The reductionist approach has dominated the fields of biology and medicine for nearly a century. Here, we present a systems science approach to the analysis of physiological waveforms in the context of a specific case, cardiovascular physiology. Our goal in this study is to introduce a methodology that allows for novel insight into cardiovascular physiology and to show proof of concept for a new index for the evaluation of the cardiovascular system through pressure wave analysis. This methodology uses a modified version of sparse time-frequency representation (STFR) to extract two dominant frequencies we refer to as intrinsic frequencies (IFs; v 1 and v 2 ). The IFs are the dominant frequencies of the instantaneous frequency of the coupled heart þ aorta system before the closure of the aortic valve and the decoupled aorta after valve closure. In this study, we extract the IFs from a series of aortic pressure waves obtained from both clinical data and a computational model. Our results demonstrate that at the heart rate at which the left ventricular pulsatile workload is minimized the two IFs are equal (v 1 ¼ v 2 ). Extracted IFs from clinical data indicate that at young ages the total frequency variation (Dv ¼ v 1 2 v 2 ) is close to zero and that Dv increases with age or disease (e.g. heart failure and hypertension). While the focus of this paper is the cardiovascular system, this approach can easily be extended to other physiological systems or any biological signal.
Analysis of carotid waveforms using intrinsic frequency methods can be used to document left ventricular ejection fraction with accuracy comparable with that of MRI. The measurements require no training to perform or interpret, no calibration, and can be repeated at the bedside to generate almost continuous analysis of left ventricular ejection fraction without arterial cannulation.
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...
In this paper, we analyse the convergence, accuracy and stability of the intrinsic frequency (IF) method. The IF method is a descendant of the sparse time frequency representation methods. These methods are designed for analysing nonlinear and non-stationary signals. Specifically, the IF method is created to address the cardiovascular system that by nature is a nonlinear and non-stationary dynamical system. The IF method is capable of handling specific nonlinear and non-stationary signals with less mathematical regularity. In previous works, we showed the clinical importance of the IF method. There, we showed that the IF method can be used to evaluate cardiovascular performance. In this article, we will present further details of the mathematical background of the IF method by discussing the convergence and the accuracy of the method with and without noise. It will be shown that the waveform fit extracted from the signal is accurate even in the presence of noise.
Childhood cancer survivors are at risk for anthracycline-related cardiac dysfunction, often developing at a time when they are least engaged in long-term survivorship care. New paradigms in survivorship care and chronic disease screening are needed in this population. We compared the accuracy of a novel handheld mHealth platform (Vivio) as well as echocardiography for assessment of cardiac function [left ventricular ejection fraction (EF)] in childhood cancer survivors with cardiac magnetic resonance (CMR) imaging (reference). Cross-sectional study design was used. Concurrent evaluation of EF was performed using Vivio, two-dimensional (2D) echocardiography, and CMR. Differences in mean EF (2D echocardiography vs. CMR; Vivio vs. CMR) were compared using Bland-Altman plots. Linear regression was used to evaluate proportional bias. A total of 191 consecutive survivors participated [50.7% female; median time from diagnosis: 15.8 years (2-44); median anthracycline dose: 225 mg/m (25-642)]. Echocardiography overestimated mean EF by 4.9% ( < 0.001); linear regression analysis confirmed a proportional bias, when compared with CMR ( = 3.1, < 0.001). There was no difference between mean EF derived from Vivio and from CMR (-0.2%, = 0.68). The detection of cardiac dysfunction via echocardiography was poor when compared with CMR [Echo EF< 45% (sensitivity 14.3%), Echo EF < 50% (sensitivity 28.6%)]. Sensitivity was substantially better for Vivio-based measurements [EF < 45% or EF < 50% (sensitivity 85.7%)]. This accessible technology has the potential to change the day-to-day practice of clinicians caring for the large number of patients diagnosed with cardiac dysfunction and heart failure each year, allowing real-time monitoring and management of their disease without the lag-time between imaging and interpretation of results. .
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