On the convergence and accuracy of the cardiovascular intrinsic frequency method

Tavallali, Peyman; Hou, Thomas Y.; Rinderknecht, Derek; Pahlevan, Niema M.

doi:10.1098/rsos.150475

Cited by 22 publications

(38 citation statements)

References 29 publications

(69 reference statements)

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“…In this study, a novel, easy-to-use, and non-invasive approach to estimate carotid-femoral PWV , from a single carotid waveform measurement, is explored. This method is based on the newly developed Intrinsic Frequency (IF) algorithm 18 , 19 . IF method solely needs one uncalibrated trace of a carotid, or aortic, pressure waveform.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Estimation of Carotid-Femoral Pulse Wave Velocity using Carotid Waveform

Tavallali¹,

Razavi²,

Pahlevan

2018

Sci Rep

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In this article, we offer an artificial intelligence method to estimate the carotid-femoral Pulse Wave Velocity (PWV) non-invasively from one uncalibrated carotid waveform measured by tonometry and few routine clinical variables. Since the signal processing inputs to this machine learning algorithm are sensor agnostic, the presented method can accompany any medical instrument that provides a calibrated or uncalibrated carotid pressure waveform. Our results show that, for an unseen hold back test set population in the age range of 20 to 69, our model can estimate PWV with a Root-Mean-Square Error (RMSE) of 1.12 m/sec compared to the reference method. The results convey the fact that this model is a reliable surrogate of PWV. Our study also showed that estimated PWV was significantly associated with an increased risk of CVDs.Cardiovascular diseases (CVDs) and stroke are among the major causes of death in the United States and the total cost related to them was more than $316 billion in 2011-2012 1,2 . New cardiovascular monitoring methods are urgently needed in order to limit the growing burden of CVDs. Arterial stiffening is one of the risk factors for CVDs 3,4 , which can be assessed non-invasively by calculating the carotid to femoral PWV 5 . This parameter is a gold standard of arterial stiffness, the rate at which pressure waves move down the aortic vessel 6 . Increased arterial stiffness is related to an increased risk of cardiovascular events; therefore, it has become an independent marker for CVDs 6,7 . Because of its clinical significance, there has been a surge in addressing arterial stiffness and PWV 8 . Arterial stiffness and its surrogates such as PWV have been suggested as one of the risk factors along with other biomarkers such as high cholesterol, diabetes, and left ventricular hypertrophy when cardiovascular risk is being evaluated 8 . Past studies have shown a strong correlation between PWV and the presence of CVDs 9-14 .Although carotid-femoral PWV measurement is non-invasive, this process is intrusive as it requires the waveform collection from inguinal region. Obtaining accurate carotid-femoral PWV measurements often requires a well-trained staff within a clinical setting 15 . The need of the medical community is an easy-to-use and non-intrusive method to measure carotid-femoral PWV with acceptable accuracy and precision; see ref. 16 .At the same time, recent advances in the field of artificial intelligence have opened up new areas and methods in creating novel modeling and predictive methods for clinical use 17 . The model and analysis in this paper are in accord to this path of introducing artificial intelligence to the field of medical sciences.In this study, a novel, easy-to-use, and non-invasive approach to estimate carotid-femoral PWV, from a single carotid waveform measurement, is explored. This method is based on the newly developed Intrinsic Frequency (IF) algorithm 18,19 . IF method solely needs one uncalibrated trace of a carotid, or aortic, pressure waveform. Our method takes an un...

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Estimation of Carotid-Femoral Pulse Wave Velocity using Carotid Waveform

Tavallali¹,

Razavi²,

Pahlevan

2018

Sci Rep

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“…A one-way ANOVA comparison of ω 1 across the pediatric and adult cohorts demonstrated a significant difference (F = 7.3, Prob>F = 0.0001). Post-hoc analysis revealed a significant difference between the adult cohort (ω 1 = 99 +/-5 bpm) and both the 0-4 year old (ω 1 = 111 +/-2 bpm; p = 0.0006) and [15][16][17][18][19] year old (ω 1 = 105 +/-5 bpm; p = 0.02) cohorts ( Fig 4A). A one-way ANOVA comparison of ω 2 across all pediatric and adult cohorts showed a significant difference (F = 4.8, Prob>F = 0.003).…”

Section: Resultsmentioning

confidence: 94%

“…IF can be computed using this formula in a fraction of second. Details regarding mathematical formulation of IF method [12], its computational procedure [12], and its convergence/accuracy [19] can be found in previous publications [12,17,19].…”

Section: Intrinsic Frequency Methodsmentioning

confidence: 99%

Proof-of-concept for a non-invasive, portable, and wireless device for cardiovascular monitoring in pediatric patients

et al. 2020

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Measurement of cardiac function is vital for the health of pediatric patients with heart disease. Standard tools to measure function including echocardiogram and magnetic residence imaging are time intensive, costly, and have limited accessibility. The Vivio is a novel, non-invasive, handheld device that screens for cardiac dysfunction by analyzing intrinsic frequencies (IF) ω 1 and ω 2 of carotid artery waveforms. Prior studies demonstrated that left ventricular ejection fraction can be derived from IFs in adults. This study 1) studies whether the Vivio can capture carotid arterial pulse waveform data in children ages 0-19 years old; 2) tests the performance of two sensor head geometries, one larger and smaller than the standard size used in adults, designed for the pediatric population; 3) compares the IFs between pediatric age groups and adults with normal function. The Vivio successfully measured a carotid artery waveform in all children over 5 years old and 28% of children under the age of five. The small head did not accurately measure a waveform in any age group. One-way analysis of variance (ANOVA) demonstrated a difference in the IF ω 1 between the adult and pediatric cohorts (F = 7.3, Prob>F = 0.0001). Post host analysis demonstrated a difference between the adult cohort (ω 1 = 99 +/-5 bpm) and the cohorts ages 0-4 (ω 1 = 111 +/-2 bpm; p = 0.0006) and 15-19 years old (ω 1 = 105 +/-5 bpm; p = 0.02). One-way ANOVA demonstrated a difference in the IF ω 2 between the adult and pediatric cohorts (F = 4.8, Prob>F = 0.003), specifically between the adult (ω 2 = 81 +/-13 bpm) and age 0-4 cohorts (ω 2 = 48 +/-8 bpm; p = 0.002). These results suggest that the Vivio can be used to capture carotid pulse waveform data in pediatric populations and that the data produced can be used to measure intrinsic frequencies.

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“…In the LV-aorta system, the decoupling time is the time of the closure of the aortic valve. It is obvious from the definition that IF frequencies are fundamentally different than Fourier harmonics or any other resonance-type frequencies [9,15]. The mathematical formulation of the IF method is as follows:…”

Section: Intrinsic Frequency Methodsmentioning

confidence: 99%

“…a 1 , a 2 , c, b 1 , b 2 , ω 1 , and ω 2 are the unknowns that can be found by solving this nonconvex minimization problem. Details regarding mathematical formulation of the IF method [9], its computational procedure [9], and its convergence/accuracy [15] were provided in previous publications [9,15,16]. Figure 1 illustrates how the IF-reconstructed waveform from ω 1 , and ω 2 represents the original waveform.…”

Section: Intrinsic Frequency Methodsmentioning

confidence: 99%

Cardiac Triangle Mapping: A New Systems Approach for Noninvasive Evaluation of Left Ventricular End Diastolic Pressure

Pahlevan

Matthews

2019

Fluids

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Noninvasive and practical assessment of hemodynamics is a critical unmet need in the treatment of both chronic and acute cardiovascular diseases. Particularly, the ability to monitor left ventricular end-diastolic pressure (LVEDP) noninvasively offers enormous benefit for managing patients with chronic congestive heart failure. Recently, we provided proof of concept that a new cardiac metric, intrinsic frequency (IF), derived from mathematical analysis of non-invasively captured arterial waveforms, can be used to accurately compute cardiovascular hemodynamic measures, such as left ventricle ejection fraction (LVEF), by using a smartphone. In this manuscript, we propose a new systems-based method called cardiac triangle mapping (CTM) for hemodynamics evaluation of the left ventricle. This method is based on intrinsic frequency (IF) and systolic time interval (STI) methods that allows computation of LVEDP from noninvasive measurements. Since the CTM method only requires arterial waveform and electrocardiogram (ECG), it can eventually be adopted as a simple smartphone-based device, an inexpensive hand-held device, or perhaps (with future design modifications) a wearable sensor. Such devices, combined with this method, would allow for remote monitoring of heart failure patients.

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On the convergence and accuracy of the cardiovascular intrinsic frequency method

Cited by 22 publications

References 29 publications

Artificial Intelligence Estimation of Carotid-Femoral Pulse Wave Velocity using Carotid Waveform

Artificial Intelligence Estimation of Carotid-Femoral Pulse Wave Velocity using Carotid Waveform

Proof-of-concept for a non-invasive, portable, and wireless device for cardiovascular monitoring in pediatric patients

Cardiac Triangle Mapping: A New Systems Approach for Noninvasive Evaluation of Left Ventricular End Diastolic Pressure

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