Signal and correction processing of anatomical structure mapping data

Abstract

To provide signal and correction processing for anatomical structure mapping data.SOLUTION: A method is provided. The method is implemented by a mapping engine. The method includes receiving biometric data of at least a portion of an anatomical structure from at least one catheter, and analyzing the biometric data based on an automatic tagging operation, a time weighted local activation time assignment operation, or a discrimination operation. The method also includes determining scar tissue of the portion of the anatomical structure based on the analysis of the biometric data.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to signal processing. More specifically, the present invention relates to signal and correction processing of anatomical structure mapping data.

Background Art

[0002] In medical procedures such as mapping the electrical activity of anatomical structures (e.g., organs such as the heart), it is important to accurately identify non-conductive tissues (e.g., scar tissue). However, current coherent mapping algorithms require user intervention (e.g., manual tagging of signals resulting from scar tissue) for the identification of scar tissue.

[0003] For example, as described in U.S. Patent Application No. 2020 / 0146579, which is incorporated herein by reference, current coherent mapping algorithms for the heart show the correspondence between a set of local activation times (LATs) when mapping the electrical activity of the heart and a set of spatial map elements. LAT is an indicator of the flow of electrical activity through the walls of the heart related to the heartbeat. Spatial map elements (e.g., triangles, etc.) may be generated from the measured locations on the heart wall. It should be noted that current coherent mapping algorithms can show the correspondence between LAT and mesh-shaped triangles that are overlaid as a mapping on a graphical representation of the heart wall. To refine this mesh form and mapping, current coherent mapping algorithms perform interpolation by calculating the velocity vector of the radio wave on the mesh form for the mesh form given to the heart wall (e.g., for each triangle, the signal arrival time and the signal velocity vector are calculated). Further, despite showing the correspondence and performing these interpolation operations, current coherent mapping algorithms still require manual tagging of scar-related signals.

Summary of the Invention

Means for Solving the Problems

[0004] According to one embodiment, a method is provided. The method is carried out by a mapping engine. The method includes receiving biometric data of at least a portion of an anatomical structure from at least one catheter and analyzing the biometric data based on an automated tagging operation, a time-weighted local excitation time assignment operation, or a discrimination operation. The method also includes determining scar tissue of a portion of an anatomical structure based on the analysis of the biometric data.

[0005] According to one or more embodiments, embodiments of the above method may be implemented as an apparatus, system, and / or computer program product. [Brief explanation of the drawing]

[0006] A more detailed understanding can be obtained from the following explanation, which is provided as an example in conjunction with the attached drawings, where similar reference numbers in the drawings indicate similar elements. [Figure 1] The diagram shows an exemplary system in which one or more embodiments can implement one or more features of the subject matter of this disclosure. [Figure 2] A block diagram of an exemplary system for signal and correction processing of anatomical structure mapping data, according to one or more embodiments, is shown. [Figure 3] Exemplary methods according to one or more embodiments are shown. [Figure 4] The graph shows automated scar measurement tagging according to one or more embodiments. [Figure 5] An exemplary interface representing automated scar measurement tagging by one or more embodiments is shown. [Figure 6] An exemplary interface representing automated scar measurement tagging by one or more embodiments is shown. [Figure 7] The graph shows automated scar measurement tagging according to one or more embodiments. [Figure 8]An exemplary interface representing automated scar measurement tagging by one or more embodiments is shown. [Figure 9] Exemplary interfaces and graphs of one or more embodiments are shown. [Figure 10] A graph illustrating one or more embodiments is shown. [Modes for carrying out the invention]

[0007] This specification discloses signal processing systems and methods. More specifically, the present invention relates to signal and correction processing of anatomical structure mapping data. Signal and correction processing is processor-executable code or software necessarily rooted in process operations by medical device equipment and their hardware processing. For ease of explanation, signal and correction processing is described herein in relation to cardiac mapping, but any anatomical structure, body part, organ, or part thereof can be targeted for mapping by the signal and correction processing described herein. According to exemplary embodiments, signal and correction processing is performed by a mapping engine.

[0008] In summary, the mapping engine generates a map of the heart by performing interpolation calculations using biometric data from a catheter. Generally, the mapping engine ensures that all electrical signals from the catheter (e.g., biometric data) used in the interpolation calculations to create the map of the heart are accurately correlated with reference times related to the heartbeat. To achieve this objective, the mapping engine automatically identifies scar tissue on the heart from the electrical signals (e.g., using the concept that scar tissue blocks the heartbeat by being largely non-conductive to electrical signals). More specifically, the mapping engine provides numerous operations (e.g., identifying scar tissue) to assign scar probability to these electrical signals (e.g., measured or annotation points) if the electrical signals of cardiac tissue detected by the catheter include indications of tissue contact (e.g., at a specific time) and low or no voltage (e.g., slow conduction or non-conductive state). That is, the mapping engine can determine whether an annotation point on the map contributes to scar tissue near the surface of the heart. Furthermore, the mapping engine can determine whether an annotation point falls into one of three categories: normal conductivity, low conductivity, or non-conductivity.

[0009] According to an exemplary embodiment, the mapping engine analyzes biometric data based on at least one of an automatic tagging operation, a time-weighted LAT assignment operation, and a discrimination operation for determining cardiac scar tissue. In this way, the mapping engine presents an advance in determining scar probability values. The technical effects and benefits of the mapping engine include reducing user intervention, improving scar identification, and enabling the distinction between low-conductivity and non-conductivity (e.g., no manual tagging). Thus, the mapping engine utilizes and transforms medical device equipment, in particular, to enable / perform signal and correction processing that is otherwise not currently available or not currently performed by current coherent mapping algorithms.

[0010] Figure 1 is a schematic diagram of a system 100 (e.g., a medical device) in which one or more features of the subject matter of this specification may be implemented according to one or more embodiments. The system 100, in whole or in part, can be used to collect information described herein (e.g., biometric data and / or training data sets) and / or to perform signal and correction processing (e.g., a mapping engine 101 for signal and correction processing of anatomical structure mapping data). The system 100 shown in the figure includes a probe 105 with a catheter 110 (including at least one electrode 111), a shaft 112, a sheath 113, and a manipulator 114. The system 100 shown in the figure also includes a physician 115 (or medical professional or clinician), a heart 120, a patient 125, and a bed 130 (or table). Note that inserts 140 and 150 show the heart 120 and catheter 110 in more detail. System 100 also includes a console 160 (including one or more processors 161 and memory 162) and a display 165, as shown in the figure. Furthermore, note that each element and / or item of System 100 represents one or more of those elements and / or items. The examples of System 100 shown in Figure 1 can be modified to implement the embodiments disclosed herein. Embodiments of this disclosure can also be applied in a similar manner using other system components and settings. Furthermore, System 100 may include further components such as elements for sensing electrical activity, wired or wireless connectors, processing and display devices.

[0011] System 100 can be used to detect, diagnose, and / or treat cardiac conditions (for example, using the mapping engine 101). Cardiac conditions such as cardiac arrhythmias remain common and dangerous medical conditions, particularly in the elderly population. For example, System 100 can be part of a surgical system (e.g., the CARTO® system sold by Biosense Webster) configured to acquire biometric data (e.g., anatomical and electrical measurements of a patient's anatomical structure or organ, such as the heart 120) and perform cardiac ablation procedures. More specifically, in the treatment of cardiac diseases such as cardiac arrhythmias, it is often necessary to obtain detailed mapping of cardiac tissue, cardiac chambers, veins, arteries, and / or electrical pathways. For example, as a prerequisite for successful catheter ablation (as described herein), the cause of the cardiac arrhythmia may be precisely localized in the cardiac chambers of the heart 120. Such localization can be performed by electrophysiological examination, during which spatially resolved potentials can be detected by a mapping catheter (e.g., catheter 110) introduced into the cardiac chambers of the heart 120. Therefore, this electrophysiological examination, so-called electroanatomical mapping, provides 3D mapping data that can be displayed on a monitor. In many cases, the mapping function and the therapeutic function (e.g., ablation) are provided by a single catheter or a group of catheters, and the mapping catheter also operates simultaneously as a therapeutic (e.g., ablation) catheter. In this case, the mapping engine 101 can be directly stored and executed by the catheter 110.

[0012] In patients with normal sinus rhythm (NSR) (e.g., patient 125), the heart (e.g., heart 120), including the atria, ventricles, and excitatory conduction tissue, is electrically excited and beats in a synchronized, patterned manner. This electrical excitation can be detected as intracardiac electrocardiogram (IC ECG) data, for example.

[0013] In patients with cardiac arrhythmias (e.g., atrial fibrillation or aFib) (e.g., patient 125), abnormal areas of cardiac tissue do not follow the synchronized beating cycle associated with normal conductive tissue, in contrast to patients with NSR. Instead, abnormal conduction occurs in adjacent tissues within the abnormal areas of cardiac tissue, disrupting the cardiac cycle and resulting in an asynchronous rhythm. It should be noted that this asynchronous rhythm can also be detected as IC ECG data. Such abnormal conduction is known to occur in various regions of the heart 120, such as the sinoatrial (SA) node region along the conduction pathway of the atrioventricular (AV) node, or the myocardial tissue forming the walls of the ventricles and atria. Other conditions exist, such as atrial flutter, in which patterns of abnormally conductive tissue lead to re-entry pathways, causing the cardiac chambers to beat in a regular pattern that can be several times more frequent than the sinus rhythm.

[0014] To assist the system 100 in detecting, diagnosing, and / or treating the condition of the heart, a physician 115 can guide the probe 105 into the heart 120 of a patient 125 lying on a bed 130. For example, the physician 115 can insert the shaft 112 through the sheath 113 while manipulating the distal end of the shaft 112 using a manipulator 114 and / or deflection from the sheath 113 near the proximal end of the catheter 110. The catheter 110 can be attached to the distal end of the shaft 112 as shown in inset 140. The catheter 110 can be inserted through the sheath 113 in a folded state and then expanded within the heart 120.

[0015] Generally, electrical activity at a point within the heart 120 can usually be measured by advancing a catheter 110 (e.g., at least one electrode 111) containing an electrical sensor at or near its distal tip into that point within the heart 120, bringing the tissue into contact with the sensor, and acquiring data at that point. One difficulty associated with mapping the cardiac chambers using catheter types containing only a single distal tip electrode is that it can take a long time to collect data point by point across the required number of points for a detailed map of the entire cardiac chamber. Therefore, multi-electrode catheters (e.g., catheter 110) have been developed to simultaneously measure electrical activity at multiple points within the cardiac chambers.

[0016] A catheter 110, which may include at least one electrode 111 and a catheter needle connected to its body, can be configured to obtain biometric data, such as electrical signals, from an internal anatomical structure (e.g., the heart 120) and / or to ablate a tissue region (e.g., the cardiac chambers of the heart 120). Note that the electrode 111 may represent any similar element, such as a tracking coil, a piezoelectric transducer, an electrode, or a combination of elements configured to ablate a tissue region or obtain biometric data. According to one or more embodiments, the catheter 110 may include one or more position sensors used to determine trajectory information. This trajectory information can be used to infer kinetic properties, such as tissue contractility.

[0017] Biometric data (e.g., patient biometrics, patient data, or patient biometric data) may include one or more of the following: local activation time (LAT), electrical activity, topology, bipolar mapping, baseline activity, ventricular activity, dominant frequency, impedance, etc. LAT may be the time of threshold activity corresponding to local activation, calculated based on a normalized initial start point. Electrical activity may be any applicable electrical signal that can be measured based on one or more thresholds and may be detected and / or augmented based on signal-to-noise ratio and / or other filters. Topology may correspond to the physical structure of a body part or part of a body part, or to variations in the physical structure of different parts of a body part or different parts of a body part. Dominant frequency may be a frequency or range of frequencies commonly found in a part of a body part and may differ in different parts of the same body part. For example, the dominant frequency of the PV in the heart may differ from the dominant frequency of the right atrium of the same heart. Impedance may be a resistance measurement in a particular region of a body part.

[0018] Examples of biometric measurement data include, but are not limited to, patient identification data, IC ECG data, bipolar intracardiac reference signals, anatomical and electrical measurements, trajectory information, body surface (BS) ECG data, historical data, brain biometric measurements, blood pressure data, ultrasonic signals, radio signals, voice signals, two-dimensional or three-dimensional image data, blood glucose data, and temperature data. Biometric measurement data can generally be used to monitor, diagnose, and treat any number of various diseases such as cardiovascular diseases (e.g., arrhythmia, cardiomyopathy, and coronary artery disease), and autoimmune diseases (e.g., type I and type II diabetes). Note that BS ECG data can include data and signals collected from electrodes on the patient's surface, IC ECG data can include data and signals collected from electrodes within the patient's body, and ablation data can include data and signals collected from ablated tissue. Further, BS ECG data, IC ECG data, and ablation data can be derived from one or more treatment records together with catheter electrode position data.

[0019] For example, catheter 110 can perform intravascular ultrasound and / or MRI catheterization using electrode 111 to image the heart 120 (e.g., acquire and process biometric measurement data). Insertion diagram 150 shows an enlarged view of catheter 110 within the cardiac chamber of heart 120. Although catheter 110 is shown as a point catheter, it will be understood that any shape including one or more electrodes 111 can be used to implement the embodiments disclosed herein.

[0020] Examples of catheter 110 include, but are not limited to, linear catheters with multiple electrodes, balloon catheters with electrodes dispersed on multiple spines forming a balloon, lasso catheters or loop catheters with multiple electrodes, or any other applicable shape. Linear catheters may be fully or partially elastic so that they can change shape in the form of twisting, bending, and / or other ways based on received signals and / or the action of external forces (e.g., cardiac tissue) on the linear catheter. Balloon catheters may be designed so that their electrodes can be kept in close contact with the endocardial surface when deployed in the patient's body. As an example, balloon catheters may be inserted into a lumen such as a pulmonary vein (PV). A balloon catheter can be inserted into a PV in a deflated state so that it does not occupy its maximum volume while inserted into the PV. A balloon catheter can be expanded while inside the PV so that the electrodes on the balloon catheter come into contact with the entire circular portion of the PV. Such contact with the entire circular portion of the PV or any other lumen allows for efficient imaging and / or ablation.

[0021] In other examples, body patches and / or surface electrodes can also be placed on or near the body of the patient 125. A catheter 110 having one or more electrodes 111 can be placed inside the body (e.g., inside the heart 120), and the position of the catheter 110 can be determined by the system 100 based on signals transmitted between one or more electrodes 111 of the catheter 110 and the body patches and / or surface electrodes. Furthermore, the electrodes 111 can sense biometric data from inside the patient 125's body, such as inside the heart 120 (e.g., the electrodes 111 sense the potential of tissue in real time). The biometric data can be associated with the determined position of the catheter 110, thereby displaying a rendering of a part of the patient's body (e.g., the heart 120) and showing the biometric data superimposed on the shape of the body part.

[0022] Probe 105 and other items of system 100 can be connected to console 160. Console 160 can include any computing device that uses signals and correction processing (represented as mapping engine 101). According to one embodiment, console 160 includes one or more processors 161 (any computing hardware) and memory 162 (any non-transitory tangible medium), where one or more processors 161 execute computer instructions regarding mapping engine 101, and memory 162 stores these instructions for execution by one or more processors 161. For example, console 160 can be configured to receive and process biometric measurement data to determine whether a particular tissue region conducts electricity. In some embodiments, console 160 can be further programmed by mapping engine 101 (within software) to perform functions such as receiving biometric measurement data of at least a portion of an anatomical structure from at least one catheter, analyzing biometric measurement data based on an auto-tagging operation, a time-weighted local excitation timing assignment operation, or a discrimination operation, and determining scar tissue of a portion of an anatomical structure based on the analysis of the biometric measurement data. According to one or more embodiments, mapping engine 101 can be external to console 160, for example, located within catheter 110, within an external device, within a mobile device, within a cloud-based device, or can be a stand-alone processor. In this regard, mapping engine 101 can be transferred / downloaded in electronic form via a network.

[0023] For example, console 160 may be any computing device described herein, including hardware such as a general-purpose computer (e.g., processor 161 and memory 162) with software (e.g., mapping engine 101) and / or suitable front-end and interface circuits for transmitting and receiving signals to and from probe 105, and for controlling other components of system 100. For example, the front-end and interface circuits include an input / output (I / O) communication interface that enables console 160 to receive signals from and / or transfer signals to at least one electrode 111. Console 160 may typically include real-time noise reduction circuitry configured as an analog-to-digital (A / D) ECG or electromyogram (EMG) signal conversion integrated circuit following a field programmable gate array (FPGA). Console 160 can transmit signals from an A / D ECG or EMG circuit to another processor and / or can be programmed to perform one or more of the functions disclosed herein.

[0024] A display 165, which may be any electronic device for visually presenting biometric data, is connected to the console 160. According to one embodiment, during a procedure, the console 160 can facilitate the presentation of a rendering of a body part to the physician 115 on the display 165 and store data representing the rendering of the body part in memory 162. For example, a map showing motor characteristics can be rendered / constructed based on trajectory information sampled at a sufficient number of points within the heart 120. As an example, the display 165 may include a touchscreen that, in addition to presenting a rendering of a body part, can be configured to receive input from the medical professional 115.

[0025] In some exemplary embodiments, the physician 115 may use one or more input devices, such as a touchpad, mouse, keyboard, or gesture recognition device, to manipulate the rendering of elements and / or body parts of the system 100. For example, the input device may be used to change the position of the catheter 110 so that the rendering is updated. Note that the display 165 may be located in the same location or in a remote location, such as another hospital or another healthcare provider network.

[0026] According to one or more embodiments, the system 100 may also obtain biometric data using ultrasound, computed tomography (CT), MRI, or other medical imaging techniques utilizing the catheter 110 or other medical devices. For example, the system 100 may use one or more catheters 110 or other sensors to obtain ECG data and / or anatomical and electrical measurements (e.g., biometric data) of the heart 120. More specifically, the console 160 may be connected by cable to a BS electrode, which includes an adhesive skin patch attached to the patient 125. The BS electrode can acquire / generate biometric data in the form of BS ECG data. For example, the processor 161 may determine the position coordinates of the catheter 110 within a body part of the patient 125 (e.g., the heart 120). The position coordinates may be based on impedance or electromagnetic fields measured between a body surface electrode and an electrode 111 of the catheter 110 or other electromagnetic component. In addition to or instead of the above, a position pad that generates a magnetic field used for navigation may be placed on the surface of the bed 130, or it may be separate from the bed 130. Biometric data can be transmitted to the console 160 and stored in memory 162. Alternatively or additionally, biometric data may be transmitted to a server, which may be local or remote, using a network as further described herein.

[0027] According to one or more embodiments, the catheter 110 may be configured to ablate tissue areas of the cardiac chambers of the heart 120. Insertion figure 150 shows a magnified view of the catheter 110 within the cardiac chambers of the heart 120. For example, an ablation electrode, such as at least one electrode 111, may be configured to deliver energy to a tissue area of ​​an internal anatomical structure (e.g., the heart 120). The energy may be thermal energy and may cause damage to the tissue area, starting from the surface of the tissue area and extending to the thickness of the tissue area. Biometric data relating to the ablation procedure (e.g., ablated tissue, ablation location, etc.) may be considered ablation data.

[0028] For example, with respect to acquiring biometric data, a multi-electrode catheter (e.g., catheter 110) can be advanced into the cardiac chambers of the heart 120. To establish the position and orientation of each electrode, anterior-posterior (AP) and lateral fluorescence images can be acquired. The ECG can be recorded against time from each of the electrodes 111 in contact with the cardiac surface, such as those related to the generation of P waves in the sinus rhythm from the BS ECG and / or signals from the electrodes 111 of catheter 110 positioned in the coronary sinus. Systems further disclosed herein can distinguish between electrodes that record electrical activity and electrodes that do not record electrical activity because they are not in close proximity to the endocardial wall. After the initial ECG is recorded, the catheter can be repositioned and fluorescence images and ECGs can be recorded again. An electrical map can then be constructed (e.g., via cardiac mapping) from iterations of the above process.

[0029] Cardiac mapping can be performed using one or more techniques. Generally, mapping of cardiac regions of the heart 120, such as cardiac areas, tissues, veins, arteries, and / or electrical pathways, can lead to the identification of problem areas such as scar tissue, arrhythmia sources (e.g., electrical rotors), and healthy areas. Cardiac regions can be mapped so that a visual rendering of the mapped cardiac region is provided using a display, as further disclosed herein. Furthermore, cardiac mapping (an example of cardiac imaging) may include mapping based on one or more modalities, such as but not limited to, local excitation time (LAT), local excitation velocity, electrical activity, topology, bipolar mapping, dominant frequency, or impedance. Data corresponding to multiple modalities (e.g., biometric data) can be acquired using a catheter inserted into the patient's body (e.g., catheter 110) and can be provided for rendering simultaneously or at different times, based on the corresponding settings and / or the preference of the physician 115.

[0030] As an example of the first technique, cardiac mapping can be performed by sensing the electrical properties of cardiac tissue, e.g., LAT, as a function of precise location within the heart 120. Corresponding data (e.g., biometric data) can be acquired by one or more catheters (e.g., catheter 110) that are advanced into the heart 120 and have electrical and position sensors (e.g., electrode 111) at their distal tip. Specifically, location and electrical activity can be initially measured at approximately 10 to 20 points on the inner surface of the heart 120. These data points are generally sufficient to generate a preliminary reconstruction or map of the cardiac surface with satisfactory quality. The preliminary map can often be combined with data measured at further points to generate a more comprehensive map of the cardiac electrical activity. In clinical practice, it is not uncommon to collect data from more than 100 locations (e.g., thousands) to generate a detailed comprehensive map of the electrical activity of the cardiac chambers. The detailed maps generated thereafter can serve as a basis for determining therapeutic actions to alter the propagation of the heart's electrical activity and restore a normal rhythm, such as decisions regarding tissue ablation as described herein.

[0031] Furthermore, cardiac mapping can be generated based on the detection of intracardiac potential fields (e.g., IC ECG data and / or bipolar intracardiac reference signals). Non-contact methods can be employed to simultaneously acquire a large amount of cardiac electrical information. For example, a catheter type with a distal end portion may be equipped with a series of sensor electrodes distributed across its surface and connected to an insulating conductor for connection to signal sensing and processing means. The size and shape of the end portion may be such that the electrodes are positioned at a large distance from the walls of the cardiac chambers. The intracardiac potential field can be detected during a single heartbeat. In one example, the sensor electrodes may be distributed on a series of circles located in planes spaced apart from each other. These planes may be perpendicular to the long axis of the end portion of the catheter. At least two additional electrodes may be arranged adjacent to both ends of the long axis of the end portion. In a more specific example, the catheter may include four circumferences, each having eight electrodes spaced equally apart on each circumference. Thus, in this particular implementation, the catheter may include at least 34 electrodes (32 circumferential electrodes and 2 end electrodes). As another, more specific example, the catheter may include other multi-spline catheters such as a flip-type catheter with five flexible branches, eight radial splines, or parallel splines (for example, each of which may have a total of 42 electrodes).

[0032] As an example of electrical or cardiac mapping, electrophysiological cardiac mapping systems and techniques can be implemented based on non-contact and non-expandable multi-electrode catheters (e.g., catheter 110 such as the multi-spline catheter described herein). ECG can be obtained using one or more catheters 110 having multiple electrodes (e.g., 42 to 122 electrodes). This implementation allows insights into the relative geometric shape of the probe and endocardium to be obtained by an independent imaging modality such as transesophageal echocardiography. After independent imaging, cardiac surface potentials can be measured using non-contact electrodes, and a map can be constructed from these surface potentials (e.g., possibly using a bipolar intracardiac reference signal). This technique may include the following steps (after the independent imaging step): Specifically, the process may include (a) measuring potential using a plurality of electrodes placed on a probe positioned within the heart 120, (b) determining the geometric relationship between the probe surface and the endocardial surface and / or other references, (c) generating a matrix of coefficients representing the geometric relationship between the probe surface and the endocardial surface, and (d) determining the endocardial potential based on the matrix of electrode potentials and coefficients.

[0033] As another example of electrical or cardiac mapping, techniques and apparatus for mapping the potential distribution of cardiac chambers can be implemented. An intracardiac multi-electrode mapping catheter assembly can be inserted into the heart 120. The mapping catheter (e.g., catheter 110) assembly may include a multi-electrode array or companion reference catheter having one or more integrated reference electrodes (e.g., one or electrode 111).

[0034] According to one or more embodiments, the electrodes can be deployed in the form of a substantially spherical array, which can be spatially referenced to a point on the endocardial surface by a reference electrode or by a reference catheter in contact with the endocardial surface. A preferred electrode array catheter may have a number of individual electrode sites (e.g., at least 24). In addition, this exemplary technique can be carried out by knowing the position of each electrode site on the array and the geometric shape of the heart. These positions are preferably determined by impedance plethysmography.

[0035] From an electrical or cardiac mapping perspective, and according to another example, the catheter 110 may also be a cardiac mapping catheter assembly that may include an electrode array defining a number of electrode sites. This cardiac mapping catheter assembly also has a lumen for receiving a reference catheter having a distal tip electrode assembly that can be used to puncture the cardiac wall. The mapping cardiac mapping catheter assembly may include a braid of insulated wires (e.g., having 24 to 64 wires in the braid), each of which can be used to form an electrode site. The cardiac mapping catheter assembly is readily deployable within the heart 120 for use in acquiring electrical activity information from a first set of non-contact electrode sites and / or a second set of contact electrode sites.

[0036] Furthermore, according to another example, a catheter 110 capable of performing electrophysiological activity mapping within the heart may include a distal tip adapted to supply stimulation pulses for pacing the heart, or an ablation electrode for ablating tissue in contact with the tip. The catheter 110 may further include at least a pair of orthogonal electrodes for generating a differential signal indicating local cardiac electrical activity in the vicinity of the orthogonal electrodes.

[0037] As described herein, the system 100 can be used to detect, diagnose, and / or treat cardiac conditions. In exemplary operation, the system 100 can perform a process for measuring electrophysiological data within the cardiac chambers. This process may include, in part, placing a set of active and passive electrodes within the heart 120, supplying current to the active electrodes to generate an electric field within the cardiac chambers, and measuring the electric field at the passive electrode sites. The passive electrodes are contained in an array placed on an inflatable balloon of a balloon catheter. In a preferred embodiment, the array is said to have 60 to 64 electrodes.

[0038] As another exemplary operation, cardiac mapping may also be performed by system 100 using one or more ultrasound transducers. The ultrasound transducers can be inserted into the patient's heart 120 and can collect multiple ultrasound slices (e.g., two-dimensional or three-dimensional slices) at various positions and orientations within the heart 120. The position and orientation of a particular ultrasound transducer may be known, and the collected ultrasound slices can be stored so that they can be viewed later. One or more ultrasound slices corresponding to the position of probe 105 (e.g., a therapeutic catheter shown as catheter 110) can be displayed, and probe 105 can be superimposed on one or more ultrasound slices.

[0039] Considering System 100, cardiac arrhythmias, including atrial arrhythmias, can be of the multi-wavelet reentrant type, characterized by multiple asynchronous loops of electrical impulses that scatter around the atria and often self-propagate (e.g., another example of IC ECG data). Alternatively, or in addition to the multi-wavelet reentrant type, cardiac arrhythmias can also have focal sources of excitation, such as when isolated areas of atrial tissue are rapidly and repeatedly excited autonomously (e.g., another example of IC ECG data). Ventricular tachycardia (V-tach or VT) is a tachycardia or rapid heart rhythm originating from one of the ventricles. It is a potentially fatal arrhythmia because it can lead to ventricular fibrillation and sudden death.

[0040] For example, aFib occurs when the normal electrical impulses generated by the sinoatrial node (e.g., another example of IC ECG data) are overwhelmed by disordered electrical impulses (e.g., signal interference) occurring in the atrial veins and PV, causing irregular impulses to be conducted to the ventricles. This results in an irregular heartbeat, which can last from minutes to weeks, or even years. Often, aFib is a chronic condition that frequently slightly increases the risk of death from stroke. The treatment strategy for aFib is medication to reduce heart rate or restore a normal rhythm. Furthermore, patients with aFib are often given anticoagulants to protect against the risk of stroke. The use of such anticoagulants carries its own risk of internal bleeding. In some patients, medication is insufficient, and their aFib is deemed drug-refractory, i.e., untreatable with standard pharmacological interventions. Synchronized electrical cardioversion can also be used to convert aFib back to a normal rhythm. Alternatively, patients with aFib may also be treated with catheter ablation.

[0041] Catheter ablation-based therapies may include mapping the electrical properties of cardiac tissue, particularly the endocardium and cardiac volume, and selectively ablating cardiac tissue by applying energy. Electrical or cardiac mapping (e.g., performed by any electrophysiological cardiac mapping systems and techniques described herein) includes creating potential maps of wave propagation along cardiac tissue (e.g., voltage maps) or maps of arrival times (e.g., LAT maps) to points located within various tissues. Electrical or cardiac mapping (e.g., cardiac maps) can be used to detect localized cardiac tissue dysfunction. Ablation, such as cardiac mapping-based ablation, can stop or alter unwanted electrical signals from propagating from one part of the heart 120 to another.

[0042] Ablation is a method that disrupts undesirable electrical pathways by forming non-conductive damaged areas. Various energy delivery methods have been disclosed to date for the purpose of forming damaged areas, including the use of microwaves, lasers, and more generally radiofrequency energy to create conduction blocks along the cardiac tissue wall. Another example of an energy delivery method is irreversible electroporation (IRE), which applies a high electric field to damage cell membranes. In a two-step procedure (mapping followed by ablation), a catheter 110 containing one or more electrical sensors (e.g., electrodes 111) is typically advanced into the heart 120, and electrical activity at points within the heart 120 is sensed and measured by acquiring data at multiple points (e.g., generally as biometric data, or specifically as ECG data). The ECG data is then used to select target regions of the endocardium to be ablated.

[0043] Cardiac ablation and other cardiac electrophysiological procedures are becoming increasingly complex when physicians treat difficult conditions such as atrial fibrillation and ventricular tachycardia. Treatment of refractory arrhythmias can now rely on the use of three-dimensional (3D) mapping systems to reconstruct the anatomical morphology of the target cardiac chambers. In this regard, the mapping engine 101 used by system 100 herein manipulates and evaluates biometric data, or specifically ECG data, to generate improved tissue data that enables more accurate diagnosis, imaging, scanning, and / or mapping for treating abnormal heartbeats or arrhythmias. For example, cardiologists rely on software such as the Complex Fractionated Atrial Electrograms (CFAE) module of the CARTO® 3 3D mapping system manufactured by Biosense Webster, Inc. (Diamond Bar, Calif.) to generate and analyze ECG data. The mapping engine 101 of system 100 enhances this software to generate and analyze improved biometric data, thereby further providing multiple pieces of information regarding the electrophysiological properties of the heart 120 (including scar tissue) that represent the cardiac matrix (anatomical and functional) of aFib.

[0044] Therefore, system 100 can implement a 3D mapping system, such as the CARTO® 3 3D mapping system, to identify the location of potential arrhythmogenic substrates in cardiomyopathy from the perspective of detecting abnormal ECGs. These substrates associated with cardiac disease are linked to the presence of fragmentation and delayed ECGs in the endocardial and / or epicardial layers of the ventricular chambers (right and left). For example, low-voltage or medium-voltage regions may indicate ECG fragmentation and delayed activity. Furthermore, low-voltage or medium-voltage regions during sinus rhythm may correspond to critical isthmuses identified in persistent, cohesive ventricular arrhythmias (e.g., unacceptable ventricular tachycardia, as well as within the atria). Generally, abnormal tissues are characterized by low-voltage ECGs. However, early clinical experience in endocardial-epidermal mapping has shown that low-voltage regions are not always present as the sole arrhythmic mechanism in such patients. In fact, low- or medium-voltage regions may show ECG segmentation and delayed activity during sinus rhythm, corresponding to the critical isthmus identified in persistent, cohesive ventricular arrhythmias (e.g., only in unacceptable ventricular tachycardia). Furthermore, ECG segmentation and delayed activity are often observed in regions showing normal or near-normal voltage amplitude (>1–1.5mV). The latter regions can be evaluated according to voltage amplitude but are not considered normal according to intracardiac signals and therefore represent true arrhythmogenic substrates. 3D mapping can pinpoint the location of arrhythmogenic substrates on the endocardial and / or epicardial layers of the right / left ventricle, whose distribution may vary depending on the progression of the major disease.

[0045] As another exemplary operation, cardiac mapping may also be performed by system 100 using one or more multi-electrode catheters (e.g., catheter 110). The multi-electrode catheter is used to stimulate and map electrical activity within the heart 120 and to ablate areas where abnormal electrical activity is observed. When used, the multi-electrode catheter is inserted into a major vein or artery, such as the femoral vein, and then guided into the cardiac chambers of the target heart 120. A typical ablation procedure involves inserting catheter 110, which has at least one electrode 111 at its distal end, into the cardiac chambers. A reference electrode is provided by being taped to the patient's skin, by a second catheter positioned in or near the heart, or by selecting one or other electrodes 111 of catheter 110. A radiofrequency (RF) current is applied to the tip electrode 111 of the ablation catheter 110, causing the current to flow through the surrounding medium (i.e., blood and tissue) toward the reference electrode. The distribution of the current depends on the amount of electrode surface in contact with tissue, compared to blood, which has higher conductivity than tissue. Tissue heating occurs due to the electrical resistance of the tissue. When the tissue is sufficiently heated, cell destruction occurs in the cardiac tissue, resulting in the formation of damaged areas within the non-conductive cardiac tissue. In this process, heating of the tip electrode 111 also occurs due to conduction from the heated tissue to the electrode itself. When the electrode temperature becomes sufficiently high, and may exceed 60°C, a thin, transparent film of dehydrated blood proteins may form on the surface of the electrode 111. As the temperature continues to rise, this dehydrated layer may gradually thicken, and blood coagulation occurs on the electrode surface. Since dehydrated biological material has a higher electrical resistance than endocardial tissue, the impedance to the flow of electrical energy into the tissue also increases. When the impedance becomes sufficiently high, an impedance surge occurs, and the catheter 110 must be withdrawn from the body and the tip electrode 111 must be cleaned.

[0046] Referring now to Figure 2, a schematic diagram of a system 200 in which one or more features of the subject matter of this disclosure may be implemented in one or more embodiments. The system 200 includes, for a patient 202 (for example, an example of patient 125 in Figure 1), an apparatus 204, a local computing device 206, a remote computing system 208, a first network 210, and a second network 211. Furthermore, the apparatus 204 may include a biometric sensor 221 (for example, an example of catheter 110 in Figure 1), a processor 222, a user input (UI) sensor 223, a memory 224, and a transceiver 225. Note that for ease of explanation and brevity, the mapping engine 101 in Figure 1 is reused in Figure 2.

[0047] According to one embodiment, the device 204 may be an example of the system 100 in Figure 1, and the device 204 may include both internal and external components of the patient. According to one embodiment, the device 204 may be an external device of the patient 202, including an attachable patch (e.g., attached to the patient's skin). According to another embodiment, the device 204 may be internal to the patient 202's body (e.g., subcutaneously implantable), and the device 204 may be inserted into the patient 202's body by any applicable method, including oral infusion, surgical insertion via vein or artery, endoscopic surgery, or laparoscopic surgery. According to one embodiment, a single device 204 is shown in Figure 2, but the exemplary system may include multiple devices.

[0048] Therefore, the device 204, the local computing device 206, and / or the remote computing system 208 can be programmed to execute computer instructions relating to the mapping engine 101. As an example, memory 223 stores these instructions for execution by processor 222 so that the device 204 can receive and process biometric data via biometric sensor 201. Thus, processor 22 and memory 223 represent the processor and memory of local computing device 206 and / or remote computing system 208.

[0049] The apparatus 204, the local computing device 206, and / or the remote computing system 208 can be any combination of software and / or hardware that individually or collectively store, execute, and implement the mapping engine 101 and its functions. Furthermore, the apparatus 204, the local computing device 206, and / or the remote computing system 208 can be an electronic computer framework that includes and / or uses any number and combination of computing devices and networks utilizing various communication technologies as described herein. The apparatus 204, the local computing device 206, and / or the remote computing system 208 can be easily scalable, expandable, and modular, allowing for modification to suit different services or the reconfiguration of some functions independently of others.

[0050] Networks 210 and 211 may be wired networks, wireless networks, or may include one or more wired and wireless networks. According to one embodiment, network 210 is an example of a short-range network (e.g., a local area network (LAN) or a personal area network (PAN)). Information can be transmitted between device 204 and local computing device 206 via the short-range network 210 using one of various short-range wireless communication protocols such as Bluetooth, Wi-Fi, Zigbee, Z-Wave, near-field communication (NFC), Ultraband, Zigbee, or infrared (IR). Furthermore, network 211 is an example of one or more of the following: an intranet, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a direct connection or a series of connections, a cellular telephone network, or any other network or medium that can facilitate communication between local computing device 206 and remote computing system 208. Information can be transmitted over network 211 using any one of various long-range wireless communication protocols (e.g., TCP / IP, HTTP, 3G, 4G / LTE, or 5G / New Radio). Wired connections between networks 210 and 211 can be implemented using Ethernet, Universal Serial Bus (USB), RJ-11, or any other wired connection, while wireless connections can be implemented using Wi-Fi, WiMAX, Bluetooth, infrared, cellular networks, satellite communications, or any other wireless connection method.

[0051] During operation, the device 204 can continuously or periodically acquire, monitor, store, process, and communicate biometric data related to the patient 202 via the network 210. Furthermore, the device 204, the local computing device 206, and / or the remote computing system 208 communicate via the networks 210 and 211 (for example, the local computing device 206 can be configured as a gateway between the device 204 and the remote computing system 208). For example, the device 204 may be an example of system 100 in Figure 1, configured to communicate with the local computing device 206 via the network 210. The local computing device 206 can be, for example, a stationary / standalone device, a base station, a desktop / laptop computer, a smartphone, a smartwatch, a tablet, or another device configured to communicate with other devices via the networks 211 and 210. A remote computing system 208, implemented as a physical server on or connected to network 211, or as a virtual server within a public cloud computing provider for network 211 (e.g., Amazon Web Services (AWS)), can be configured to communicate with a local computing device 206 via network 211. This allows biometric data related to patient 202 to be communicated throughout the entire system 200.

[0052] The elements of the device 204 are described here. The biometric sensor 221 may include, for example, one or more transducers configured to convert one or more environmental conditions into electrical signals so that different types of biometric data can be observed / acquired / obtained. For example, the biometric sensor 221 may include one or more of the following: electrodes (e.g., electrode 111 in Figure 1), temperature sensors (e.g., thermocouples), blood pressure sensors, blood glucose sensors, blood oxygen sensors, pH sensors, accelerometers, and microphones.

[0053] In executing the mapping engine 101, the processor 222 can be configured to receive, process, and manage biometric data acquired by the biometric sensor 221, and to communicate the biometric data to the memory 224 for storage and / or to the entire network 210 via the transceiver 225. Biometric data from one or more other devices 204 may also be received by the processor 222 via the transceiver 225. Furthermore, as will be described in more detail below, the processor 222 can be configured to selectively respond to different tapping patterns (e.g., single tap or double tap) received from the UI sensor 223 so that different tasks on a patch (e.g., data acquisition, storage, or transmission) are triggered based on the detected pattern. In some embodiments, the processor 222 can generate audible feedback with respect to gesture detection.

[0054] The UI sensor 223 includes, for example, a piezoelectric or capacitive sensor configured to receive user input such as a tap or touch. For example, the UI sensor 223 may be controlled to perform capacitive coupling in response to a patient 202 tapping or touching the surface of the device 204. Gesture recognition can be implemented via any one of various capacitive types, such as resistive capacitive, surface capacitive, projected capacitive, surface ultrasonic, piezoelectric, and infrared touch. The capacitive sensor may be positioned over a small area or length of the surface so that a tap or touch on the surface activates the monitoring device.

[0055] Memory 224 is any non-temporary tangible medium such as magnetic, optical, or electronic memory (e.g., any suitable volatile and / or non-volatile memory such as random access memory or a hard disk drive). Memory 224 stores computer instructions executed by processor 222.

[0056] The transceiver 225 may include a separate transmitter and a separate receiver. Alternatively, the transceiver 225 may include a transmitter and receiver integrated into a single device.

[0057] During operation, the device 204 utilizing the mapping engine 101 observes / acquires biometric data of the patient 202 via the biometric sensor 221, stores the biometric data in memory, and shares this biometric data throughout the system 200 via the transceiver 225. The mapping engine 101 can then utilize fuzzy logic, models, neural networks, machine learning, and / or artificial intelligence to reduce user intervention, improve scar recognition, and enable the distinction between low-conductivity and non-conductivity. Generally, the mapping engine 101 provides a number of operations for assigning scar probabilities. These operations include, but are not limited to, automatic tagging, time-weighted LAT assignment, and the determination of low-conductivity or non-conductivity states.

[0058] Referring to Figures 3 to 5, the automatic tagging operation is explained for the mapping engine 101.

[0059] As shown in Figure 3, Method 300 (e.g., carried out by the mapping engine 101 in Figures 1 and / or 2) is shown in one or more exemplary embodiments. Method 300 by the mapping engine 101 generally automatically identifies scar tissue on the heart from electrical signals (e.g., using the concept that scar tissue blocks the heartbeat by not conducting electrical signals very well). Method 300 by the mapping engine 101 then addresses the shortcomings of current coherent mapping algorithms by providing multi-stage manipulation of electrical signals, which enables a more accurate and further understanding of electrophysiology.

[0060] This method is initiated in block 330, in which the mapping engine 101 receives biometric data from at least the catheter 110. The biometric data includes the patient's anatomical structure (e.g., heart 120) and electrical measurements of one or more reference signals.

[0061] In block 350, the mapping engine 101 determines which electrical measurements of the biometric data have associated indications. This determination generates a sub-dataset of electrical measurements. According to one embodiment, this indication may be the tissue proximity indicator (TPI) function of the mapping engine 101. The TPI can detect the proximity of the catheter 110 to tissue using an impedance-based algorithm, thereby determining whether there is tissue proximity using any real-time changes in impedance at each electrode of the catheter 110. Thus, an active TPI for a particular measurement indicates that the mapping engine 101 can recognize that the catheter 110 is in contact with tissue.

[0062] In block 370, the mapping engine 101 assigns a scar probability to each electrical measurement in the subdataset. The scar probability is assigned based on voltage, for example, using fuzzy logic (instead of a threshold). Fuzzy logic is a mathematical logic form in which the truth value of a variable can be any real number between 0 and 1. For example, if a low voltage is present on the catheter 110 and active TPI is present when an electrical measurement is taken, then contact (with the tissue by the catheter 110) is present. The mapping engine 101 can then automatically assign this point to the electrical measurement, which will be a point contributing to the scar (e.g., a scar point relating to a general or specific feature).

[0063] In block 390, the mapping engine 101 uses scar probabilities to define scar tissue on a triangular mesh when generating a map. The technical effects and benefits of method 300 include reducing user intervention, improving scar identification, and enabling the distinction between low-conductivity and non-conductivity.

[0064] Figure 4 shows graph 400, which represents automated scar measurement tagging (as performed by mapping engine 101). Automated scar measurement tagging, as shown in graph 400, eliminates reliance on manual scar tagging by the user by assigning scar probabilities based on the voltage and TPI combination of the measurements.

[0065] For example, in the case of measurements using TPI, the mapping engine 101 assigns scar probabilities based on voltage for the purpose of defining scar tissue on a triangular mesh. In this way, the mapping engine 101 uses the catheter 110 to measure a point, the voltage at that point, and the signal arrival time at that point. If a point has a very low voltage (e.g., indicating the presence of scar tissue), the mapping engine 101 provides automatic scar measurement tagging. According to another embodiment, the mapping engine 101 can implement an integrated scheme such that the probability of a wall surface being a scar depends on the distance to the nearest scar measurement in order to define scar tissue on the mesh.

[0066] As shown in Figure 5, exemplary interfaces 500 and 501 demonstrate automated scar measurement tagging (as performed by the mapping engine 101). Interface 500 is an example of mapping of the entire cardiac map rendered by the mapping engine 101 (e.g., a map of the heart generated by performing interpolation). Interface 501 is an example of mapping of a selected zone rendered by the mapping engine 101 and includes a legend 502. Measurement 510 has low voltage and TPI and can therefore be tagged as a scar measurement by the mapping engine 101 (e.g., the arrow to measurement 510 points to a square without a dot inside). Measurement 520 has low voltage without TPI and is therefore not tagged as a scar measurement by the mapping engine 101 (e.g., the arrow to measurement 520 points to a square with a dot inside, meaning there was a no TPI indication for legend 502).

[0067] Referring here to Figures 6 to 8, the mapping engine 101 is further described in terms of time-weighted LAT assignment in one or more embodiments. Generally, time-weighted LAT assignment includes determining the duration of a time window and identifying / weighting signals within that time window with respect to normal bipolar beats and blockages.

[0068] Figure 6 shows a frequency vs. LAT graph 600 representing the distribution of LAT for points contributing to the boundary. The first distribution includes the unweighted LAT distribution of points contributing to the LAT assignment before reweighting, and the second distribution includes the weighted LAT distribution of points. Furthermore, note that in graph 600, each distribution has two peaks.

[0069] Considering Figure 6, the mapping engine 101 finds that the distribution of points may be branched based on the signal flow from the catheter 110 (associated with the tissue on one side / associated with the other side). During operation, the mapping engine 101 can perform a peak search of the distribution of points contributing to all triangles in the triangular mesh. If branching exists, the mapping engine 101 applies an iterative algorithm of two peak locations so that previous iterations (e.g., previous solutions with respect to the LAT time assigned to any particular triangle) are used to measure the peaks. As shown in Figure 6, the weight of a point can be determined by the time difference with the face LAT of the previous iteration (see face LAT 650 of the previous iteration), thus weakening contributions from smaller distributions. If branching exists, the mapping engine 101 selects only the distributions that are closer to the time of the previous iteration / solution (e.g., discard one bump and use the other).

[0070] With respect to time-weighted LAT assignment, the mapping engine 101 determines a time window and identifies / weights the signals within that time window with respect to normal bipolar pulsations and occlusions. According to one or more embodiments, the time-weighted LAT assignment operation overcomes the problems of current coherent mapping algorithms.

[0071] Traditionally, physicians operating current coherent mapping algorithm systems must determine the time period to be analyzed. Therefore, once an electroanatomical map is acquired, the clinician selects a reference signal. This reference signal may originate from electrodes within a catheter placed within an anatomical structure (e.g., the coronary sinus) or be derived at some point in time from a combination of signals. The time period around this reference signal can be defined by the clinician. Signals from a catheter within a portion of an anatomical structure (e.g., a chamber) are assigned a negative LAT value if they arrive before the reference signal, and a positive LAT value if they arrive after the reference signal (e.g., some signals arrive before a given reference time, and others after). Situations may arise where the clinician defines a time period that is too short, resulting in the complete lack of detection of subdivided signals. Conversely, situations may arise where the clinician defines a time period that is too long, resulting in the same signal being included twice. In some cases, errors in setting these time periods are less likely to be due to user error and more likely to be caused by slight variations in the time between reference signals.

[0072] According to one embodiment, the mapping engine 101 automatically defines a time window, which is the time interval (e.g., duration) between two reference signal annotations, to include any subdivided signals and exclude overlapping signals. In this way, the mapping engine 101 can be configured to automatically select a time window (e.g., maximum or minimum). According to another embodiment, the mapping engine 101 automatically selects a time window based on input from a physician 115. For example, the mapping engine 101 takes in two known reference signals, and the physician 115 needs to select a predetermined window between the two reference signals (instead of the physician taking one reference point and then selecting an arbitrary duration for the window). Thus, the mapping engine 101 determines the duration of the time window and also automatically identifies the signal corresponding to a normal bipolar pulsation and the dual potential signal corresponding to an occlusion (e.g., block).

[0073] One or more advantages, technical effects, and benefits of the mapping engine 101 can significantly reduce user intervention from the requirements of current coherent mapping algorithms, where the physician takes a reference point and arbitrarily selects the duration of the period. Thus, the mapping engine 101 reduces physician errors and artifacts by reducing user intervention.

[0074] As seen in Figure 7, exemplary interfaces 700 and 701 representing automated scar measurement tagging (as performed by the mapping engine 101) are shown. Interface 700 is an example of mapping of the entire cardiac map rendered by the mapping engine. Interface 701 is an example of mapping of a selected zone rendered by the mapping engine 101. Figure 7 shows how the mapping engine 101, in relation to improved scar identification (e.g., at point 705), correctly detects lines of block via time-weighted LAT measurements (e.g., from Figure 6). In the selected zone of interface 701, there is a boundary 710 between two widely separated LAT distributions. The mapping engine 101 will see a block at this boundary 710. For example, the mapping engine 101 can reweight measurements using bimodal LAT so that a block can be detected. A block refers to the concept that scar tissue interferes with the transmission of heartbeats by not conducting electrical signals.

[0075] Next, the mapping engine 101 finds a clear break in the time separation of adjacent iterations when the mapping is tilted to one side relative to the other. This clear time separation allows the mapping engine 101 to create a block line. For example, Figure 8 shows an interface 800 with a block line 810. The interface 800 in Figure 8 can be compared to the interface 700 in Figure 7 (which does not have a block line).

[0076] Referring here to Figures 9-10, the mapping engine 101 is further described in relation to the determination of slow conduction or non-conductivity states (sometimes referred to as low voltage or no voltage states) according to one or more embodiments. More specifically, the mapping engine 101 can determine whether a triangle (e.g., an annotation point) falls into one of three categories (e.g., normal conductivity, low conductivity, and non-conductivity) for the purpose of integrating all the capabilities of the mapping engine 101. In this regard, the mapping engine 101 recognizes that a signal can actually pass through the lines of a block. For example, if the catheter 110 is at a point with indication (e.g., active TPI is a function of the system), and there is low voltage on the catheter at acquisition, the mapping engine 101 automatically assigns this point as contributing to scarring as described herein. Again in this case, the technical benefit is that the physician 115 does not set the threshold itself, because the mapping engine 101 uses fuzzy logic (instead of a threshold) and requires proximity indication. Furthermore, the mapping system can utilize these points with respect to slow-velocity or non-conductive (SNO) zones. In this reface, the wall of the heart 120 has a probability of being either a normal zone or an SNO zone. The mapping system 101 starts by classifying the SNO zones. The mapping system 101 uses inputs from biometric data such as voltage, dual potential, subdivision, and velocity direction. Figure 9 shows the interface 900 and graph 940 in one or more embodiments. The interface 900 shows that there is no waiting for a dedicated subdivision algorithm so that the mapping engine 101 can collect cases with manually tagged subdivisions. Graph 940 shows these cases. In Figure 10, graph 1001 shows the integration of subdivision signals in one or more embodiments. If the triangle initially identified as non-conductive is near the subdivision signal 1020, the mapping engine 101 reconstructs the continuity state into the entire triangle of the resolution.The mapping engine 101 can add an additional set of (weighted) measurement inputs at the location of the subdivided measurement, extending over the time of the subdivided signal. This may extend over the time of the adjacent measured signal 1030, and the subdivided signal in Figure 10 is obtained from the atrium.

[0077] The flowcharts and block diagrams in the figures illustrate the structure, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for performing the indicated logical function. In some alternative implementations, the functions shown in a block may be performed in an order other than that shown in the figure. For example, two consecutively shown blocks may actually be executed substantially simultaneously, or they may sometimes be executed in reverse order depending on the relevant functionality. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, may be performed by a dedicated hardware-based system that performs a specific function or operation, or they may operate or execute a combination of dedicated hardware and computer instructions.

[0078] While features and elements are described above in specific combinations, those skilled in the art will understand that each feature or element can be used individually or in combination with other features and elements. In addition, the methods described herein may be implemented in computer programs, software, or firmware incorporated into a computer-readable medium for execution on a computer or processor. The computer-readable medium as used herein should not be interpreted as being a transient signal in itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses passing through fiber optic cables), or electrical signals transmitted through moving wires.

[0079] Examples of computer-readable media include electrical signals (transmitted via wired or wireless connections) and computer-readable storage media. Examples of computer-readable storage media, though not limited to these, include registers, cache memory, semiconductor memory devices, magnetic media such as internal hard disks and removable disks, magneto-optical media, optical media such as compact disks (CDs) and digital versatile disks (DVDs), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), and memory sticks. A processor can be used with software to implement a radio frequency transceiver for use in terminals, base stations, or any host computer.

[0080] The terms used herein are intended solely to describe specific embodiments and are not intended to be limiting. Where used herein, unless otherwise specified in the context, the singular forms "a," "an," and "the" also include the plural forms. The terms "comprise" and / or "comprising," as used herein, indicate the presence of a described feature, integer, process, operation, element, and / or component, but do not exclude the presence or addition of one or more other features, integers, processes, operations, elements, components, and / or groups thereof.

[0081] The descriptions of different embodiments in this specification are for illustrative purposes only and are not intended to be exhaustive or limitful to the embodiments disclosed. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the embodiments described. The terms used herein have been selected to best describe the principles, practical applications, or technical improvements of the embodiments compared to the art available on the market, or to enable those skilled in the art to understand the embodiments disclosed herein.

[0082] [Implementation Method] (1) A method, A mapping engine, run by one or more processors, receives biometric data of at least a portion of an anatomical structure from at least one catheter, The mapping engine analyzes the biometric data based on at least one of the following: automatic tagging, time-weighted local excitation time allocation, and discrimination. A method comprising determining scar tissue of a portion of the anatomical structure based on the analysis of the aforementioned biometric data. (2) The automatic tagging operation described above is: The mapping engine determines the electrical measurements of the biometric data having an indication associated with the mapping engine, and generates a sub-dataset of the measurements. The method according to Embodiment 1, comprising assigning a scarring probability to each measurement in the subdataset using the mapping engine. (3) The method according to Embodiment 2, further comprising determining a time window and which of the electrical measurements within the time window have the indication for generating the subdataset. (4) The method according to Embodiment 2, wherein the indication includes a tissue proximity indicator function of the mapping engine for identifying contact by the catheter with the portion of the anatomical structure. (5) The method according to Embodiment 1, wherein the scarring probability is assigned based on the voltage of the corresponding electrical measurement.

[0083] (6) The method according to Embodiment 1, wherein the scarring probability is assigned using fuzzy logic. (7) The method according to Embodiment 1, wherein the scar probability is used to define scar tissue on a triangular mesh. (8) The method according to Embodiment 1, wherein the biometric data includes one or more reference signals and the electrical measurement values. (9) The method according to Embodiment 1, wherein the time-weighted local excitation time-assignment operation includes automatically selecting a signal from a set of signals associated with the tissue location of the portion of the anatomical structure, the set of signals having a bimodal time distribution. (10) The method according to embodiment 9, wherein the selected signal includes a time that is closer to the local excitation time assigned to the triangle in the previous iteration.

[0084] (11) The method according to Embodiment 1, wherein the discrimination operation includes determining whether the annotation point of the biometric measurement data falls into the category of normal conductivity, low conductivity, or non-conductivity. (12) A system, Memory for storing processor-executable code for the mapping engine, At least one processor, which executes the processor executable code in the memory, to the system To receive biometric data of at least a portion of an anatomical structure from at least one catheter, The biometric data is analyzed based on at least one of the following: automatic tagging, time-weighted local excitation time allocation, and discrimination. A system comprising at least one processor configured to perform the mapping engine by determining scar tissue of a portion of the anatomical structure based on the analysis of the biometric data. (13) The automatic tagging operation described above is: The mapping engine determines the electrical measurements of the biometric data having an indication associated with the mapping engine, and generates a sub-dataset of the measurements. The system according to Embodiment 12, comprising assigning a scarring probability to each measurement in the subdataset using the mapping engine. (14) The at least one processor executes the processor executable code in the memory to the system The system according to embodiment 13, configured to perform the mapping engine by having it perform a time window and determine which of the electrical measurements within the time window have the indication for generating the subdataset. (15) The system according to Embodiment 13, wherein the indication includes a tissue proximity indicator function of the mapping engine for identifying contact by the catheter with the portion of an anatomical structure.

[0085] (16) The system according to embodiment 12, wherein the scarring probability is assigned based on the voltage of the corresponding electrical measurement. (17) The scarring probability is assigned using fuzzy logic in the system according to Embodiment 12. (18) The system according to Embodiment 12, wherein the scar probability is used to define scar tissue on a triangular mesh. (19) The system according to Embodiment 12, wherein the biometric data includes one or more reference signals and the electrical measurement values. (20) The time-weighted local excitation time-assignment operation comprises automatically selecting a signal from a set of signals associated with the tissue location of the portion of an anatomical structure, the set of signals having a bimodal time distribution, according to Embodiment 12.

Claims

1. It is a system, Memory for storing processor-executable code for the mapping engine, At least one processor, which executes the processor executable code in the memory, to the system Receiving biometric data of at least a portion of an anatomical structure from at least one catheter, The biometric data is analyzed based on the time-weighted local excitation time allocation behavior, The system comprises at least one processor configured to perform the mapping engine by determining scar tissue of a portion of the anatomical structure based on the analysis of the aforementioned biometric data, The time-weighted local excitation time assignment operation includes automatically selecting a signal from a subset of electrical measurements related to the location of the portion of the anatomical structure on the scar tissue, wherein the subset of electrical measurements has two peaks of bimodal local excitation time, and the signal is weighted based on the time difference from the previous local excitation time assigned to that location.

2. The analysis of the biometric measurement data is further based on an automatic tagging operation, and the automatic tagging operation is The mapping engine determines the electrical measurements of the biometric data having an indication associated with the mapping engine, and generates the subdataset. The system according to claim 1, comprising assigning a scarring probability to each electrical measurement value in the subdataset using the mapping engine.

3. The at least one processor executes the processor executable code in the memory to the system. The system according to claim 2, wherein the mapping engine is configured to perform the following: determine a time window, and which of the electrical measurements within the time window have the indication for generating the subdataset.

4. The system according to claim 2, wherein the indication includes a tissue proximity indicator function of the mapping engine for identifying contact by the catheter with the portion of the anatomical structure.

5. The system according to claim 2, wherein the scarring probability is assigned based on the voltage of the corresponding electrical measurement.

6. The system according to claim 2, wherein the scarring probability is assigned using fuzzy logic.

7. The system according to claim 2, wherein the scar probability is used to define scar tissue on a triangular mesh.

8. The system according to claim 1, wherein the biometric measurement data includes one or more reference signals and the electrical measurement values.

9. A method for operating a system, One or more processors of the system receive biometric data of at least a portion of an anatomical structure from at least one catheter via the system's mapping engine, The processor analyzes the biometric data based on time-weighted local excitation time allocation operation using the mapping engine, The processor includes determining scar tissue of a portion of the anatomical structure based on the analysis of the biometric data, The time-weighted local excitation time assignment operation comprises automatically selecting a signal from a subset of electrical measurements relating to the location of the portion of the anatomical structure on the scar tissue, wherein the subset of electrical measurements has two peaks of bimodal local excitation time, and the signal is weighted based on the time difference from the previous local excitation time assigned to that location.

10. The analysis of the biometric measurement data is further based on an automatic tagging operation, and the automatic tagging operation is The processor determines the electrical measurements of the biometric data having indications associated with the mapping engine using the mapping engine, and generates the subdataset. A method of operating the system according to claim 9, comprising the processor assigning a scar probability to each measurement value in the subdataset by the mapping engine.

11. The method of operating the system according to claim 10, further comprising the processor determining a time window and which of the electrical measurements within the time window have the indication for generating the subdataset.

12. A method of operating the system according to claim 10, wherein the indication includes a tissue proximity indicator function of the mapping engine, which allows the processor to identify contact by the catheter with the portion of the anatomical structure.

13. The method of operating the system according to claim 10, wherein the scarring probability is assigned by the processor based on the voltage of the corresponding electrical measurement.

14. The method of operating the system according to claim 10, wherein the scar probability is assigned by the processor using fuzzy logic.

15. The method of operating the system according to claim 10, wherein the scar probability is used by the processor to define scar tissue on a triangular mesh.

16. The method of operating the system according to claim 9, wherein the biometric measurement data includes one or more reference signals and the electrical measurement values.

17. The method of operating the system according to claim 9, wherein the selected signal includes a time that is closer to the local excitation time assigned to a triangle in a previous solution relating to the local excitation time assigned to any particular triangle.

18. The method of operating the system according to claim 9, wherein the analysis of the biometric data is further based on a discrimination operation, the discrimination operation includes the processor determining whether the annotation points of the biometric data fall into the categories of normal conductivity, low conductivity, or non-conductivity.

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