Removal of far field signals from electrophysiology information

Abstract

In one example, a computer-implemented method includes calculating a first reconstructed electrophysiological signal on a heart envelope based on geometry data and electrophysiological data. The electrophysiological data represents an electrophysiological signal measured non-invasively from a location distributed on a body surface, and the geometry data represents a geometry of the heart envelope and a geometry distributed on the body surface of the location where the electrophysiological signal is measured. A second reconstructed electrophysiological signal is calculated on the heart envelope based on the geometry data and the electrophysiological data, wherein the second reconstructed electrophysiological signal represents a far-field signal component. A near-field component of the electrophysiological signal is determined over at least a portion of the cardiac envelope based on the first reconstructed electrophysiological signal and the second reconstructed electrophysiological signal.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of priority to U.S. Provisional Patent Application No. 63 / 390,314, filed on July 19, 2022, which is incorporated herein by reference in its entirety. Technical Field

[0003] The present technology generally relates to removing far-field signals from electrophysiological information. Background Art

[0004] Electrophysiology involves the measurement of voltage changes or current or the manipulation of such as associated electrophysiological signals of the heart, brain, or other anatomical structures. Electrophysiology studies are performed to measure and record electrophysiological signals from a patient's body, such as by placing one or more electrodes on and / or in the body. Some examples of EP studies that may be performed include electrocardiograms, electroencephalograms, electromyograms, etc. During these and other EP studies, there may be a variety of sources of interference (including far-field signals) that may affect the signal measurements. Summary of the invention

[0005] The techniques of the present disclosure are generally directed to determining far-field signal components from electrophysiological information. The techniques described herein can also be used to recover near-field components based on far-field components.

[0006] In one aspect, the present disclosure provides a computer-implemented method, the computer-implemented method comprising calculating a first reconstructed electrophysiological signal on a cardiac envelope based on geometry data and electrophysiological data. The electrophysiological data represents an electrophysiological signal measured non-invasively from locations distributed on a body surface, and the geometry data represents the geometry of the cardiac envelope and the geometry of the locations distributed on the body surface where the electrophysiological signal is measured. A second reconstructed electrophysiological signal is calculated on the cardiac envelope based on the geometry data and the electrophysiological data, wherein the second reconstructed electrophysiological signal represents a far-field signal component. A near-field component of the electrophysiological signal is determined on at least a portion of the cardiac envelope based on the first reconstructed electrophysiological signal and the second reconstructed electrophysiological signal. In another example, one or more non-transitory computer-readable media having instructions that, when executed by a processor, perform the method.

[0007] In another aspect, the present disclosure provides a system comprising a memory and at least one processor. The memory may store data and executable instructions, wherein the data includes electrophysiological data and geometric data. The electrophysiological data represents electrophysiological signals measured from locations distributed on a body surface. The geometric data represents the geometry of a surface of interest and the geometry of the locations distributed on the body surface where the electrophysiological signals are measured. The processor is configured to access the memory and execute the instructions to at least:

[0008] calculating a first reconstructed electrophysiological signal on the surface of interest based on the geometric data and the electrophysiological data;

[0009] A second reconstructed electrophysiological signal is calculated on the surface of interest based on the geometric structure data and the electrophysiological data, wherein the second reconstructed electrophysiological signal represents a far-field signal component; and a near-field component of the electrophysiological signal of at least a portion of the surface of interest is determined based on the first reconstructed electrophysiological signal and the second reconstructed electrophysiological signal of the portion of the surface of interest.

[0010] The details of one or more aspects of the present disclosure are set forth in the following drawings and description. Other features, objectives, and advantages of the techniques described in the present disclosure will be apparent from the description and drawings, and from the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 is a block diagram illustrating an example system for generating near-field electrophysiological data.

[0012] Figure 2 is a block diagram of the source node placement function.

[0013] Figure 3 A and Figure 3 B is a conceptual diagram illustrating a portion of an example method for reconstructing an electrophysiological signal.

[0014] Figure 4 A and Figure 4 B is a conceptual diagram illustrating a portion of another example method for reconstructing an electrophysiological signal.

[0015] Figure 5 is a block diagram illustrating an example reconstruction engine.

[0016] Figure 6 is a block diagram illustrating an example system for mapping and analyzing electrophysiological signals.

[0017] Figure 7 is a flow chart of a method for determining near-field electrophysiological data.

[0018] Figures 8 to 17 Describes the Figure 6system or Figure 7 Example graphical output and associated signals generated by the method. DETAILED DESCRIPTION

[0019] The present specification relates to systems and methods for providing a measure of a near-field electrophysiological signal. As described herein, this can be achieved by removing the contribution of the corresponding far-field signal from the electrophysiological signal of interest.

[0020] As described herein, the systems and methods described herein may be implemented as machine-readable instructions executable by a processor. The processor includes a code (e.g., a reconstruction engine) programmed to calculate a first reconstructed electrophysiological signal on the cardiac envelope or other surface of interest (e.g., a cardiac surface or a virtual surface) based on the geometric structure data and the electrophysiological data. For example, the reconstruction engine uses a fundamental solution method (MFS) to solve an inverse problem for reconstructing electrophysiological signals on the cardiac envelope, such as disclosed in U.S. Patent No. 7,983,743, which is incorporated herein by reference in its entirety. The electrophysiological data represents electrophysiological signals measured non-invasively from locations distributed on the body surface (e.g., monopolar signals measured by a body surface sensor arrangement). The geometric structure data represents the spatial geometry of the cardiac envelope and the spatial geometry of the locations (e.g., sensor locations) of the measured electrophysiological signals distributed on the body surface. For example, the geometric structure data may represent corresponding positions in a common three-dimensional spatial coordinate system (e.g., as absolute spatial coordinates or relative spatial coordinates).

[0021] The reconstruction engine is also programmed to calculate a second reconstructed electrophysiological signal on the cardiac envelope based on the geometry data and the electrophysiological data. However, the reconstruction engine (e.g., using MFS) is programmed to calculate the second reconstructed electrophysiological signal based on the position of a virtual source node different from the virtual source node used to calculate the first reconstructed electrophysiological signal. For example, the virtual source node outside the body can be moved further from (away from) the geometric center of the body. Additionally or alternatively, the virtual source node in the heart can be moved toward (closer to) the geometric center of the heart. Therefore, the second reconstructed electrophysiological signal represents (or emphasizes) a far-field signal component. As used herein, a far-field signal or signal component refers to a signal originating from a position (node) away from the measurement position (e.g., a distance greater than a threshold distance from the measurement position) and / or a reconstructed signal on the cardiac envelope. In some examples, the position of the source node can be adaptively determined relative to the corresponding position on the cardiac envelope and / or the body surface. The processor also includes code (e.g., a near-field calculator) that is programmed to determine a near-field component of the electrophysiological signal on at least a portion of the cardiac envelope based on the difference between the first reconstructed electrophysiological signal and the second reconstructed electrophysiological signal. The systems and methods described herein may use a near-field component of an electrophysiological signal reconstructed over at least a portion of the cardiac envelope to control the delivery of therapy.

[0022] Figure 1 An example system 100 for generating near-field electrophysiological data 102 representing electrophysiological signals reconstructed on one or more surfaces of interest is depicted. In some examples, the surface of interest is the cardiac envelope. As used herein, the cardiac envelope can refer to any two-dimensional or three-dimensional surface residing within the patient's body on which the electrical signals are to be reconstructed. As an example, the surface corresponds to a virtual surface (e.g., a sphere or other three-dimensional structure). As another example, the surface corresponds to one or more surfaces of an anatomical structure, such as an epicardial surface, an endocardial surface, or both an epicardial surface and an endocardial surface. Thus, the cardiac envelope can be configured as a cardiac surface model having a three-dimensional geometric structure that is aligned or can be aligned to a spatial coordinate system of the patient's anatomical structure. The cardiac surface model can include cardiac nodes distributed on the geometric structure representing the cardiac envelope.

[0023] As an example, the system 100 may be implemented as a computing device including a memory 104 and a processor configured to execute instructions. Figure 1 , shown as a mapping system 106. The memory 104 can be implemented as one or more non-transitory machine-readable media configured to store data and instructions. The processor is configured to access the memory and execute instructions to perform methods and functions corresponding to the mapping system 106. The memory 104 stores electrophysiological data 108, such as representing a unipolar electrophysiological signal measured at one or more time intervals by an arrangement of electrodes (e.g., electrodes distributed on the surface of the body and / or invasive electrodes). The electrophysiological data 108 may include real-time measurements and / or previous measurements, which may generally vary depending on whether the system 100 (e.g., during an electrophysiological study) is used for real-time analysis or post-operative analysis.

[0024] The memory 104 also stores geometry data 110. The geometry data 110 includes data representing the body surface geometry of locations distributed on the body surface where electrophysiological signals are measured. For example, the locations on the body surface correspond to corresponding electrode locations of a sensing system (e.g., a sensor arrangement) that is positioned on the patient's chest and configured to sense body surface electrophysiological signals from such electrode locations. Examples of non-invasive sensing systems that can be used to measure body surface electrophysiological signals are shown and described in U.S. Patent No. 9,655,561 and International Application No. WO2010 / 054352, each of which is incorporated herein by reference in its entirety.

[0025] The geometry data 110 includes data representing the geometry of a surface of interest, such as a cardiac envelope for which a reconstructed electrophysiological signal is determined. The cardiac envelope may correspond to a three-dimensional epicardial surface geometry of the heart. Alternatively or additionally, the cardiac envelope may correspond to a three-dimensional endocardial surface geometry of the heart. As yet another alternative, the cardiac envelope may correspond to virtually any geometric surface between a region within the patient's heart and an outer surface of the patient's torso where electrical measurements are taken. The geometry data 110 may correspond to actual patient anatomical geometry, a preprogrammed generic model, or a combination thereof (e.g., a model modified based on the patient's anatomy).

[0026] As an example, the geometry data 110 may be derived from processing image data acquired for a patient via an imaging system (not shown). For example, the imaging system may be implemented according to any imaging modality (such as computed tomography (CT), magnetic resonance imaging (MRI), X-ray examination, fluoroscopy, ultrasound examination, etc.) to acquire three-dimensional image data of a patient's torso. Such image processing may include extracting and segmenting anatomical features from a digital image set, including one or more organs and other structures. Additionally, the position of each of the electrodes in the sensing system may be included in the geometry data 110, such as by acquiring an image while the electrode is set on the patient and identifying the electrode position in a coordinate system by appropriate extraction and segmentation. Imaging may be performed simultaneously with recording electrophysiological signals for generating patient measurement data 170, or imaging can be performed separately (e.g., before or after acquiring the measurement data). In another example, one or more non-imaging-based techniques may also be used to provide three-dimensional positioning of the electrode in a coordinate system, such as a digitizer or manual measurement results.

[0027] exist Figure 1 In an example of, the mapping system 106 includes a reconstruction engine 112. The reconstruction engine 112 is programmed to reconstruct electrophysiological signals on the cardiac envelope based on the electrophysiological data 108 and the geometric structure data 110. As described herein, the reconstruction engine 112 is programmed to calculate a first set of reconstructed electrophysiological signals and a second set of reconstructed electrophysiological signals for a surface of interest (such as the cardiac envelope). For example, the reconstruction engine 112 is configured to calculate each set of reconstructed electrophysiological signals in a corresponding set of reconstructed electrophysiological signals for a plurality of cardiac nodes spatially distributed on the cardiac envelope based on the same electrophysiological data 108 measured non-invasively at one or more time intervals. In some examples, the number of cardiac nodes may be greater than 1,000 or 2,000 or more, depending on the desired resolution. For example, one or more surface areas defining the cardiac envelope and / or time intervals for which the reconstructed electrophysiological signals are calculated may be set in response to a user input instruction entered through the user interface 114 and stored in the memory 104.

[0028] exist Figure 1 In an example of , the reconstruction engine 112 includes code programmed to implement the MFS 116 and the source node placement function 118. The MFS 116 includes an inverse calculation 120 and a forward calculation 122 that solve an inverse problem to generate corresponding first and second electrophysiological signals on a surface of interest based on the electrophysiological data 108, the geometry data 110, and the source node data provided by the source node placement function 118. For example, the geometry data 110 defines the spatial coordinates of nodes on the surface of interest (e.g., cardiac nodes on the spatial envelope) to which the reconstructed electrophysiological signals will be applied and the spatial coordinates of nodes on the body surface (e.g., body surface nodes) that define electrode locations from which the electrophysiological signals are measured to provide the electrophysiological data 108. The spatial coordinates of the corresponding nodes can be provided in a three-dimensional coordinate system aligned with the patient's anatomy.

[0029] The source node placement function 114 is programmed to provide source node data of a plurality of source nodes (e.g., virtual nodes or imaginary points) in a three-dimensional coordinate system representing the geometric structure data 110. The source nodes may include body surface source nodes and heart source nodes. The body surface source nodes may represent virtual nodes at locations radially spaced outward from the outer surface of the patient's body where the electrophysiological signal measurement is performed. The heart source nodes may represent virtual nodes radially spaced inward from the heart surface (or other surface of interest). As described herein, the source nodes used by the MFS 116 to calculate the second reconstructed electrophysiological signal are different from the source nodes used by the MFS to calculate the first reconstructed electrophysiological signal. By using different source nodes in this manner, one of the first electrophysiological signal or the second electrophysiological signal is smoothed so that it can better represent the far-field signal component than the other (e.g., it emphasizes the far-field signal component). In the following example, the second reconstructed electrophysiological signal is described as representing a far-field signal.

[0030] As an example, the source node placement function 114 is programmed to provide first source node data describing a first source node location to be used (e.g., by the MFS 116) to determine a first set of reconstructed electrophysiological signals. The source node placement function 114 may provide the first source node location at a default or fixed spatial distance relative to the body surface node and the heart node. For example, referring to Figure 2, a first body source node may be placed at a location spaced radially outwardly at a predetermined distance along a virtual first surface 130 from a body surface 132 where the body surface nodes reside. Similarly, a first cardiac source node may be placed at a location spaced radially inwardly at a predetermined distance along a surface 134 from a cardiac envelope (conceptually shown as 136) on which an EP signal is being reconstructed. In an example, the source node placement function 118 includes a distance calculator programmed to calculate a distance from a corresponding surface node (e.g., a body surface node or a cardiac surface node) to a corresponding location of the source node, such as a distance along a normal line drawn relative to a tangent line at the corresponding surface node.

[0031] The source node placement function 118 (e.g., its distance calculator) is also programmed to determine the positions of the corresponding second body source nodes and the second cardiac source nodes for use in determining the second set of reconstructed electrophysiological signals (e.g., by the MFS 116). The corresponding second body source nodes and the second cardiac source nodes may be placed uniformly or adaptively, such as based on the positions of the first source nodes.

[0032] In the example, refer to Figure 3 , each of the second body source nodes may be placed on the surface 140 spaced radially outwardly from the corresponding position of the first body source node (e.g., on the virtual surface 130) at a predetermined (e.g., uniform) distance (shown as D2). The predetermined distance of the second body source nodes may be calculated by applying a multiplier greater than one (e.g., 1.5x, 2x, 3x, 5x, or greater) to the distance D1 between the corresponding first body source node and the body surface 132. Each of the second cardiac source nodes may likewise be placed on the surface 142 spaced radially inwardly from the corresponding position of the first cardiac source node (e.g., on the surface 134) at a predetermined (e.g., uniform) distance. For example, the distance for placing the second cardiac source node radially inwardly from the heart surface may be calculated by applying a multiplier less than one (e.g., 0.9x, 0.8x, 0.7x, 0.5, or less) to the distance between the corresponding first cardiac source node and the geometric center of the heart. Other multipliers or distance calculations may be used to evenly position the respective second body source nodes and second cardiac source nodes relative to the body surface and the cardiac surface.

[0033] In another example, the source node placement function 118 is programmed to adaptively determine the positions of the corresponding second body source nodes and the second cardiac source nodes. The adaptive source node placement function can set the distance of the corresponding source nodes according to a predefined function and / or in response to user input entered through the user interface. The source node placement function 118 can determine the position of the selected node in the second source nodes to adjust (e.g., increase) the smoothness of the far-field signal component in the second set of reconstructed electrophysiological signals.

[0034] For example, refer to Figure 4 , the source node placement functionality 118 may place each of the second body source nodes 146 at a corresponding position spaced outwardly from the first surface 130 in which the first body source node is placed. Alternatively, the distance may be determined from the body surface 132. The distance D3 may be variable for each of the second body source nodes 146. In an example, the distance D3 between the first surface 130 and a given second body source node 146 depends on (e.g., is inversely proportional to) the distance between one or more associated cardiac nodes on the cardiac envelope 134 and the nearest electrode location on the body surface 132. Thus, a second body source node 146 associated with a cardiac node on the envelope 134 that is closer to the body surface electrode may be moved further away from the body surface 132 than other second body source nodes associated with a cardiac node on the envelope 134 that is spaced farther from the corresponding electrode on the body surface. As an example such as Figure 4 The result of such adaptive placement of the second body source node shown is similar to that of Figure 3 Compared to the uniform placement of the second body source nodes shown, the reconstructed electrophysiological signal calculated by the MFS 116 using the second body source nodes may exhibit a smooth increase in far-field signal components.

[0035] As another example, the source node placement function 118 is programmed to determine the closest distance between the corresponding cardiac node on the cardiac envelope 134 and the electrode position (node) on the body surface 132. Subsequently, the source node placement function 118 may identify a group of one or more cardiac nodes whose distance is less than a distance threshold. The distance threshold may be a default value or a variable, such as in response to a user input via the user interface 114. Subsequently, the source node placement function 118 may place a second body surface source node associated with the identified cardiac node to a position spaced from the body surface by a distance inversely proportional to the determined distance. Other second body surface source nodes that are not associated with the identified cardiac node may be placed (e.g., maintained) at their corresponding first body surface source node positions. Alternatively, such other second body surface source nodes may be uniformly placed at a distance outward from the body surface or outward from the corresponding first body surface source node positions, such as described herein. This results in the corresponding second body surface node positions being farther away from the body surface than their corresponding first body surface source node positions.

[0036] In addition, if Figure 4As shown, the source node placement function 118 is programmed to place the second cardiac source node at a position radially inward from the cardiac envelope. The second cardiac source node position can be placed uniformly or adaptively. In one example, the source node placement function 118 is programmed to place the second cardiac source node at a position radially inwardly spaced a predetermined (e.g., uniform) distance from the cardiac envelope 134 along a virtual surface 148, on which the EP signal is being reconstructed. The surface 148 can also be radially inward from the surface 142 on which the first cardiac source node is placed. In another example, the source node placement function 118 is programmed to place the second cardiac source node at a position along the same surface 142 on which the first cardiac source node is placed (e.g., the first cardiac source node and the second cardiac source node can be the same). In yet another example, the source node placement function 118 is programmed to place the second cardiac source node at a position adaptively determined based on the distance between the cardiac envelope 134 and the nearest electrode on the body surface 132.

[0037] Return to reference Figure 1 , the MFS 116 may be programmed to use the source node data for both the inverse calculation 120 and the forward calculation 122. For example, the MFS 116 is further programmed (e.g., to include or otherwise utilize a matrix calculator) to derive an analytical expression for a basic solution method including a transfer matrix A (also referred to herein as matrix A). As disclosed herein, matrix A includes coefficients that relate the location of each source node (e.g., including both cardiac source nodes and body surface source nodes) to the location of a body surface node at which the electrophysiological signal is measured distributed on the body surface. The inverse calculation 120 is programmed to perform an inverse calculation on the matrix A and the non-invasively measured electrophysiological signal provided by the electrophysiological data 108 to calculate a plurality of source node coefficients.

[0038] The MFS 116 is also programmed (e.g., to include or otherwise utilize another matrix calculator) to determine a coefficient matrix B (also referred to as matrix B) that relates each cardiac node position on the cardiac envelope to each source node position. The forward calculation 122 is programmed to perform a forward calculation based on the matrix B and the plurality of source node coefficients to calculate a cardiac electrophysiological signal on the cardiac envelope. Thus, the reconstruction engine 112 may calculate a first reconstructed electrophysiological signal and a second reconstructed electrophysiological signal on the cardiac envelope for each of a plurality of consecutive time samples in one or more time intervals. As described herein, the reconstruction engine 112 calculates the first reconstructed electrophysiological signal based on the EP data 108 and the geometry data 110 using an MFS that uses a first source node (e.g., source node data provided by the source node placement function 118 to describe a first body source node and a first cardiac source node). The reconstruction engine 112 also employs the MFS using the second source nodes (e.g., source node data provided by the source node placement function 118 to describe the second body source nodes and the second first cardiac source nodes) to compute a second reconstructed electrophysiological signal based on the EP data 108 and the geometry data 110. As described herein, the first reconstructed electrophysiological signal and the second reconstructed electrophysiological signal are computed on the same cardiac envelope based on the same set of electrophysiological data 108 and the geometry data 110.

[0039] In another example, the reconstruction engine 112 is programmed to calculate a second reconstructed electrophysiological signal on a surface of interest (e.g., a cardiac envelope) based on the first reconstructed electrophysiological signal. For example, the reconstruction engine 112 may calculate an average (or other smoothing function) of the first reconstructed electrophysiological signal of a spatial neighborhood of a corresponding cardiac node on the cardiac envelope. In this manner, the second reconstructed electrophysiological signal represents a smoothed signal on the cardiac envelope within the spatial distance of the corresponding cardiac node. The size of the neighborhood can be set to the number of nodes or the spatial distance on the cardiac envelope. The neighborhood size can be set in response to a user input instruction entered through a user interface 114 (e.g., a knob, button, or sliding graphical user interface) to control the amount of smoothing implemented to generate the second reconstructed electrophysiological signal.

[0040] As another example, the reconstruction engine 112 can be programmed to implement a normalized weighted average. For example, the weighted average can be implemented using a Gaussian convolution that assigns higher weights to nearby nodes than other nodes, and where the sum of the weights adds up to 1. The application of the weighting function can also be controlled based on spatial distance (e.g., distance <3 cm) or based on the neighborhood level and sigma value that can be set for the Gaussian kernel.

[0041] The mapping system 106 also includes a near-field calculator 124 configured to provide near-field electrophysiological data 102 based on the first reconstructed electrophysiological signal and the second reconstructed electrophysiological signal. In an example, the MFS calculates the first reconstructed electrophysiological signal and the second reconstructed electrophysiological signal (the electrophysiological signal attributed to far-field smoothing) on ​​the same cardiac envelope based on the same set of electrophysiological data 108 and the geometric structure data 110. Therefore, by subtracting the second reconstructed electrophysiological signal from the first reconstructed electrophysiological signal, the far-field signal component can be reduced or even removed, so that the resulting near-field electrophysiological data 102 is more representative of the near-field signal component. The near-field electrophysiological data 102 can be determined for the entire cardiac envelope (e.g., the surface of the heart) or for a selected area of ​​the cardiac envelope. For example, a user provides user input through the user interface 114 to select one or more signal intervals and / or specify a region of interest on the heart. The reconstruction engine 112 can calculate the near-field electrophysiological data 102 in response to the user input. The corresponding output data can be generated and presented as a corresponding graphical output for display on an output device, such as described herein.

[0042] Figure 5 An example of a system 200 configured to reconstruct electrophysiological signals on a cardiac envelope is depicted. The system 200 includes a reconstruction engine 202, such as a Figure 1 1 , which is shown as a workflow diagram of program code elements that can be executed by one or more processors. There can be multiple (e.g., two or more) instances of the reconstruction engine 202 operating in parallel. For example, each instance of the reconstruction engine 202 is programmed to calculate a respective set of reconstructed EP data 204 representing an electrophysiological signal reconstructed on a surface of interest. Thus, the near-field calculator can determine a near-field component of the electrophysiological signal based on combining the respective set of reconstructed EP data, as described herein.

[0043] exist Figure 5 In an example of the present invention, each instance of the reconstruction engine 202 implements an MFS (e.g., an example of the MFS 116) to calculate reconstructed electrophysiological signal data 204 based on geometry data 206 and electrophysiological data 208 (e.g., corresponding to the EP data 108 and the geometry data 110). The geometry data 206 is generated to specify the geometric relationship between the electrode positions and the cardiac envelope on which the electrophysiological signals are being reconstructed. Additionally, the electrophysiological data 208 may represent a unipolar EP signal measured by each electrode in the electrode arrangement (non-invasively at the body surface node locations represented in the geometry data). In some examples, the electrophysiological data 208 may also include EP signals measured by one or more electrodes positioned within the body (e.g., invasive EP measurements).

[0044] As another example, the reconstruction engine 202 includes a source node placement function 210, which is configured to determine the positions of a first source node and a second source node, which are virtual nodes (e.g., imaginary points) used by the MFS implemented by the reconstruction engine 202. As disclosed herein, the source nodes include a first corresponding source node and a second group of corresponding source nodes, each of which includes a body source node and a heart source node. In some examples, the source node placement function 210 places the first body source node in a spatial arrangement and distribution that is radially outward from the spatial distribution of electrode locations measuring electrophysiological signals on the body surface. The second body source node may be positioned in a three-dimensional spatial arrangement and distribution that is radially outward from the spatial arrangement and distribution of the first body source node. As described herein, the source node placement function 210 may uniformly or adaptively position the body source nodes in three-dimensional space, such as by controlling the distance of such nodes relative to the body surface, the cardiac envelope, or another anatomical surface or virtual location.

[0045] Additionally, the source node placement function 210 places the first cardiac source node in a three-dimensional spatial arrangement and distribution that is positioned radially inward from the spatial arrangement and distribution of cardiac nodes on the cardiac envelope. The second cardiac source node may be placed in a three-dimensional spatial arrangement and distribution that is positioned at the same location of the first cardiac source node or positioned radially inward from the first cardiac source node. In some examples, the first source node and the second source node may be implemented using the same number of nodes or a different number of nodes as the corresponding body nodes (e.g., electrode locations) and the cardiac nodes on the cardiac envelope. For example, each group of first source nodes and second source nodes may have a greater or lesser number of nodes than the corresponding cardiac nodes and body surface nodes. The number of nodes and their spatial distribution may be set to a default or user-programmable value (e.g., in response to user input).

[0046] The reconstruction engine 202 also includes a first matrix calculator 212 that is programmed to calculate a transfer matrix A that relates the location of each source node (e.g., determined by the source node placement function 210) to the geometry of the body surface nodes that correspond to the locations of the measured electrophysiological signals distributed on the body surface. Thus, the first matrix calculator 212 calculates the respective transfer matrices A for the first set of source nodes and the second set of source nodes. The coefficients in the transfer matrix A represent the "strength" of each source node. For example, the electrophysiological signals measured on the body surface can be expressed as a vector (V BS ):

[0047] V BS =AΓ

[0048] The transfer matrix A is a 2N×P+1 matrix,

[0049] Where: N represents the total number of body surface nodes, and

[0050] P represents the total number of source nodes.

[0051] In this example, the first matrix calculator 212 may be configured to calculate each entry (a) in the matrix A according to the distance between each body surface node and each source node. j,k ). For example, each entry a in matrix A j,k The value of the node (TN j ) and the source node (SN k ) varies with the distance between them, so that:

[0052]

[0053] where r j,k Equal to the body surface node TN in space j With the source node SN k The distance between.

[0054] For example, the distance between each body surface node and each of the source nodes (e.g., each of the body surface source nodes and the heart source nodes) may be calculated between the respective positions of such nodes according to a Euclidean calculation or other distance calculation. j,k Each value of can be easily calculated from the known coordinates of each trunk node and each source node, so the entry a in the matrix A is j,k The same is known.

[0055] Thus, the combination function 214 of the reconstruction engine 202 may employ the calculated transfer matrix A to express the non-invasively measured electrophysiological data 208 as a function of the transfer matrix A and Γ, such as described above. Thus, the 1×P+1 vector Γ is the only unknown in the expression. The inverse solution calculator 216 is programmed to perform an inverse calculation on the matrix A and the non-invasively measured electrophysiological signal to calculate a plurality of source node coefficients. In this manner, the inverse solution calculator 216 determines the value of the inverse matrix of the transfer matrix (e.g., Γ=A -1 *V BS). Since the calculation of Γ is an ill-posed problem, the inverse solution calculator 216 may employ any of a variety of mathematical schemes to estimate the values ​​in the matrix Γ. Examples of schemes believed to provide efficient results for calculating Γ include Tikhonov zero-order regularization and the generalized minimum residual (GMRes) method. For example, the inverse solution calculator 216 may be programmed to implement Tikhonov regularization such as described in U.S. Patent No. 6,772,004, or GMRes regularization such as described in U.S. Patent No. 7,016,719, each of which is incorporated herein by reference in its entirety.

[0056] The reconstruction engine 202 also includes a second matrix calculator 220 for calculating a matrix B. The matrix B operates to convert the source node coefficients determined via the inverse solution calculator 216 into corresponding electrophysiological signals on the cardiac envelope of interest at each cardiac node location (e.g., endocardial node and / or epicardial node). As an example, each entry b in the matrix B is j,k The value of each heart node CN j With the source node SN k The distance between them varies so that:

[0057]

[0058] where r j,k Equal to the heart node CN j With the source node SN k The distance between.

[0059] exist Figure 5 In the example of FIG. 1 , another combination function 222 is configured to express the cardiac electrophysiological signal as a function of the transfer matrix B and Γ, such as determined by the inverse solution calculator 216 above. For example, the combination function 222 may express the cardiac electrophysiological signal (V CE ) is expressed as a function of the matrices B and Γ, such as:

[0060] V CE =BΓ

[0061] Where B is an M×P+1 matrix,

[0062] Where M represents the total number of cardiac nodes, and

[0063] P represents the total number of source nodes (eg, heart nodes and body source nodes).

[0064] Due to r j,k The distance to each value of can be easily calculated, so the entries in the matrix B are also known, which allows V to be calculated directly from B and Γ CE. For example, the forward calculator 224 is configured to calculate corresponding estimates of the reconstructed electrophysiological data 204 at cardiac nodes distributed on the cardiac envelope. In some examples, in response to user input, the locations of the plurality of cardiac nodes are set to reside, such as on a selected one or more of an epicardial surface and an endocardial surface or another cardiac envelope. Thus, the reconstruction engine 202 is configured to calculate the reconstructed electrophysiological data 204 to include first electrophysiological data 230 and second electrophysiological data 232. For example, one instance of the reconstruction engine 202 calculates the first electrophysiological data 230 based on the electrophysiological data 208 and the geometry data 206 and according to a first source node (e.g., determined by the source node placement function 210). Another instance of the reconstruction engine 202 calculates the second electrophysiological data 232 based on the electrophysiological data 208 and the geometry data 206 and according to a second set of source nodes (e.g., determined by the source node placement function 210). The first electrophysiological data 230 and the second electrophysiological data 232 may be stored in a memory for further processing (eg, to determine a near-field electrophysiological signal), as described herein.

[0065] Figure 6 An example of a system 300 that can be used to perform diagnosis and / or treatment of a patient is depicted. In some examples, the system 300 can be implemented to generate corresponding graphical outputs for signals and / or a graphical map of the patient's heart 302 in real time as part of a diagnostic procedure (e.g., monitoring signals during an electrophysiology study) to assist in evaluating electrophysiological signals of the patient's heart. Additionally or alternatively, the system 300 can be used as part of a treatment procedure, such as to provide and / or assist a physician in determining one or more parameters for delivering a treatment (e.g., a delivery location, delivery amount, and / or delivery type) and to provide visualization and / or other outputs to control and / or facilitate determining when to end delivery of the treatment.

[0066] For example, an invasive device 306, such as a catheter or other probe, may be inserted into the patient's body 304. The invasive device 306 may include one or more electrodes attached thereto to deliver therapy (e.g., with or without contact) to the patient's heart 302 through the endocardium or epicardium. Those skilled in the art will understand and appreciate the various types and configurations of the device 306, which may vary depending on the type of therapy and procedure. The placement of the device 306 may be guided via a positioning or tracking system (not shown), which may be operable to locate the device 306 in a 3D coordinate system.

[0067] The device may be implemented as part of an invasive system 308. The invasive system 308 may include a control 310 configured to (electrically) process and control the capture of the measured signals so as to provide corresponding invasive EP measurement data 309. The control 310 may also be configured to control the delivery of a therapy by the device 306, such as based on a near-field component of the electrophysiological signal estimated for at least a portion of the cardiac envelope.

[0068] In an example, device 306 may include one or more electrodes disposed thereon at a predetermined position relative to the device. Each such electrode may be configured to deliver an electrical signal that can be located. Device 306 may provide a signal to deliver location-specific therapy, such as ablation, a pacing signal, or deliver another therapy (e.g., providing electrical therapy or controlling the delivery of chemotherapy, acoustic therapy, or any combination thereof). For example, device 306 may include one or more electrodes located at the tip of a pacing catheter to respond to an electrical signal (e.g., a pacing pulse) provided by system 308 to, for example, pace the heart. Other types of therapy may also be delivered via system 308 and device 306 positioned within body 304. The therapy delivery device may be on the same catheter as the catheter used to invasively sense electrophysiological signals or on a different catheter probe.

[0069] As another example, the system 308 may be located outside the patient's body 304 and configured to control the therapy being delivered by the device 306, such as based on the output data 324. For example, the system 308 may also control the electrical signal provided via the conductive link electrically connected between the delivery device (e.g., one or more electrodes) 306 and the system 308. The control system 310 may control the signal parameters (e.g., current, voltage, repetition rate, trigger delay, sensing trigger amplitude) provided to the device 306 for delivering the therapy (e.g., ablation or stimulation) to one or more locations on or within the heart 302 via the electrodes on the invasive device 306. The control may be based on the output data 324, which is provided according to the near-field component of the electrophysiological signal determined for at least a portion of the cardiac envelope. The control circuit 310 may set the therapy parameters and apply the stimulation or other therapy based on automatic, manual (e.g., user input), or a combination of automatic and manual (e.g., semi-automatic control). One or more sensors (not shown, but may be part of the device) may also transmit sensor information back to the control 310. In some examples, invasive system 308 and device 306 may be omitted from system 300 .

[0070] The sensing system 314 includes one or more sensors configured to non-invasively measure electrophysiological signals from the patient's body 304. For example, the sensing system 314 may correspond to a high-density arrangement of body surface sensors that are distributed over a portion of the patient's external body surface (e.g., the chest) for measuring electrophysiological signals associated with the patient's heart (e.g., as part of an electrocardiogram mapping procedure). Examples of non-invasive sensors that can be used to implement the sensing system 314 are shown and described in U.S. Patent No. 9,655,561, International Patent Publication No. WO2010054352A1, each of which is incorporated herein by reference. Other arrangements and numbers of sensors can be used as the sensing system 314. As an example, the sensor can be configured as a sheet or patch that does not cover the entire torso of the patient and is designed to measure electrophysiological signals for a specific purpose (e.g., an electrode arrangement specifically designed for analyzing a selected type of arrhythmia) and / or for monitoring electrophysiological signals at a predetermined spatial region of the heart.

[0071] The electrophysiological signals (e.g., electrical potentials) measured non-invasively via the sensing system 314 are provided to a measurement system 316. The measurement system 316 may include appropriate controls and signal processing circuitry 318 for providing corresponding EP measurement data 320 of the electrophysiological signals measured by the electrodes in the sensing system 314. The measurement data 320 may include analog and / or digital information (e.g., corresponding to the electrophysiological data 108).

[0072] Non-invasive measurement controls 318 may also be configured to control data acquisition processes (e.g., sampling rate, line filtering, baseline filtering, etc.) for measuring electrophysiological signals and providing non-invasive EP data 320. In some examples, controls 318 may control acquisition of measurement data 320 separately from therapy system operation, such as in response to user input. In other examples, measurement data 320 may be acquired simultaneously and synchronously with delivery of therapy using device 306, such as to detect electrophysiological signals of heart 302 in response to application of a given therapy (e.g., according to therapy parameters).

[0073] The EP mapping system 312 includes an electrogram reconstruction engine 330 (e.g., corresponding to the reconstruction engines 112, 202), which is programmed to reconstruct electrophysiological signals on the cardiac envelope, such as disclosed herein. For example, the reconstruction engine 330 includes an MFS, which is programmed to perform inverse calculations and forward calculations on electrophysiological signals reconstructed on the cardiac envelope based on the geometry data 322 and the EP data 320. As described herein, the reconstruction engine 330 can implement the MFS based on the geometry data 322 and the EP data 320 to derive a first set of reconstructed electrophysiological signals and a second set of reconstructed electrophysiological signals using different source node locations. The near-field calculator 332 is programmed to determine an electrophysiological signal representing a near-field signal based on the difference between the first set of reconstructed electrophysiological signals and the second set of reconstructed electrophysiological signals. That is, by determining that the second set of reconstructed electrophysiological signals will represent far-field electrophysiological signals, the calculator 332 can subtract such signals from the first set to describe the near-field electrophysiological signals on the cardiac envelope. The cardiac envelope of the reconstructed signal may describe the entire 3D cardiac surface or a region of interest, such as may be selected in response to user input (via GUI 334). In an example, the GUI may include a selection tool 336 by which a user may select one or more signal intervals of interest (e.g., one or more beats) in response to the user input. Additionally or alternatively, the user may employ the selection tool 336 to select one or more spatial regions of interest on the cardiac envelope in response to the user input, and the reconstruction engine 330 may cause the MFS to adaptively reconstruct the signal over the selected region of interest of the cardiac envelope.

[0074] The output generator 338 may generate corresponding output data 324. As described herein, the output generator 338 of the mapping system 312 may provide output data 324 based on the near-field component of the electrophysiological signal determined for at least a portion (e.g., including up to all) of the cardiac envelope. The output data may also include instructions programmed to present the output data 324 as a corresponding graphical output (e.g., a map) 344 in a display 342. For example, the output generator 338 provides the output data 324 to a graphics pipeline of a computing device, which provides a graphical map via an interface to an output device (such as the display 342). The display 342 may include a screen, wearable augmented reality glasses, a head-up display, etc., which are configured to display a graphical visualization generated based on the generated reconstructed electrophysiological signal, such as including the map 344. The graphical output 344 may also include a representation of the electrophysiological signal (e.g., voltage potential) reconstructed on the cardiac envelope or a signal feature derived from such a reconstructed electrophysiological signal. For example, the electrophysiological signals may represent near-field electrophysiological signals that may be displayed as a graphical map 344 on a graphical representation of the patient's anatomy (eg, superimposed on a heart surface) for one or more time intervals.

[0075] Additionally, in some examples, the output data 324 may be utilized by the system 308 in conjunction with controlling the delivery of treatment and / or monitoring electrical characteristics. For example, the implemented control 310 may be fully automated control, semi-automated control (partially automated and responsive to user input), or manual control based on the output data 324 (e.g., including near-field components of electrophysiological signals). In some examples, the control 310 of the treatment system 306 is configured to utilize the output data 324 to control one or more parameters that are used by the device 306 to deliver the corresponding treatment. In other examples, an individual may view a map 344 generated on a display 342 to manually control the treatment system at a location determined based on the present disclosure. Other types of treatments and devices may also be controlled based on the output data 324 and the corresponding graphical map 344.

[0076] In view of the above structural and functional characteristics, Figure 7 shows that it can be performed (for example, by Figure 1 , Figure 5 and / or Figure 6 400 for determining near-field electrophysiological data. Therefore, for examples of hardware and software that can be configured to implement method 400, reference may be made to Figure 1 , Figure 5 and Figure 6 In other examples, different combinations of hardware and software may be used to implement method 400. Although for the purpose of simplifying the description, Figure 7 The method 400 is shown and described as being performed sequentially, but it should be understood and appreciated that the present disclosure is not limited by the illustrated order, as portions of the method may occur in a different order and / or simultaneously than that shown and described herein. Additionally, for example, the method 400 may be performed by various components configured as machine-readable instructions stored in a memory (e.g., one or more non-transitory media) and executable by one or more processors. Moreover, not all of the features shown may be required to implement the method.

[0077] At 402, method 400 includes calculating a first reconstructed electrophysiological signal on the cardiac envelope based on the geometry data and the electrophysiological data (e.g., by a reconstruction engine 112, 202, or 330). As described herein, the electrophysiological data represents electrophysiological signals measured (e.g., non-invasively) from locations distributed on a body surface. In some examples, the electrophysiological data may also include electrophysiological signals measured invasively. The geometry data represents the geometry of the cardiac envelope and the geometry of the locations of the measured electrophysiological signals distributed on the body surface, such as representing points and surfaces in a 3D spatial coordinate system. For example, the calculation at 402 may be implemented using source nodes at corresponding first source node locations in the 3D spatial coordinate system (e.g., by a reconstruction engine programmed to perform MFS). As described herein, the source nodes may include body source nodes located outside the body (e.g., radially outward from the body surface) and cardiac source nodes located inside the body (e.g., radially inward from the cardiac envelope).

[0078] At 404, the first source node location used at 402 may be located at a second source node location (e.g., by the source node placement function 118, 210) to increase the influence of the far-field signal component on the cardiac envelope during the second EP reconstruction. At 406, the method 400 includes calculating a second reconstructed electrophysiological signal on the cardiac envelope based on the geometry data and the electrophysiological data (e.g., by the reconstruction engine 112, 202, or 330). The second EP reconstruction at 406 uses the locations of the corresponding source nodes, including the source node locations determined at 404. For example, the second source node locations include a body source node (e.g., located at a radially outward position farther from the body surface than the body source node used at 402) and a cardiac source node (e.g., located at the same or radially inward position farther from the cardiac envelope than the cardiac source node used at 402).

[0079] At 408, the method includes determining a near-field signal component of the electrophysiological signal over at least a portion of the cardiac envelope based on a difference between the first reconstructed electrophysiological signal and the second reconstructed electrophysiological signal. For example, the determination at 406 may be calculated by subtracting the second reconstructed electrophysiological signal from the first reconstructed electrophysiological signal at a corresponding node on the cardiac envelope. At 410, a graphical output (e.g., on a display 432) may be provided based on the near-field signal component of the electrophysiological signal determined at 408. In some examples, the method 400 may also use the near-field signal component to control the delivery of therapy, such as by setting one or more therapy parameters used by a therapy device (e.g., device 306) based on the near-field signal component to achieve a desired therapeutic (or sub-therapeutic) effect.

[0080] Figures 8 to 17 It shows that Figure 6 System 300 or Figure 7Different examples of graphical outputs and associated signals generated by method 400 of . In each of these examples, a near-field electrocardiogram image (ECGI) map is generated and demonstrates improved accuracy of activation timing across a range of arrhythmia conditions. Figures 8 to 17 The illustrated maps and EP signals may be used (eg, by systems 100, 300, and / or method 400) to control one or more parameters of a device configured to deliver therapy (eg, to achieve a desired therapeutic or subtherapeutic effect), such as described herein.

[0081] For example, Figure 8 and Fig. 9 Examples of EP signals and ventricular sinus rhythm maps are shown. Figure 8 and Fig. 9 A system ECGI map 520 of an EP signal reconstructed on the cardiac envelope (e.g., epicardial surface), a graphical map 522 of a far-field component of the EP signal reconstructed on the cardiac envelope, and a graphical map 524 of a near-field component of the EP signal reconstructed on the cardiac envelope are shown. The system map 520 is generated by a reconstruction engine, such as using nominal source node locations (e.g., as part of an MFS), and the far-field map 522 is generated using source node locations adjusted to emphasize far-field signal components. As described herein, the near-field map 524 can be derived by subtracting the far-field map from the graphical map 522. Figure 8 Also shown is a graph 526 that includes reconstructed EP signals 528, 530, and 532 for respective locations on the cardiac envelope (shown as nodes 1423 on map 520) for each of maps 520, 522, and 524. Signal 528 represents the system signal from a given cardiac node of map 520, signal 530 represents the far-field signal from a given cardiac node of map 522, and signal 532 represents the resulting near-field signal from a given cardiac node of map 524.

[0082] Fig. 9 A graph 540 is shown that includes reconstructed EP signals 542, 544, and 546 for each of maps 520, 522, and 524 at another corresponding location on the cardiac envelope (shown as node 661 on map 520). Signal 542 represents the system signal from a given cardiac node of map 520, signal 544 represents the far-field signal from a given cardiac node of map 522, and signal 546 represents the resulting near-field signal from a given cardiac node of map 524.

[0083] Fig.10Examples of corresponding activation maps 550, 552, and 554 derived from a reconstructed EP signal on the cardiac envelope based on EP data and geometry data are depicted. Activation map 550 shows activation of the system reconstructed EP signal (e.g., using nominal source node locations), and far-field map 552 is generated from the reconstructed EP signal using source node locations that are adjusted to emphasize (increase the contribution of) far-field signal components. Near-field activation map 554 is derived by determining activation times of the EP signal determined on the cardiac envelope based on differences between the system electrogram and the far-field reconstructed electrogram. Additionally, near-field map 554 shows improved near-field activation at regions 556 and 558, where the effects of the far-field signal have been reduced.

[0084] As another example, Figures 11 to 13 An example of an EP signal and a sinus rhythm map from a patient with supraventricular tachycardia (SVT) is shown. Fig.11 A system ECGI map 580 of an EP signal reconstructed on the cardiac envelope (e.g., epicardial surface), an ECGI map 582 of a far-field component of the EP signal reconstructed on the cardiac envelope, and an ECGI map 584 of a near-field component of the EP signal reconstructed on the cardiac envelope are shown. The system map 580 is generated by a reconstruction engine, such as using nominal source node locations (e.g., as part of an MFS), and the far-field map 582 is generated using source node locations adjusted to emphasize far-field signal components. As described herein, the near-field map 584 can be derived by subtracting the far-field map from the graphical map 522.

[0085] Fig.11 Also shown is a graph 586 that includes reconstructed EP signals 588, 590, and 592 at respective locations on the cardiac envelope (shown as node 1339 on map 582) for each of maps 580, 582, and 584. Signal 588 represents the system signal from a given cardiac node of map 580, signal 590 represents the far-field signal from a given cardiac node of map 582, and signal 592 represents the resulting near-field signal from a given cardiac node of map 584. Fig.12 A graph 600 is shown that includes reconstructed EP signals 602, 604, and 606 at another corresponding location on the cardiac envelope (shown as node 1547 on map 580) for each of maps 520, 522, and 524. Signal 602 represents the system signal from a given cardiac node of map 580, signal 604 represents the far-field signal from a given cardiac node of map 582, and signal 606 represents the resulting near-field signal from a given cardiac node of map 584.

[0086] Fig.13 Examples of corresponding activation maps 610, 612, and 614 derived from a reconstructed EP signal on the cardiac envelope based on EP data and geometry data are depicted. Activation map 610 shows activation of the system reconstructed EP signal (e.g., using nominal source node locations), and far-field map 612 is generated from the reconstructed EP signal using source node locations that are adjusted to emphasize (increase the contribution of) far-field signal components. Near-field activation map 614 is derived by determining activation times of the EP signal determined on the cardiac envelope based on differences between the system electrogram and the far-field reconstructed electrogram. Additionally, near-field map 614 shows improved near-field activation at region 616, in which the effects of the far-field signal have been reduced.

[0087] As another example, Figures 14 to 17 An example of an EP signal from a patient with premature ventricular contractions (PVCs) and a ventricular map in sinus rhythm is shown. Fig.14 , Fig.15 and Fig.16 A system ECGI map 720 of an EP signal reconstructed on the cardiac envelope (e.g., epicardial surface), a graphical map 722 of a far-field component of the EP signal reconstructed on the cardiac envelope, and a graphical map 724 of a near-field component of the EP signal reconstructed on the cardiac envelope are shown. Fig.14 and Fig.16 In FIG. 1 , the map is shown with the same view of the cardiac envelope (eg, heart), and in FIG. Fig.15 . A system map 720 is generated by a reconstruction engine, such as using nominal source node positions (e.g., as part of an MFS), and a far-field map 722 is generated using source node positions adjusted to emphasize far-field signal components. As described herein, a near-field map 724 can be derived by subtracting the far-field map from the graphical map 722.

[0088] Fig.14 Also shown is a graph 726 that includes reconstructed EP signals 728, 730, and 732 at respective locations on the cardiac envelope (shown as node 1342 on map 720) for each of maps 720, 722, and 724. Signal 728 represents the system signal for a given cardiac node from map 520, signal 730 represents the far-field signal for a given cardiac node from map 722, and signal 732 represents the resulting near-field signal for a given cardiac node from map 724. Fig.15A graph 740 is shown that includes reconstructed EP signals 742, 744, and 746 at another location on the cardiac envelope (shown as node 143 on map 722) for respective maps 720, 722, and 724. Signal 742 represents the system signal from a given cardiac node of map 720, signal 744 represents the far-field signal from a given cardiac node of map 722, and signal 746 represents the resulting near-field signal from a given cardiac node of map 724. Fig.16 A graph 750 is shown that includes reconstructed EP signals 752, 754, and 756 at another location on the cardiac envelope (shown as node 529 on map 720) for respective maps 720, 722, and 724. Signal 752 represents the system signal at a given cardiac node 529 from map 750, signal 754 represents the far-field signal from a given node of map 722, and signal 756 represents the resulting near-field signal from a given cardiac node of map 724.

[0089] Fig.17 Depicted are examples of corresponding activation maps 760, 762, and 764 derived from a reconstructed EP signal on the cardiac envelope based on EP data and geometry data. Activation map 760 shows activation of the system reconstructed EP signal (e.g., using nominal source node locations), and far-field map 762 is generated from the reconstructed EP signal using source node locations that are adjusted to emphasize (increase the contribution of) far-field signal components. Near-field activation map 764 is derived by determining activation times of the EP signal determined on the cardiac envelope based on the difference between the system electrogram and the far-field reconstructed electrogram. In addition, near-field map 764 shows improved near-field activation at regions 766, 768, and 770, where the effects of the far-field signal have been reduced, as shown by near-field component signals 732, 746, and 756, respectively.

[0090] According to one example, a computer-implemented method includes: calculating a first reconstructed electrophysiological signal on a cardiac envelope based on geometry data and electrophysiological data, wherein the electrophysiological data represents an electrophysiological signal measured non-invasively from locations distributed on a body surface, and the geometry data represents a geometry of the cardiac envelope and a geometry of locations distributed on the body surface where the electrophysiological signal is measured. The method also includes: calculating a second reconstructed electrophysiological signal on the cardiac envelope based on the geometry data and the electrophysiological data, the second reconstructed electrophysiological signal representing a far-field signal component. The method also includes: determining a near-field component of the electrophysiological signal on at least a portion of the cardiac envelope based on the first reconstructed electrophysiological signal and the second reconstructed electrophysiological signal.

[0091] In some implementations, calculating the first reconstructed electrophysiological signal and the second reconstructed electrophysiological signal on the cardiac envelope includes using a fundamental solution method.

[0092] In some implementations, using the base solution method includes placing at least some source nodes for computing the second reconstructed electrophysiological signal further away from the cardiac envelope than corresponding source nodes used for computing the first reconstructed electrophysiological signal.

[0093] In some implementations, positioning at least some of the source nodes includes positioning the body source nodes radially outward from a surface of the body at a first uniform distance.

[0094] In some implementations, placing at least some of the source nodes includes calculating distances between locations on the body surface where measurements are made and cardiac nodes on the cardiac envelope, and adaptively placing body source nodes radially outward from the body surface based on the calculated distances.

[0095] In some implementations, positioning at least some of the source nodes further comprises positioning the body source nodes radially inward from the cardiac envelope at a second uniform distance.

[0096] In some implementations, placing at least some of the source nodes includes: calculating distances between locations where measurements are made on the body surface and corresponding cardiac nodes on the cardiac envelope; and adaptively placing cardiac source nodes radially inward from the cardiac envelope based on the calculated distances.

[0097] In some specific implementations, calculating the second reconstructed electrophysiological signal includes: calculating an average value of the first reconstructed electrophysiological signal in corresponding cardiac nodes in a spatial neighborhood on the cardiac envelope.

[0098] In some implementations, the near-field component of the electrophysiological signal is determined based on a difference between the first reconstructed electrophysiological signal and the second reconstructed electrophysiological signal.

[0099] In some implementations, the electrophysiological signals non-invasively measured from locations distributed over the body surface include monopolar signals.

[0100] In some implementations, the method further includes providing a graphical representation based on a near-field component of the electrophysiological signal.

[0101] In some implementations, one or more non-transitory computer-readable media have instructions that, when executed by a processor, perform any of the methods, alone or in any combination.

[0102] According to another example, a system includes a memory for storing data and executable instructions, the data including electrophysiological data and geometric data, the electrophysiological data representing electrophysiological signals measured from locations distributed on a body surface, the geometric data representing the geometry of a surface of interest and the geometry of locations distributed on the body surface where the electrophysiological signals are measured. At least one processor is configured to access the memory and execute instructions to at least: calculate a first reconstructed electrophysiological signal on the surface of interest based on the geometric data and the electrophysiological data; calculate a second reconstructed electrophysiological signal on the surface of interest based on the geometric data and the electrophysiological data, the second reconstructed electrophysiological signal representing a far-field signal component; and determine a near-field component of the electrophysiological signal of at least a portion of the surface of interest based on the first reconstructed electrophysiological signal and the second reconstructed electrophysiological signal of the portion of the surface of interest.

[0103] In some embodiments, the system further includes an electrode arrangement configured to measure electrophysiological signals from locations distributed on a surface of the body and a display configured to display a graphical visualization generated based on at least one of the reconstructed electrophysiological signals.

[0104] In some implementations, the surface of interest includes a cardiac envelope, and the processor is configured to compute each of the first reconstructed electrophysiological signal and the second reconstructed electrophysiological signal on the cardiac envelope using a fundamental solution method.

[0105] In some embodiments, the basic solution method includes instructions to perform at least the following: derive an analytical expression for the basic solution method, the analytical expression including a matrix A that relates the position of each source node to the position of a measured electrophysiological signal distributed on the body surface; perform inverse calculations on matrix A and the measured electrophysiological signals to calculate a plurality of source node coefficients; determine a coefficient matrix B that relates the position of each cardiac node on the cardiac envelope to the corresponding source node position; and perform a forward calculation using B and the plurality of source node coefficients to calculate a first reconstructed electrophysiological signal on the cardiac envelope.

[0106] In some implementations, the basic solution method includes node placement instructions for placing at least some of the source nodes further away from a cardiac envelope used to calculate the second reconstructed electrophysiological signal than corresponding source nodes used to calculate the first reconstructed electrophysiological signal.

[0107] In some implementations, the node placement instructions are programmed to place the body source nodes at uniform distances radially outward from the body surface.

[0108] In some embodiments, the node placement instructions of the system are programmed to: calculate the distance between the location where the measurement is made on the body surface and the cardiac node on the cardiac envelope; and adaptively place the body source node radially outward from the body surface based on the calculated distance.

[0109] In some implementations, the node placement instructions are further programmed to place the cardiac source nodes a second uniform distance radially inward from the cardiac envelope.

[0110] In some embodiments, the node placement instructions are further programmed to: calculate the distance between the location where the measurement is made on the body surface and the corresponding cardiac node on the cardiac envelope; and adaptively place the cardiac source node radially inward from the cardiac envelope based on the calculated distance.

[0111] In some implementations, the instructions are further programmed to compute a second reconstructed electrophysiological signal on the surface of interest, and are programmed to compute an average of the first reconstructed electrophysiological signal in spatially neighboring corresponding cardiac nodes on the surface of interest.

[0112] In some implementations, the instructions for calculating the near-field component of the electrophysiological signal are programmed to determine the near-field component of the electrophysiological signal based on a difference between the first reconstructed electrophysiological signal and the second reconstructed electrophysiological signal.

[0113] In some implementations, the electrophysiological signals of the system measured from locations distributed over a body surface include monopolar signals.

[0114] It should be understood that the various aspects disclosed herein may be combined in combinations different from those specifically presented in the specification and drawings. It should also be understood that, depending on the example, certain actions or events of any of the processes or methods described herein may be performed in a different order, may be added, combined, or omitted entirely (e.g., not all described actions or events may be required to perform these techniques). In addition, although for clarity, certain aspects of the present disclosure are described as being performed by a single module or unit, it should be understood that the techniques of the present disclosure may be performed by a combination of units or modules associated with, for example, a medical device.

[0115] In one or more examples, the described techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or codes on a computer-readable medium and executed by a hardware-based processing unit. The computer-readable medium may include a non-transitory computer-readable medium corresponding to a tangible medium, such as a data storage medium (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and can be accessed by a computer).

[0116] Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuits. Thus, the term "processor" as used herein may refer to any of the foregoing structures or any other physical structure suitable for implementing the described techniques. Additionally, these techniques may be fully implemented in one or more circuits or logic elements.

Claims

1. A computer-implemented method, the computer-implemented method comprising: calculating a first reconstructed electrophysiological signal on the cardiac envelope based on geometry data and electrophysiological data, wherein the electrophysiological data represents electrophysiological signals measured non-invasively from locations distributed on a body surface, and the geometry data represents a geometry of the cardiac envelope and a geometry of the locations distributed on the body surface at which the electrophysiological signals were measured; calculating a second reconstructed electrophysiological signal on the cardiac envelope based on the geometry data and the electrophysiological data, the second reconstructed electrophysiological signal representing a far-field signal component; as well as A near-field component of the electrophysiological signal is determined over at least a portion of the cardiac envelope based on the first reconstructed electrophysiological signal and the second reconstructed electrophysiological signal.

2. The method according to claim 1, wherein calculating the first reconstructed electrophysiological signal and the second reconstructed electrophysiological signal on the cardiac envelope comprises: Use the fundamental solution method, and Wherein using the basic solution method includes: placing at least some source nodes for calculating the second reconstructed electrophysiological signal farther away from the cardiac envelope than corresponding source nodes used for calculating the first reconstructed electrophysiological signal.

3. The method of claim 2, wherein placing at least some of the source nodes comprises: placing body source nodes radially outward from the body surface at a first uniform distance; or calculating a distance between the location on the body surface where the measurement is made and a cardiac node on the cardiac envelope; as well as Body source nodes are adaptively positioned radially outward from the body surface based on the calculated distances.

4. The method of claim 3, wherein placing at least some of the source nodes further comprises: The cardiac source nodes are positioned a second uniform distance radially inward from the cardiac envelope.

5. The method of claim 3, wherein placing at least some of the source nodes comprises: calculating a distance between the location on the body surface where the measurement is made and a corresponding cardiac node on the cardiac envelope; as well as A cardiac source node is adaptively positioned radially inward from the cardiac envelope based on the calculated distance.

6. The method according to claim 1, wherein calculating the second reconstructed electrophysiological signal comprises: An average value of the first reconstructed electrophysiological signal is calculated in spatially neighboring corresponding cardiac nodes on the cardiac envelope.

7. The method of any preceding claim, wherein the near-field component of the electrophysiological signal is determined based on a difference between the first reconstructed electrophysiological signal and the second reconstructed electrophysiological signal.

8. The method according to any one of claims 1 to 6, wherein the electrophysiological signals measured non-invasively from locations distributed on the body surface include monopolar signals.

9. The method according to any one of claims 1 to 6, further comprising: A graphical representation is provided based on the near-field component of the electrophysiological signal.

10. One or more non-transitory computer readable media having instructions which, when executed by a processor, perform the method of any one of claims 1 to 9.

11. A system, comprising: a memory for storing data and executable instructions, the data comprising electrophysiological data and geometric data, the electrophysiological data representing electrophysiological signals measured from locations distributed on a body surface, the geometric data representing a geometric structure of a surface of interest and a geometric structure of the locations distributed on the body surface at which the electrophysiological signals are measured; and at least one processor configured to access the memory and execute the instructions to at least: calculating a first reconstructed electrophysiological signal on the surface of interest based on the geometric data and the electrophysiological data; calculating a second reconstructed electrophysiological signal on the surface of interest based on the geometric data and the electrophysiological data, the second reconstructed electrophysiological signal representing a far-field signal component; as well as A near-field component of the electrophysiological signal of at least a portion of the surface of interest is determined based on the first and second reconstructed electrophysiological signals of the at least a portion of the surface of interest.

12. The system of claim 11, wherein the instructions for calculating the near-field component of the electrophysiological signal are programmed to determine the near-field component of the electrophysiological signal based on a difference between the first reconstructed electrophysiological signal and the second reconstructed electrophysiological signal, and the system further comprises: an electrode arrangement configured to measure the electrophysiological signals from the locations distributed on the body surface; and A display is configured to display a graphical visualization generated based on at least one of the reconstructed electrophysiological signals.

13. The system of claim 12, wherein the surface of interest comprises a cardiac envelope, and wherein the processor is configured to compute each of the first reconstructed electrophysiological signal and the second reconstructed electrophysiological signal on the cardiac envelope using a basic solution method, wherein the basic solution method comprises instructions to: deriving an analytical expression for the basic solution method, the analytical expression comprising a matrix A, the matrix A relating the location of each source node to the locations distributed on the body surface at which the electrophysiological signals are measured; Performing inverse calculation on the matrix A and the measured electrophysiological signal to calculate a plurality of source node coefficients; determining a coefficient matrix B that relates each cardiac node location on the cardiac envelope to a corresponding source node location; as well as A forward calculation is performed using B and the plurality of source node coefficients to calculate the first reconstructed electrophysiological signal on the cardiac envelope.

14. The system of claim 14, wherein the basic solution method includes node placement instructions for placing at least some source nodes further away from the cardiac envelope for computing the second reconstructed electrophysiological signal than corresponding source nodes used for computing the first reconstructed electrophysiological signal, wherein the node placement instructions are programmed to do at least one of the following: placing body source nodes at uniform distances radially outward from the body surface; or calculating the distance between the location on the body surface where the measurement is made and a cardiac node on the cardiac envelope; and Body source nodes are adaptively positioned radially outward from the body surface based on the calculated distances.

15. The system of claim 14, wherein the node placement instructions are further programmed to: placing the cardiac source nodes a second uniform distance radially inward from the cardiac envelope; or calculating the distance between the location on the body surface where the measurement is made and the corresponding cardiac node on the cardiac envelope; and A cardiac source node is adaptively positioned radially inward from the cardiac envelope based on the calculated distance.

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