Probe cavity motion modeling

Abstract

To provide systems, apparatuses and methods that provide probe-cavity location data and probe-cavity motion data based on probe location data and respiration data.SOLUTION: Motion of a probe or catheter is modeled. The present disclosure provides systems, processes and methods that estimate motion of a probe chamber in real time while inside the chamber by continuously assessing and considering respiration motion based on a measured location of the probe. In one aspect, the probe motion is determined based on compensating for respiration motion from the measured location of the probe.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present application provides systems, devices, and methods for modeling the motion of a probe or catheter. [Background technology]

[0002] Surgical procedures involving cavities or chambers, such as the heart, require precise information regarding the location of a probe or catheter within the cavity. In one aspect, mapping of the cardiac chambers is essential for identifying problems with cardiac arrhythmias (e.g., atrial fibrillation (AF)), which can be treated via intracorporeal procedures.

[0003] Breathing is divided into inhalation (inhalation or inspiration) and exhalation (exhalation or expiration). Due to human physiology, the diaphragm moves up and down during this process, causing a cyclical displacement of the heart. During the expiration phase, there is a moment called the end-expiratory phase when this displacement from the diaphragm is at its smallest in a given time period.

[0004] Currently, site stability, i.e., the stability of the probe or catheter relative to the cavity or cavity wall, is based on end-expiration position. Due to this configuration, site stability data can only be updated once every 5 to 8 seconds based on a person's normal breathing cycle and end-expiration. Any onset or end-expiration stability that occurs between two consecutive end-expirations will only be determined at a later time. Furthermore, the exact onset or rest time may vary in some cases. Therefore, a short, sudden movement that occurs during end-expiration may result in a complete failure to tag one long ablation site instead of two shorter ablation sites. Summary of the Invention [Means for solving the problem]

[0005] In one aspect, the present disclosure provides systems, processes, and methods for estimating probe-cavity motion intracavitarily and in real time by continuously evaluating and accounting for respiratory motion based on the measured position of the probe. In one aspect, probe motion is determined based on compensating for respiratory motion from the measured position of the probe.

[0006] In one aspect, disclosed herein is a site stability process and system that identifies time segments when the probe is stable against the cavity or vessel wall, thereby allowing for further analysis. [Brief explanation of the drawings]

[0007] A more detailed understanding may be had from the following description, given by way of example in conjunction with the accompanying drawings, in which: [Figure 1] FIG. 1 illustrates an exemplary system, according to one embodiment. [Figure 2] 1 is an exemplary image produced using the disclosed embodiments. [Figure 3] FIG. 1 is a schematic diagram illustrating a system according to one aspect. [Figure 4A] 1 is a first portion of a flow diagram illustrating aspects of the disclosed embodiment; [Figure 4B] 4B is a second part of the flow diagram from FIG. 4A. [Figure 5] 10 is a graph illustrating a respiratory indicator signal during ablation. [Figure 6] 10 is a graph illustrating an example low pass filter and derivative filter with respect to weights. [Figure 7A] 1 is a first part of a flow diagram of the pre-processing stage. [Figure 7B] 7B is a second part of the flow diagram from FIG. 7A. [Figure 8] 10 is a graph illustrating a respiratory vector during ablation. [Figure 9] 10 is a graph illustrating a recent coefficient. [Figure 10] 10 is a graph illustrating a velocity coefficient. [Figure 11] 1 is a graph illustrating an ablation coefficient. [Figure 12] 1 is a graph illustrating several stability factors. [Figure 13] 1 illustrates a flow diagram of a method according to one embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0008] As disclosed herein, systems, apparatus, and methods are provided for determining or estimating probe motion while intraluminally by taking respiratory motion into account. The term probe is used interchangeably herein with the term catheter, and one skilled in the art will understand that any type of position sensing device may be implemented in the configurations disclosed herein.

[0009] Figure 1 illustrates one embodiment for implementing aspects of the disclosed subject matter. As shown in Figure 1, a surgeon is navigating a probe or catheter 1 relative to a patient. In one embodiment, the surgeon is navigating the catheter 1 inside the patient's heart 2. On monitor 3, the surgeon can view various data sets and models related to respiration, absolute catheter motion and position, and catheter-heart motion and position. These data sets are shown in more detail in Figure 2.

[0010] A computing system 4 is provided that is configured to implement the various processes and algorithms disclosed herein. The computing system 4 may include a control unit 4a, a processor 4b, and a memory unit 4c. The control unit 4a may be configured to analyze signals from the catheter 1 and sensors to determine the coordinates and position of the catheter 1, as well as various other information. The memory unit 4c may be of various types and is typically configured to track position data, respiratory data, time data, and other types of data related to the catheter 1 and sensors. The computing system 4 may be configured to perform any of the steps, processes, methods, configurations, features, etc. disclosed herein.

[0011] In one aspect, the sensor 5 is attached directly to the patient's body. In one embodiment, the sensor 5 is a patch configured to detect magnetic and / or electrical signals. Those skilled in the art will understand based on this disclosure that the embodiments disclosed herein are not limited to the heart, but can be implemented to analyze any type of body part or organ. In one aspect, the sensor 5 can be configured to measure interimpedance from within the sensor 5.

[0012] In another aspect, at least one single-axis magnetic sensor mounted on the tip of the catheter is configured to operate in conjunction with at least one external magnetic sensor in a patient pad (i.e., underneath the patient). In one embodiment, there are three magnetic sensors mounted on the catheter, oriented in three different directions (i.e., spaced 120 degrees apart) and configured to operate in conjunction with the three external magnetic sensors. Details of such technology are provided in the following documents: U.S. Patent Nos. 5,391,199; 5,443,489; 5,558,091; 6,172,499; 6,177,792; 6,690,963; 6,788,967; and 6,892,091, each of which is incorporated by reference as if fully set forth herein.

[0013] In another aspect, an impedance sensor on a catheter is provided that is configured to be used without any external sensor. Details of such technology are provided in the following documents: U.S. Patent Nos. 5,944,022, 5,983,126, and 6,456,864, each of which is incorporated by reference as if fully set forth herein. As used herein, the term sensor refers to any of the sensor configurations described herein, whether the sensor is integrated with the catheter or includes a combination of a sensor integrated with the catheter and an additional sensor, such as an external sensor.

[0014] Regardless of the sensor configuration, these sensors model the patient's respiratory cycle and help determine when the patient's lungs inhale or exhale. In one embodiment, impedance increases as the lungs inflate or fill with air. The sensors can also generate a magnetic field, which can be used to detect the absolute position of catheter 1. Those skilled in the art will appreciate from this disclosure that a variety of methods and sensors can be used to determine a patient's respiratory cycle or the position of the catheter. The aggregated respiratory motion from the sensors can be used to generate an ellipsoid (i.e., element 20 in FIG. 2 ) that provides a model of the respiratory cycle.

[0015] 2 shows an exemplary image 10 that includes data regarding respiratory motion 20, catheter motion 30, reconstruction motion 40, and intracardiac motion 50. This information is configured to be displayed on a monitor 3. Each of these components is described in further detail herein.

[0016] The term respiratory motion 20 refers to motion based on respiratory data, which can be aggregated or collected in various ways. For example, in one embodiment, a probe, sensor, or patch (such as sensor 5 of FIG. 1 ) is attached to the patient's body, or the sensor is integrated into catheter 1, which can be used in conjunction with external sensor 5. This data reflects changes in the patient's lung volume. Respiratory motion 20 can be based on the movement of the patient's diaphragm during breathing. The signal can be derived by placing electrodes on the body surface. In one embodiment, respiratory motion 20 is based on electrical signals between probes on the body surface. In another aspect, the probes 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 processes disclosed herein. In one aspect, a primary or main signal is generated by a sensor. This primary or main signal is then further processed to determine the signal's derivative. Respiratory indicators and respiratory vectors related to respiratory motion 20 are described herein.

[0017] The catheter motion 30 illustrated in FIG. 2 is also known as probe motion, sensor motion, or original catheter motion and reflects data regarding the absolute position of the catheter 1. This data can be generated using any known tracking or sensing configuration, including a catheter, probe, and sensor. In one embodiment, sensors are used to generate magnetic fields and detect the position of the catheter 1. For example, catheter motion data can be acquired using a magnetic transmitter external to the patient that generates a signal based on the position of the catheter 1. In one embodiment, the sensor configuration includes at least three magnetic sensors oriented in three different directions relative to one another, mounted on the tip of the catheter and operating in conjunction with multiple sensors external to the patient. In another embodiment, an impedance sensor is provided, but no external sensors are required. The catheter motion data can be further processed via any combination of filters, smoothing, or post-acquisition signal processing.

[0018] The reconstructed motion 40 of Figure 2 is a control element in one embodiment. In other words, the reconstructed motion 40 is generated to provide a validity and error check for whether an undesirable amount of difference exists between itself and the original catheter motion 30. In one embodiment, the reconstructed motion 40 is equal to the respiratory motion 20 plus the original catheter motion 30. An error variable, also referred to as ERR and described in more detail herein, can be determined based on the reconstructed motion 40 from the original catheter motion 30. Such a determination based on the reconstructed motion 40 can be by subtraction or addition to the original catheter motion 30.

[0019] Intracardiac motion 50 is also referred to as catheter-to-heart motion or probe-cavity motion. In one aspect, intracardiac motion 50 is measured in millimeters, but may be measured in any metric. This motion data may generally refer to the motion data of any wall of a cavity or any type of probe, catheter, or sensor relative to a cavity. This data can ultimately be used to determine site stability, particularly related to ablation within a patient's heart. Intracardiac motion 50 is generally important to surgeons because this information indicates when the catheter is stable relative to the ventricular wall. With intracardiac motion 50, it is possible to find a stability point, also known as a stable position or site. This information is important for determining various parameters related to the ablation lesion, such as location and size. During a breath, it is generally expected that the catheter and heart will move up and down based on the contraction of the diaphragm. If the catheter is not stable relative to the heart wall or boundary during ablation (i.e., the catheter does not make contact with the heart wall or boundary), the ablation may not be successful or may not ablate the desired target location. In one aspect, the present disclosure is directed to providing more reliable and accurate mapping of intracardiac motion 50 (i.e., catheter-heart motion or probe-cavity motion) based at least in part on respiratory motion and other characteristics, which addresses problems associated with any sudden motion that occurs during end-expiration. In other words, by providing more accurate information regarding the relative positions of the catheter and cavity boundaries, any motion that occurs during end-expiration can be taken into account by the surgeon and used to facilitate decisions related to the ablation procedure.

[0020] In one aspect, the systems, methods, and processes are based on three features: (1) signal processing, (2) respiratory compensation, and (3) station finding or site stability location. Those skilled in the art will appreciate 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 implemented for procedures specifically involving the heart, and specifically involving ablation of specific regions of the heart. Any of the features described herein can be used or adapted to determine cavity aspects and are not specifically limited to being implemented solely with respect to the ventricles (i.e., navigation in the lungs).

[0021] In one aspect, signal processing includes converting respiration indicators (RI) into a primary respiration signal and its derivative, i.e., respiration vectors (RV). Respiration data is aggregated based on a sensor in one embodiment, which may include either a catheter integrated with the sensor, an external sensor, or any combination thereof. In one aspect, before converting RI to RV, ablation effects are considered using ablation offset correction, and low-pass filtering can be used to reduce cardiac motion. In one aspect, the disclosed subject matter is based on decomposing catheter motion into two components: respiratory motion and catheter-heart motion. Respiratory motion is generally assumed to be correlated to RV via a correlation matrix A.

[0022] The catheter-heart motion is assumed to be smoothed and can be modeled via a spline curve. Based on the above, Equation 1 is given as follows:

[0023]

number

[0024] Here, the correlation matrix A and the spline parameters are adjusted incrementally and simultaneously to minimize the root mean square (RMS) deviation between the measured motion and the reconstructed motion after a lower bandpass in order to minimize the error.

[0025] In one aspect, the correlation matrix A is regressed at step n to fit the respiratory motion at step n-1, which is the global motion minus the intracardiac motion at step n-1. In other words, A is regressed at the current state to fit the respiratory motion of the previous state, which is equal to the global motion minus the intracardiac motion of the previous state.

[0026] The correlation matrix A is a 2 row by 3 column data set. The correlation matrix A is configured to transform the two-dimensional RV in one embodiment into an ellipsoid in three-dimensional space. The matrix values ​​are scaled or selected to fit by multiplying the RV values ​​by the respiratory ellipsoid size (i.e., in mm). In one embodiment, the RV values ​​are relatively small (i.e., 1.e -3 ), which are multiplied by a large value in the A matrix (ie, 3,000) to produce a translation in three-dimensional space (ie, a 3.0 mm translation).

[0027] With respect to respiratory motion, in one embodiment, there are five RI channels. One skilled in the art will understand that the number of RI channels can vary. RI generally indicates lung motion and lung volume changes over time. RI typically includes similar signal and noise, respectively. In one embodiment, 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 to generate the RI. In one embodiment, this RI is typically given in mV.

[0028] A filter can be applied to the RI to remove noise or other components. For example, a low-pass finite impulse response (FIR) filter can be used to remove the heartbeat signature in the RI signal. In one embodiment, this step uses 151 weight vectors multiplied by 151 elements on each channel of the raw data to generate one sample of filtered data. In other words, the filter multiplies corresponding samples in a sliding window manner. Those skilled in the art will understand that these parameters can be varied.

[0029] Singular value decomposition (SVD) can be used to find weights that, when multiplied by the five measured RIs, select the most prominent respiratory signals. This filtering and processing step provides a smoother output signal, eliminating unwanted noise and disruptions caused by heartbeat.

[0030] Based on the RI, the dominant respiratory vector is extracted. In one embodiment, this extraction is performed by calculating the first eigenvector within the SVD.

[0031] Generally, SVD is configured to extract the most significant common motion from several similar respiration-dependent signals. This common signal is found as a linear combination of the individual RI signals. In one embodiment, to verify the consistency of the linear weights, SVD is performed three times: once for the first 30 seconds, once for the second 30 seconds, and once for the entire 60 seconds of the RI buffer. If the three weight sets are sufficiently similar, the last set is selected. In other words, similarity here refers to V from SVD. This V then becomes the weight vector, i.e., the RI2RV weight vector. In one embodiment, the similarity parameter has a Pearson correlation of at least 0.5.

[0032] In one embodiment, this process involves determining the first eigenvector in the SVD of the five RI signals over several respiratory cycles. For example, a 60-second buffer can be used, which can correspond to 10 respiratory cycles. By using multiple cycles, the RI signals can be assumed to be accurate and reliable.

[0033] The disclosed subject matter also adds a phase-shift signal to provide an ellipse-like motion or model, as shown by the estimated respiratory motion 20 in FIG. 2. This phase-shift signal is calculated by finding the derivative of the RI signal and combining the derivatives using equal weights. This process is similar to the phase shift between a Cos(x) and a Sin(x) function.

[0034] In one aspect, a respiration vector (RV) is generated based on the primary respiration vector and the derivative of that primary respiration 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 equations are given:

[0035]

number

[0036] Equation 2 is generally similar to Equation 1, except that Equation 2 uses RV instead of RI.

[0037] To determine the catheter position, the raw position data for the catheter is filtered. In one aspect, the raw position data is filtered to remove cardiac motion using the same low-pass FIR filter applied to the RI signal. This processing results in a composite and filtered respiration vector and catheter position.

[0038] In summary, at the end of signal processing, the process disclosed herein provides signals 20 and 30 of FIG.

[0039] This feature generally involves estimating the correlation matrix A and spline parameters that represent the actual motion of the catheter. The two RVs are then transformed into three-dimensional space.

[0040] With respect to the RV, the process disclosed herein generally converts this 2D vector into a 3D ellipse or ellipsoid in 3D space, as shown by element 20 in Figure 2. The ellipse depicted in Figure 2 can be oriented in any direction, and the primary purpose of respiratory compensation involves modeling the patient's breathing movement on the catheter or probe.

[0041] The correlation matrix A (also called a transformation matrix) transforms the two-dimensional vectors into a three-dimensional ellipsoid. The correlation matrix A, in one aspect, is determined based on regression. The root mean square error (RMSE) is minimized between the modeled motion (i.e., respiratory motion + catheter motion) and the measured motion to provide the correlation matrix A.

[0042] As shown in Figure 3, a system 200 is disclosed that includes a site stability module 210. The system 200 may be implemented, integrated, or configured to interface with the computing system 4 shown in Figure 1. As used herein, the term module may refer to any computing component or interface, and may include any hardware or software component.

[0043] The site stability module 210 may 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 inputs. For example, position data 202, respiratory indicator data 204, status data 206 (such as RI position status and status), and initialization or algorithm parameters 208 may each be provided to the site stability module 210. In one embodiment, data is provided in 16.67 ms time intervals. One skilled in the art will understand based on this disclosure that the data streaming parameters and cycles or periods may vary.

[0044] From the site stability module 210, the session stability time segments 212 (i.e., start-end segments) and catheter-heart position data 214 are sent to the central module 220. In other words, the site stability module 210 provides output parameters including the last stability segment parameters and the parameters of the last set of sites in the last ablation session. In one aspect, stability is a segment of 3 seconds or more duration during which the catheter-heart rate is below a certain rate threshold. The site is part of a stable segment during the ablation time, and a stable segment during ablation can be divided into several sites.

[0045] In one embodiment, the central module 220 may 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 a CARTO VISITAG™ processing unit. Those skilled in the art will appreciate that various types of configurations for communicating with the site stability module 210 may be provided.

[0046] In 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. in the site stability module 210. A recording module 230 can optionally be provided. This recording module 230 can be configured to assist in debugging and post-mortem analysis. Element 224 in Figure 3 represents a recalculation step, in which information from the central module 220 is continuously provided back to the site stability module 210 to refine and recalculate information regarding site stability.

[0047] 4A and 4B, a flow diagram is provided to illustrate various steps of process 300. As shown in FIGS. 4A and 4B, process 300 generally includes a pre-processing stage 310 and a stability detection stage 330. Pre-processing stage 310 provides position data 312 and RI 314. In other words, pre-processing stage 310 includes at least two channel groups: a position channel and an RI channel. This data can be set or generated in relatively small increments. For example, the data can be set at least every 16.67 ms.

[0048] The position data 312 may be further processed. For example, the position data 312 may be processed by applying a low pass filter or a low pass FIR filter in step 316. After this step, the filtered position, i.e., the final filtered position, is determined in step 324.

[0049] The RI 314 can also be further processed through a series of steps or processes 318, 320, and 322. As shown in FIG. 3A, the RI 314 can first be analyzed for ablation correction in step 318, which accounts for any offset caused by the ablation. This offset from the ablation affects the ability to estimate respiratory motion. Therefore, 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 ablation is activated, 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 from the RI signal measured just before ablation begins to the RI signal measured just after the ablation begins. The effect of the ablation offset is illustrated in FIG. 5, which shows the ablation offset due to the shift in RI during ablation.

[0050] Returning to FIG. 4A , the data is filtered in step 320. In one embodiment, the filters include 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, pre-processing includes the following steps: First, the original five RI channels are acquired. Second, an ablation offset correction is performed. Third, low-pass and derivative-pass filters are performed to create filtered respiration indicators (FRIs) and derivative respiration indicators (DRIs) using a sliding window of 151 samples of the FIR filter. Fourth, SDV is performed to find the most significant weight. Fifth, the most significant weight is implemented on the FRI to obtain RV(1) and on the DRI to obtain RV(2). Those skilled in the art will understand that additional steps can be performed using additional filters.

[0051] Figure 6 shows an example of a filter that can be used in step 320, showing the weights for the low pass filter and its derivative signal. As shown in Figure 6, different weighting factors are applied to the low pass filter and the derivative filter over time.

[0052] In step 322, the data regarding the RI 314 is further processed by calculating the first eigenvector of the RI 314 and calculating the derivative of the first eigenvector of the RI 314. This SVD is used to find the first eigenvector of the filtered RI to reduce redundancy and noise. The mean compensated (e.g., subtracted or added) filtered respiration indicator (FRI) and derivative respiration indicator (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.

[0053] In one aspect, SVD is performed only once at the beginning of a predetermined period when the buffer is filled with valid signals. This SVD generates five coefficients of the first eigenvector (V1), which are used to generate a respiration vector (RV) in step 326. In one embodiment, RV is equal to FRIxV1, DRIxV1.

[0054] At the end of the pre-processing stage 310, the last minute of RV data (produced in step 326) and the filtered position data produced in step 324 are synchronously stored in a last minute buffer.

[0055] The data from steps 324 and 326 is provided to a stability detection stage 350. During the stability detection stage 350, motion decomposition 352 is performed. More details regarding motion decomposition are provided in FIG. 7B. From motion decomposition 352, the process includes calculating regional stability and region 354. This step returns data to the regional stability module 210, which is configured to receive catheter motion data and provide session motion data. Information regarding catheter motion and region information is stored with the regional stability module 210. Regional stability parameters may be recalculated based on the information provided to the regional stability module 210.

[0056] A more detailed overview of the pre-processing stage 310 is shown in Figures 7A and 7B. As shown in process 600, the pre-processing stage begins with obtaining a data set, i.e., step 602. The data set may include RI, location, status, and various other information.

[0057] As shown on the right side of FIG. 7A, an ablation correction stage 604 is provided. This stage is configured to correct offsets in the RI signal due to ablation interference. The ablation correction stage 604 includes setting up a buffer in step 604a. Next, in step 604b, the offset is estimated for each channel by comparing the RI signal edges (i.e., the 5th and 95th percentiles of the signal) just before and just after the activation of the ablation device. Then, in step 604c, this estimated offset is compensated (e.g., subtracted or added) from the RI signal. In one aspect, the ablation offset is measured and corrected for each ablation session, and is performed dynamically and continuously. For each ablation, the estimation becomes more accurate due to the offset measurements being averaged over time.

[0058] As shown in Figure 7A, the buffer in step 604a can include a 1.5 second buffer of the RI signal to allow for fast estimation. The buffer can contain at least 151 samples of the RI signal. Parameters for considering the effect of ablation on the RI signal can vary depending on the specific situation, such as the number of RI channel signals, ablation strength and duration, patient characteristics, etc. Data from step 604c is ultimately transmitted to step 616, which is described in more detail below.

[0059] Continuing from step 602, a validation step 608 is performed to determine whether the data set is complete. In one aspect, a manager in the computing system 4 or software module verifies the validity of the data. As used herein, the term manager may refer to any computing interface, such as an interface in the CARTO VISITAG™ processing unit or central module 220. This component is configured to insert or input data and receive data output. If the data is invalid, the manager sends a reset or soft reset signal to the algorithm processor, which empties all buffers and, if an error is detected, begins refilling the buffers with new data. The algorithm ensures that the buffers are filled with valid data before proceeding to subsequent steps.

[0060] If the data is determined to be invalid due to a problem with the position data, the buffer is reset in step 610 and the position filter buffer is updated in step 614. In one embodiment, step 614 includes 151x3 samples.

[0061] If the data is determined to be invalid due to RI channel related issues, the buffer is reset in step 612 and the RI filter buffer is updated in step 616. In one embodiment, step 616 includes 151 x 5 samples.

[0062] If the data is determined to be valid, the position filter buffer and the RI filter buffer are also updated, as described with respect to steps 614 and 616, but the buffers do not need to be reset.

[0063] Continuing from 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 in step 618. In one embodiment, the filter in step 618 is a low pass FIR filter. The RI signal data from step 616 is also processed. In one embodiment, the filter in step 612 is a low pass and derivative FIR filter.

[0064] Step 622 follows step 618 and then updates the position buffer. Step 624 follows step 620 and includes updating the RI buffer. From step 622, the position data is further processed in step 626, which includes obtaining the last 1 minute buffer and obtaining filtered catheter motion.

[0065] With respect to the RI signal data, after step 624, this data is further processed using an eigenvector estimation stage 606. Generally, stage 606 involves transforming every sample in the 5D FRI vector to its first eigenvector using the eigenvector coefficients of vector V. In one aspect, vector V is a five element weight vector that multiplexes the five RIs to obtain the most prominent respiration vector. In one aspect, vector V is derived from an SVD decomposition and RI=U * S * It is obtained via V'.

[0066] Step 606a involves measuring disruptions in the derivative RI (DRI) signal. Step 606b involves checking that there are no breathing gaps and that the data is valid. Step 606c involves calculating V using SVD. SVD is calculated only when the FRI buffer is filled with a valid number and no breathing gaps are found. In one embodiment, this step may include calculating V at 1 second intervals. Once V is obtained in step 606d, the data may be further processed in steps 628 and 630. Step 606e is a validity check; V is reset to zero if a soft reset signal is received.

[0067] Step 628 involves checking whether (V1)>0. If no, step 630 involves waiting for the next data cycle. If yes, step 632 defines RV=[FRIxV, DRIxV]. Calculating V (or RI2RV as used in one aspect of the algorithm) involves the following steps: Once the RV buffer is full, two second tests can be performed to determine whether there are any breathing gaps (i.e., due to apnea) in the DRI buffer. If the buffer is full and there are no gaps, a calculation is performed to determine three different Vs by three different SVDs: one for the first 30 seconds, one for the last 30 seconds, and one for the entire 60-second DRI buffer. If all three Vs are correlated (i.e., within a predetermined value of each other), the 60-second V is selected as the new V advances. The process then verifies that the minimum value of the resulting respiratory signal RV(1) correlates with the maximum value of the probe's y-position. In one embodiment, the minimum value of the respiratory signal (RV1) preferably represents end-expiration (relative to inspiration). For this process, the corresponding Y value of the catheter position is examined, and the process ensures that the Y value of the respiratory minimum is greater than the Y value of the respiratory maximum, since the Y value is maximum with each end-expiration in humans. In this orientation, when the patient is lying supine, the Y direction is oriented toward the patient's heart-to-head.

[0068] If the resulting respiratory signal RV(1) does not correlate with the maximum value of the probe's y-position, the selected V is inverted to be negative (i.e., -V). Finally, step 634 provides the RV, including the last 1 minute buffer used in step 626.

[0069] As shown at the top of Figure 7B, the filtered motion is provided from step 626, and its RV is provided from step 634. Step 636 includes providing a correlation matrix A, which accounts for weight buffering and breathing gaps. In general, Figure 7B shows steps for motion decomposition according to one aspect. Motion decomposition is based on the premise that filtered catheter motion (FCM) can be modeled as a sum of two main components: (1) respiration compensation vector (RCV) = correlation matrix AxRV, and (2) catheter-heart motion = Smooth(FCM-RCV) = Spline(FCM-RCV).

[0070] As used herein, the term "smooth(x)" generally refers to a mathematical smoothing function that can be implemented by any computer interface, electronic device, or programming tool, such as MATLAB®. Various other functions used herein, such as "smooth," "exp," "pinv," "spline," "rms," ​​and "norm," can be implemented using any known computer interface, electronic device, or programming tool, such as MATLAB®. These computer interfaces, electronic devices, or programming tools are typically configured to provide matrix, array, and data manipulation, drawing functions, data implementation, and / or algorithm implementation.

[0071] The term "smoothing" when used with respect to a function or formula herein refers to a method of noise reduction or a data set in one aspect. In one aspect, the smoothing function uses a moving average, a filter, or a smoothing spline.

[0072] In one aspect, when used with respect to a function or formula herein, the term "spline(x)" refers to a function that calculates, returns, or generates an array, matrix, or vector having interpolated values ​​corresponding to points in a data set x. In one aspect, cubic spline interpolation can be used.

[0073] In one aspect, when used with respect to a function or formula herein, the term "exp(x)" refers to the exponential function e x refers to determining each element in an array, matrix, or data set x.

[0074] In one aspect, the term "pinv(x)" when used with respect to functions or formulas herein refers to a function that calculates, returns, or generates the Moore-Penrose generalized inverse matrix in an array, data set, or matrix x.

[0075] In one aspect, the term "norm(x)" when used with respect to a function or formula herein refers to a function that determines a scalar or magnitude value, calculates, returns, or generates a measure of the magnitude of the elements in an array, matrix, or dataset x.

[0076] In one aspect, the term "max(x)" when used with respect to a function or formula herein refers to a function that calculates, returns, or generates the maximum element in an array, matrix, or dataset x.

[0077] In one aspect, the term "rms(x)" when used with respect to a function or formula herein refers to a function that calculates, returns, or generates the root mean square of an array, matrix, or data set x.

[0078] Any one or more of the functional terms used herein can be implemented using any computing element or software program configured to perform computational analyses and functions. One example of such a program is MATLAB®. Those skilled in the art will understand that these mathematical or programming functions, and equivalents or variations thereof, can be implemented to perform the specific functions, calculations, or manipulations of data, matrices, arrays, and information described herein. Any of the functions described herein can be implemented on a computing system 4 or any other electrical or computer hardware and software.

[0079] In one embodiment, the decomposition is calculated every 100 ms. These 100 ms segments can be calculated over the last minute of data to allow for slow application of the correlation matrix A over the last minute.

[0080] In one aspect, two versions of catheter-heart motion are used by controlling the curvature of the spline. In one version, the intermediate catheter motion is calculated using a single curvature or smoothing parameter. 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, which is equivalent to assuming slow acceleration. This intermediate catheter motion is used to estimate the correlation matrix A. In particular, the correlation matrix A is adjusted using segments with slow intermediate catheter motion velocities (i.e., slow segments). In these slow segments, the catheter acceleration is small relative to the respiratory motion, and the correlation matrix (A) is more accurately estimated. During step 638, the intermediate catheter motion is estimated using a smoothing function to limit the resulting curvature.

[0081] In one aspect, intermediate catheter motion (ICM) = Smooth(T0, (FCM-RCV), SmoothP), where T0 is a vector of reference times (0-60 seconds in 0.1 second increments). The imprecision is estimated by a norm function of the estimated error. Using step 638, ICM has an error component (ICMerr), where ICMerr = Norm(FCM-RCV-ICM), and SmoothV = Exp(-2 * ICMerr), and SmoothP=0.005. This process corrects or updates ICM to allow for more curvature when ICMerr is unacceptably large. If ICM equals FCM-RCV, ICMerr is zero.

[0082] SmoothP is a smoothing parameter that limits the spline curve second derivative (i.e., acceleration) and is typically defined as a smoothing parameter for controlling the curvature through the segment. SmoothV is typically defined as a smoothing vector for controlling the curvature per data point. During this step, 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. ICMerr, or the error of the ICM, is an estimated error, which is equal to Norm(FCM-RCV-ICM). ICMerr is used to estimate the accuracy of the FCM reconstruction. Based on this, the spline is corrected when the error of slow motion is large. In one aspect, the smoothing function uses a moving average filter to smooth the data.

[0083] In another aspect, as shown in step 648, the catheter motion (CM) is corrected using a correction vector (SmoothV) to allow for fast accelerations where intermediate catheter motion is determined to be inaccurate. This correction vector (SmoothV) allows for different curvatures (i.e., accelerations) at different spots along the last minute of 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, allowing for larger curvatures at points where the reconstructed error is larger.

[0084] Step 650 includes estimating the catheter motion based on the following equation: Smooth(FCM-RCV,SmoothV). In one aspect, a function or process is implemented to smooth the catheter motion using a spline model by constraining the spline curvature (i.e., the second derivative of the curvature).

[0085] When estimating the CM, the fast segments allow for increased curvature to provide a more accurate and better reconstruction. The smoothing spline function f is given by:

[0086]

number

[0087] In this equation, |z|2 represents the sum of the squares of all inputs of z, n is the number of inputs of x, and the integral is over the smallest time interval that includes all inputs of x. The default value of the weighting vector W in the error measurement is 1(size(x)). The default value of the piecewise constant weighting function smoothV in the roughness measurement is the constant function 1.

[0088] In Equation 3, D 2f denotes the second derivative of the function f. The parameter smoothP controls the smoothing strength. When smoothP=1, the spline passes through all of the original data points without curvature constraints. When smoothP=0, the spline cannot contain curvature and therefore the result is a straight line. In this situation, smoothing can be used to control the acceleration the catheter is allowed to achieve.

[0089] For intermediate catheter motion (ICM), only non-zero smoothP parameters are used, and sharp motions are smoothed or filtered out. To model fast or sharp motions, smoothV vectors with values ​​less than 1 are used. SmoothV values ​​less than 1 can include more curvature at those locations to give extra weight or emphasis to certain aspects.

[0090] The overall concept of smoothing in this embodiment is to strike a balance between two opposing factors. The first factor generally seeks to remain true or accurate to the input vector, even in the presence of noise (i.e., many opposing direction changes). The second factor seeks to be as smooth as possible with minimal direction and velocity changes. SmoothV is a vector value that directs the smoothing function to favor one element over another, or vice versa, in different parts of the input vector. Thus, the closer the SmoothV value is to 0, the more accurate and less smooth the smoothing function needs to be. In one embodiment, smoothV allows smoothP in Equation 3 to be localized.

[0091] From step 650, catheter motion is used for site segment detection in step 652. In step 654, catheter motion data and site segment detection data are sent to a central module 655 configured to store data related to site segments and catheter motion. An iterative process of analyzing the site motion and recalculating the ablation site is then performed. The ablation is divided into sites and recalculated at every step, which can vary significantly according to the compensated catheter-heart motion. This compensated catheter position can be saved in a central module or storage unit for site recalculation after the user or surgeon adjusts various control parameters.

[0092] Step 640 includes calculating the RCV over the last minute of information and data. In one embodiment, RCV = AxRV. In one aspect, the correlation matrix A is also specified as RV2RCV. Estimating the correlation matrix A is generally performed using weighted mean squares and matrix inversion according to one embodiment. The weights (W) used in these calculations reflect how each data point in the last minute is configured to estimate the correlation matrix A according to at least one of the following criteria: (i) weight decay over time, (ii) preference for slow intermediate catheter movement speeds, (iii) RV adequacy, and (iv) duration since ablation.

[0093] With regard to factor (i), older samples generally receive less weight than recent samples. With regard to factor (ii), slower velocities in longer segments receive more weight. With regard to factor (iii), during apnea, RV drops to very close to zero and cannot be used to estimate the correlation matrix A. Therefore, apnea segments receive a weight of zero. Apnea detection is performed in step 642. Apnea is also commonly known as deep or shallow breaths. Factor (iv) results in recent ablation segments receiving more weight. The weight vector is identified as stability weights, and the step of estimating the stability weights is generally shown as step 644 in FIG. 7B. In other words, the intermediate catheter motion from step 638 is used to estimate the stability weights in step 644.

[0094] Apnea detection, associated with factor (iii) and step 642, is defined in more detail. Respiratory apneas are time segments during which the respiratory signal, i.e., RV, drops due to apnea or other forms of shallow breathing. Because of their effect, the correlation matrix A should generally not be estimated during these segments. The effect of apnea or shallow breathing is minimized by using weights associated with each sample (including RV and position data). Weights during shallow breathing (i.e., small RV signals) are zeroed, thereby eliminating the adverse effect of these measurements on the overall estimation and correlation matrix A.

[0095] Figure 8 illustrates the effect of apnea on the RV during ablation. A respiratory gap is generally defined as a time segment during which the respiratory derivative fails to reach a predetermined extreme value, reaching only less than 85% of the predetermined extreme value. Used in this context, these large and small parameters are defined by analyzing the 15th to 85th percentiles and ignoring signals above and below these percentiles. Consecutive samples falling within the 15th to 85th percentiles are counted and analyzed. If a signal within a consecutive segment happens to fall within these thresholds but does not cross them, all samples within this consecutive segment are designated as a respiratory gap, i.e., a shallow breath, and can therefore be discarded. The percentile value is selected within the last minute of data and defines a threshold for the respiratory derivative amplitude. If the respiratory derivative signal falls between the thresholds for a predetermined period, such as 5 seconds (roughly corresponding to one respiratory cycle), the respiratory signal is considered too weak and should therefore be discarded. The percentile value can be modified and generally selected to identify shallow breathing. Those skilled in the art will appreciate that other filtering or analysis may be used to identify shallow breathing.

[0096] Further details regarding estimating stability weights are provided herein. Generally, the decomposition process into respiratory-related and catheter-related motion is performed more accurately when the catheter is relatively stable or sliding slowly against another surface, i.e., tissue. Identifying relatively stable segments is important for generating reliable output. A stable segment is generally defined herein as a time segment in which the mean catheter motion velocity is relatively slow (i.e., <2.5 mm / sec) for a specific period (i.e., >3-5 seconds). Estimating the correlation matrix A is performed using stable or essentially stable segments. Stable segments are emphasized by assigning a greater weight to samples during stable times. The stability weight corresponds to the last minute and is converted into a vector of numbers representing the relevance of that point to the correlation matrix A estimate (i.e., the RV2RCV matrix). In addition to velocity stability, factors related to recency (i.e., age, or how old the segment is) and data segments collected during ablation are also considered. The most recent samples receive a greater weight than older samples, and ablation segments receive a greater weight than non-ablation segments.

[0097] Figure 9 illustrates one embodiment of the recency coefficient for stability weights. As shown in Figure 9, greater weights are assigned to more recent samples (i.e., the right-hand side of the graph), and older samples receive less weight. As shown in Figure 9, the S-shaped curve provides a smooth transition between the most recent and older signals.

[0098] Figure 10 illustrates one embodiment of a velocity coefficient for stability weighting. As shown in Figure 10, a larger weight is used when the catheter-estimated velocity is small, and a smaller weight is used when the catheter-estimated velocity is large. In one embodiment, the weight is decreased when the duration of the low velocity is shorter than the average respiratory cycle. As shown in Figure 10, the duration is adjusted by artificially increasing the estimated velocity.

[0099] Figure 11 illustrates one embodiment of the ablation coefficient in relation to stability weighting. Generally, a larger weight is assigned to the signal when ablation is occurring, as shown in Figure 12.

[0100] In one aspect, the three coefficients described above with respect to Figures 9, 10, and 11 are used to generate the overall stability weight. In other words, this overall stability weight vector may be the sample-by-sample product of all three coefficients. Those skilled in the art will appreciate that more than three coefficients may be used. Figure 12 illustrates how the stability weight coefficient may be measured over time. In one aspect, the recent coefficient is calculated only at initialization, since this coefficient is fixed.

[0101] Returning to FIG. 7B, using the weights (W), in step 646 the correlation matrix A is estimated using the following formula: A(2x3)=Pinv(RV)xW(FCM-RCV). When used in this manner, A(2x3) transforms RV (a two column matrix representing the respiratory signal and its derivatives) into RCV (a three row matrix representing the XYZ of the respiratory matrix). The matrix A is the transformation matrix between the respiratory vector (RV) and the 3D respiratory ellipse. This formula represents the decomposition equation FCM=A * Since FCM is RV + CM + err, it represents regression using the mean square. Because FCM cannot be completely decomposed into these models, there will be some error (err) that represents the difference between FCM or actual motion and the sum of the two models. In effect, these processes adjust the transformation matrix A to minimize the error. To minimize the error, the correlation matrix A is regressed so that A = ((FCM - (CM)) / RV is obtained by Pinv(RV) * A weight W is added to give more weight to time segments with slow (CM), so in one aspect the formula is A=P inv(W * RV) * W *(FCM-(CM)).

[0102] Then, as shown in Figure 7B, the correlation matrix A in step 646 is sent back to step 636, providing a roundabout or continuous cycle for continuously estimating and updating the correlation matrix A. The process of Figure 7B is iterative, so that the correlation matrix A constantly updates and becomes more accurate through each iteration. The correlation matrix A is generally updated based on the calculated mid-catheter motion and any one or more of the following steps: estimating apnea segments based on RV, estimating stability weights based on mid-catheter motion, and regressing the correlation matrix A based on A = (FCM - ICM) / RV.

[0103] In one aspect, it is assumed that the 3D RCV can be modeled as the product of the 2D RV (equal to the main respiratory signal plus phase shift) and a correlation matrix A (2x3). The parameters of the correlation matrix A are estimated based on the RV, a coarse estimate of the respiratory vector (equal to the filtered catheter motion - intermediate catheter motion), and a stability weight vector by minimizing the mean square error. By providing inputs of (i) FCM, (ii) intermediate catheter motion, and (iii) stability weights, the correlation matrix A is generated as the output.

[0104] The mid-catheter motion assumes a relatively slow acceleration of the catheter and cannot represent the true catheter motion in the high-velocity segments. The mid-catheter motion error coefficient can be estimated based on segments with faster motion. The catheter motion is recalculated, increasing the curvature for the high-velocity segments, which allows for even faster motion. Using this approach, ICM error (ICMerr) = Norm(FCM - ICM - RCV). In other words, the respiration data equals the total catheter motion minus the mid-catheter motion, and the total catheter motion minus respiration equals the intracardiac catheter motion. In one embodiment, ICMerr is the Euclidean length per sample in R3 between the measured position FCM and the slow model reconstructed position ICM + RCV. In one aspect, SmoothV is used to correct for the mid-catheter motion and find the intracardiac catheter motion.

[0105] Station finding, also known as position finding or site finding, generally involves identifying periods when the catheter or probe is stable. This process involves executing logic and functions to generate station time edge and center positions based on compensated motion. The stations of the last session are continuously updated along with the correlation matrix A and the resulting compensated motion. Stability is generally defined as consecutive time segments during which the estimated catheter velocity is less than a user-defined threshold.

[0106] The catheter velocity is calculated by taking the derivative of the catheter position over the last minute. The last minute position is updated at least every 0.1 seconds and saved for the entire minute. These values ​​may vary depending on the specific requirements of a particular application. The last catheter velocity value from this buffer is saved and stored in a last velocity buffer.

[0107] For example, in one embodiment, the end-5 value of the catheter velocity buffer is the catheter velocity updated for the current iteration using the end-5 and end-7 values ​​of the catheter's 1-minute position buffer, where the end-5 value of the last velocity buffer is the last catheter velocity calculated five iterations prior.

[0108] The minimum value of the catheter velocity and the last velocity over the last minute are defined as the minimum velocity and are used to detect when the catheter is stable. A minimum velocity buffer contains all minimum velocities estimated during the last minute. Segments of continuous velocity less than the threshold velocity are identified within this buffer, simultaneously attempting to find a time period during which the catheter is stable.

[0109] Estimations close to the current time are not accurate enough because the speed can change dramatically near the end of the buffer without a reliable indication of the estimated speed. Therefore, any decision regarding stability may be delayed by at least one second, and this predetermined delay may vary.

[0110] The boundaries of the current stable segment may be updated and then terminated if necessary. If a current stable segment is not found or it was previously terminated, a search for a new stable segment in the last few seconds of the estimated catheter motion is performed.

[0111] If a stable segment has already been found, the boundary is updated according to the updated minimum velocity buffer. The start point of the current stable segment can also be updated by looking backward from the last index of the stable segment to the earliest point where the velocity is less than the threshold.

[0112] The end point of the stability segment can be updated by looking forward from the last index of the stable end to the most recent point where the catheter velocity is less than a threshold.

[0113] A stable segment does not end until a predetermined period (i.e., approximately 1 second) has elapsed from the current time, to avoid segment termination due to edge inaccuracies. If a stable segment has not yet been found, a stability search is performed from the current time to find the earliest instant when a predetermined threshold was crossed.

[0114] In addition to the above process for determining when a segment is stable, the present disclosure is also directed to defining a localized segment as a time segment whose all points (i.e., distance from its own center of gravity) are less than a threshold. In other words, if we imagine a curve in 3D space, we can calculate the XYZ average of the curve, which is its center of gravity. From this value, we can calculate the distance from the center (DFC) for all points on that line.

[0115] This process involves positioning a site (i.e., a time segment) when the ablation is on and the catheter is stable and localized. One aspect of this process is described in more detail herein. In one aspect, a stable time segment during ablation is identified.

[0116] The processes and algorithms disclosed herein can separate, segment, or divide stable segments during ablation into localized segments (i.e., regions) whose maximum DFC is less than a predetermined threshold. In one aspect, the predetermined threshold is 3.0 mm. Those skilled in the art will appreciate that this value may vary. The algorithm for dividing stable segments into regions practically obtains the minimum number of subsegments that maintains the localization criteria. In one aspect, the algorithm is a recursive function that aborts or stops if the current subsegment is localized. In one embodiment, this includes determining whether the maximum DFC is less than a threshold, i.e., 3.0 mm in one aspect.

[0117] In one aspect, an algorithm is provided that practically divides or segments a curve in a 3D dimensional space (i.e., intracardiac motion 50, catheter-heart motion, probe cavity motion, etc.) into localized curves. As used herein, the term curve is defined as a vector of XYZ coordinates in 3D space that represents the motion process of an object. In one aspect, this process includes determining the centroid of the curve defined by the dataset, where the centroid is defined as the point in 3D space that represents the average value of each coordinate of the curve. The DFC, in one aspect, is defined as a one-dimensional vector that represents the Euclidean length between each point of the curve relative to the centroid. Finally, a localized curve is defined as a curve whose maximum DFC is less than a predetermined threshold distance. In one aspect, this distance is 3.0 mm.

[0118] In one aspect, the dividing curve of the 3D dimensional data is defined by a recursive function. First, the process includes finding a central localization curve based on the obtained data (i.e., intracardiac motion 50, catheter-heart motion, probe cavity motion, etc.). This step may include determining whether the test curve (i.e., input curve) defined by the data set is localized, i.e., whether the maximum DFC is less than a threshold. During this step, the index of the minimum DFC for the given curve may be identified, and the index of the maximum DFC for the given curve may be identified. If the maximum index is less than the minimum index (i.e., if the maximum DFC is before the minimum DFC), the first point of the test curve data may be excluded, i.e., the data point is ignored. If the maximum index is not less than the minimum index, the last point of the given curve data may be excluded, i.e., ignored. Finally, the process recognizes or registers the central localization curve as the test curve. The test curve is essentially a potential candidate for a centrally localized curve and is repeatedly cut from either side until it is localized and therefore declared or identified as the centrally localized curve. In certain situations, the input curve or initial test curve will be immediately localized, and therefore the cutting step can be omitted. In these situations, the input curve is a centrally localized curve, and therefore no segmentation algorithm is required.

[0119] The process then checks whether any curves exist before the central localization curve. In one aspect, this step is performed by using a division function in a recursive manner based on the excluded or ignored pre-center points (disclosed above with respect to the subscript comparison step). This process generates a list of pre-center localization curves for analysis based on the previous step. This process also checks whether any curves exist after the central localization curve. In one aspect, this step is performed by using a division function in a recursive manner based on the excluded or ignored posterior center points. This process generates a list of posterior central localization curves for analysis based on the previous step. The list of all localization curves includes the previous localization curve, the central localization curve, and the current localization curve. In other words, the algorithm effectively segments or breaks the 3D curve into multiple localization curves for further analysis. In particular, this analysis determines whether the curve has a maximum DFC that is less than a predetermined distance threshold.

[0120] FIG. 13 shows a flow diagram illustrating various steps for method 1300. As shown in FIG. 13, step 1310 includes acquiring respiratory data. This data can be acquired by sensors attached to the patient's body. In one aspect, this data can be acquired by a series of patches or sensors attached to the patient's body (i.e., sensors 5 of FIG. 1), configured to detect impedance values ​​based on the patient's lungs filling with air. In another aspect, this data is acquired through a sensor configuration that includes a catheter integrated with sensors as well as external sensors, or based solely on sensors integrated with sensors. Breathing is generally based on the patient's diaphragm moving up and down, which also displaces the patient's heart. Therefore, acquiring respiratory data is important to map the movement of the catheter within the patient's heart during breathing.

[0121] Step 1320 includes acquiring probe position data, which may be generated via any known tracking method or system. The probe position data may also be acquired using a sensor attached to the patient's body (i.e., sensor 5 of FIG. 1). In one aspect, the sensor is configured to generate a magnetic field, and movement of the catheter inside the patient may generate a magnetic signal indicating the position of the catheter.

[0122] Step 1330 includes generating probe cavity position data by compensating (e.g., subtracting or adding) the respiration data from the probe position data. Step 1340 includes identifying a period during which the probe is stable relative to the cavity boundary. Step 1350 includes identifying a region of stability based on a period during which the probe velocity is less than a predetermined velocity during the predetermined period. In one aspect, the predetermined velocity may be 2 mm / sec and the predetermined period may be at least 3 seconds. In one embodiment, method 1300 includes applying a filter to the respiration data and the probe position data.

[0123] Step 1360 includes notifying the surgeon of the period during which the probe is stable relative to the cavity boundary. This step may include providing notification via a visual alert or indicia (which may be displayed via monitor 3), an audible alert or indicia (which may be played via computing system 4), or any other notification by which the surgeon is notified of the stable period. In one aspect, a marker, tag, or visual indicator is overlaid or inserted onto a three-dimensional mapping or image of the position data and displayed on a monitor (e.g., monitor 3 of FIG. 1).

[0124] Step 1310 may further include transforming the respiratory indicator to obtain a first eigenvector and a first eigenvector derivative by using singular value decomposition (SVD). The first eigenvector may correspond to the primary respiratory signal, and the first eigenvector derivative may correspond to the phase-shifted respiratory signal. Method 1300 may include transforming the primary respiratory signal and the phase-shifted respiratory signal from a two-dimensional to a three-dimensional ellipsoid based on the correlation matrix. Method 1300 may 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 1300 may include determining the correlation matrix based on a weighting factor including at least one of (i) age of the respiratory data, (ii) probe velocity, (iii) depth of respiration, or (iv) data acquired during ablation. In one embodiment, method 1300 includes generating 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. This image can then be displayed to the surgeon on a monitor along with an indicator, tag, or visual indicator showing the period during which the probe is stable relative to the cavity boundary.

[0125] The disclosed subject matter is not limited to use in connection with the heart, but can be used in a variety of applications to analyze characteristics of any type of object, such as a cavity.

[0126] Any of the functions and methods described herein can be implemented in a general-purpose computer, processor, or processor core. Suitable processors include, by way of example, a general-purpose processor, a special-purpose processor, a conventional processor, a digital signal processor (DSP), multiple microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) circuit, any other type of integrated circuit (IC), and / or a state machine. Such processors can be manufactured by configuring a manufacturing process using the results of processed hardware description language (HDL) instructions and other intermediate data, such as a netlist (such instructions can be 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 produce a processor implementing features of the present disclosure.

[0127] Any of the functions and methods described herein can be implemented in a computer program, software, or firmware embodied in a non-transitory computer-readable storage medium and executed 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).

[0128] It should be understood that many variations are possible based on the disclosure herein, and although features and elements are described above in particular combinations, each feature or element may be used alone without other features and elements, or in various combinations with other features and elements, with or without other features and elements.

[0129] [Embodiment] (1) A method comprising: (i) acquiring respiratory data of a patient via at least one sensor; (ii) acquiring probe position data for a probe positioned within the cavity; (iii) generating probe cavity position data by compensating the respiration data from the probe position data; and (iv) identifying a period during which the probe is stable relative to a cavity boundary based on the probe-cavity position data; (v) notifying a surgeon of the period during which the probe is stable relative to the cavity boundary. (2) The method of claim 1, further comprising applying a filter to the respiratory data and the probe position data. (3) The method of embodiment 1, wherein step (i) further comprises obtaining a respiration indicator via the at least one sensor. (4) The method of embodiment 3, wherein step (i) further comprises transforming the respiratory indicator 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. (5) The method of embodiment 4, wherein the primary respiratory signal and the phase-shifted respiratory signal are transformed from a two-dimensional to a three-dimensional ellipsoid based on a correlation matrix.

[0130] (6) The method of claim 5, further comprising determining the correlation matrix based on the probe position data and the probe cavity motion data. (7) The method of embodiment 5, wherein the correlation matrix is ​​determined based on weighting factors including at least one of: (i) age of the respiratory data; (ii) speed of the probe; (iii) depth of respiration; or (iv) data acquired during ablation. (8) generating an image, the image comprising: an estimated respiratory movement based on the respiratory data; Probe movement based on the probe position data; and 2. The method of claim 1, further comprising: moving the probe cavity based on the probe cavity position data. (9) The method of embodiment 1, wherein step (v) 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. (10) The method of embodiment 1, further comprising identifying a site stability site based on a period during which the probe speed is less than 2 mm / sec for at least 3 seconds.

[0131] (11) A system comprising: a probe configured to be inserted into a body cavity of a patient; at least one sensor configured to generate respiration data, the probe and the at least one sensor configured to generate probe position data; 1. A processor, comprising: generating probe cavity position data based on the probe position data and the respiratory 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 notifying a surgeon of the period during which the probe is stable relative to the cavity boundary. (12) The system of claim 11, wherein the processor is further configured to apply a filter to the respiratory data and the probe position data. (13) The system of embodiment 11, wherein the processor is further configured to generate a respiration indicator based on the respiration data from the at least one sensor. (14) The system of embodiment 13, wherein the processor is further configured to transform the respiratory indicator 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. (15) The system of embodiment 14, wherein the primary respiratory signal and the phase-shifted respiratory signal are transformed from a two-dimensional to a three-dimensional ellipsoid based on a correlation matrix.

[0132] (16) The system of embodiment 15, wherein the correlation matrix is ​​determined based on weighting factors including at least one of: (i) age of the respiratory data, (ii) speed of the probe, (iii) depth of respiration, or (iv) data acquired during ablation. (17) The system of claim 11, wherein the processor is further configured to estimate a correlation matrix based on the probe position data and the probe cavity motion data. (18) The system of embodiment 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. (19) The system of embodiment 11, wherein notifying the surgeon of the period during which the probe is stable relative to the cavity boundary includes displaying a visual indicator on a monitor. (20) The system of embodiment 11, wherein the processor is further configured to identify a site stability site based on a period during which the probe speed is less than 2 mm / sec for at least 3 seconds.

Claims

1. 1. A system comprising: a probe configured to be inserted into a body cavity of a patient; at least one sensor configured to generate respiration data, the probe and the at least one sensor configured to generate probe position data; 1. A processor, comprising: generating compensated probe cavity position data by subtracting or adding the respiration data from the probe position data; identifying periods during which the probe is stable with respect to respiratory movement of cavity boundaries based on the probe cavity position data; and notifying a surgeon of the period during which the probe is stable relative to the cavity boundary.

2. The system of claim 1 , wherein the processor is further configured to apply a filter to the respiration data and the probe position data.

3. The system of claim 1 , wherein the processor is further configured to generate a respiration indicator based on an electrical signal from the at least one sensor patch attached to the patient.

4. 4. The system of claim 3, wherein the processor is further configured to transform the respiratory indicator 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.

5. 5. The system of claim 4, wherein the primary respiratory signal and the phase-shifted respiratory signal are transformed from a two-dimensional to a three-dimensional ellipsoid based on a correlation matrix.

6. 6. The system of claim 5, wherein the correlation matrix is ​​determined based on weighting factors including at least one of: (i) age of the respiratory data, (ii) speed of the probe, (iii) depth of respiration, or (iv) data acquired during ablation.

7. 2. The system of claim 1, 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.

8. The system of claim 1 , wherein informing the surgeon of the period during which the probe is stable relative to the cavity boundary comprises displaying a visual indicator on a monitor.

9. 2. The system of claim 1, wherein the processor is further configured to identify a site of site stability based on a period during which a probe velocity calculated by taking a derivative of a probe position based on the probe position data is less than 2 mm / sec for at least 3 seconds.

10. 1. A method comprising: (i) acquiring patient respiratory data via at least one sensor; (ii) acquiring probe position data for a probe positioned within the cavity; (iii) generating compensated probe cavity position data by subtracting or adding the respiration data from the probe position data; (iv) identifying periods during which the probe is stable with respect to respiratory movement of cavity boundaries based on the probe cavity position data; (v) notifying a surgeon of the period during which the probe is stable relative to the cavity boundary.

11. The method of claim 10 , further comprising applying a filter to the respiration data and the probe position data.

12. 11. The method of claim 10, wherein step (i) further comprises obtaining a respiration indicator based on an electrical signal from the at least one sensor patch attached to the patient.

13. 13. The method of claim 12, wherein step (i) further comprises transforming the respiratory indicator 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.

14. 14. The method of claim 13, wherein the primary respiratory signal and the phase-shifted respiratory signal are transformed from a two-dimensional to a three-dimensional ellipsoid based on a correlation matrix.

15. 15. The method of claim 14, wherein the correlation matrix is ​​determined based on weighting factors including at least one of: (i) age of the respiratory data, (ii) speed of the probe, (iii) depth of respiration, or (iv) data acquired during ablation.

16. generating an image, the image comprising: an estimated respiratory movement based on the respiratory data; Probe movement based on the probe position data; and 11. The method of claim 10, further comprising: probe cavity movement based on the probe cavity position data.

17. 11. The method of claim 10, wherein step (v) includes informing the surgeon of the period during which the probe is stable relative to the cavity boundary via a visual indicator displayed on a monitor.

18. The method of claim 10, further comprising identifying a site stability site based on a period during which the probe velocity, calculated by taking a derivative of the probe position based on the probe position data, is less than 2 mm / sec for at least 3 seconds.

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