Non-invasive method and system for measuring myocardial ischemia, stenosis identification, localization and fractional flow reserve estimation

Abstract

The present disclosure facilitates the evaluation of broadband phase gradient information of cardiac tissue to assess, for example, the presence of cardiac ischemic heart disease. Notably, the present disclosure provides an improved and efficient method for identifying and risk stratifying coronary artery stenosis of the heart using high resolution and broadband cardiac gradients acquired from patients. In some embodiments, patient data is derived from cardiac gradient waveforms of one or more leads, resulting in high-dimensional data and long cardiac gradient records exhibiting complex nonlinear changes. Through numerical wavelet operators, spatiotemporal analysis is used to study the morphology of cardiac gradient data as a phase space dataset by extracting dynamic and geometric properties from the phase space dataset.

Description

[0001] Related applications [0002] This application claims priority to and the benefit of US Provisional Application No. 62 / 354,673, filed June 24, 2016, and US Provisional Application No. 62 / 409,176, filed October 17, 2016, each of which is hereby incorporated in its entirety by Incorporated herein by reference. technical field [0003] The present disclosure generally relates to non-invasive methods and systems for characterizing cardiovascular circulation. More particularly, the present disclosure relates to utilizing unfiltered broadband cardiac phase gradient data to generate residual subspace and noise subspace data, for example, for predicting and localizing coronary stenosis, localizing and / or estimating fractional flow reserve, and A non-invasive method to characterize myocardial ischemia. Background technique [0004] Vascular disease typically manifests as decreased blood flow due to atherosclerotic occlusion of blood vessels. For example, occlusion of the co...

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Problems solved by technology

Furthermore, although information related to the effect of myocardial tissue structure on conduction properties is inherent in surface cardiac gradients, the challenge is to discern relevant information from these long quasi-periodic cardiac gradient signals while excluding noise contamination.

Furthermore, there is a marked lack of non-invasive tools available to enhance identification of high-risk patients and thus conduct experimental prevention strategies in a non-invasive manner

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