Respiratory compensation for mapping

Abstract

The name of the invention is breathing compensation for mapping. A system and method are disclosed. The systems and methods include obtaining breathing data of a patient via at least one sensor; obtaining probe position data of a probe positioned within the cavity; generating probe-cavity position data by compensating the respiration data from the probe position data; identifying a period of time during which the probe is stable relative to a cavity boundary based on the probe-cavity position data; and capturing data based on the generated probe-cavity position during the identified time period to produce a map.

Description

Technical Field

[0001] The present application provides systems, devices, and methods for compensating for respiration during mapping. Background Art

[0002] Surgical procedures involving cavities or chambers, such as the heart, require accurate information about the position of a probe or catheter within the cavity. In one aspect, mapping of the heart chambers is crucial for identifying problems related to cardiac arrhythmias (e.g., atrial fibrillation (AF)), which can be treated via in vivo procedures.

[0003] Respiration is divided into inspiration (inhalation or intake of air) and expiration (exhalation or expulsion of air). Due to human physiology, the diaphragm moves up and down during this process and displaces the heart in a cyclic manner. During the expiration phase, there is a moment called the end-expiratory phase, during which the displacement of the diaphragm is minimal over a period of time. Summary of the Invention

[0004] A system and method are disclosed. The system and method include: obtaining respiratory data of a patient via at least one sensor; obtaining probe position data of a probe positioned within a cavity; generating probe-cavity position data by compensating the respiratory data from the probe position data; identifying a period of time during which the probe is stable relative to the cavity boundary based on the probe-cavity position data; and capturing data based on the generated probe-cavity position during the identified period of time to produce a map. Description of the Drawings

[0005] A more detailed understanding can be obtained from the following description given by way of example in conjunction with the drawings, in which:

[0006] Figure 1 An example catheter-based electrophysiological mapping and ablation system according to one or more embodiments is depicted;

[0007] Figure 2 An example image produced using the disclosed embodiments;

[0008] Figure 3 A schematic diagram illustrating a system according to one aspect;

[0009] Figure 4A A first part of a flowchart showing aspects of the disclosed embodiments;

[0010] Figure 4B is Figure 4A a second part of the flowchart of

[0011] Figure 5 A graph illustrating a respiratory metric signal during ablation;

[0012] Figure 6 is a graph illustrating exemplary low-pass and derivative filters with respect to weights;

[0013] Figure 7A is the first part of a flowchart of a preprocessing stage;

[0014] Figure 7B is Figure 7A the second part of a flowchart of;

[0015] Figure 8 is a graph illustrating the respiratory vector during ablation;

[0016] Figure 9 is a graph illustrating a proximate factor;

[0017] Figure 10 is a graph illustrating a velocity factor;

[0018] Figure 11 is a graph illustrating an ablation factor;

[0019] Figure 12 is a graph illustrating multiple stability factors;

[0020] Figure 13 illustrates a flowchart for defining a segmentation curve;

[0021] Figure 14 illustrates a flowchart of a method according to one embodiment;

[0022] Figure 15 illustrates a method of capturing mapping data with respiratory compensation; and

[0023] Figure 16 illustrates a graph of respiratory data points with and without respiratory compensation. DETAILED DESCRIPTION

[0024] As disclosed herein, systems, devices, and methods are provided for determining or estimating the movement of a probe within a chamber by considering respiratory movement. As used herein, the term "probe" may be used interchangeably with the term "catheter", and one of ordinary skill in the art will understand that any type of sensing device may be implemented with the configurations disclosed herein.

[0025] A system and method are disclosed. The system and method include: obtaining respiratory data of a patient via at least one sensor; obtaining probe position data of a probe positioned within a chamber; generating probe-chamber position data by compensating the respiratory data from the probe position data; identifying a period of time during which the probe is stable relative to the chamber boundary based on the probe-chamber position data; and capturing data based on the generated probe-chamber position during the identified period of time to generate a map.

[0026] The method includes: obtaining respiratory data of a patient via at least one sensor; obtaining probe position data of a probe positioned within a cavity; generating compensated position data by compensating the obtained probe position data with the obtained respiratory data; and generating a map based on the generated compensated position data. The method may include applying a filter to the respiratory data. Obtaining the respiratory data may include obtaining a respiratory index (abbreviated herein as "RI") via at least one sensor. Obtaining the respiratory data may include: transforming the respiratory index by using singular value decomposition (SVD) to obtain a first eigenvector and a first eigenvector derivative, the first eigenvector corresponding to a primary respiratory signal and the first eigenvector derivative corresponding to a phase-shifted respiratory signal. Transferring the primary respiratory signal and the phase-shifted respiratory signal from two dimensions to a three-dimensional ellipsoid based on a correlation matrix. Determining the correlation matrix based on a weighting factor, the weighting factor including at least one of the following: the age of the respiratory data; the speed of the probe; and the respiratory depth. The method may include generating an image that includes an estimated respiratory motion based on the respiratory data, a probe motion based on the probe position data, and a probe-cavity motion based on the probe-cavity position data. The method may include notifying a surgeon of a period during which the probe is stable relative to the cavity boundary. Notifying the surgeon includes notifying the surgeon of the period during which the probe is stable relative to the cavity boundary via a visual indicator displayed on a monitor. The method may include identifying a site stability site based on a period during which the probe speed is less than 2 mm / second for at least three seconds.

[0027] The system may include: a probe configured to be inserted into a body cavity of a patient; at least one sensor configured to obtain respiratory data, the probe and the at least one sensor being configured to obtain probe position data; and a processor configured to generate compensated position data by compensating the obtained probe position data with the obtained respiratory data; and generate a mapping based on the generated compensated position data. The processor may be configured to apply a filter to the respiratory data and the probe position data. The processor may be configured to generate a respiratory metric based on the respiratory data from the at least one sensor. The processor may be configured to: transform the respiratory metric by using singular value decomposition (SVD) to obtain a first eigenvector and a first eigenvector derivative, the first eigenvector corresponding to a primary respiratory signal, and the first eigenvector derivative corresponding to a phase-shifted respiratory signal. Transfer the primary respiratory signal and the phase-shifted respiratory signal from two dimensions to a three-dimensional ellipsoid based on a correlation matrix. Determine the correlation matrix based on a weighting factor, the weighting factor including at least one of the following: the age of the respiratory data; the speed of the probe; and the respiratory depth. The processor may be configured to generate an image including an estimated respiratory motion based on the respiratory data, a probe motion based on the probe position data, and a probe-cavity motion based on the probe-cavity position data. The processor may be configured to notify a surgeon of a period of time during which the probe is stable relative to the cavity boundary. Notifying the surgeon of a period of time during which the probe is stable relative to the cavity boundary includes displaying a visual indicator on a monitor. The processor may be configured to identify a site stability site based on a period of time during which the probe speed is less than 2 mm / second for at least three seconds.

[0028] Reference Figure 1 , which shows an example system shown as system 10 (e.g., a medical device setup and / or a catheter-based electrophysiology mapping and ablation system), in which one or more features of the subject matter herein may be implemented according to one or more embodiments. As described herein, all or part of system 100 may be used to collect information (e.g., biometric data and / or training datasets) and / or to implement machine learning and / or artificial intelligence algorithms. As shown, system 10 includes a recorder 11, a heart 12, a catheter 14, a model or anatomical map 20, an electrogram 21, a spline 22, a patient 23, a physician 24 (representing any medical professional, technician, or clinician), a position pad 25, one or more electrodes 26, a display device 27, a distal tip 28, a sensor 29, a coil 32, a patient interface unit (PIU) 30, an electrode skin patch 38, an ablation energy generator 50, and a workstation 55. It should also be noted that each element and / or item of system 10 represents one or more of that element and / or that item. Figure 1An example of the system 10 shown can be modified to implement the embodiments disclosed herein. The embodiments disclosed in the present invention can be similarly applied using other system components and settings. Additionally, the system 10 can include additional components such as elements for sensing electrical activity, wired or wireless connectors, processing and display devices, etc.

[0029] The system 10 includes a plurality of catheters 14 that are inserted by a physician 24 through the patient's vascular system via the skin into the chambers or vascular structures of the heart 12. Generally, a delivery sheath catheter is inserted into the left atrium or right atrium near the desired location in the heart 12. Thereafter, a plurality of catheters can be inserted into the delivery sheath catheter to reach the desired location. The plurality of catheters 14 can include a catheter dedicated to sensing intracardiac electrogram (IEGM) signals, a catheter dedicated to ablation, and / or a catheter dedicated to both sensing and ablation. An example catheter 14 configured for sensing IEGM is illustrated herein. The physician 24 brings the distal end 28 of the catheter 14 into contact with the heart wall for sensing a target site in the heart 12. For ablation, the physician 24 similarly brings the distal end of the ablation catheter to the target site for ablation.

[0030] The catheter 14 is an exemplary catheter that includes at least one (preferably a plurality of) electrodes 26 that are optionally distributed on a plurality of splines 22 at the distal end 28 and are configured to sense IEGM signals. Additionally, the catheter 14 can further include a sensor 29 that is embedded in or near the distal end 28 for tracking the positioning and orientation of the distal end 28. Optionally and preferably, the position sensor 29 is a magnetic-based position sensor that includes three magnetic coils for sensing three-dimensional (3D) position and orientation.

[0031] The sensor 29 (e.g., a positioning or magnetic-based sensor) can operate with a position pad 25 that includes a plurality of magnetic coils 32 configured to generate a magnetic field in a predefined workspace. The real-time positioning of the distal end 28 of the catheter 14 can be tracked based on the magnetic field generated by the position pad 25 and sensed by the sensor 29. Details of magnetic-based position sensing techniques are described in U.S. Patents Nos. 5,539,199, 5,443,489, 5,558,091, 6,172,499, 6,239,724, 6,332,089, 6,484,118, 6,618,612, 6,690,963, 6,788,967, and 6,892,091.

[0032] System 10 includes one or more electrode patches 38 that are positioned to contact the skin of patient 23 to establish a position reference for impedance-based tracking of position pad 25 and electrodes 26. For impedance-based tracking, current is directed toward electrodes 26 and sensed at patches 38 (e.g., electrode skin patches) such that the position of each electrode can be triangulated via patches 38. Details of impedance-based position tracking techniques are described in U.S. Pat. Nos. 7,536,218, 7,756,576, 7,848,787, 7,869,865, and 8,456,182, which are incorporated herein by reference.

[0033] Recorder 11 displays electrogram 21 captured with electrodes 18 (e.g., surface electrocardiogram (ECG) electrodes) and intracardiac electrogram (IEGM) captured with electrodes 26 of catheter 14. Recorder 11 may include pacing capabilities for pacing the heart rhythm and / or may be electrically connected to an independent pacemaker.

[0034] System 10 may include an ablation energy generator 50 that is adapted to conduct ablation energy to one or more of electrodes 26 at the distal end 28 of catheter 14 that is configured for ablation. The energy generated by ablation energy generator 50 may include, but is not limited to, radiofrequency (RF) energy or pulsed field ablation (PFA) energy (including monopolar or bipolar high voltage DC pulses that may be used to effect irreversible electroporation (IRE)), or combinations thereof.

[0035] PIU 30 is an interface configured to establish electrical connectivity between the catheter, electrophysiology equipment, power supply, and workstation 55 for controlling the operation of System 10. The electrophysiology equipment of System 10 may include, for example, a plurality of catheters 14, position pads 25, surface ECG electrodes 18, electrode patches 38, ablation energy generator 50, and recorder 11. Optionally and preferably, PIU 30 further includes processing capabilities for performing real-time calculations of the position of the catheter and for performing ECG calculations.

[0036] Workstation 55 includes a memory, a processor unit with a memory or storage device loaded with appropriate operating software, and user interface capabilities. Workstation 55 may provide a plurality of functions, optionally including: (1) performing three-dimensional (3D) modeling of endocardial anatomy and rendering a model or anatomical map 20 for display on display device 27; (2) displaying, on display device 27, an activation sequence (or other data) compiled from the recorded electrograms 21 as representative visual markers or images superimposed on the rendered anatomical map 20; (3) displaying the real-time position and orientation of a plurality of catheters within a heart chamber; and (5) displaying, on display device 27, sites of interest (such as where ablation energy has been applied). A commercial product embodying the elements of system 10 may be the CARTO TM 3 system, which is available from Biosense Webster, Inc., 31A Technology Drive, Irvine, CA 92618.

[0037] For example, system 10 may be part of a surgical system (e.g., a system sold by Biosense Webster ), which is configured to obtain biometric data (e.g., anatomical and electrical measurements of a patient's organ such as heart 12, as described herein) and perform a cardiac ablation procedure. More specifically, the treatment of cardiac disorders such as arrhythmias typically requires obtaining a detailed map of the heart tissue, chambers, veins, arteries, and / or electrical pathways. For example, a prerequisite for successfully performing catheter ablation (as described herein) is that the cause of the arrhythmia is accurately located within the chambers of heart 12. Such localization can be accomplished via an electrophysiological study during which the electrical potential is spatially resolved using a mapping catheter (e.g., catheter 14) introduced into the chambers of heart 12. This electrophysiological study (so-called electroanatomical mapping) thus provides 3D mapping data that can be displayed on display device 27. In many cases, the mapping function and the treatment function (e.g., ablation) are provided by a single catheter or a group of catheters, such that the mapping catheter also operates as a treatment (e.g., ablation) catheter simultaneously.

[0038] In another aspect, at least one uniaxial magnetic sensor mounted on the distal end of the catheter is configured to work in conjunction with at least one external magnetic sensor in a patient pad (i.e., under the patient). In one embodiment, there are three magnetic sensors mounted on the catheter, which are oriented in three different directions (i.e., spaced 120 degrees apart) and are configured to work in conjunction with three external magnetic sensors. Details of this technique are provided in the following documents: U.S. Patent 5,391,199, U.S. Patent 5,443,489, U.S. Patent 5,558,091, U.S. Patent 6,172,499, U.S. Patent 6,177,792, U.S. Patent 6,690,963, U.S. Patent 6,788,967, and U.S. Patent 6,892,091, each of which is incorporated herein by reference as if fully set forth herein.

[0039] In another aspect, an impedance sensor on a catheter is provided that is configured to be used without any external sensors. Details of this technique are provided in the following documents: U.S. Patent 5,944,022, U.S. Patent 5,983,126, and U.S. Patent 6,456,864, each of which is incorporated herein by reference as if fully set forth herein. As used herein, the term sensor refers to any of the sensor arrangements described herein, whether the sensor is integrated with the catheter or includes a combination of a sensor integrated with the catheter and additional sensors (such as external sensors).

[0040] Regardless of the sensor arrangement, these sensors assist in modeling the patient's respiratory cycle and identifying when the patient's lungs are inhaling or exhaling. In one embodiment, when the lungs are inflated or filled with air, the impedance increases. The sensors can also generate a magnetic field, and this magnetic field can be used to detect the absolute position of the catheter 14. Through this disclosure, those of ordinary skill in the art will understand that various methods and sensors can be used to determine the patient's respiratory cycle or the position of the catheter. The respiratory movements collected from the sensors can be used to generate an ellipsoid for providing a respiratory cycle model. (i.e., Figure 2 element 120 in).

[0041] Figure 2 Exemplary image 110 is illustrated, which includes data regarding respiratory movement 120, catheter movement 130, reconstruction movement 140, and intracardiac movement 150. This information is configured to be displayed on monitor 3. Each of these components is described in more detail herein.

[0042] The term respiratory movement 120 refers to movement based on respiratory data, which can be centralized or collected in various ways. For example, in one embodiment, a probe, sensor, or patch attached to the patient's body or sensor (such as Figure 1The sensor 18) therein is integrated with the catheter 14 that can be used in combination with an external sensor 18. This data reflects the volume change of the patient's lungs. The respiratory movement 120 can be based on the movement of the patient's diaphragm during respiration. Signals can be extracted by placing electrodes on the body surface. In one embodiment, the respiratory movement 120 is based on the electrical signal between the electrodes on the probe and the patch on the body surface. On the other hand, the probe can measure the displacement of one part of the body relative to another part of the body. This information is collected and further processed using the methods disclosed herein. In one aspect, the main or significant signal is generated by a sensor (such as Figure 1 the sensor 18) therein. Then the main or significant signal is further processed, and the derivative of the signal is determined. Respiratory metrics and respiratory vectors related to the respiratory movement 120 are described herein.

[0043] Figure 2 The catheter movement 130 (also referred to as probe or sensor movement or raw catheter movement) illustrated therein reflects data regarding the absolute position of the catheter 14. Any known tracking or sensing configuration for the catheter, probe, sensor, etc. can be used to generate this data. In one aspect, a magnetic field and sensors 29 located on the catheter 14 are then used to detect the position of the catheter 14. For example, a magnetic transmitter outside the patient can be used to obtain catheter movement data, and the magnetic transmitter generates a signal based on the position of the catheter 14. In one aspect, the sensor configuration includes at least three magnetic sensors that are oriented in three different directions relative to each other, and the at least three magnetic sensors are mounted on the end of the catheter and work in combination with a plurality of sensors outside the patient. On the other hand, an impedance sensor is provided and no external sensors are required. The catheter movement data can be further processed via any combination of filtering, smoothing, or post-acquisition signal processing.

[0044] Figure 2 The reconstructed movement 140 is a control element in one aspect. In other words, the reconstructed movement 140 is generated to provide a validity and error check regarding whether there is an undesired amount of difference between the reconstructed movement itself and the raw catheter movement 130. In one aspect, the reconstructed movement 140 is equal to the respiratory movement 120 plus the raw catheter movement 30. An error variable, also referred to as ERR and described in more detail herein, can be determined based on the offset of the reconstructed movement 140 from the raw catheter movement 130. This determination based on the reconstructed movement 140 can be achieved by subtracting or adding the raw catheter movement 130.

[0045] Intracardiac motion 150 is also referred to as catheter - cardiac motion or probe - cavity motion. In one aspect, intracardiac motion 150 is measured in millimeters, but any metric can be used to measure it. This motion data can generally refer to any type of motion data of a probe, catheter, or sensor relative to any wall of the chamber or the chamber itself. This data can ultimately be used to determine site stability, particularly site stability related to ablation in a patient's heart. Since this information indicates when the catheter is stable relative to the heart chamber wall, intracardiac motion 150 is generally important to a surgeon. Using intracardiac motion 150, stability points, also referred to as stability positions or sites, can be found. This information is important for determining various parameters regarding ablation lesions, such as location, size, etc. During respiration, it is generally assumed that the catheter and the heart move up and down based on diaphragm contraction. If the catheter is unstable relative to the heart wall or boundary during ablation (i.e., the catheter is not in contact with the heart wall or boundary), the ablation will likely be unsuccessful or may not ablate the desired target location. In one aspect, the present disclosure aims to provide a more reliable and accurate mapping of intracardiac motion 150 (i.e., catheter - cardiac motion or probe - cavity motion) based at least in part on respiratory motion and other features, which addresses issues associated with any sudden movements that occur between end - exhalations. In other words, by providing more accurate information about the relative position of the catheter and the cavity boundary, a surgeon can account for any movements that occur between end - exhalations and use this movement to facilitate decisions regarding the ablation procedure.

[0046] In one aspect, the system, method, and process are based on three features: (1) signal processing, (2) respiratory compensation, and (3) site discovery or site stability position. Those skilled in the art will understand that any one or more of these features can be used independently of the other features. For example, features (1) and (2) can be implemented without feature (3). In another aspect, feature (3) is specifically implemented as a procedure involving the heart and specifically relates to ablation of a particular region of the heart. Any of the features described herein can be used for or are suitable for determining aspects of a chamber and are not specifically limited to being implemented only relative to a heart chamber (i.e., navigation in the lungs).

[0047] In one aspect, signal processing includes converting a respiratory index (RI) into a primary respiratory signal and its derivative, i.e., a respiratory vector (RV). In one implementation, respiratory data is collected based on sensors, which can include a catheter integrated with the sensor, an external sensor, or any combination thereof. In one aspect, before converting the RI to the RV, ablation effects are accounted for with ablation offset correction, and a low - pass filter can be used to reduce heartbeat movement. In one aspect, the disclosed subject matter is based on decomposing catheter motion into two components: respiratory motion and catheter - cardiac motion. Respiratory motion is generally assumed to be related to the RV via a correlation matrix A.

[0048] Assume that the catheter - heart motion is smooth and can be modeled by a spline curve. Based on the above, Equation 1 is provided:

[0049] Measured motion = Respiratory motion (A×RV)+Catheter - heart motion+ERR

[0050] (Equation 1)

[0051] Here, the correlation matrix A and the spline parameters are incremented and tuned simultaneously so as to minimize the root mean square (RMS) deviation between the measured motion after low - pass filtering and the reconstructed motion, thereby minimizing the error.

[0052] In one aspect, the correlation matrix A is regressed in step n to match the respiratory motion of step n - 1, which is the total motion in step n - 1 minus the in - heart motion. In other words, A is regressed in the current state to match the respiratory motion of an earlier state, and the respiratory motion is equal to the total motion minus the in - heart motion in the earlier state.

[0053] The correlation matrix A is a two - row by three - column data set. In one aspect, the correlation matrix A is configured to transform a two - dimensional RV into an ellipsoid in three - dimensional space. The matrix values are tuned or selected so as to adjust the size of the respiratory ellipsoid (i.e., in mm) by multiplying the matrix values by the RV values. In one aspect, the RV values are relatively small (i.e., 1.e -3 ), and these values are multiplied by larger values (i.e., 3,000) in the A matrix to produce a movement (i.e., a 3.0 mm movement) in three - dimensional space.

[0054] Regarding the respiratory motion, in one aspect, there are five RI channels. Those skilled in the art will understand that the number of RI channels can vary. RI generally represents the changes in lung motion and lung volume over time. RI generally each contain similar signals and noise. In one aspect, the RI is collected by attaching multiple probes directly to the patient. For example, six patches or probes can be attached to the patient. The electrical signals between the six patches or probes are measured and the RI is generated. The electrical signals can include voltage, resistance, and / or impedance. In one embodiment, the RI corresponding to each patch or probe can be provided in mV.

[0055] Specifically, in one embodiment, the RI is a measure of the change in resistance (impedance) between the patches, where the resistance is extracted via time decomposition to obtain the respiratory - related frequency band. The RI reflects the change in the amount of air flowing into and present in the lungs. The change in the airflow into the lungs and the air within the lungs may change the resistance measurements between the patches (among the six patches).

[0056] The filter can be applied to the RI to remove noise or other elements. For example, a low-pass finite impulse response (FIR) filter can be used to remove the imprint of the heartbeat on the RI signal. In one aspect, this step multiplies 151 elements on each original data channel by a 151 weight vector to produce a filtered data sample. In other words, the filter multiplies the corresponding samples in a sliding window manner. Those skilled in the art will understand that these parameters can vary.

[0057] Singular value decomposition (SVD) can be used to find the weights that select the most prominent respiratory signal when multiplied by the measure of five RIs. This filtering step and processing step provide a smoother output signal and eliminate the unwanted noise and interference caused by the heartbeat.

[0058] Extract the principal respiratory vector based on the RI. In one aspect, this extraction is performed by calculating the first eigenvector in the SVD.

[0059] Generally, SVD is configured to extract the most prominent common motion from several similar respiration-dependent signals. The common signal is found to be a linear combination of individual RI signals. In one aspect, to verify the consistency of the linear weights, three SVDs are performed, including: the first on the first 30 seconds of the RI buffer, the second on the subsequent 30 seconds, and the third on the entire 60 seconds. In the case where the three sets of weights are similar enough, the last set is selected. In other words, the similarity here refers to V from the SVD. Then, this V becomes the weight vector, that is, the RI2RV weight vector. In one aspect, the similarity parameter has a Pearson correlation of at least 0.5.

[0060] In one aspect, the process involves determining the first eigenvector in the SVD of five RI signals over several respiratory cycles. For example, a sixty-second buffer can be used, which can correspond to ten respiratory cycles. By using multiple cycles, the RI signals can be ensured to be accurate and reliable.

[0061] The disclosed subject matter also adds a phase-shifted signal to provide an elliptical-like motion or model, as shown by the estimated respiratory motion 120 in Figure 2 The phase-shifted signal is calculated by finding the derivative of the RI signal and combining the derivative using the same weights. This process is similar to the phase shift between the Cos(x) function and the Sin(x) function.

[0062] In one aspect, a respiratory vector (RV) is generated based on the principal respiratory vector and the derivative of the principal respiratory vector. In other words, the five RIs are processed using the steps disclosed herein to generate or determine two RVs. Once these RVs are determined, the following equation is provided:

[0063] Measured motion = respiratory motion (A × RV) + catheter - cardiac motion + ERR

[0064] (Equation 2)

[0065] Equation 2 is generally similar to Equation 1, except that Equation 2 uses RV instead of RI.

[0066] To determine the catheter position, the raw position data regarding the catheter is filtered. In one aspect, the raw position data is filtered using the same low - pass FIR filter applied to the RI signal to remove the heartbeat motion. As a result of the processing, a synchronized and filtered respiratory vector and catheter position are generated.

[0067] In summary, at the end of the signal processing, the process disclosed herein provides Figure 2 the signals 120 and 130 in

[0068] This feature generally includes estimating the correlation matrix A and spline parameters representing the actual motion of the catheter. Then the two RVs are transferred to a three - dimensional space.

[0069] Regarding the RV, the process disclosed herein generally transfers the 2D vector to a 3D ellipse or ellipsoid in 3D space, as shown in Figure 2 element 120 in Figure 2 The ellipse plotted in

[0070] can be oriented in any direction, and the main purpose of compensating for respiration involves modeling the movement of the patient's respiration on the catheter or probe. The correlation matrix A (also called the transformation matrix) converts a two - dimensional vector into a three - dimensional ellipsoid. In one aspect, the correlation matrix A is determined based on regression. The root - mean - square error (RMSE) between the modeled motion (i.e., respiratory motion + catheter motion) and the measured motion is minimized to provide the correlation matrix A.

[0071] As shown in Figure 3 a system 200 including a site stability module 210 is disclosed. The system 200 can be implemented, integrated, or configured to interact with the Figure 1 computing system 4 shown in

[0072] The site stability module 210 can include any one or more of the features disclosed herein. In one aspect, the site stability module 210 is configured to receive data from various input terminals. For example, position data 202, respiratory metric data 204, status data 206 (such as the position status and the status of RI), and initialization or algorithm parameters 208 can each be provided to the site stability module 210. In one embodiment, the streaming data is provided at intervals of 16.67 ms. Based on the present disclosure, those skilled in the art will understand that the data stream parameters and cycles or periods can vary.

[0073] The session stability time segment 212 (i.e., the start - end segment) and the catheter - heart position data 214 are transmitted from the site stability module 210 to the central module 220. In other words, the site stability module 210 provides output parameters that include the stability segment parameters in the last ablation session and the parameters of the last set of sites. In one aspect, stability is a segment of three seconds or longer duration in which the catheter - heart velocity is below a certain velocity threshold. A site is a part of a segment that is stable during ablation time, and the stability segments during ablation can be divided into several sites.

[0074] In one embodiment, the central module 220 can include any centralized computing system configured to receive, process, and / or transmit data to and from the site stability module 210. In one example, the central module 220 is CARTOVISITAG TM processing unit. Those skilled in the art will understand that various types of configurations for communicating with the site stability module 210 can be provided.

[0075] At the central module 220, the compensated position data 222 for each session can be saved and stored. This data 222 can be further processed, filtered, etc. at the site stability module 210. Optionally, a recording module 230 can be provided. The recording module 230 can be configured to assist in debugging and post - analysis of data collection. Figure 3 Element 224 in represents a recalculation step where information from the central module 220 is continuously provided to the site stability module 210 in order to refine and recalculate information regarding site stability.

[0076] Reference Figure 4A and Figure 4B , flowcharts are provided to illustrate the various steps of process 300 for providing site stability. As Figure 4A and Figure 4BAs shown, for site stability, process 300 generally includes two different phases: a preprocessing phase 310 and a stability detection phase 330. The preprocessing phase 310 is generally a phase in which the raw signal from the measurement results is filtered and transformed to provide a filtered catheter motion vector and a respiration vector. The filtered catheter motion may still include a respiration factor. The decomposition 350 in the stability detection phase 330 is where the filtered catheter motion is separated into a respiration motion and a non-respirator catheter motion. The stability detection phase 330 is a phase in which a sliding motion is used during ablation to detect ablation sites within a stable section. The preprocessing phase 310, the decomposition 350, and the stability detection phase 330 are described in more detail below.

[0077] In the preprocessing phase 310, position data 312 and RI 314 are provided. In other words, the preprocessing phase 310 includes at least two sets of channels: a position channel and an RI channel. The data may be set or generated in relatively small increments. For example, the data may be set at least once every 16.67 milliseconds.

[0078] The position data 312 is further processed. For example, the position data 312 may be processed by applying a low-pass filter or a low-pass FIR filter at step 316. After this step, the filtered position, i.e., the final filtered position, is determined at step 324.

[0079] The RI 314 may also be further processed through a series of steps or processes 318, 320, and 322. As Figure 4A shown, the RI 314 may first be analyzed at step 318 for ablation correction, which takes into account any offset caused by the ablator. The offset from the ablator affects the ability to estimate respiration motion. Thus, reducing the effect of the offset provides an accurate output for the RI 314. In one aspect, an ablation offset correction factor is determined by measuring the RI signal before and during ablation. Whenever the ablator is turned on, the estimated offset for each channel is compensated (e.g., subtracted or added) from the RI signal. The offset is estimated based on the change in the RI signal between the RI signal measured just before ablation starts and the RI signal measured just after the ablator is turned on. The effect of ablation offset is shown in Figure 5 which illustrates an ablation offset caused by a shift in the RI during ablation.

[0080] Return Figure 4A, the data is filtered at step 320. In one embodiment, the filter includes a low-pass filter and a derivative filter. In one embodiment, the low-pass filter and the derivative filter are configured as FIR filters with a 151-sample kernel. In one aspect, the preprocessing includes the following steps. First, obtain five raw RI channels. Second, perform ablation offset correction. Third, perform low-pass and derivative filters to create filtered respiratory indices (FRI) and derivative respiratory indices (DRI) using a 151-sample sliding window of the FIR filter. Fourth, perform SDV to find the most prominent weights. Fifth, implement the most prominent weights on the FRI to obtain RV(1) and implement the most prominent weights on the DRI to obtain RV(2). Those skilled in the art will understand that additional filters may be used and additional steps may be implemented.

[0081] Figure 6 An example of a filter that can be used at step 320 is illustrated, and the weights for the low-pass filter and for its derivative signal are illustrated. As Figure 6 shown, different weighting factors are applied over time for the low-pass filter and the derivative filter.

[0082] At step 322, the data regarding RI 314 is further processed by calculating the first eigenvector of RI 314 and by calculating the derivative of the first eigenvector of RI 314. SVD is used to find the first eigenvector of the filtered RI in order to reduce redundancy and noise. The averaged compensated (e.g., subtracted or added) filtered respiratory index (FRI) and derivative respiratory index (DRI) can be stored. In one embodiment, this information is stored in a one-minute buffer. Those skilled in the art will understand that the buffer duration can vary.

[0083] In one aspect, when the buffer is full of valid signals, SVD is performed only once at the start of a predetermined time period. SVD produces five coefficients of the first eigenvector (V1), and these five coefficients are used to produce the respiratory vector (RV) at step 326. In one embodiment, RV is equal to FRI×V1, DRI×V1.

[0084] At the end of the preprocessing stage 310, the last minute of the RV data (produced at step 326) and the filtered position data produced at step 324 are synchronized and stored in the last-minute buffer.

[0085] The data from steps 324 and 326 is provided to the stability detection stage 350. During the stability detection stage 350 (as Figure 4A shown at the bottom and Figure 4B top), motion decomposition 352 is performed. Figure 7BMore details regarding motion decomposition are provided. According to motion decomposition 352, the process includes calculating site stability and site 354. This step feeds data back to the site stability module 210, which is configured to receive catheter motion data and provide session motion data. Information regarding catheter motion and site information are stored in the site stability module 210. Site stability parameters can be recalculated based on the information provided to the site stability module 210.

[0086] Figure 7A and Figure 7B A more detailed overview of the preprocessing stage 310 is illustrated in and. As shown in process 600, the preprocessing stage begins with obtaining a data set, i.e., step 602. The data set can include RI, position, status, and various other information.

[0087] As Figure 7A shown on the right, an ablation correction stage 604 is provided. This stage is configured to correct the offset caused in the RI signal due to ablation interference. The ablation correction stage 604 includes setting a buffer at step 604a. Next, at step 604b, the offset for each channel is estimated by comparing the RI signal edges (i.e., the signal 5 and 95 percentiles) just before and just after the ablator is turned on. Then, at step 604c, this estimated offset is compensated (e.g., subtracted or added) from the RI signal. In one aspect, the ablation offset for each ablation session is measured and corrected, and the ablation offset is performed dynamically and continuously. For each ablation, the estimate becomes more accurate as the offset measurement results are averaged over time.

[0088] As Figure 7A shown, at step 604a, the buffer can include a 1.5 - second buffer of the RI signal to enable fast estimation. The buffer can include at least 151 RI signal samples. The parameters used to illustrate the effect of ablation on the RI signal can vary depending on the circumstances, such as the number of RI channel signals, ablation intensity and duration, patient characteristics, etc. The data from step 604c is ultimately sent to step 616, which is described in more detail below.

[0089] Continuing with step 602, a verification step 608 is performed to determine whether the data set is complete. In one aspect, the computing system 4 or the manager in the software module confirms the validity of the data. As used herein, the term manager can refer to any computing interface, such as VISITAG TMAn interface within the processing unit or central module 220. This component is configured to insert or input data and also receive the output of the data. In the case where the data is invalid, the manager sends a reset or soft reset signal to the algorithm processor to clear all buffers, and if an error is detected, the buffers are filled again with new data. The algorithm ensures that the buffers are filled with valid data before proceeding to the subsequent steps.

[0090] If it is determined that the data is invalid due to location data problems, the buffer is reset at step 610, and the position filter buffer is updated at step 614. In one embodiment, step 614 includes 151×3 samples.

[0091] If it is determined that the data is invalid due to RI channel-related problems, the buffer is reset at step 612, and the RI filter buffer is updated at step 616. In one embodiment, step 616 includes 151×5 samples.

[0092] If it is determined that the data is valid, the position filter buffer and the RI filter buffer are still updated as described with respect to steps 614 and 616, but there is no need to reset the buffer.

[0093] Continuing with steps 614 and 616, both the position data and the RI signal data undergo a filtering step. The position data from step 614 is filtered at step 618. In one embodiment, the filter at step 618 is a low-pass FIR filter. The RI signal data from step 616 is treated similarly. In one embodiment, the filter at step 612 is a low-pass and derivative FIR filter.

[0094] Then, the position buffer is updated at step 622 after step 618. Step 624 comes after step 620 and includes the updated RI buffer. After step 622, the position data is further processed at step 626, which includes obtaining the last minute buffer and obtaining the filtered catheter movement.

[0095] Regarding the RI signal data, after step 624, the data is further processed using the eigenvector estimation stage 606. Generally, stage 606 includes transforming each sample in the 5D RI vector into its first eigenvector using the eigenvector coefficients in vector V. In one aspect, vector V is a five-element vector of weights that multiplies five RIs to obtain the most prominent respiratory vector. In one aspect, vector V is obtained via SVD decomposition, and RI = U*S*V'.

[0096] Step 606a includes measuring breakpoints in the derivative RI (DRI) signal. Step 606b includes checking that there are no breathing gaps and that the data is valid. Step 606c includes calculating V using SVD. The SVD is calculated only if the FRI buffer is full of valid digits and no breathing gaps are found. In one embodiment, this step may include calculating V at one second intervals. Once V is obtained at step 606d, the data may be further processed at steps 628 and 632. Step 606e is a validity check and if a soft reset signal is received, the vector V is reset to zero.

[0097] Step 628 includes checking if (V1)>0. If not, step 630 includes waiting for the next round of data. If so, step 632 defines RV = [FRI×V, DRI×V]. Calculating V (or RI2RV as used in one aspect of the algorithm) includes the following steps. Once the RV buffer is full, two second tests may be performed to determine if there are any breathing gaps (i.e., due to apnea) within the DRI buffer. If the buffer is full and no gaps are present, calculations are made to determine three different Vs by three different SVDs: one on the first 30 seconds, one on the last 30 seconds, and one on the entire 60 second DRI buffer. If all three Vs are related (i.e., within a predetermined value of each other), the 60 second V is selected as the new positive V. Then, the process verifies that the minimum value of the resulting respiratory signal RV(1) is related to the maximum value of the y position of the probe. In one aspect, the minimum value of the respiratory signal (RV1) preferably represents the end of exhalation (as opposed to the beginning of exhalation). For this process, the corresponding Y value of the catheter position is checked and the process ensures that the Y value in the respiratory minimum is greater than the Y value in the respiratory maximum because a human has the maximum Y value at the end of exhalation. In this orientation, the Y direction is oriented along the patient's heart-head direction when the patient is supine.

[0098] If the resulting respiratory signal RV(1) is not related to the maximum value of the y position of the probe, the selected V is reversed to negative (i.e., -V). Finally, step 634 includes using the last minute buffer used in step 626 to provide RV.

[0099] As Figure 7B shown at the top of (including in Figure 7A to provide an overlapping reference), filtered motion is provided from step 626 and RV is provided from step 634. Step 636 includes providing a correlation matrix A that takes into account weight buffering and breathing gaps. Generally, Figure 7BShows steps for motion decomposition according to one aspect. Motion decomposition assumes that the filtered catheter motion (FCM) can be modeled as the sum of the following two main components: (1) Respiratory Compensation Vector (RCV) = Correlation Matrix A × RV; and (2) Catheter-Heart Motion = Smooth(FCM - RCV) = Spline(FCM - RCV).

[0100] As used herein, the term "smooth(x)" generally refers to a mathematical smoothing function, which can be implemented according to any computer interface, electronic device, or programming tool such as to implement. Various other functions used herein such as "smooth", "exp", "pinv", "spline", "rms", "norm", etc. can be implemented using any known computer interface, electronic device, or programming tool such as to implement. These computer interfaces, electronic devices, or programming tools are generally configured to provide matrix, array, and data manipulation, plotting functions, data implementation, and / or algorithm implementation.

[0101] The term "smoothing" used with respect to the functions or equations herein refers to a method of noise reduction or, in one aspect, to a data set. In one aspect, the smoothing function uses a moving average, a filter, or a smoothing spline.

[0102] In one aspect, the term "spline(x)" used with respect to the functions or equations herein refers to a function that calculates, returns, or generates a vector with interpolated values corresponding to the points in an array, matrix, or data set x. In one aspect, cubic spline interpolation can be used.

[0103] In one aspect, the term "exp(x)" used with respect to the functions or equations herein refers to the exponential function e x and determines each element in the array, matrix, or data set x.

[0104] In one aspect, the term "pinv(x)" used with respect to the functions or equations herein refers to a function that calculates, returns, or generates the Moore-Penrose pseudoinverse matrix of an array, data set, or matrix x.

[0105] In one aspect, the term "norm(x)" used with respect to the functions or equations herein refers to a function that determines a scalar or quantitative value and calculates, returns, or generates a measure of the magnitude of the elements in an array, matrix, or data set x.

[0106] In one aspect, the term "max(x)" used with respect to the functions or equations herein refers to a function that calculates, returns, or generates the maximum element in an array, matrix, or data set x.

[0107] In one aspect, the term "rms(x)" as used with respect to a function or equation herein refers to a function that calculates, returns, or generates the root mean square of an array, matrix, or data set x.

[0108] Any one or more of the functional terms used herein can be implemented using any computing element or software program configured to perform computational analysis and functions. One such example of a program is Those skilled in the art will understand that these mathematical or programming functions and their equivalents or variants can be implemented to perform the specific functions, computations, or operations on the data, matrices, arrays, and information described herein. Any of the functions described herein can be implemented on a computing system 4 or any other electronic or computer hardware and software.

[0109] In one embodiment, the decomposition is calculated every 100 ms. These 100 ms segments can be calculated within the last full minute of the data to allow the correlation matrix A to adjust slowly within the last minute.

[0110] In one aspect, two versions of catheter cardiac motion are used by controlling the curvature of the spline. In one version, a single curvature or smoothing parameter is used to calculate the intermediate catheter motion. Step 638 includes estimating the intermediate catheter motion using the following calculation: Smooth(FCM - RCV). This smoothing parameter limits the rate of change of the spline direction and is equivalent to assuming slow acceleration. The intermediate catheter motion is used in the estimation of the correlation matrix A. Specifically, segments with a slow intermediate catheter motion speed (i.e., slow segments) are used to tune the correlation matrix A. On these slow segments, the catheter acceleration is small relative to the respiratory motion and the correlation matrix (A) is estimated more accurately. During step 638, a smoothing function is used to estimate the intermediate catheter motion to limit the resulting curvature.

[0111] In one aspect, Intermediate Catheter Motion (ICM) = Smooth(T0, (FCM - RCV), SmoothP). In this equation, T0 is a vector of reference times (0 seconds to 60 seconds, incrementing by 0.1 seconds). The inaccuracy is estimated by the norm function of the estimation error. Using step 638, ICM has an error component (ICMerr) and ICMerr = Norm(FCM - RCV - ICM), and SmoothV = Exp(-2 * ICMerr), and SmoothP = 0.005. The process corrects or updates the ICM where ICMerr is unacceptably large to allow for greater curvature. If ICM equals FCM - RCV, then ICMerr will be zero.

[0112] SmoothP is a smoothing parameter that limits the second derivative (i.e., acceleration) of the spline curve and is typically defined as a smoothing parameter to control the curvature through a section. SmoothV is typically defined as a smoothing vector to control the curvature at each data point. During step 638, it is assumed that slow acceleration helps converge the correlation matrix A. Slow acceleration (i.e., intermediate catheter motion (ICM)) is used in combination with faster acceleration (i.e., catheter motion (CM)) to provide a more robust and accurate reconstruction. The ICMerr or error of ICM is an estimation error and this is equal to Norm(FCM - RCV - ICM). ICMerr is used to estimate the accuracy of the reconstruction of FCM. Based on this, when the error of the slow motion is high, the spline is corrected. In one aspect, the smoothing function uses a moving average filter to smooth the data.

[0113] In another aspect, as shown in step 648, the catheter motion (CM) is the ICM corrected using a correction vector (SmoothV), and fast acceleration is allowed in cases where the intermediate catheter motion is determined to be inaccurate. This correction vector (SmoothV) allows different curvatures (i.e., accelerations) at different points along the last minute of the data. CM is equal to: Smooth(T0, (FCM - RCV), SmoothP, smoothV). This process is a way to force the smoothing function to be more flexible, which allows for a larger curvature in the points in cases where the reconstruction error is large.

[0114] Step 650 includes estimating the catheter motion based on the following equation: Smooth(FCM - RCV, SmoothV). In one aspect, a function or process is performed to smooth the catheter motion by limiting the spline curvature (i.e., the second derivative of the curvature) using a spline model.

[0115] When estimating CM, fast sections are allowed to have an increased curvature to provide a more accurate and better reconstruction. The smoothing spline function f is shown in the following equation:

[0116]

[0117] In this equation, |z|2 represents the sum of the squares of all the entries of z, n is the number of entries of x, and the integral is taken over the smallest interval containing all the entries of x. The default value of the weight vector W in the error measure is ones(size(x)). The default value of the piecewise constant weight function smoothV in the roughness measure is the constant function 1.

[0118] In equation 3, D 2f represents the second derivative of the function f. The parameter smoothP controls the smoothing intensity. When smoothP = 1, the spline passes through all the original data points without any curvature constraints. When smoothP = 0, the spline cannot include any curvature, and thus the result is a straight line. In this case, smoothing can be used to control the allowed acceleration of the catheter.

[0119] For intermediate catheter motion (ICM), only non-zero smoothP parameters are used, and sharp movements are smoothed or filtered out. To simulate fast or sharp movements, a smoothV vector with a value less than 1 is used. SmoothV values less than 1 impose additional weights or emphasis on certain aspects and can thus include higher curvature in those places.

[0120] In this regard, the complete concept of smoothing is to balance between two conflicting elements. Even in the presence of noise (i.e., many inconsistent direction changes), the first element generally tries to remain true or accurate to the input vector. The second element is as smooth as possible, with minimal direction and speed changes. SmoothV is a vector of values that guides the smoothing function to favor one element over the other in different parts of the input vector or vice versa. Thus, when the SmoothV value is closer to 0, the smoothing function should be more accurate and smoother. In one aspect, smoothV allows smoothP in Equation 3 to be localized.

[0121] According to step 650, at step 652, the catheter motion is used for site segment detection. At step 654, the catheter motion and site segment detection data are sent to the central module 655, which is configured to store data regarding the site segments and the catheter motion. Then, an iterative process is performed that analyzes the session motion and recalculates the ablation sites. The ablation is divided into sites and recalculated at each step and can vary significantly based on the compensated catheter-heart motion. The compensated catheter position can be saved in the central module or a storage unit for site recalculation after the user or surgeon has adjusted various control parameters.

[0122] Step 640 includes calculating the RCV in the last minute of information and data. In one embodiment, RCV = A × RV. In one aspect, the correlation matrix A is also identified as RV2RCV. According to one embodiment, the estimation of the correlation matrix A is typically performed using weighted mean square values and matrix transpose. The weights (W) used in these calculations reflect how each data point in the last minute is configured to estimate the correlation matrix A based on at least one of the following criteria: (i) weight decay over time, (ii) preference for slow intermediate catheter motion speed, (iii) RV validity, and (iv) duration since ablation start.

[0123] Regarding factor (i), older samples typically receive a lower weight than more recent samples. Regarding factor (ii), slower speeds over long segments receive a higher weight. Regarding factor (iii), during apnea, the RV decreases to very close to zero and cannot be used to estimate the correlation matrix A. Thus, apnea segments receive a zero weight. Apnea detection occurs at step 642. Apnea is also commonly referred to as respiratory depth, or hypopnea. Factor (iv) causes more recent ablation segments to receive a higher weight. The weight vector is identified as the stability weight, and the step of estimating the stability weight is typically shown as Figure 7B step 644 in

[0124] More specifically defines the apnea detection associated with factor (iii) and step 642. Apnea is a time segment during which the respiratory signal (i.e., RV) decreases due to apnea or other forms of hypopnea. Due to its effect, the correlation matrix A should generally not be estimated during these segments. By using the weights associated with each sample (which includes RV and position data), the effect of apnea or hypopnea is minimized. The weights during hypopnea (i.e., small RV signals) are set to zero, which eliminates the effect of these measurements on the overall estimate and the correlation matrix A.

[0125] Figure 8 Illustrates the effect of apnea on RV during ablation. A gap in respiration is typically defined as a time segment during which the respiratory derivative cannot reach a predetermined extreme value and can only reach below 85% of the predetermined extreme value. As used herein, these small and large parameters are defined by analyzing the 15 - 85 percentiles, and signals above and below these percentiles are ignored. The continuous samples within the 15 - 85 percentiles are counted and analyzed. If the signals in a continuous segment occur within these thresholds and do not cross the thresholds, the segment of all samples in that continuous segment is indicated as a respiration gap (i.e., hypopnea) and can thus be discarded. The percentile values during the last minute of the selected data are chosen and these percentile values define the threshold amplitude of the respiratory derivative. When the respiratory derivative signal persists between the thresholds for a predetermined period such as 5 seconds (which roughly corresponds to one respiratory cycle), the respiratory signal is considered too weak and should thus be discarded. The percentile values can be modified and are typically chosen to identify hypopnea. Those skilled in the art will understand that other filtering or analysis can be used to identify hypopnea.

[0126] This document provides additional details regarding the estimation of stability weights. Generally, when the catheter is relatively stable or sliding slowly against another surface (i.e., tissue), the decomposition process into respiration-related motion and catheter-related motion is performed more accurately. Identifying relatively stable segments is important for generating reliable outputs. A stable segment is generally defined herein as a segment in which the intermediate catheter motion speed is relatively slow (i.e., <2.5 mm / sec) for a specific time period (i.e., >3 seconds to 5 seconds). Stable segments or substantially stable segments are used to estimate the correlation matrix A. The stable segments are emphasized by assigning higher weights to the samples during the stable time. The stability weights are converted into a vector of numbers corresponding to the last minute and represent the point correlation of the correlation matrix A estimate (i.e., the RV2RCV matrix). In addition to velocity stability, recency factors (i.e., age or newness of the segment) and the data segments collected during ablation are also considered. More recent samples receive higher weights than older samples, and ablation segments receive higher weights than non-ablation segments.

[0127] Figure 9 An aspect of the recency factor regarding stability weights is illustrated. As Figure 9 shown, higher weights are assigned to more recent samples (i.e., the right side of the figure), and older samples receive lower weights. As Figure 9 shown, an S-shaped smooth transition is provided between the most recent and older signals.

[0128] Figure 10 An aspect of the velocity factor regarding stability weights is illustrated. As Figure 10 shown, higher weights are used when the catheter estimated velocity is low, and lower weights are used when the catheter estimated velocity is high. In one aspect, the weight is reduced when the low velocity duration is shorter than the average respiratory cycle. As Figure 10 shown, the duration is adjusted by artificially increasing the estimated velocity.

[0129] Figure 11 An aspect of the ablation factor regarding stability weights is illustrated. Generally, when ablation is occurring, higher weights are assigned to the signals, as Figure 12 shown. In one aspect, the three factors described above regarding Figure 9 , Figure 10 and Figure 11 are used to generate the total stability weight. In other words, the total stability weight vector can be the per-sample product of all three factors. Those skilled in the art will understand that more than three factors can be used. Figure 12 An example of how the stability weighting factor can be measured over time is illustrated. In one aspect, the recency factor is calculated only at initialization because this factor is fixed.

[0130] Return Figure 7B , using the weight (W), the correlation matrix A is estimated at step 646 using the following equation: A(2×3) = Pinv(RV) × W(FCM - RCV). As used in this aspect, A(2×3) transforms RV (a two-column matrix representing the respiratory signal and its derivative) into RCV (a three-column matrix representing the X-Y-Z respiratory matrix). The matrix A is the transformation matrix between the respiratory vector (RV) and the 3D respiratory ellipse. This equation represents regression using the mean square because FCM = A*RV + CM + err, which represents the decomposition equation. Since FCM cannot be fully decomposed into these models, there will be some error (err), which represents the difference between FCM or the actual movement and the sum of the two models. Basically, these processes tune the transformation matrix A to minimize the error. To minimize the error, the correlation matrix A is regressed such that A = ((FCM - (CM)) / RV, which is equal to Pinv(RV)*(FCM - (CM)). In one aspect, a weight W is added to give more weight to time segments with a slow (CM), and the equation then becomes A = Pinv(W*RV)*W*(FCM - (CM)).

[0131] As Figure 7B shown, the correlation matrix A is then sent back to step 636 at step 646 to provide a loop or continuous cycle for continuous estimation and updating of the correlation matrix A. Figure 7B The process in

[0132] is iterative such that the correlation matrix A is continuously updated and becomes more accurate with each iteration. The correlation matrix A is typically updated based on the calculated intermediate catheter movement and any one or more of the following steps: estimating the apnea segment based on RV; estimating the stability weight based on the intermediate catheter movement; and regressing the correlation matrix A based on A = (FCM - ICM) / RV.

[0133] Intermediate catheter motion assumes that the catheter has a relatively slow acceleration and cannot represent the true catheter motion in the high-speed section. The intermediate catheter motion error factor can be estimated based on the faster moving section. Recalculating the catheter motion when increasing the curvature of the high-speed section also allows for fast motion. Using this method, ICM error (ICMerr) = Norm(FCM - ICM - RCV). In other words, the respiratory data is equal to the total catheter motion minus the intermediate catheter motion, and the total catheter motion minus the respiration is equal to the intracardiac catheter motion. In one embodiment, ICMerr is the Euclidean distance for each sample in R3 between the measured position FCM and the slow model reconstruction position ICM + RCV. In one aspect, SmoothV is used to correct the intermediate catheter motion to obtain the intracardiac catheter motion.

[0134] Site discovery, also known as position discovery or locus discovery, generally includes identifying periods of time when the catheter or probe is stable. The process includes performing logic and functions that generate the time margins and central positions of the site based on the compensated motion. The sites in the last session are continuously updated along with the correlation matrix A and the resulting compensated motion. Stability is generally defined as a continuous time period where the estimated catheter speed is below a user-specified threshold.

[0135] The catheter speed is calculated by taking the derivative of the catheter position within the last minute. As an example, the position in the last minute is updated at least every 0.1 seconds and saved for the entire minute. These values can vary according to the specific requirements of a particular application. The last catheter speed value from this buffer is saved and accumulated in the last speed buffer.

[0136] For example, in one embodiment, the end-5 value in the catheter speed buffer is the catheter speed using the end-5 value and the end-7 value in the catheter one-minute position buffer updated in the current iteration. Here, the end-5 value in the last speed buffer is the last catheter speed calculated for the previous 5 iterations.

[0137] The minimum of the catheter speed and the last speed within the last minute is defined as the minimum speed and is used to detect when the catheter is stable. The minimum speed buffer includes all the minimum speeds estimated during the last minute. Identify the periods of consecutive speeds in this buffer that are less than the threshold speed while attempting to find the period when the catheter is stable.

[0138] The estimate near the current time is not accurate enough because the speed can change significantly near the end of the buffer without a reliable indication of the estimated speed. Therefore, any determination regarding stability can be delayed by at least one second, and this predetermined delay can vary.

[0139] The boundaries of the current stable segment can be updated and then terminated if necessary. If the current stable segment is not found or if it was previously terminated, a search for a new stable segment of the estimated catheter movement in the most recent few seconds is performed.

[0140] If a stable segment is found, the boundaries are updated according to the updated minimum velocity buffer. The starting point of the current stable segment can also be updated by looking back from the last index of the stable segment until the earliest point where the velocity is below the threshold.

[0141] The end point of the stable segment can be updated by looking back from the last index of the stable end until the most recent point where the catheter velocity is below the threshold.

[0142] The stable segment is not terminated until a predetermined period of time (i.e., approximately one second) has elapsed since the current time, in order to avoid terminating the segment due to edge inaccuracies. If a stable segment has not been found, a search for stability is performed by retrieving back from the current time to find the earliest moment that crosses a predetermined threshold.

[0143] In addition to the above process for determining when a segment is stable, the present disclosure also relates to defining a localized segment as a time segment in which all of its points (i.e., the distance from its own center of gravity) are below a threshold. In other words, when considering a curve in 3D space, the XYZ average of the curve can be calculated and it is the center of gravity. Based on this value, the distance from the center (DFC) can be calculated for each point on the line.

[0144] This process involves locating a site (i.e., a time segment) when the ablator is on and the catheter is stable and localized. One aspect of this method is described in more detail herein. In one aspect, stable time segments during ablation are identified.

[0145] The processes and algorithms disclosed herein can divide, segment, or otherwise split the stable segments during ablation into localized segments (i.e., sites) in which the maximum DFC is below a predetermined threshold. In one aspect, the predetermined threshold is 3.0 mm. Those skilled in the art will understand that this value can vary. The algorithm for splitting the stable segment into sites essentially obtains the minimum number of sub - segments that maintain the localization criteria. In one aspect, the minimum number of sub - segments is a recursive function that ends or stops when the current sub - segment is localized. In one embodiment, this involves determining whether the maximum DFC is less than the threshold, i.e., 3.0 mm in one aspect.

[0146] In one aspect, an algorithm is provided that essentially divides curves in a 3D dimensional space (i.e., intra - cardiac motion 50, catheter - heart motion, probe - cavity body motion, etc.) into localized curves. As used herein, the term curve is defined as a vector of XYZ coordinates in 3D space, which represents the motion process of an object. In one aspect, the process involves determining the centroid of a curve defined by a data set, where the centroid is defined as the point that is the average of each coordinate representing the curve in 3D space. In one aspect, the DFC is defined as a one - dimensional vector representing the Euclidean distance between each point in the curve and the centroid. Finally, a localized curve is defined as a curve where the maximum DFC is less than a predetermined threshold distance. In one aspect, this distance is 3.0 mm.

[0147] In one aspect, the recursive function defines the segmented curves of the 3D dimensional data. The process for defining the segmented curves is provided in the flowchart 1300 of Figure 13 First, the flowchart 1300 includes finding a central localized curve based on the data obtained at 1310 (i.e., intra - cardiac motion 50, catheter - heart motion, probe - cavity motion, etc.). Step 1310 may include determining at 1320 whether a test curve (i.e., the input curve) defined by the data set is localized, i.e., whether the maximum DFC is less than the threshold. During 1310, the index of the minimum DFC of a predetermined curve may be identified at 1330, and the index of the maximum DFC of the predetermined curve may be identified at 1340. In the case where the index of the maximum value is less than the index of the minimum value (i.e., in the case where the maximum DFC is before the minimum DFC), then the first point of the test curve data may be excluded at 1350, i.e., these data points are ignored. In the case where the index of the maximum value is not less than the index of the minimum value, then the last point of the predetermined curve data may be excluded (i.e., ignored) at 1360. The flowchart 1300 identifies the central localized curve at 1370 or registers the central localized curve as the test curve. The test curve (which is essentially a potential candidate for the central localized curve) is iteratively cut from either side until it is localized and thus declared or identified as the central localized curve. In some cases, the input curve or the initial test curve will be immediately localized, and thus the cutting step may be omitted. In these cases, the input curve is the central localized curve, and thus no segmentation algorithm is required.

[0148] Flowchart 1300 checks whether there are any curves before the central localization curve at 1380. In one aspect, this step is performed recursively by using a segmentation function based on the pre - central points that are excluded or ignored (disclosed above with respect to the index comparison step). The process generates a list of pre - central localization curves for analysis at 1390 based on 1380. Flowchart 1300 checks whether there are any curves after the central localization curve at 1385. In one aspect, this step is performed recursively by using a segmentation function based on the post - central points that are excluded or ignored. The process generates a list of post - central localization curves for analysis at 1395 based on 1385. The list of all localization curves at 1399 includes the previous localization curves, the central localization curve, and the subsequent localization curves. In other words, the algorithm essentially segments or breaks the 3D curve into multiple localization curves for further analysis. In particular, the analysis determines whether the curve has a maximum DFC less than a predetermined distance threshold.

[0149] Figure 14 An example flowchart illustrates the various steps of a method 1400 for illustrating or minimizing the effect of respiratory compensation in catheter motion data. As Figure 14 shown, step 1410 includes obtaining respiratory data. This data can be obtained through sensors attached to the patient's body. In one aspect, the data is obtained through a series of patches or sensors attached to the patient's body (i.e., sensors 18 from Figure 1 . The series of patches or sensors are configured to detect impedance values based on the air - filled lungs of the patient. In another aspect, the data is obtained via a sensor arrangement that includes a catheter integrated with sensors and external sensors, or based solely on sensors integrated with the catheter. Respiration is generally based on the up - and - down movement of the patient's diaphragm, which also moves the patient's heart. Therefore, obtaining respiratory data is important for mapping the movement of the catheter within the patient's heart during respiration.

[0150] Step 1420 includes obtaining probe position data, which can be generated via any known tracking method or system. The probe position data can also be obtained using sensors attached to the patient's body (i.e., sensors 18 from Figure 1 . In one aspect, the sensors are configured to generate a magnetic field, and the movement of the catheter within the patient's body can generate magnetic signals to indicate the position of the catheter.

[0151] Step 1430 includes generating probe-cavity position data by compensating (e.g., subtracting or adding) respiratory data from the probe position data. Step 1440 includes identifying a period of time during which the probe is stable relative to the boundary of the cavity. Step 1450 includes identifying a site stability site based on a period of time during which the probe velocity is less than a predetermined velocity for a predetermined period of time. In one aspect, the predetermined velocity can be 2 mm / second and the predetermined period of time can be at least three seconds. In one embodiment, method 1400 includes applying a filter to the respiratory data and the probe position data.

[0152] Step 1460 includes notifying the surgeon of the period of time during which the probe is stable relative to the cavity boundary. This step can include providing the notification via a visual alert or marker (which can be displayed via monitor 3), an audio alert or marker (which can be played via computing system 4), or any other notification such that the surgeon is aware of the stable period of time. In one aspect, the marker, label, or visual indicator is overlaid or inserted onto the three-dimensional mapping or image of the position data and displayed on a monitor (e.g., Figure 1 monitor 3 in

[0153] Step 1410 described above can also include transforming the respiratory metric by using singular value decomposition (SVD) to obtain a first eigenvector and a first eigenvector derivative. As will be understood by one of ordinary skill in the art, the first eigenvector can correspond to the primary respiratory signal and the first eigenvector derivative can correspond to the phase-shifted respiratory signal. Method 1400 can include transferring the primary respiratory signal and the phase-shifted respiratory signal from two dimensions to a three-dimensional ellipsoid based on a correlation matrix. Method 1400 can also include determining the correlation matrix by calculating the root mean square deviation between the probe position data and the probe-cavity motion data. Method 1400 can include determining the correlation matrix based on weighting factors including at least one of the following: (i) the age of the respiratory data; (ii) the velocity of the probe; (iii) the respiratory depth; or (iv) data obtained during ablation. In one embodiment, method 1400 includes generating an image that includes an estimated respiratory motion based on the respiratory data, a probe motion based on the probe position data, and a probe-cavity motion based on the probe-cavity position data. Then, the image and an indicator, label, or visual indicator showing the period of time during which the probe is stable relative to the cavity boundary can be displayed together on a monitor.

[0154] Figure 15 An example of a method 1500 for capturing mapping data using respiratory compensation is illustrated. In a mapping configuration, as described above, the predetermined velocity of the probe position data can be very small and can even be zero. Additionally, in a mapping configuration where there is no ablation, the input for ablation is also zero. Thus, in such configurations, the movement of the electrode relative to the tissue wall can be primarily the result of respiratory motion.

[0155] At step 1510, in a mapping configuration, signals from multiple electrodes are recorded over a period of about 2.5 seconds. As a result of using the 2.5 - second period, the beating of the heart can be taken into account. As a result of considering the duration of the heart beat, movement caused by respiration is the main remaining factor affecting mapping. This respiration - induced movement can cause data trailing.

[0156] At step 1520, to reduce or eliminate data trailing, respiratory movement can be compensated. As described above Figure 2 this movement can be significant. As described above, since respiratory movement is known and understood, mapping points collected during respiratory movement can be compensated. For example, all points captured during a respiratory cycle can be adjusted to the end of the respiratory phase to produce a better anatomical map (less blurry). In such a configuration, by adjusting the data points to remove respiratory movement, the image clarity is improved. It should be understood that the compensation of mapping points can be located at a reference point in any part of the respiratory cycle, including the start, mid - point, or any other part of the respiratory cycle. The end of respiration is used as an example to aid in understanding this specification. This data collection can be referred to as compensated motion mapping.

[0157] At step 1530, additionally or alternatively, the stability of each electrode (or spline of the catheter) can be monitored as described herein. When collecting data, stability is used to determine mapping values. This determination can include at least one of adjusting mapping data points based on stability and only calculating mapping values for electrodes that are considered stable. For example, when data collection occurs, data from stable electrodes can be recorded while unstable electrodes are excluded, or data from unstable electrodes can be recollected. Alternatively, a weighting system can be used based on the stability level of the collected data. In either example of waiting or weighting (which can also be used simultaneously), the data is improved and less blurry. Using a stability - based gating function, the collected data has improved quality.

[0158] At step 1540, respiratory data can be obtained. In order to compensate for respiratory data at step 1550, respiratory data can be obtained. This data can be obtained by a sensor attached to the patient's body. In one aspect, this data is obtained by a series of patches or sensors attached to the patient's body (i.e., sensors 5 from Figure 1 ), which are configured to detect impedance values based on the air - filled lungs of the patient. In another aspect, this data is obtained via a sensor arrangement that includes a catheter integrated with sensors and external sensors, or based only on sensors integrated with sensors. Respiration is generally based on the up - and - down movement of the patient's diaphragm, which also moves the patient's heart. Therefore, obtaining respiratory data is important for mapping the movement of the catheter within the patient's heart during respiration.

[0159] At step 1550, the respiratory data can be transformed into metrics by using singular value decomposition (SVD) to obtain a first eigenvector and a first eigenvector derivative. The first eigenvector can correspond to the primary respiratory signal, and the first eigenvector derivative can correspond to the phase-shifted respiratory signal. Method 1400 can include transferring the primary respiratory signal and the phase-shifted respiratory signal from two dimensions into a three-dimensional ellipsoid based on a correlation matrix. Method 1400 can also include determining the correlation matrix by calculating the root mean square deviation between the probe position data and the probe-cavity motion data. Method 1400 can include determining the correlation matrix based on weighting factors that include at least one of the following: (i) the age of the respiratory data; (ii) the speed of the probe; (iii) the respiratory depth; or (iv) data obtained during ablation. In one embodiment, method 1400 includes generating an image that includes an estimated respiratory motion based on the respiratory data, a probe motion based on the probe position data, and a probe-cavity motion based on the probe-cavity position data. Then, the image can be displayed on a monitor along with an indicator, label, or visual indicator showing a period of time during which the probe is stable relative to the cavity boundary.

[0160] Figure 16 Graph 1600 illustrates respiratory data points with and without respiratory compensation. Graph 1600 includes data on the X-axis from -5 mm to +5 mm. Graph 1600 includes data on the Y-axis from -4 mm to +4 mm. In Graph 1600, data point 1610 represents data without respiratory compensation. Data point 1620 represents the same data with compensation. Additionally, data point 1620 includes points where the data moves from a continuum of the respiratory cycle to a point such as at the end of the cycle. As shown, the data points 1610 without respiratory compensation have a Y-axis spread of approximately 8 mm and an X-axis spread of approximately 5 mm. The compensated data points 1620 have a Y-axis spread of less than 0.5 mm and an X-axis spread of less than 0.5 mm.

[0161] The subject matter disclosed by the present invention is not limited to use in connection with the heart. The subject matter disclosed by the present invention can be used in a variety of applications to analyze the characteristics of any type of object (such as a cavity).

[0162] Any of the functions and methods described herein can be implemented in a general-purpose computer, processor, or processor core. By way of example, suitable processors include general-purpose processors, dedicated processors, conventional processors, digital signal processors (DSPs), multiple microprocessors, one or more microprocessors associated with DSP cores, controllers, microcontrollers, application specific integrated circuits (ASICs), field programmable gate array (FPGA) circuits, any other type of integrated circuit (IC), and / or state machines. Such processors can be manufactured by configuring a manufacturing process using hardware description language (HDL) instructions for the processing and the results of other intermediate data including a netlist (such instructions being capable of being stored on a computer-readable medium). The result of such processing can be a mask work, which is then used in a semiconductor manufacturing process to fabricate a processor implementing the features of the present disclosure.

[0163] Any of the functions and methods described herein can be implemented in a computer program, software, or firmware that is incorporated into a non-transitory computer-readable storage medium for execution by a general-purpose computer or processor. Examples of non-transitory computer-readable storage media include read-only memory (ROM), random access memory (RAM), registers, cache memory, semiconductor memory devices, magnetic media (such as internal hard disks and removable disks), magneto-optical media, and optical media (such as CD-ROM disks and digital versatile disks (DVDs)).

[0164] It should be understood that many variations are possible based on the disclosure herein. While the features and elements have been described above in specific combinations, each feature or element can be used alone without the other features and elements or in various combinations with or without the other features and elements.

Claims

1. A method comprising: obtaining respiratory data of the patient via at least one sensor; obtaining probe position data of a probe positioned within the cavity; generating compensated position data by compensating the obtained probe position data with the obtained respiration data; as well as A map is produced based on the generated compensated position data. 2 . The method of claim 1 , further comprising applying a filter to the respiration data.

3. The method according to claim 1, wherein: Obtaining breathing data further includes obtaining a breathing index via the at least one sensor.

4. The method according to claim 3, wherein: Obtaining respiration data further includes transforming the respiration index using singular value decomposition (SVD) to obtain a first eigenvector and a first eigenvector derivative, the first eigenvector corresponding to a main respiration signal, and the first eigenvector derivative corresponding to a phase-shifted respiration signal.

5. The method according to claim 4, wherein: The main respiration signal and the phase-shifted respiration signal are transferred from two dimensions into a three-dimensional ellipsoid based on a correlation matrix.

6. The method according to claim 5, wherein: The correlation matrix is ​​determined based on weighting factors including at least one of: age of the respiratory data; velocity of the probe; and depth of respiration.

7. The method of claim 1 , further comprising generating an image, the image comprising: estimating a respiratory motion based on the respiratory data, Probe movement based on the probe position data, and Probe-cavity motion based on probe-cavity position data.

8. The method of claim 1, further comprising generating a notification when the probe is stable relative to a boundary of the cavity.

9. The method according to claim 8, wherein: The period of time during which the probe is stable relative to the boundary is communicated via a visual indicator displayed on a monitor.

10. The method of claim 1, further comprising identifying a site stability site based on a period of time in which a probe velocity is less than a predetermined rate for at least a predetermined period of time.

11. A system comprising: a probe configured to be inserted into a body cavity of a patient; at least one sensor, the at least one sensor configured to obtain respiration data, the probe and the at least one sensor configured to obtain probe position data; and A processor, the processor being configured to: generating compensated position data by compensating the obtained probe position data with the obtained respiration data, and A map is produced based on the generated compensated position data.

12. The system according to claim 11, wherein: The processor is further configured to apply a filter to the respiration data and the probe position data.

13. The system according to claim 11, wherein: The processor is further configured to generate a breathing metric based on the breathing data from the at least one sensor.

14. The system according to claim 13, wherein: The processor is further configured to transform the respiration indicator by using singular value decomposition (SVD) to obtain a first eigenvector and a first eigenvector derivative, the first eigenvector corresponding to a main respiration signal, and the first eigenvector derivative corresponding to a phase-shifted respiration signal.

15. The system of claim 14, wherein: The main respiration signal and the phase-shifted respiration signal are transferred from two dimensions into a three-dimensional ellipsoid based on a correlation matrix.

16. The system of claim 15, wherein: determining the correlation matrix based on weighting factors, the weighting factors comprising at least one of: an age of the respiratory data; the speed of the probe; and the depth of breathing.

17. The system of claim 11, wherein: The processor is further configured to generate an image including estimated respiratory motion based on the respiratory data, probe motion based on the probe position data, and probe-cavity motion based on the probe-cavity position data.

18. The system of claim 11, wherein: The processor is further configured to notify the probe of a time period during which it is stable relative to the cavity boundary.

19. The system of claim 18, wherein: Notifying the time period during which the probe is stable relative to the boundary includes displaying a visual indicator on a monitor.

20. The system of claim 11, wherein: The processor is further configured to identify a site stability site based on a probe velocity being less than 2 mm / second for a period of at least three seconds.

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