Patent Description:
The present disclosure generally relates to non-invasive methods and systems for characterizing cardiovascular circulation. More specifically, the present disclosure relates to non-invasive methods that utilize unfiltered wide-band cardiac phase gradient data to generate residue subspace and noise subspace data, for example, to be used in the prediction and localization of coronary artery stenoses, localizing and/or estimating fractional flow reserve, and characterizing myocardial ischemia.

<CIT> is prior art showing obtaining high-resolution ECG signals and determining a phase space representation of ECG data. Vascular diseases are often manifested by reduced blood flow due to atherosclerotic occlusion of vessels. For example, occlusion of the coronary arteries supplying blood to the heart muscle is a major cause of heart disease. Invasive procedures for relieving arterial blockage such as bypass surgery and stent placement with a catheter rely on estimates of occlusion characteristics and blood flow through the occluded artery. These estimates are based on measurements of occlusion size and / or blood flow. Unfortunately, current methods of occlusion size and blood flow measurement require invasive procedures such as coronary angiography, which requires cardiac catheterization. This procedure involves a long, thin, flexible catheter being placed into a blood vessel in the arm, groin (upper thigh), or neck; the catheter is then threaded to the heart. Through the catheter, a physician can perform a visual evaluation of the inner diameter of a vessel with cineangiography or fluoroscopy and/or use a small sensor on the tip of the wire (commonly a transducer) to measure parameters such as pressure, temperature, and flow to determine the severity of the lesion; and fractional flow reserve (FFR). These minimally invasive diagnostic tests on the heart carry the risk of stroke, heart attack, injury to the catheterized artery/ heart, irregular heart rhythms, kidney damage, infection, and radiation exposure from X-rays. These procedures are time consuming, require expertise in the interpretation of the results and are expensive.

Stenosis geometry is also important in the therapeutic phase when balloon angioplasty, stenting or drug delivery procedures are subsequently performed. For example, precise stent placement is critical for reducing the risk of restenosis. Thus, decisions on whether or not to use any of the blockage relieving methods and which of the methods should be used are often based on partial information and do not take into account coronary collateralization. The ischemic stress often induces the increase in collateral circulation in coronary small vessel which at times will compensate for distal vessel blockage. Further, the evaluation of therapeutic success is also problematic, where both occlusion opening and stent position have to be evaluated. One class of methods, predominantly used today, require a lengthy procedure to find and determine severity, blockage to blood flow, of the lesion or lesions. Contemporary techniques evaluate the cardiac gradient phase-space changes and correlate the changes with cardiac computed tomography (CT), myocardial perfusion imaging, and cardiac angiography. The surface cardiac gradient contains detailed information on the electrophysiology of the chambers recorded. Because surface cardiac gradient represents the summation of the individual action potentials from each and every cardiac cell in syncytium, in theory, any information that might be determined from measurement of the orchestrated cellular action potential should be available on a "global" level in the surface. Moreover, although information relating to the influence of myocardial tissue architecture on conduction properties is inherent in the surface cardiac gradient, the challenge is in the discrimination of the pertinent information from these long quasi-periodic cardiac gradient signals while excluding noise contamination. Still further, there is a distinct lack of non-invasive tools available to enhance identification of high-risk patients and thus to trial preventive strategies in a non-invasive manner.

The components in the drawings are not necessarily to scale relative to each other and like reference numerals designate corresponding parts throughout the several views:.

The components in the drawings are not necessarily to scale relative to each other and like reference numerals designate corresponding parts throughout the several views.

<FIG> is a diagram of a system for non-invasively determining arterial flow characteristics in the heart using wide-band cardiac gradient data, in accordance with an illustrative embodiment. As shown in <FIG>, the system <NUM> includes a wide-band biopotential measuring equipment <NUM> and an analysis subsystem <NUM>. The wide-band biopotential measuring equipment <NUM> collects wide-band biopotential signals <NUM> (shown as 112a. n) (also referred to herein as wide-band cardiac gradient signal data <NUM>) from a subject or patient <NUM>, via at least one electrode <NUM> (shown as surface electrodes 106a, 106b ,. , 106n), and corresponding common-mode reference lead <NUM>, all of which are in the system of <FIG> are attached to the surface of the mammalian subject or patient <NUM> (e.g., the skin of an animal or a person). The wide-band biopotential measuring equipment <NUM> may be any device configured to capture unfiltered electrophysiological signals such that the spectral component(s) of the signals are not altered. That is, all of the captured signal, if not a significant portion of the captured signal, includes components conventionally perceived or treated as being noise, e.g., those in the frequency range of greater than about <NUM>. To this end, the wide-band biopotential measuring equipment <NUM> captures, converts, and analyzes the collected wide-band biopotential signals <NUM> without any filtering (via hardware circuitry, or digital signal processing) that affects phase linearity of the signal of the wide-band biopotential signals <NUM>. That is, only phase deterministic operations, numeric or analytical, are performed in the phase space transformation and analysis. Phase distortions are non-deterministic distortions that cause shifts in the frequency component of a signal.

An example wide-band biopotential measuring equipment <NUM> is described in <CIT>, published as <CIT>, titled "Method and Apparatus for Wide-Band Gradient Signal Acquisition,". In some embodiments, the wide-band biopotential measuring equipment <NUM> is configured to record unfiltered physiologic signals at a rate of about <NUM> at a number of observation points on the patient or subject (in a resting position) for <NUM> seconds. The resultant signal recording is then securely transmitted to a cloud-based repository whereupon it is automatically queued for processing. In some embodiments, the resultant signal recording is securely transmitted to a cloud-based repository whereupon it is automatically queued for processing. The processing pipeline derives the phase energy of the thoracic system by taking the multi-dimensional (spatial temporal) transformation of the signals and subsequently reconstructs this into a phase space model of the patient's heart.

The inventors have discovered that wide-band biopotential signals, having energy and frequency components beyond those of conventional electrocardiography (ECG) and traditionally perceived or treated as random noise, includes measurable data of the heart physiology that can be discriminated by genetic algorithms (and other machine learning algorithms) to assess regional flow characteristics of the heart, including for example an estimated value for stenosis and the identification of ischemia and a fractional flow reserve (FFR) of specific arteries and branches thereof. Noise removal (e.g., by applying cleaning techniques to the data resulting in the same amount of data as prior to noise removal) is a fundamental step in signal processing. However, the exemplified method and system process the entire obtained biopotential signals without any noise removal operations. What has heretofore been perceived and/or classified as unwanted noise in the wide-band data is, in many cases, the signal of interest. Examples of noise removal that is not performed include, but are not limited to, analog-based low-pass filters, band-pass filters, high-pass filters as well as digital-based filters such as FIR filters, Butterworth filters, Chebyshev filters and median filters (among others) that are configured to change the phase linearity of the processed signals. It is noted that analog-based low-pass filters, band-pass filters, high-pass filters as well as digital-based filters, that are configured to be phase linear, may be used. In some embodiments, the signal may be processed via phase linear operations to allow for analysis of specific aspects of the highfrequency wide-band data.

As described in <CIT>, in some embodiments, the wide-band biopotential measuring equipment <NUM> is configured to capture one or more biosignals, such as biopotential signals, in microvolt or sub-microvolt resolutions - resolutions that are at, or significantly below, the noise-floor of conventional electrocardiographic and biosignal acquisition instruments. In some embodiments, the wide-band biopotential measuring equipment <NUM> is configured to acquire and record wide-band phase gradient signals (e.g., wide-band cardiac phase gradient signals, wide-band cerebral phase gradient signals) that are simultaneously sampled, in some embodiments, having a temporal skew or "lag" of less than about <NUM>, and in other embodiments, having a temporal skew or lag of not more than about <NUM> femtoseconds. Notably, the exemplified system minimizes non-linear distortions (e.g., those that can be introduced via certain filters) in the acquired wide-band phase gradient signal so as to not affect the information therein.

Referring still to <FIG>, the analysis system <NUM> is configured to generate a phase space map to be used in subsequent phase space analysis <NUM> later described herein. The output of the phase space analysis is then evaluated using machine learning analysis <NUM> to assess parameters <NUM> associated with a presence of a disease or physiological characteristic such as regional arterial flow characteristics. In some embodiments, the machine learning analysis <NUM> may use a library <NUM> of quantified FFR, stenosis, and ischemia data in the assessment of the obtained wide-band cardiac gradient signal data <NUM>. The output <NUM> of a processor performing the analysis <NUM> is then transmitted to a graphical user interface, such as, e.g., a touchscreen or other monitor, for visualization. The graphical user interface, in some embodiments, is included in a display unit configured to display parameters <NUM>. In some embodiments, the graphical user interface displays intermediate parameters such as a 3D phase space plot representation of the biopotential signal data and virtual biopotential signal data. In other embodiments, the output of the processor is then transmitted to one or more non-graphical user interfaces (e.g., printout, command-line or text-only user interface), directly to a database or memory device for, e.g., later retrieval and/or additional analysis, or combinations thereof.

As used herein, the term "processor" refers to a physical hardware device that executes encoded instructions for performing functions on inputs and creating outputs. The processor may include one or more processors, each configured to execute instructions and process data to perform one or more functions associated with a computer for indexing images. The processor may be communicatively coupled to RAM, ROM, storage, database, I/O devices, and interface. The processor may be configured to execute sequences of computer program instructions to perform various processes.

<FIG> (reproduced in <FIG>) is a diagram of an example wide-band cardiac gradient signal <NUM> shown as time series data, in accordance with an embodiment. <FIG> (reproduced also in <FIG>) is a diagram of the example wide-band cardiac gradient signal <NUM> of <FIG> shown in the frequency domain, in accordance with an embodiment. As shown in <FIG>, the wide-band cardiac gradient signal <NUM> has a frequency component greater than <NUM>, which is significantly higher than conventional electrocardiogram measurements. In some embodiments, the wide-band cardiac gradient signal <NUM> has a frequency component up to about <NUM> (e.g., about <NUM> to about <NUM>). In some embodiments, the wide-band cardiac gradient signal <NUM> has a frequency component up to about <NUM> (e.g., about <NUM> to about <NUM>). In some embodiments, the wide-band cardiac gradient signal <NUM> has a frequency component up to about <NUM> (e.g., about <NUM> to about <NUM>). In some embodiments, the wide-band cardiac gradient signal <NUM> has a frequency component up to about <NUM> (e.g., about <NUM> to about <NUM>). In some embodiments, the wide-band cardiac gradient signal <NUM> has a frequency component up to about <NUM> (e.g., about <NUM> to about <NUM>). In some embodiments, the wide-band cardiac gradient signal <NUM> has a frequency component up to about <NUM> (e.g., about <NUM> to about <NUM>). In some embodiments, the wide-band cardiac gradient signal <NUM> has a frequency component up to about <NUM> (e.g., about <NUM> to about <NUM>). In some embodiments the wide-band cardiac gradient signal <NUM> has a frequency component up to <NUM> (e.g., about <NUM> to about <NUM>).

<FIG> is a time-series plot of an example wide-band cardiac gradient signal <NUM> associated with a single surface electrode, in accordance with an embodiment. The plot shows the signal in mV over time (in seconds).

<FIG> is a frequency plot of an example wide-band cardiac gradient signal <NUM> associated with three surface electrodes, in accordance with an embodiment. As shown, <FIG> includes frequency components of the wide-band cardiac gradient signal <NUM> up to <NUM>. As further shown, the presented wide-band cardiac gradient signal <NUM> has power, in the frequency domain, between -<NUM> dB and <NUM> dB at frequencies greater than <NUM>. This portion <NUM> of the wide-band cardiac gradient signal <NUM> includes topologic and functional information about the cardiac tissue and its underlying structure that can be used to determine regional flow characteristics such as estimation of regional FFR, estimation of region stenosis, and identification and/or estimation of a degree of regional ischemia.

Wide-band cardiac gradient signals (e.g., having frequencies between about <NUM> and about <NUM>) facilitate phase space analysis on unevenly sampled data. In some embodiments, the wide-band cardiac gradient signals have higher sampling rates during intervals of interest and lower sampling rates during other intervals to facilitate minimization of the resulting data set size. This varying sampling rate may be used in application where data storage is limited. Many non-linear functions (e.g., such as those used in phase space analysis) operate more effectively at identifying amplitudes with points that are unevenly spaced in the time domain. The much higher sampling rate as compared to those of the highest frequencies of interest (e.g., <NUM> times greater than those of the highest frequencies of interest) facilitates a correctly characterized shape of the signal system. This is similar to Lorenz systems, where very high frequencies are beneficial to correctly model the shape of the system in phase space. Example of the Lorenz system is described in <NPL>).

In some embodiments, the exemplified method and system are used to classify ultra-wide-band cardiac gradient signals having negative spectral energy signatures as high as about <NUM> (e.g., having frequencies between about <NUM> and about <NUM>).

<FIG> is a frequency plot of an example ultra-wide-band cardiac gradient signal, in accordance with an illustrative embodiment. As shown, <FIG> is sampled at a frequency of about <NUM> and includes frequency components of an ultra-wide-band cardiac gradient signal up to about <NUM> (according to the Nyquist sampling theorem). Notably, as shown, the presented ultra-wide-band cardiac gradient signal includes negative spectral energy signatures in frequencies greater than about <NUM>-<NUM> (shown as frequencies <NUM>); the negative spectral energy signatures, in the frequency domain, having energy between about <NUM> dB and about <NUM> dB. The data suggest that low-energy signatures in ultra-wide-band electrocardiograms may have information that could be used to image morphologies or functions of the body and/or for diagnostics.

<FIG> is a diagram of a method <NUM> of processing the wide-band biopotential signal data <NUM> (and ultra-wide-band biopotential signal data), in accordance with an illustrative embodiment. As shown in <FIG>, the method <NUM> includes collecting the wide-band gradient cardiac signal data <NUM> (shown as "Wide-band gradient signals Unfiltered RAW ADC data" <NUM>) and pre-processing <NUM> the wide-band gradient cardiac signal data to generate a phase space dataset (shown as "residue subspace" dataset <NUM> and "noise subspace" dataset <NUM>) in phase space analysis, whereby features of the phase space dataset (<NUM>, <NUM>) are extracted (operations <NUM>) and evaluated in nested non-linear functions <NUM> to generate stenosis and FFR estimation values <NUM>.

The wide-band gradient cardiac signal data <NUM> may be collected from one or more electrodes (e.g., surface electrodes, non-contact electrodes). In some embodiments, the wide-band gradient cardiac signal data <NUM> are simultaneously collected from between <NUM> and about <NUM> or more electrodes (e.g., <NUM> electrode, <NUM> electrodes, <NUM> electrodes, <NUM> electrodes, <NUM> electrodes, <NUM> electrodes, <NUM> electrodes, <NUM> electrodes, <NUM> electrodes, <NUM> electrodes, <NUM> electrodes, <NUM> electrodes, <NUM> electrodes, <NUM> electrodes, <NUM> electrodes, <NUM> electrodes, <NUM> electrodes, <NUM> electrodes, <NUM> electrodes, and <NUM> or more electrodes). In some embodiments, the sampling of these electrodes may have a less than a <NUM>-femtosecond skew or "lag". In other embodiments, the sampling of these electrodes may have a less than a <NUM>-femtosecond skew or lag. In other embodiments, the sampling of these electrodes may have a less than a few picosecond skew or lag. In some embodiments, the wide-band cardiac gradient signal <NUM> has a frequency component up to about <NUM> (e.g., about <NUM> to about <NUM>; about <NUM> to about <NUM>; about <NUM> to about <NUM>; about <NUM> to about <NUM>; about <NUM> to about <NUM>; about <NUM> to about <NUM>; or about <NUM> to about <NUM>). In some embodiments, the wide-band cardiac gradient signal <NUM> has a frequency component up to about <NUM> (e.g., about <NUM> to about <NUM>). In some embodiments, the wide-band gradient cardiac signal data <NUM> has a voltage resolution of about ½ µV sensitivity. In other embodiments, the wide-band gradient cardiac signal data <NUM> has a voltage resolution greater than about ½ µV sensitivity (e.g., about <NUM>µV, about <NUM>µV,about <NUM>µV,or about 1mV). In some embodiments, the resolution of the signal data is about <NUM> bits. In some embodiments, the effective resolution is <NUM> bits, <NUM> bits, <NUM> bits, or <NUM> or more bits. In some embodiments, the effective resolution is less than <NUM> bits (e.g., <NUM> bits or <NUM> bits or fewer).

In some embodiments, the phase space plot analysis uses geometrical contrast that arises from the interference in the phase plane of the depolarization wave with any other orthogonal leads. The presence of noiseless subspaces allows the recording of the phase of these waves. In general, the amplitude resulting from this interference can be measured; however, the phase of these orthogonal leads still carries the information about the structure and generates geometrical contrast in the image. The phase space plot analysis takes advantage of the fact that different bioelectric structures within, e.g., the heart and its various types of tissue have different impedances, and so spectral and non-spectral conduction delays and bends the trajectory of phase space orbit through the heart by different amounts. These small changes in trajectory can be normalized and quantified beat-to-beat and corrected for abnormal or poor lead placement and the normalized phase space integrals can be visualized on, or mapped to, a geometric mesh using a genetic algorithm to map <NUM> myocardial segments in the ventricle to various tomographic imaging modalities of the heart from retrospective data.

Referring still to <FIG>, three separate phase space analyses are performed to generate sets of metrics and variables (shown as 712a, 712b, and 712c) to be used in the non-linear functions <NUM> to generate regional FFR estimation values, regional stenosis values, and regional ischemia values <NUM>. Table <NUM> is an example output matrix <NUM>.

As shown, Table <NUM> includes a fractional flow reserve (FFR) parameter, an estimated stenosis parameter, and an estimated ischemia parameter for a plurality of segments corresponding to major vessels in the heart. In some embodiments, the matrix <NUM> includes a fractional flow reserve (FFR) parameter, an estimated stenosis parameter, and an estimated ischemia parameter for a standardized myocardial segment map having <NUM> segments of the heart including the Left Main Artery (LMA), the Proximal Left Circumflex Artery (Prox LCX), the Mid- Left Circumflex Artery (Mid LCX), the Distal Left Circumflex Artery (Dist LCX), the Left Posterior Atrioventricular (LPAV), the First Obtuse Marginal Branch (OM1), the Second Obtuse Marginal Brach (OM2), the Third Obtuse Marginal Branch (OM3), the Proximal Left Anterior Descending Artery (Prox LAD), the Mid Left Anterior Descending Artery (Mid LAD), the Distal Left Anterior Descending Artery (Dist LAD), the Left Anterior Descending First Diagonal Branch (LAD D1), the Left Anterior Descending Second Diagonal Branch (LAD D2), the Proximal Right Coronary Artery (Prox RCA), the Mid Right Coronary Artery (Mid RCA), the Distal Right Coronary Artery (Dist RCA), and the Acute Marginal Brach Right of the Posterior Descending Artery (AcM R PDA). In Table <NUM>, the parameters for myocardial ischemia estimation, stenosis identification, and/or fractional flow reserve estimation are shown in a range of <NUM> to <NUM>. Other scaling or ranges may be used.

Tables <NUM>-<NUM> show example non-linear functions to generate FFR estimations for several segments corresponding to major vessels in the heart. In Table <NUM>, an example function to determine a FFR estimation for the left main artery ("FFR_LEFTMAIN") is provided.

As shown in Table <NUM>, the FFR estimation for the left main artery is determined based on extracted metrics and variables such as a Z-component parameter associated with the noise subspace <NUM> ("noisevectorRz"), a Alphahull ratio parameter ("Alpharatio"), and a signal density cloud volume <NUM> ("DensityV4").

In Table <NUM>, an example function to determine a FFR estimation for the mid right coronary artery ("FFR_MIDRCA") is provided.

As shown in Table <NUM>, the FFR estimation for the mid right coronary artery is determined based on extracted metrics and variables such as a Y-component parameter associated with the noise subspace <NUM> ("noisevectorRy"), the Alphahull ratio parameter ("Alpharatio"), and a signal density cloud volume <NUM> ("DensityV3").

In Table <NUM>, an example function to determine a FFR estimation for the mid left artery descending ("FFR_MIDLAD") is provided.

As shown in Table <NUM>, the FFR estimation for the mid left artery descending is determined based on extracted metrics and variables such as a ratio of volume to surface area for cloud cluster <NUM> ("AspectRatio3") and a wavelet residue mean XYZ ("residueLevelMean").

In Table <NUM>, an example function to determine a FFR estimation for the proximal left circumflex artery ("FFR_PROXLCX") is provided.

As shown in Table <NUM>, the FFR estimation for the proximal left circumflex artery is determined based on extracted metrics and variables such as a wavelet residue volume XYZ ("residueLevelVolume"), vector cloud <NUM> volume ("vectorcloud6"), and a signal density cloud volume <NUM> ("DensityV4").

Referring again to <FIG>, a wavelet operator <NUM> (shown as "wavelets cleaning" <NUM>) can perform an operation on the wide-band gradient signal data <NUM> (or a derived data therefrom). It should be understood to those skilled in the art that other intermediate phase linear processing may be perform on the signal data <NUM> prior to operation by the wavelet operator <NUM>. In some embodiments, the wavelet operator <NUM> comprises a Biorthogonal wavelet <NUM> transform. <FIG> are diagrams of an example wavelet transformation (i.e., Biorthogonal wavelet <NUM>) used to generate a multi-dimensional wavelet-cleansed dataset, in accordance with an illustrative embodiment. <FIG> shows a decomposition scaling function φ. <FIG> shows a decomposition wavelet function ψ. <FIG> is a diagram of an example output <NUM> of the wavelet cleaning operation. The output <NUM> is show in conjunction with the input <NUM> to the wavelet cleaning operation. The output, in some embodiments, is a time series dataset.

Referring still to <FIG>, the output of the wavelet operator <NUM> is combined and transformed, via phase space transformation <NUM>, to produce the multi-dimensional wavelet-cleansed dataset <NUM>. Feature topology analysis (also shown in block <NUM>) is performed on the multi-dimensional wavelet-cleansed dataset <NUM> to extract metrics and variables 712a. The extracted metrics and variables 712a, in some embodiments, include morphological, topologic, or functional features of the multi-dimensional wavelet-cleansed dataset including, for example, 3D volume value, a void volume value, a surface area value, a principal curvature direction value, and a Betti number value. In some embodiments, the multi-dimensional wavelet-cleansed dataset may be segmented, or partitioned, into sub-regions to which metrics and variables of these sub-regions are extracted. In some embodiments, a void volume value, a surface area value, a principal curvature direction value, and a Betti number value is also determined for each sub-region. In some embodiments, the number of generated sub-regions (also referred to as number of segment) is between about <NUM> and about <NUM> (e.g., <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM>). In some embodiments, the number of sub-regions is greater than <NUM>.

<FIG> is a diagram of a method <NUM> of performing feature topology analysis of the multi-dimensional wavelet-cleansed dataset. In some embodiments, the method <NUM> may be similarly performed on other datasets, such as the multi-dimensional noise subspace dataset 712b and multi-dimensional wavelet-cleansed dataset 712c generated by each of the phase space analyses (e.g., residue subspace analysis and noise subspace analysis) as described in relation to <FIG>. As shown in <FIG>, a morphological, topologic, or functional feature extraction analysis (shown as "Topology Analysis" <NUM>) includes, in some embodiments, computing 3D volumes, voids, and surface area for at least one cycle of the multi-dimensional wavelet-cleansed dataset as a space-time domain dataset using an alpha-hull operator <NUM>. In some embodiments, the alpha-hull operator uses a static alpha radius. Further detail of the alpha-hull operator is described in <NPL>). Other topologic or geometric encapsulation operations may be used, including, for example, but not limited to Delauney triangulation. Delaunay triangulations are triangulations on a set of points such that no point is within the circumcircle of any triangle in the triangulation, and the minimum angle of all the angles in each triangle in the triangulation is maximized.

Referring still to <FIG>, the generated multi-dimensional output of the alpha-hull operator <NUM> may be further extracted to compute (operator <NUM>) principal direction, curvature direction, Betti numbers, and Betti values. In addition, the dataset as a space-time domain dataset is further segmented (via operator <NUM>) into sub-regions and volume, surface area and aspect ratios are computed for these sub-regions also using an alphahull operator <NUM>. As shown, the space-time domain dataset is segmented into <NUM> regions, <NUM> regions, and <NUM> regions to which volume, surface area and aspect ratio parameters are computed for some of all of these regions. In some embodiments, the three group of regions comprising <NUM> regions may generate <NUM> parameters for each regions to provide <NUM> metrics or variables 712a. In combination with the computed volume, surface area, and aspect ratios of the alphahull output and the principal direction, curvature direction, Betti numbers, and Betti values thereof, there may be <NUM> metrics or variables 712a. The metrics and variables 712a may be provided as a matrix (shown as a "topology matrix" <NUM>).

It should be appreciated that other topologic features may be extracted in addition, or in substitute, those discussed herein. These features may include properties such as energy, surface variations, etc., or geometric features such as size.

It should be appreciated that other metrics and variables may be extracted and used depending on the number of operations performed and that the example provided herein is merely for illustrative purposes.

Referring again to <FIG>, attention is directed to a second phase space analysis performed to determine metrics and variables 712b for a multi-dimensional residue subspace dataset <NUM>.

<FIG> is a diagram of an example wavelet-based operation <NUM> to generate the multi-dimensional residue subspace dataset <NUM> as described in relation to <FIG>, in accordance with an illustrative embodiment. As shown in both <FIG> and <FIG>, multi-dimensional residue subspace dataset <NUM> is generated as a residue (e.g., a subtraction operator <NUM> in <FIG>) of two wavelet operators (e.g., <NUM> and <NUM>). The first wavelet operator may be the wavelets cleaning <NUM>, for example, using the biorthogonal wavelet <NUM> operator. The second wavelet operator may be a Reverse Biorthogonal Wavelet <NUM> operator <NUM>. <FIG> are diagrams of an example wavelet transformation (i.e., Reverse Biorthogonal wavelet <NUM>) used to generate a multi-dimensional residue subspace dataset, in accordance with an illustrative embodiment. <FIG> shows a decomposition scaling function φ. <FIG> shows a decomposition scaling function ψ. It should be that other phase linear wavelet operators may be used.

Referring still to <FIG>, each residue output of the wavelet operator <NUM> and wavelet operator <NUM> for each of the gradient signals are combined and transformed, via phase space transformation, to produce the multi-dimensional residue subspace dataset <NUM>. Feature topology analysis (also shown in block <NUM>) is performed on the multi-dimensional wavelet residue dataset to extract metrics and variables 712b. The extracted metrics and variables 712b may include morphological, topologic, or functional features of the multi-dimensional wavelet residue dataset including, for example, 3D volume value, a void volume value, a surface area value, a principal curvature direction value, and a Betti number value. In some embodiments, the multi-dimensional wavelet cleansed dataset may be segmented, or partitioned, into sub-regions to which metrics and variables of these sub-regions are extracted. In some embodiments, a void volume value, a surface area value, a principal curvature direction value, and a Betti number value is also determined for each sub-region. In some embodiments, the number of generated sub-regions (also referred to as number of segment) is between <NUM> and about <NUM> (e.g., <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM>). In some embodiments, the number of sub-regions is greater than <NUM>. In some embodiments, a similar or same topology extraction analysis as described in relation to <FIG> may be performed.

<FIG> is a depiction of an example residue subspace <NUM> which results from subtracting wavelet models (e.g., <NUM> and <NUM>) using biorthogonal <NUM> and reverse biorthogonal <NUM> operations. The residue subspace represents parts of the biological signal that are effectively too complex and non-linear to fit (i.e., represented) with a single wavelet function. This residue subspace is processed, transformed into representative features, and used to study the dynamical and geometrical properties of the cardiac gradient data.

<FIG> is a depiction of an example dynamical phase space volume object that has been colored by the residue subspace. The phase space volume object is generated by overlaying the value of the residue subspace as a color intensity mapping upon the input wide-band gradient signal data. The lack of intensive coloring is indicative of the absence of ischemic myocardial tissue. That is, <FIG> is an example dynamical phase space volume object of a healthy person.

<FIG> is a depiction of an example dynamical phase space volume object associated with an ischemic patient. That is, the dynamical phase space volume object was generated using wide-band gradient signal data of a patient diagnosed with an ischemic myocardium. The dynamical phase space volume object has been colored by the residue subspace by overlaying the value of the residue subspace as a color intensity mapping upon the input wide-band gradient signal data. The intensive coloring (corresponding to arrow <NUM>) is indicative of the presence of an ischemic myocardium.

Referring again to <FIG>, attention is directed to a third phase space analysis performed to determine metrics and variables 712c for a multi-dimensional noise subspace dataset <NUM>. As shown in <FIG>, the multi-dimensional noise subspace dataset <NUM> may be computed by subtracting, via a subtraction operator <NUM>, the input wide-band gradient signal data <NUM> (or a dataset derived therefrom) and the output of the wavelet cleansed signal data <NUM>. The outputs of the subtraction operation are combined and transformed, via phase space transformation, to produce the multi-dimensional residue subspace dataset <NUM>. Feature topology analysis (also shown in block <NUM>) is performed on the multi-dimensional noise subspace dataset to extract metrics and variables 712c. The extracted metrics and variables 712c may include morphological, topologic, or functional features of the multi-dimensional wavelet residue dataset including, for example, 3D volume value, a void volume value, a surface area value, a principal curvature direction value, and a Betti number value. In some embodiments, the multi-dimensional wavelet cleansed dataset may be segmented, or partitioned, into sub-regions to which metrics and variables of these sub-regions are extracted. In some embodiments, a void volume value, a surface area value, a principal curvature direction value, and a Betti number value is also determined for each sub-region. In some embodiments, the number of generated sub-regions (also referred to as number of segments) is between <NUM> and about <NUM> (e.g., <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM>). In some embodiments, the number of sub-regions is greater than <NUM>. In some embodiments, a similar or same topology extraction analysis as described in relation to <FIG> may be performed.

<FIG> is a depiction of the noise subspace <NUM>, which is the result of subtracting a biorthogonal <NUM> wavelet model (e.g., <NUM>) from the input wide-band gradient data <NUM>. Similar to the residue subspace <NUM>, it contains complex dynamical information. Specifically, the noise subspace contains chaotic information that cannot be effectively captured in a model. This noise subspace is processed, transformed into representative features, and used to study the dynamical and geometrical properties of the cardiac gradient data.

<FIG> is a depiction of an example noise subspace phase space object that has been colored by the noise subspace. The phase space object is generated by overlaying the value of the noise subspace as a color intensity mapping upon a derivative transformation (e.g., a numeric fractional derivative) of the input wide-band gradient signal data (or a derived data thereof). As shown in <FIG>, the lack of intensive coloring is indicative of the absence of an ischemic myocardium. That is, <FIG> is an example noise subspace phase space object of a healthy person.

<FIG> is a depiction of an example noise subspace phase space object associated with an ischemic patient. The phase space object has been colored by the noise subspace by overlaying the value of the noise subspace as a color intensity mapping upon a derivative transformation (e.g., a numeric fractional derivative) of the input wide-band gradient signal data (or a derived data thereof). The intensive coloring (corresponding to arrow <NUM>) is indicative of the presence of an ischemic myocardium.

<FIG> is a diagram of a method of performing machine learning analysis to create and select non-linear models to identify and/or estimate a degree of myocardial ischemia, identify one or more stenoses, and/or localize and/or estimate fractional flow reserve, as described in relation to <FIG>, in accordance with an illustrative embodiment. As shown in <FIG>, angiographic dataset <NUM> and fractional flow dataset <NUM> are used to create (via operation <NUM>) candidate non-linear models to identify stenosis (and/or estimate a degree thereof) and estimate fractional flow reserve <NUM>. Examples of the generation of non-linear models, e.g., to estimate cardiac chamber size and mechanical function, are described, for example, in U. Application No. <NUM>/<NUM>,<NUM>, title "Noninvasive electrocardiographic method for estimating mammalian cardiac chamber size and mechanical function".

In some embodiments, machine learning algorithms <NUM> are then used to select a family of non-linear models <NUM> from the candidate non-linear models using wide-band gradient cardiac signal data <NUM> of patients or subjects with some degree of stenosis and ischemia. In some embodiments, the machine learning algorithms are based on Regression Random Forest algorithms or a modified variation thereof. In some embodiments, the machine learning algorithms are based on deep learning algorithms.

In some embodiments, the machine learning phase invokes a meta-genetic algorithm to automatically select a subset of features drawn from a large pool. This feature subset is then used by an AdaBoost algorithm to generate predictors to diagnose significant coronary artery disease across a population of patients representing both positive and negative cases. The performances of the candidate predictors are determined through verification against a previously unseen pool of patients. Further description of the AdaBoost algorithm is provided in <NPL>).

In some embodiments, space-time quantities can mapped to complex Phase Space differences in <NUM>-dimensional space. Spatial changes in the phase space matrix can be extracted using a non-Fourier integral which creates the <NUM>-dimensional space-time density metrics. These metrics for the ventricle are modeled using a genetic algorithm to link <NUM> nonlinear nested sinusoidal Gaussian equations, for the ventricles <NUM> Segments of the Coronary Arterial Territories, as perfusion blockages. Perfusion images were visually scored using a <NUM> -segment model of the left ventricle and a <NUM>-point scale (<NUM>=normal tracer uptake, <NUM>=mildly reduced, <NUM>=moderately reduced, <NUM>=severely reduced, <NUM>=no uptake). The amount of ischemic myocardial tissue (IM) was calculated as the summed difference score (the difference between summed stress and summed rest scores) divided by <NUM>. Patients were classified as: no ischemia or equivocal (IM<<NUM>%), mild ischemia (<NUM>%≤IM<<NUM>%) and moderate/severe ischemia (IM≥<NUM>%). The output of these equations provides the amount and location of the ischemic myocardial tissue.

In some embodiments, the wide-band biopotential data <NUM> are operated upon with a modified matching pursuit (MMP) algorithm to create a sparse mathematical model. Detail of the MMP algorithm is provided in <NPL>).

Characteristics of the model, including residue quantification, can be included in the feature set. The characteristics of the model may be extracted, in a feature extraction operation <NUM> (<FIG>), to determine geometric and dynamic properties of the model. These subspaces may include, but are not limited to complex sub harmonic frequency (CSF) trajectory, quasi-periodic and chaotic subspaces, low/high energy subspaces, and fractional derivatives of the low/high energy subspaces. These subspaces are exemplars of the family of subspaces that characterize the dynamics of the system, whether pathological or normal.

<FIG> is a diagram of a method of visualizing the estimated arterial flow characteristics in the heart, in accordance with an illustrative embodiment. As shown in <FIG>, a visualization engine <NUM> receives the determined arterial flow characteristics and renders the characteristics onto a 3D visualization output. In some embodiments, the visualization engine <NUM> provides, in a graphical user interface (GUI), a system-level view of all of the arterial flow characteristics and their interactions. In some embodiments, the GUI presents the cascading effects of upstream modifications to the arterial flow upon the downstream circulation.

A coronary artery disease learning and formula development study conducted under a clinical protocol collects resting phase signals from human subjects prior to coronary angiography. The collected signals were evaluated using the non-invasive acquisition and analysis methods described herein to detect the presence of significant coronary artery disease in symptomatic adult patients or subjects. In addition, the collected signals were evaluated to assess the left ventricular ejection fraction and to identify the location of significant coronary artery disease. The performance of the non-invasive acquisition and analysis methods described herein were evaluated using a comparative paired trial design; the results are shown in <FIG>.

Further description of this clinical protocol is provided in <CIT>, titled "Method and System for Collecting Phase Signals for Phase Space Tomography Analysis,".

<FIG> are diagrams showing results of this study, which was conducted on <NUM> human subjects, in accordance with an illustrative embodiment. The presented data involves a prospective, non-randomized trial to refine the non-invasive acquisition and analysis methods described herein to detect and assess significant coronary artery disease (CAD) using paired phase signals with clinical outcomes data as assessed during a catheterization procedure (i.e., either a ≥ <NUM>% stenosis or a reduced fractional flow rate of <<NUM>).

In the presented data, data sets (total of <NUM>) of <NUM> subjects are used as the training data set, and data sets of <NUM> subjects are used as the verification population to assess sensitivity and specificity of non-invasive acquisition and analysis methods described herein. For a candidate predictor A (<FIG>), the study provides a ROC curve of <NUM> with a positive predictor value (PPV) of <NUM>% and a negative predictor value (NPV) of <NUM>% as compared to angiography results. For a candidate predictor B (<FIG>), the study provides a ROC curve of <NUM> with a positive predictor value (PPV) of <NUM>% and a negative predictor value (NPV) of <NUM>% as compared to angiography results. Candidate predictors A and B are internal parameters (such as training classifiers) used in the machine training process.

As compared with diagnostic performance of non-invasive myocardial perfusion imaging using single-photon emission computed tomography, cardiac magnetic resonance, and positron emission tomography for the detection of obstructive coronary artery disease as published in <NPL>) (shown as "SPECT"), the non-invasive acquisition and analysis methods described herein (shown as "cPSTA") performs comparably well. These solutions regularly achieved AUC-ROC scores greater than <NUM> in the verification phase, performing as well or better than previous human-guided methods. Table <NUM> below shows diagnostic performance between that study and the study herein.

Having thus described several embodiments of the present disclosure, it will be rather apparent to those skilled in the art that the foregoing detailed disclosure is intended to be presented by way of example only, and is not limiting. Many advantages for non-invasive method and system for location of an abnormality in a heart have been discussed herein. Various alterations, improvements, and modifications will occur and are intended to those skilled in the art, though not expressly stated herein. Additionally, the recited order of the processing elements or sequences, or the use of numbers, letters, or other designations therefore, is not intended to limit the claimed processes to any order except as may be specified in the claims. Accordingly, the present disclosure is limited only by the following claims and equivalents thereto.

For example, further examples of phase space processing that may be used with the exemplified method and system are described in <CIT>, title "Latent teratogen-induced heart deficits are unmasked postnatally with mathematical analysis and machine learning on ECG signals"; <CIT>, title "Methods and Systems Using Mathematical Analysis and Machine Learning to Diagnose Disease"; <CIT>, published as <CIT>, title "Method and system for characterizing cardiovascular systems from single channel data"; <CIT>, issued as <CIT>, title "Noninvasive method for estimating glucose, glycosylated hemoglobin and other blood constituents"; <CIT>, published as <CIT>, title "Noninvasive electrocardiographic method for estimating mammalian cardiac chamber size and mechanical function"; <CIT>, title "Noninvasive electrocardiographic method for estimating mammalian cardiac chamber size and mechanical function"; <CIT>, issued as <CIT>, title "Non-invasive method and system for characterizing cardiovascular systems and all-cause mortality and sudden cardiac death risk"; <CIT>, published as <CIT>, title "Non-invasive method and system for characterizing cardiovascular systems"; <CIT>, issued as <CIT>, title "Non-invasive method and system for characterizing cardiovascular systems"; <CIT>, titled "Method and System for Phase Space Analysis to Determine Arterial Flow Characteristics"; and <CIT>, title "Non-invasive method and system for characterizing cardiovascular systems".

For example, the exemplified methods and systems may be used generate stenosis and FFR outputs for use with interventional system configured to use the FFR/stenosis outputs to determine and/or modify a number of stents and their placement intra operation.

Claim 1:
A method comprising:
obtaining a plurality of wide-band gradient signals (<NUM>) simultaneously acquired from a subject via at least one electrode (<NUM>), including a first electrode and a second electrode, wherein each of the plurality of wide-band gradient signals comprises cardiac data in a frequency domain having frequency components of at least one of about <NUM>, about <NUM>, about <NUM>, about <NUM>, about <NUM>, about <NUM>, about <NUM>, about <NUM>, about <NUM>, and about <NUM>;
determining, via one or more processors, a three-dimensional phase space dataset in a space-time domain associated with a shape of the plurality of wide-band gradient signals; and
characterized in that the method further comprises:
extracting a first set of morphologic features from the three-dimensional phase space dataset (<NUM>)
using an alpha-hull operator; wherein the first set of extracted morphologic features include parameters selected from the group consisting of a three-dimensional (3D) volume value, a void volume value, a surface area value, a principal curvature direction value, and a Betti number value.