji
- Swiss Federal Laboratories for Materials Science and Technology (EMPA), Dubendorf, Switzerland
| | - Niema M. Pahlevan
- Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, California, United States of America
- Cardiovascular Research Institute, Huntington Medical Research Institutes, Pasadena, CA, United States of America
- Division of Cardiovascular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
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Niroumandi S, Alavi R, Wolfson AM, Vaidya AS, Pahlevan NM. Assessment of Aortic Characteristic Impedance and Arterial Compliance from Non-invasive Carotid Pressure Waveform in The Framingham Heart Study. Am J Cardiol 2023; 204:195-199. [PMID: 37544144 DOI: 10.1016/j.amjcard.2023.07.076] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 07/08/2023] [Accepted: 07/13/2023] [Indexed: 08/08/2023]
Abstract
The primary goal of this study was to test the hypothesis that a hybrid intrinsic frequency-machine learning (IF-ML) approach can accurately evaluate total arterial compliance (TAC) and aortic characteristic impedance (Zao) from a single noninvasive carotid pressure waveform in both women and men with heart failure (HF). TAC and Zao are cardiovascular biomarkers with established clinical significance. TAC is lower and Zao is higher in women than in men, so women are more susceptible to the consequent deleterious effects of them. Although the principles of TAC and Zao are pertinent to a multitude of cardiovascular diseases, including HF, their routine clinical use is limited because of the requirement for simultaneous measurements of flow and pressure waveforms. For this study, the data were obtained from the Framingham Heart Study (n = 6,201, 53% women). The reference values of Zao and TAC were computed from carotid pressure and aortic flow waveforms. IF parameters of carotid pressure waveform were used in ML models. IF models were developed on n = 5,168 of randomly selected data and blindly tested the remaining data (n = 1,033). The final models were evaluated in patients with HF. Correlations between IF-ML and reference values in all HF and HF with preserved ejection fraction for TAC were 0.88 and 0.90, and for Zao were 0.82 and 0.80, respectively. The classification accuracy in all HF and HF with preserved ejection fraction for TAC were 0.9 and 0.93, and for Zao were 0.81 and 0.89, respectively. In conclusion, the IF-ML method provides an accurate estimation of TAC and Zao in all subjects with HF and in the general population.
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Affiliation(s)
- Soha Niroumandi
- Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, Los Angeles, California
| | - Rashid Alavi
- Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, Los Angeles, California
| | - Aaron Michael Wolfson
- Division of Cardiovascular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, California
| | - Ajay Shrikrishna Vaidya
- Division of Cardiovascular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, California
| | - Niema Mohammed Pahlevan
- Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, Los Angeles, California; Division of Cardiovascular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, California.
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Cheng AL, Liu J, Bravo S, Miller JC, Pahlevan NM. Screening left ventricular systolic dysfunction in children using intrinsic frequencies of carotid pressure waveforms measured by a novel smartphone-based device. Physiol Meas 2023; 44:10.1088/1361-6579/acba7b. [PMID: 36753767 PMCID: PMC11073485 DOI: 10.1088/1361-6579/acba7b] [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: 12/03/2022] [Accepted: 02/08/2023] [Indexed: 02/10/2023]
Abstract
Objective.Children with heart failure have higher rates of emergency department utilization, health care expenditure, and hospitalization. Therefore, a need exists for a simple, non-invasive, and inexpensive method of screening for left ventricular (LV) dysfunction. We recently demonstrated the practicality and reliability of a wireless smartphone-based handheld device in capturing carotid pressure waveforms and deriving cardiovascular intrinsic frequencies (IFs) in children with normal LV function. Our goal in this study was to demonstrate that an IF-based machine learning method (IF-ML) applied to noninvasive carotid pressure waveforms can distinguish between normal and abnormal LV ejection fraction (LVEF) in pediatric patients.Approach. Fifty patients ages 0 to 21 years underwent LVEF measurement by echocardiogram or cardiac magnetic resonance imaging. On the same day, patients had carotid waveforms recorded using Vivio. The exclusion criterion was known vascular disease that would interfere with obtaining a carotid artery pulse. We adopted a hybrid IF- Machine Learning (IF-ML) method by applying physiologically relevant IF parameters as inputs to Decision Tree classifiers. The threshold for low LVEF was chosen as <50%.Main results.The proposed IF-ML method was able to detect an abnormal LVEF with an accuracy of 92% (sensitivity = 100%, specificity = 89%, area under the curve (AUC) = 0.95). Consistent with previous clinical studies, the IF parameterω1was elevated among patients with reduced LVEF.Significance.A hybrid IF-ML method applied on a carotid waveform recorded by a hand-held smartphone-based device can differentiate between normal and abnormal LV systolic function in children with normal cardiac anatomy.
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Affiliation(s)
- Andrew L Cheng
- Division of Pediatric Cardiology, Children’s Hospital Los Angeles, Los Angeles, CA, United States of America
| | - Jing Liu
- Division of Cardiovascular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
| | - Stephen Bravo
- Division of Pediatric Cardiology, Children’s Hospital Los Angeles, Los Angeles, CA, United States of America
| | - Jennifer C Miller
- Division of Pediatric Cardiology, Children’s Hospital Los Angeles, Los Angeles, CA, United States of America
| | - Niema M Pahlevan
- Division of Cardiovascular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
- Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA, United States of America
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A coupled atrioventricular-aortic setup for in-vitro hemodynamic study of the systemic circulation: Design, fabrication, and physiological relevancy. PLoS One 2022; 17:e0267765. [PMID: 36331977 PMCID: PMC9635706 DOI: 10.1371/journal.pone.0267765] [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: 04/12/2022] [Accepted: 10/13/2022] [Indexed: 11/06/2022] Open
Abstract
In-vitro models of the systemic circulation have gained a lot of interest for fundamental understanding of cardiovascular dynamics and for applied hemodynamic research. In this study, we introduce a physiologically accurate in-vitro hydraulic setup that models the hemodynamics of the coupled atrioventricular-aortic system. This unique experimental simulator has three major components: 1) an arterial system consisting of a human-scale artificial aorta along with the main branches, 2) an artificial left ventricle (LV) sac connected to a programmable piston-in-cylinder pump for simulating cardiac contraction and relaxation, and 3) an artificial left atrium (LA). The setup is designed in such a way that the basal LV is directly connected to the aortic root via an aortic valve, and to the LA via an artificial mitral valve. As a result, two-way hemodynamic couplings can be achieved for studying the effects that the LV, aorta, and LA have on each other. The collected pressure and flow measurements from this setup demonstrate a remarkable correspondence to clinical hemodynamics. We also investigate the physiological relevancies of isolated effects on cardiovascular hemodynamics of various major global parameters found in the circulatory system, including LV contractility, LV preload, heart rate, aortic compliance, and peripheral resistance. Subsequent control over such parameters ultimately captures physiological hemodynamic effects of LV systolic dysfunction, preload (cardiac) diseases, and afterload (arterial) diseases. The detailed design and fabrication of the proposed setup is also provided.
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Aghilinejad A, Alavi R, Rogers B, Amlani F, Pahlevan NM. Effects of vessel wall mechanics on non-invasive evaluation of cardiovascular intrinsic frequencies. J Biomech 2021; 129:110852. [PMID: 34775340 DOI: 10.1016/j.jbiomech.2021.110852] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 10/04/2021] [Accepted: 10/26/2021] [Indexed: 11/29/2022]
Abstract
Intrinsic Frequency (IF) is a systems-based approach that provides valuable information for hemodynamic monitoring of the left ventricle (LV), the arterial system, and their coupling. Recent clinical studies have demonstrated the clinical significance of this method for prognosis and diagnosis of cardiovascular diseases. In IF analysis, two dominant instantaneous frequencies (ω1 and ω2) are extracted from arterial pressure waveforms. The value of ω1 is related to the dynamics of the LV and the value of ω2 is related to the dynamics of vascular function. This work investigates the effects of vessel wall mechanics on the accuracy and applicability of IFs extracted from vessel wall displacement waveforms compared to IFs extracted from pressure waveforms. In this study, we used a computational approach employing a fluid-structure interaction finite element method for various wall mechanics governed by linearly elastic, hyperelastic, and viscoelastic models. Results show that for vessels with elastic wall behavior, the error between displacement-based and pressure-based IFs is negligible. In the presence of stenosis or aneurysm in elastic arteries, the maximum errors associated with displacement-based IFs is less than 2%. For non-linear elastic and viscoelastic arteries, errors are more pronounced (where the former reaches up to 11% and the latter up to 27%). Our results ultimately suggest that displacement-based computations of ω1 and ω2 are accurate in vessels that exhibit elastic behavior (such as carotid arteries) and are suitable surrogates for pressure-based IFs. This is clinically significant because displacement-based IFs can be measured non-invasively.
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Affiliation(s)
- Arian Aghilinejad
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, USA
| | - Rashid Alavi
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, USA
| | - Bryson Rogers
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, USA
| | - Faisal Amlani
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, USA
| | - Niema M Pahlevan
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, USA; Division of Cardiovascular Medicine, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, USA.
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Aghilinejad A, Amlani F, Liu J, Pahlevan NM. Accuracy and applicability of non-invasive evaluation of aortic wave intensity using only pressure waveforms in humans. Physiol Meas 2021; 42. [PMID: 34521071 DOI: 10.1088/1361-6579/ac2671] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 09/14/2021] [Indexed: 01/09/2023]
Abstract
Background.Wave intensity (WI) analysis is a well-established method for quantifying the energy carried in arterial waves, providing valuable clinical information about cardiovascular function. The primary drawback of this method is the need for concurrent measurements of both pressure and flow waveforms.Objective. We have for the first time investigated the accuracy of a novel methodology for estimating wave intensity employing only single pressure waveform measurements; we studied both carotid- and radial-based estimations in a large heterogeneous cohort.Approach.Tonometry was performed alongside Doppler ultrasound to acquire measurements of both carotid and radial pressure waveforms as well as aortic flow waveforms in 2640 healthy and diseased participants (1439 female) in the Framingham Heart Study. Patterns consisting of two forward waves (Wf1, Wf2) and one backward wave (Wb1) along with reflection metrics were compared with those obtained from exact WI analysis.Main Results. Carotid-based estimates correlated well for forward peak amplitudes (Wf1,r = 0.85,p < 0.05; Wf2,r = 0.72,p < 0.05) and peak time (Wf1,r = 0.94,p < 0.05; Wf2,r = 0.98,p < 0.05), and radial-based estimates correlated fairly to poorly for amplitudes (Wf1,r = 0.62,p < 0.05; Wf2,r = 0.42,p < 0.05) and peak time (Wf1,r = 0.04,p = 0.10; Wf2,r = 0.75,p < 0.05). In all cases, estimated Wb1 measures were not correlated. Reflection metrics were well correlated for healthy patients (r = 0.67,p < 0.05), moderately correlated for valvular disease (r = 0.59,p < 0.05) and fairly correlated for CVD (r = 0.46,p < 0.05) and heart failure (r = 0.49,p < 0.05).Significance. These findings indicate that pressure-only WI produces accurate results only when forward contributions are of primary interest and only for carotid pressure waveforms. The pressure-only WI estimations of this work provide an important opportunity to further the goal of uncovering clinical insights through wave analysis affordably and non-invasively.
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Affiliation(s)
- Arian Aghilinejad
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, United States of America
| | - Faisal Amlani
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, United States of America
| | - Jing Liu
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, United States of America
| | - Niema M Pahlevan
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, United States of America.,Division of Cardiovascular Medicine, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, United States of America
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Mogadam E, Shavelle DM, Giesler GM, Economides C, Lidia SP, Duquette S, Matthews RV, Pahlevan NM. Intrinsic frequency method for instantaneous assessment of left ventricular-arterial coupling after transcatheter aortic valve replacement. Physiol Meas 2020; 41:085002. [DOI: 10.1088/1361-6579/aba67f] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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