Signal analysis of reference electrode migration in a coronary sinus catheter.

Abstract

To provide a method and a system for signal processing.SOLUTION: A method is provided. The method is implemented by a device orientation engine being executed by one or two or more processors. The method includes determining a movement between each group of electrodes of a catheter to provide movements, and determining a total movement of electrodes of the catheter. The method also includes removing a standard component from the movements and the total movement and outputting a movement indication for the catheter on the basis of the movements and the total movement with the standard component.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a method and system for signal processing, and more particularly to signal analysis of the movement of a reference electrode of a catheter within the coronary sinus (CS) vein. [Background technology]

[0002] Currently, cardiac electrophysiology systems are used to map or visualize the real-time calculated position and orientation of a catheter within a patient's heart. In some cases, cardiac electrophysiology systems utilize fiducial points to visualize activation waves on the heart beat by beat and / or for each point taken during the mapping process. Additionally, cardiac electrophysiology systems can track the time per fiducial point.

[0003] In general, the CS vein is an excellent reference point for cardiac electrophysiology systems because it is located between the atria and ventricles (meaning, for example, that the catheter can monitor both atrial and ventricular activity). The CS vein is also ideal as a reference point because it is a stable location for catheter placement and the catheter is expected to maintain the same position throughout mapping. Because cardiac electrophysiology systems rely on the catheter's electrode as a reference point within the CS, it is important that the electrode does not move. If the reference electrode within the CS moves, further mapping should not be performed because this movement essentially compromises the accuracy of all measurements. In particular, if the electrode moves, the timing of atrial tachycardia (AT) or ventricular tachycardia (VT) measurements becomes unreliable and must be remapped, which prolongs the procedure.

[0004] Currently, no techniques exist to improve electrode / catheter stability and / or account for their movement. Such techniques could be beneficial for cardiac electrophysiology systems. Summary of the Invention [Means for solving the problem]

[0005] According to one embodiment, a method is provided. The method is implemented by a device orientation engine executed by one or more processors. The method includes determining a movement between each electrode group of a catheter to provide a movement and determining a total movement of the electrodes of the catheter. The method also includes removing a normal component from the movement and the total movement, and outputting a catheter movement indication based on the movement and the total movement with the normal component.

[0006] According to one or more embodiments, the above method embodiments may be implemented as an apparatus, a system, and / or a computer program product. [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 like reference numerals indicate similar elements and in which: [Figure 1] 1 shows a diagram of an exemplary system in which one or more features of the subject matter of this disclosure can be implemented, in accordance with one or more embodiments. [Figure 2] 1 shows a block diagram of an exemplary system for signal analysis of reference electrode movement of a catheter within a coronary sinus (CS) vein, according to one or more embodiments. [Figure 3] 1 illustrates an exemplary method according to one or more embodiments. [Figure 4] 1 shows a graphical depiction of an artificial intelligence system, according to one or more embodiments. [Figure 5] FIG. 1 illustrates a block diagram of an example neural network and a method implemented within the neural network, according to one or more embodiments. [Figure 6] 1 illustrates an exemplary method according to one or more embodiments. [Figure 7]1 illustrates a diagram of vector determination according to one or more embodiments. [Figure 8] 1 shows a graph of movement over time according to one or more embodiments. [Figure 9] 1 illustrates a graph of peak detection in accordance with one or more embodiments. [Figure 10] 10 illustrates a graph of a plot after interpolation in accordance with one or more embodiments. [Figure 11] 10 shows a graph of a movement display after utilizing a low pass filter, according to one or more embodiments. [Figure 12] 1 illustrates a visualization according to one or more embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0008] Disclosed herein are methods and systems for signal processing, more particularly, the present invention relates to signal analysis of the movement of a reference electrode of a catheter within the coronary sinus (CS) vein.

[0009] For example, the device orientation engine is processor-executable code or software that is inherently rooted in the process operations of the medical device equipment and its hardware. According to an exemplary embodiment, the device orientation engine can include machine learning / artificial intelligence (ML / AI) algorithms. The device orientation engine tracks the movement of the CS catheter within the CS while mapping the CS. For example, the device orientation engine tracks the movement of the CS catheter along the axial axis of the CS, which can affect the stability of the map's fiducials, and tracks deviations from the fiducial position obtained at the start of mapping.

[0010] Technical effects and advantages of the device orientation engine include providing cardiac surgeons and medical personnel with a visual representation of the original catheter position relative to the displaced catheter position after inadvertent catheter movement. Thus, the device orientation engine utilizes and transforms medical device equipment to, among other things, enable / implement CS catheter displacement estimation that is otherwise not currently available or currently performed by cardiac electrophysiology systems.

[0011] According to one or more embodiments, a device orientation engine executed by one or more processors implements a method that includes determining a movement between each group of electrodes of a catheter to provide a movement and determining a total movement of the electrodes of the catheter. The method also includes removing a normal component from the movement and the total movement, and outputting a catheter movement indication based on the movement and the total movement with the normal component.

[0012] According to one or more embodiments or any of the method embodiments herein, the device orientation engine can utilize the positions per timestamp for the electrodes of each electrode group and the reference position as inputs for determining movement between each electrode group.

[0013] According to one or more embodiments or any of the method embodiments herein, movement between each electrode group may be determined based on at least one set of two vectors constructed for each electrode group.

[0014] According to one or more embodiments or any of the method embodiments herein, the movement between each electrode group can be determined based on a third vector between a set of two vectors for each electrode group.

[0015] According to one or more embodiments or any of the method embodiments herein, the total movement may be based on the average movement of three central electrode pairs of the plurality of electrodes.

[0016] According to one or more embodiments or any of the method embodiments herein, the processor-executable code may be further executed to cause the system to determine a median of each movement between each electrode pair, select the three movement measurements closest to the median to provide a selected measurement, and determine an average of the selected measurements to provide an average movement.

[0017] According to one or more embodiments or any of the method embodiments herein, the standard component may include respiratory motion or cardiac motion.

[0018] According to one or more embodiments or any of the method embodiments herein, the movement indication may be for every input position relative to a reference position.

[0019] According to one or more embodiments or any of the method embodiments herein, the movement indication can be along an axial insertion axis of the catheter into the coronary sinus.

[0020] According to one or more embodiments, a system includes a memory and one or more processors. The memory stores processor-executable code for a device orientation engine. The one or more processors execute the processor-executable code to cause the system and device orientation engine to determine a movement between each electrode group of the catheter to provide the movement and to determine a total movement of the electrodes of the catheter. The processor-executable code further causes the system and device orientation engine to remove a normal component from the movement and the total movement, and to output a catheter movement indication based on the movement and the total movement with the normal component.

[0021] According to one or more embodiments or any of the system embodiments herein, the device orientation engine can utilize the positions per timestamp for the electrodes of each electrode group and the reference position as inputs for determining movement between each electrode group.

[0022] According to one or more embodiments or any of the system embodiments herein, movement between each electrode group can be determined based on at least one set of two vectors constructed for each electrode group.

[0023] According to one or more embodiments or any of the system embodiments herein, movement between each electrode group can be determined based on a third vector between a set of two vectors for each electrode group.

[0024] According to one or more embodiments or any of the system embodiments herein, the total movement may be based on the average movement of three central electrode pairs of the plurality of electrodes.

[0025] According to one or more embodiments or any of the system embodiments herein, the processor executable code may be further executed to cause the system to determine a median of each movement between each electrode pair, select the three movement measurements closest to the median to provide a selected measurement, and determine an average of the selected measurements to provide an average movement.

[0026] According to one or more embodiments or any of the system embodiments herein, the standard component may include respiratory motion or cardiac motion.

[0027] According to one or more embodiments or any of the system embodiments herein, the movement indication may be for every input position relative to a reference position.

[0028] According to one or more embodiments or any of the system embodiments herein, the movement indication can be along the axial insertion axis of the catheter into the coronary sinus.

[0029] According to one or more embodiments or any of the system embodiments herein, the catheter may be a straight catheter.

[0030] 1 is a schematic diagram of a system 100 (e.g., a medical device instrument) in which one or more features of the subject matter herein may be implemented, according to one or more embodiments. All or a portion of system 100 may be used to collect information (e.g., biometric data and / or training data sets) and / or may be used to implement a device orientation engine 101 as described herein.

[0031] The illustrated system 100 includes a probe 105 with a catheter 110 (including at least one electrode 111), a shaft 112, a sheath 113, and a manipulator 114. The illustrated system 100 also includes a physician 115 (or medical professional, clinician, technician, etc.), a heart 120, a patient 125, and a bed 130 (or table). Note that insets 140 and 150 show the heart 120 and catheter 110 in greater detail. The system 100 also includes a console 160 (including one or more processors 161 and memory 162) and a display 165, as shown. Further, note that each element and / or item in the system 100 represents one or more of that element and / or item. The example system 100 shown in FIG. 1 can be modified to implement the embodiments disclosed herein. The embodiments of the present disclosure can be similarly applied using other system components and configurations. Additionally, system 100 may include additional components, such as elements for sensing electrical activity, wired or wireless connectors, processing and display devices, etc.

[0032] System 100 may be utilized to detect, diagnose, and / or treat cardiac conditions (e.g., using device orientation engine 101). Cardiac conditions, such as cardiac arrhythmias, remain common and dangerous medical disorders, especially in the elderly population. For example, system 100 may be part of a surgical system (e.g., a Carto® system sold by Biosense Webster) configured to acquire biometric data (e.g., anatomical and electrical measurements of a patient's organs, such as heart 120) and perform cardiac ablation procedures.

[0033] In a patient (e.g., patient 125) having normal sinus rhythm (NSR), the heart (e.g., heart 120), including the atria, ventricles, and excitable conduction tissue, is electrically excited to beat in a synchronized, patterned manner, which can be detected, for example, as intracardiac electrocardiogram (IC ECG) data.

[0034] In patients (e.g., patient 125) with cardiac arrhythmias (e.g., atrial fibrillation or aFib), abnormal regions of cardiac tissue do not follow the synchronous beating cycle associated with normally conductive tissue, in contrast to patients with NSR. In contrast, the abnormal regions of cardiac tissue conduct abnormally to adjacent tissue, disrupting the cardiac cycle and resulting in asynchronous cardiac rhythms. Note that this asynchronous cardiac rhythm can also be detected in IC ECG data. Such abnormal conduction has previously been known to occur in various regions of the heart 120, such as, for example, in the region of the sinoatrial (SA) node along the conduction pathway of the atrioventricular (AV) node, or in the myocardial tissue forming the walls of the ventricles and atria.

[0035] To assist system 100 in detecting, diagnosing, and / or treating a cardiac condition, physician 115 can guide probe 105 into heart 120 of patient 125 reclining on bed 130. For example, physician 115 can insert shaft 112 through sheath 113 while manipulating the distal end of shaft 112 using manipulator 114 near the proximal end of catheter 110 and / or deflection from sheath 113. As shown in inset 140, catheter 110 can be attached to the distal end of shaft 112. Catheter 110 can be inserted through sheath 113 in a collapsed state and then expanded within heart 120.

[0036] The catheter 110, which may include at least one electrode 111 and a catheter needle coupled on its body, may be configured to obtain biometric data, such as electrical signals, of a body organ (e.g., the heart 120) and / or ablate a tissue region thereof (e.g., a chamber of the heart 120). It should be noted that the electrode 111 represents any similar element, such as a tracking coil, piezoelectric transducer, electrode, or combination of elements configured to ablate a tissue region or obtain biometric data. According to one or more embodiments, the catheter 110 may include one or more position sensors used to determine trajectory information. This trajectory information may be used to infer motion characteristics, such as tissue contractility.

[0037] The biometric data (e.g., patient biometrics, patient data, or patient biometric data) may include one or more of local time activation (LAT), electrical activity, topology, bipolar mapping, dominant frequency, impedance, etc. LAT may be a time point of threshold activity corresponding to local activation calculated based on a normalized initial starting point. Electrical activity may be any applicable electrical signal that can be measured based on one or more thresholds and detected and / or enhanced based on signal-to-noise ratio and / or other filters. Topology may correspond to the physical structure of a body part or portion of a body part, and may correspond to changes in the physical structure for different portions of the body part or for different body parts. The dominant frequency may be a frequency or range of frequencies commonly found in a portion of a body part and may differ in different portions of the same body part. For example, the dominant frequency of the PVs of a heart may differ from the dominant frequency of the right atrium of the same heart. Impedance may be a resistance measurement in a particular region of a body part.

[0038] Examples of biometric data include, but are not limited to, patient identification data, IC ECG data, anatomical and electrical measurements, trajectory information, body surface (BS) ECG data, medical history data, brain biometrics, blood pressure data, ultrasound signals, radio signals, audio signals, two-dimensional or three-dimensional image data, blood glucose data, and temperature data. Biometric data may generally be used to monitor, diagnose, and treat any number of various diseases, such as cardiovascular diseases (e.g., arrhythmias, cardiomyopathies, and coronary artery disease) and autoimmune diseases (e.g., type I and type II diabetes). Note that BS ECG data may include data and signals collected from electrodes on the patient's surface, IC ECG data may include data and signals collected from electrodes inside the patient's body, and ablation data may include data and signals collected from ablated tissue. Furthermore, BS ECG data, IC ECG data, and ablation data, along with catheter electrode position data, may be derived from one or more procedure records.

[0039] For example, catheter 110 may use electrodes 111 to perform intravascular ultrasound and / or MRI catheterization to image (e.g., acquire and process biometric data for) heart 120. Inset 150 shows a close-up of catheter 110 within a chamber of heart 120. While catheter 110 is shown as a point catheter, it will be understood that any shape that includes one or more electrodes 111 may be used to implement the embodiments disclosed herein.

[0040] Examples of the catheter 106 include, but are not limited to, a linear catheter with multiple electrodes, a balloon catheter including electrodes distributed on multiple spines forming a balloon, a lasso or loop catheter with multiple electrodes, or any other applicable shape. The linear catheter may be fully or partially elastic so that it can twist, bend, and / or otherwise change its shape based on received signals and / or the action of external forces (e.g., cardiac tissue) on the linear catheter. The balloon catheter may be designed to hold its electrodes in intimate contact with the endocardial surface when deployed within a patient's body. As an example, the balloon catheter may be inserted into a lumen such as a pulmonary vein (PV). The balloon catheter may be inserted into the PV in a deflated state so that the balloon catheter does not occupy its maximum volume while inserted into the PV. The balloon catheter may be expanded while inside the PV so that the electrodes on the balloon catheter contact the entire circular portion of the PV. Such contact with the entire circular portion of the PV or any other lumen enables efficient imaging and / or ablation.

[0041] The probe 105 and other items of the system 100 can be connected to a console 160. The console 160 can include any computing device capable of using the ML / AI algorithms of the device orientation engine 101. According to one embodiment, the console 160 includes one or more processors 161 (any computing hardware) and memory 162 (any non-transitory tangible medium), where the one or more processors 161 execute computer instructions for the device orientation engine 101 and the memory 162 stores these instructions for execution by the one or more processors 161. For example, the console 160 can be configured to receive and process biometric data to determine whether a particular tissue region conducts electricity. In some embodiments, the console 160 can be further programmed by the device orientation engine 101 (in software) to perform the functions of determining the movement between each electrode group of the catheter to provide a movement, determining the total movement of the electrodes of the catheter, removing the normalized component from the movement and the total movement, and outputting a catheter movement indication based on the movement and the total movement with the normalized component. According to one or more embodiments, the device orientation engine 101 may be external to the console 160, for example, located within the catheter 110, within an external device, within a mobile device, within a cloud-based device, or may be a stand-alone processor. In this regard, the device orientation engine 101 may be transferable / downloadable in electronic form over a network.

[0042] In one example, console 160 may be any computing device including software (e.g., device orientation engine 101) and / or hardware (e.g., processor 161 and memory 162), such as a general-purpose computer, with front-end and interface circuitry suitable for transmitting signals to and receiving signals from probe 105 and for controlling other components of system 100, as described herein. For example, the front-end and interface circuitry may include an input / output (I / O) communication interface that allows console 160 to receive signals from and / or transfer signals to at least one electrode 111. Console 160 may include real-time noise reduction circuitry, typically configured as an analog-to-digital (A / D) ECG or electrocardiogram / electromyogram (EMG) signal conversion integrated circuit followed by a field programmable gate array (FPGA). The console 160 may communicate signals from the A / D ECG or EMG circuitry to a separate processor and / or may be programmed to perform one or more of the functions disclosed herein.

[0043] A display 165, which may be any electronic device for visually presenting biometric data, is connected to the console 160. According to one embodiment, during a procedure, the console 160 may facilitate the presentation of a rendering of the body part to the physician 115 on the display 165 and store data representing the rendering of the body part in the memory 162. For example, a map indicative of motion characteristics may be rendered / constructed based on trajectory information sampled at a sufficient number of points within the heart 120. As an example, the display 165 may include a touch screen that may be configured to receive input from the physician 115 in addition to presenting the rendering of the body part.

[0044] In some exemplary embodiments, physician 115 can use one or more input devices, such as a touchpad, mouse, keyboard, gesture recognizer, etc., to manipulate renderings of elements of system 100 and / or body parts. For example, the input devices can be used to change the position of catheter 110 so that renderings are updated. Note that display 165 can be located at the same location or at a remote location, such as another hospital or another healthcare provider network.

[0045] According to one or more embodiments, the system 100 can also obtain biometric data using ultrasound, computed tomography (CT), MRI, or other medical imaging techniques utilizing the catheter 110 or other medical equipment. For example, the system 100 can obtain ECG data and / or anatomical and electrical measurements (e.g., biometric data) of the heart 120 using one or more catheters 110 or other sensors. More specifically, the console 160 can be connected by a cable to BS electrodes, including adhesive skin patches, attached to the patient 125. The BS electrodes can acquire / generate biometric data in the form of BS ECG data. For example, the processor 161 can determine position coordinates of the catheter 110 within a body part (e.g., the heart 120) of the patient 125. The position coordinates can be based on impedance or electromagnetic fields measured between body surface electrodes and electrodes 111 of the catheter 110 or other electromagnetic components. Additionally or alternatively, a location pad may be placed on the surface of the bed 130 or may be separate from the bed 130. The biometric data may be transmitted to the console 160 and stored in memory 162. Alternatively or additionally, the biometric data may be transmitted to a server, which may be local or remote, using a network as further described herein.

[0046] According to one or more embodiments, catheter 110 can be configured to ablate a tissue region of a chamber of heart 120. Inset 150 shows an enlarged view of catheter 110 within a chamber of heart 120. For example, an ablation electrode, such as at least one electrode 111, can be configured to apply energy to a tissue region of an internal organ (e.g., heart 120). The energy can be thermal energy and can cause damage to the tissue region starting from the surface of the tissue region and extending through the thickness of the tissue region. Biometric data related to the ablation procedure (e.g., ablated tissue, ablation location, etc.) can be considered ablation data.

[0047] Referring now to FIG. 2 , a schematic diagram of a system 200 in which one or more features of the presently disclosed subject matter may be implemented is shown, according to one or more embodiments. System 200 includes, for a patient 202 (e.g., an example of patient 125 in FIG. 1 ), an apparatus 204, a local computing device 206, a remote computing system 208, a first network 210, and a second network 211. Additionally, apparatus 204 may include a biometric sensor 221 (e.g., an example of catheter 110 in FIG. 1 ), a processor 222, a user input (UI) sensor 223, a memory 224, and a transceiver 225. Note that for ease of explanation and brevity, device orientation engine 101 from FIG. 1 is reused in FIG. 2 .

[0048] According to one embodiment, device 204 may be an example of system 100 of FIG. 1 , where device 204 may include both patient-internal and patient-external components. According to one embodiment, device 204 may be a device external to patient 202 that includes an attachable patch (e.g., attached to the patient's skin). According to another embodiment, device 204 may be internal to the body of patient 202 (e.g., subcutaneously implantable), where device 204 may be inserted into patient 202 by any applicable method, including oral infusion, surgical insertion via a vein or artery, endoscopic surgery, or laparoscopic surgery. According to one embodiment, while a single device 204 is shown in FIG. 2 , an exemplary system may include multiple devices.

[0049] Accordingly, device 204, local computing device 206, and / or remote computing system 208 may be programmed to execute computer instructions related to device orientation engine 101. As an example, memory 224 stores these instructions for execution by processor 222 such that device 204 can receive and process biometric data via biometric sensor 201. In this manner, processor 222 and memory 224 are representative of the processor and memory of local computing device 206 and / or remote computing system 208.

[0050] The device 204, the local computing device 206, and / or the remote computing system 208 may be any combination of software and / or hardware that individually or collectively stores, executes, and implements the device orientation engine 101 and its functions. Furthermore, the device 204, the local computing device 206, and / or the remote computing system 208 may be an electronic computer framework that includes and / or uses any number and combination of computing devices and networks utilizing various communication technologies, as described herein. The device 204, the local computing device 206, and / or the remote computing system 208 may be easily scalable, extensible, and modular, capable of being tailored for different services or reconfigured with some functions independently of others.

[0051] Networks 210 and 211 may be wired networks, wireless networks, or may include one or more wired and wireless networks. According to one embodiment, network 210 is an example of a short-range network (e.g., a local area network (LAN) or a personal area network (PAN)). Information can be transmitted between apparatus 204 and local computing device 206 over short-range network 210 using any one of a variety of short-range wireless communication protocols, such as Bluetooth, Wi-Fi, Zigbee, Z-Wave, near field communications (NFC), Ultraband, Zigbee, or infrared (IR). Furthermore, network 211 is an example of one or more of an intranet, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a direct connection or series of connections, a cellular telephone network, or any other network or medium capable of facilitating communication between local computing device 206 and remote computing system 208. Information can be transmitted over network 211 using any one of a variety of long-range wireless communication protocols (e.g., TCP / IP, HTTP, 3G, 4G / LTE, or 5G / New Radio). It should be noted that the wired connections of networks 210 and 211 can be implemented using Ethernet, Universal Serial Bus (USB), RJ-11, or any other wired connection, and the wireless connections can be implemented using Wi-Fi, WiMAX, Bluetooth, infrared, cellular networks, satellite communications, or any other wireless connection method.

[0052] In operation, device 204 may continuously or periodically acquire, monitor, store, process, and communicate biometric data related to patient 202 via network 210. Additionally, device 204, local computing device 206, and / or remote computing system 208 communicate via networks 210 and 211 (e.g., local computing device 206 may be configured as a gateway between device 204 and remote computing system 208). For example, device 204 may be an example of system 100 of FIG. 1 configured to communicate with local computing device 206 via network 210. Local computing device 206 may be, for example, a fixed / standalone device, a base station, a desktop / laptop computer, a smartphone, a smartwatch, a tablet, or any other device configured to communicate with other devices via networks 211 and 210. A remote computing system 208, implemented as a physical server on or connected to the network 211 or as a virtual server within a public cloud computing provider of the network 211 (e.g., Amazon Web Services (AWS)®), can be configured to communicate with the local computing device 206 over the network 211, thereby enabling biometric data related to the patient 202 to be communicated throughout the system 200.

[0053] The elements of device 224 are now described. Biometric sensor 221 may include, for example, one or more transducers configured to convert one or more environmental conditions into electrical signals so that different types of biometric data may be observed / acquired / obtained. For example, biometric sensor 221 may include one or more of an electrode (e.g., electrode 111 of FIG. 1 ), a temperature sensor (e.g., a thermocouple), a blood pressure sensor, a blood glucose sensor, a blood oxygen sensor, a pH sensor, an accelerometer, and a microphone.

[0054] In executing device orientation engine 101, processor 222 may be configured to receive, process, and manage biometric data obtained by biometric sensor 221 and to transmit the biometric data to memory 224 for storage and / or over network 210 via transceiver 225. Biometric data from one or more other devices 204 may also be received by processor 222 via transceiver 225. Additionally, as described in more detail below, processor 222 may be configured to selectively respond to different tapping patterns (e.g., single tap or double tap) received from UI sensor 223 such that different tasks of the patch (e.g., acquiring, storing, or transmitting data) are initiated based on the detected pattern. In some embodiments, processor 222 may generate audible feedback regarding detection of the gesture.

[0055] The UI sensor 223 may include, for example, a piezoelectric or capacitive sensor configured to receive user input, such as a tap or touch. For example, the UI sensor 223 may be controlled to perform capacitive coupling in response to the patient 202 tapping or touching the surface of the device 204. Gesture recognition may be implemented via any one of a variety of capacitive types, such as resistive capacitive, surface capacitive, projected capacitive, surface ultrasonic, piezoelectric, and infrared touch. The capacitive sensor may be positioned over a small area or the length of the surface, such that a tap or touch on the surface activates the monitoring device.

[0056] The memory 224 may be any non-transitory, tangible medium, such as magnetic, optical, or electronic memory (e.g., any suitable volatile and / or non-volatile memory, such as a random access memory or a hard disk drive). The memory 224 stores computer instructions that are executed by the processor 222.

[0057] The transceiver 225 may include a separate transmitter and a separate receiver, or the transceiver 225 may include a transmitter and receiver integrated into a single device.

[0058] During operation, the device 204 utilizing the device orientation engine 101 observes / acquires biometric data of the patient 202 via the biometric sensor 221, stores the biometric data in memory, and shares this biometric data throughout the system 200 via the transceiver 225. The device orientation engine 101 can then utilize models, neural networks, ML, and / or AI to provide cardiac surgeons and medical personnel with a visual representation of the original catheter position relative to the displaced catheter position following inadvertent catheter movement.

[0059] Referring now to FIG. 3, a method 300 (e.g., performed by the device orientation engine 101 of FIG. 1 and / or FIG. 2) is shown, according to one or more embodiments. The method 300 addresses the need for reliable measurement and mapping by providing a multi-stage signal analysis of electrical signals representative of the movement of a reference electrode of a catheter 110 within a CS vein, enabling more accurate and improved understanding of electrophysiology. In this example, the catheter 110 is a straight catheter having multiple electrodes 111. The multiple electrodes 111 of the catheter 110 may be grouped (e.g., paired) to provide position information over time.

[0060] The method begins at block 330, where the device orientation engine 101 determines movement between each electrode group of the plurality of electrodes 111 of the catheter 110 to provide multiple movements. An electrode group may include two or more electrodes, such as pairs, triplets, etc. The device orientation engine 101 utilizes described position information (e.g., the positions of the electrodes 111 of each electrode group per timestamp) and a reference position as inputs for determining movement between each electrode group. According to one embodiment, movement between each electrode group is determined based on at least one set of two vectors constructed for each electrode group and a third vector between the set of two vectors for each electrode group.

[0061] In block 350, the device orientation engine 101 determines a total movement of the plurality of electrodes 111. The total movement may be based on an average movement of three central electrode pairs of the plurality of electrodes 111. For example, the device orientation engine 101 determines the median of each movement between each electrode pair, selects the three movement measurements closest to the median to provide selected measurements, and determines the average of the selected measurements to provide the average movement.

[0062] In block 370, the device orientation engine 101 removes normal components from the multiple movements and total movements. The normal components may include respiratory and / or cardiac movements (e.g., may include gating, compensation, and / or the like).

[0063] In block 380, the device orientation engine 101 outputs a visualization 390 including a movement indication of the catheter 110 based on the multiple movements and the total movement with the standard component removed. The movement indication can be for all input positions relative to a reference position and / or along the axial insertion axis of the catheter 110 into the CS.

[0064] Technical effects and advantages of method 300 include allowing a cardiac surgeon to experience visualization 390. Visualization 390 includes graphics that allow for correction of axial movements (e.g., multiple movements and total movements) along the CS that affect the timing of signals and measurements. More specifically, as shown in FIG. 3 , visualization 390 can show where catheter 110 originally was (e.g., baseline position) in relation to inadvertent catheter movement of catheter 110, utilizing axial vector calculations to show the direction of inadvertent catheter movement relative to the baseline position. Measurements provided can be in any length unit, such as millimeters.

[0065] That is, using method 300, when the device orientation engine 101 detects movement of the catheter 110 along the CS, a dialog box (e.g., visualization 390) may provide an alert. The alert may indicate a threshold or delta threshold to indicate how far the catheter 110 has moved (e.g., proximally or distally) from the baseline position. In this manner, the dialog box may indicate the baseline position relative to the real-time position, particularly for axial movement of the catheter 110 along the CS. Such an alert allows the physician to use the delta value as a guide to determine whether to return the catheter 110 to the baseline position. Thus, visualization 390 provides an enhanced ability to track the position of the catheter 110, distinguish between lateral and axial movement, and correct inadvertent catheter movement along the CS.

[0066] FIG. 4 shows a graphical depiction of an AI system 400 according to one or more embodiments. The AI ​​system 400 includes data 410 (e.g., biometric data), a machine 420, a model 430, an outcome 440, and (underlying) hardware 450. Where appropriate, for ease of understanding, the description of FIGS. 4-5 will be made with reference to FIGS. 1-3. For example, the machine 420, the model 430, and the hardware 450 may represent aspects of (e.g., the ML / AI algorithms therein) the device orientation engine 101 of FIGS. 1-2, while the hardware 450 may also represent the catheter 110 of FIG. 1, the console 160 of FIG. 1, and / or the device 204 of FIG. 2. In general, the ML / AI algorithms of the AI ​​system 400 (e.g., as implemented by the device orientation engine 101 of FIGS. 1-2) operate on the hardware 450 using the data 410 to train the machine 420, build the model 430, and predict the outcome 440.

[0067] For example, machine 420 may act as a controller or data acquisition associated with hardware 450 and / or may be associated with hardware 450. Data 410 (e.g., biometric data as described herein) may be ongoing data or output data associated with hardware 450. Data 410 may also include currently collected data, historical data, or other data from hardware 450, may include measurements during a surgical procedure and may be associated with the outcome of the surgical procedure, may include temperatures of heart 120 of FIG. 1 that are collected and correlated with the outcome of the cardiac procedure, and may be associated with hardware 450. Data 410 may be divided by machine 420 into one or more subsets.

[0068] Further, the machine 420 is trained, such as with the hardware 450. This training may also include analysis and correlation of the collected data 410. For example, in the cardiac case, the temperature and outcome data 410 may be trained to determine if a correlation or association exists between the temperature and outcome of the heart 120 of FIG. 1 during a cardiac procedure. According to another embodiment, training the machine 420 may include self-training by the device orientation engine 101 of FIG. 1 utilizing one or more subsets. In this regard, the device orientation engine 101 of FIG. 1 learns to detect point-by-point case classifications.

[0069] Additionally, model 430 is constructed on data 410 associated with hardware 450. Constructing model 430 can include physical hardware or software modeling, algorithmic modeling, and / or similar modeling intended to represent collected and trained data 410 (or a subset thereof). In some aspects, constructing model 430 is part of a self-training operation by machine 420. Model 430 can be configured to model the operation of hardware 450 and to model data 410 collected from hardware 450 to predict outcome 440 obtained by hardware 450. Predicting outcome 440 (of model 430 associated with hardware 450) can use trained model 430. By way of example, and to further understand the present disclosure, for the heart, if temperatures during treatment between 36.5°C and 37.89°C (i.e., 97.7°F and 100.2°F) result in positive outcomes from cardiac treatments, then outcome 440 can be predicted for a particular treatment using these temperatures. Thus, the predicted outcome 440 can be used to configure the machine 420, model 430, and hardware 450 accordingly.

[0070] Thus, the ML / AI algorithms therein may include neural networks, as AI system 400 uses data 410 to operate on hardware 450 to train machines 420, build models 430, and predict outcomes 440. Generally, a neural network is a network or circuit of neurons, or in the modern sense, an artificial neural network (ANN) made up of artificial neurons or nodes or cells.

[0071] For example, an ANN contains a network of processing elements (artificial neurons) that can exhibit complex global behavior determined by the connections between the processing elements and element parameters. These connections in a neuronal network or circuit are modeled as weights. Positive weights reflect excitatory connections, while negative values ​​represent inhibitory connections. Inputs are modified by the weights and summed using linear combinations. An activation function can control the amplitude of the output. For example, the acceptable range of the output is typically 0 to 1, but can also be -1 to 1. ANNs are often adaptive systems that change their structure based on external or internal information flowing through the network.

[0072] In more practical terms, neural networks are nonlinear statistical data modeling or decision-making tools that can be used to model complex relationships between inputs and outputs or to find patterns in data. Therefore, ANNs can be used for predictive modeling and adaptive control applications while being trained through a data set. Note that self-learning arising from experience can occur within ANNs, allowing them to draw conclusions from complex and seemingly unrelated sets of information. The usefulness of artificial neural network models lies in the fact that they can be used to estimate and use functions from observations. Unsupervised neural networks can also be used to learn representations of inputs that capture salient features of the input distribution and, more recently, in deep learning algorithms that can implicitly learn distribution functions for observed data. Training with neural networks is particularly useful in applications where the complexity of the data (e.g., biometric data) or the task (e.g., monitoring, diagnosing, and treating any number of different diseases) makes the design of such functions impractical.

[0073] Neural networks can be used in a variety of fields. Thus, in AI systems 400, the ML / AI algorithms therein can include neural networks that are generally divided according to the tasks to which they are applied. These divisions tend to fall into the categories of regression analysis (e.g., function approximation), including time series prediction and modeling; classification, including pattern and sequence recognition; novel detection and sequential decision making; data processing, including filtering; clustering; blind signal separation; and compression. For example, application areas of ANNs include nonlinear system identification and control (vehicle control, process control), game playing and decision making (backgammon, chess, racing), pattern recognition (radar systems, face identification, object recognition), sequence recognition (gesture, speech, handwritten text recognition), medical diagnosis and treatment, financial applications, data mining (or knowledge discovery in databases, i.e., KDD), visualization, and email spam filtering. For example, it is possible to create semantic profiles of patient biometric data obtained from medical procedures.

[0074] According to one or more embodiments, the neural network may implement a long-short-term memory neural network architecture, a convolutional neural network (CNN) architecture, or the like. The neural network may be configurable with respect to a number of layers, a number of connections (e.g., encoder / decoder connections), regularization techniques (e.g., dropout), and optimization features.

[0075] Long-short-term memory neural network architectures contain feedback connections and can process single data points (e.g., images) along with entire sequences of data (e.g., speech or video). The units of a long-short-term memory neural network architecture can consist of cells, input gates, output gates, and forget gates, where cells store values ​​over any time interval and gates regulate the flow of information to and from the cells.

[0076] The CNN architecture is a shared-weight architecture with translational invariance, where each neuron in one layer is connected to every neuron in the next layer. The regularization techniques of the CNN architecture can exploit hierarchical patterns in the data and organize more complex patterns using smaller, simpler patterns. When a neural network implements a CNN architecture, other configurable aspects of the architecture may include the number of filters in each stage, the kernel size, and the number of kernels per layer.

[0077] Referring now to FIG. 5, a block diagram of an example neural network 500 and a method 501 performed within the neural network 500 is shown in accordance with one or more embodiments. The neural network 500 operates to assist in the implementation of the ML / AI algorithms described herein (e.g., as implemented by the device orientation engine 101 of FIGS. 1-2). The neural network 500 may be implemented in hardware, such as the machine 420 and / or hardware 450 of FIG. 4. As provided herein, the description of FIGS. 4-5 will be made with reference to FIGS. 1-3, where appropriate, for ease of understanding.

[0078] In an example operation, the device orientation engine 101 of FIG. 1 includes collecting data 410 from hardware 450. In the neural network 500, the input layer 510 is represented by multiple inputs (e.g., inputs 512 and 514 of FIG. 5 ). With reference to block 520 of the method 501, the input layer 510 receives the inputs 512 and 514. The inputs 512 and 514 may include biometric data. For example, collecting the data 410 may be aggregating biometric data (e.g., BS ECG data, IC ECG data, and ablation data) from one or more treatment records of the hardware 450 into a dataset (represented by data 410).

[0079] In block 525 of method 501, neural network 500 encodes inputs 512 and 514 using any portion of data 410 (e.g., datasets and predictions generated by AI system 400) to generate a latent representation or data encoding. The latent representation includes one or more intermediate data representations derived from multiple inputs. According to one or more embodiments, the latent representation is generated by an element-by-element activation function (e.g., a sigmoid function or a rectified linear function) of device orientation engine 101 of FIG. 1. As shown in FIG. 5, inputs 512 and 514 are provided to hidden layer 530, which is shown to include nodes 532, 534, 536, and 538. Neural network 500 executes processing through hidden layer 530 of nodes 532, 534, 536, and 538 to exhibit complex global behavior determined by the connections between processing elements and element parameters. Thus, the transition between layers 510 and 530 can be viewed as an encoder stage that takes inputs 512 and 514 and forwards them to the deep neural network (in hidden layer 530) to learn a particular smaller representation of the input (e.g., the resulting latent representation).

[0080] The deep neural network may be a CNN, a long-short-term memory neural network, a fully connected neural network, or a combination thereof. The inputs 512 and 514 may be intracardiac ECG, a surface ECG, or both intracardiac ECG and surface ECG. This encoding results in dimensionality reduction of the inputs 512 and 514. Dimensionality reduction is the process of reducing the number of random variables (in the inputs 512 and 514) being considered by obtaining a set of key variables. For example, dimensionality reduction may be feature extraction, which transforms data (e.g., inputs 512 and 514) from a high-dimensional space (e.g., greater than 10 dimensions) to a low-dimensional space (e.g., 2-3 dimensions). Technical effects and advantages of dimensionality reduction include reducing the time and storage space requirements of the data 410, improving visualization of the data 410, and improving parameter interpretability for ML. This data transformation may be linear or nonlinear. The operations of receiving (block 520) and encoding (block 525) may be considered the data preparation portion of a multi-stage data manipulation by the device orientation engine 101.

[0081] At block 545 of method 501, neural network 500 decodes the latent representation. The decoding stage receives the encoder output (e.g., the resulting latent representation) and attempts to reconstruct a particular form of inputs 512 and 514 using another deep neural network. In this regard, nodes 532, 534, 536, and 538 are combined to generate output 552 in output layer 550, as shown at block 560 of method 501. That is, output layer 550 recovers inputs 512 and 514 with reduced dimensionality but without signal interference, signal artifacts, and signal noise. An example of output 552 includes cleaned biometric data (e.g., a clean / denoised version of IC ECG data). Technical effects and benefits of cleaned biometric data include enabling more accurate monitoring, diagnosis, and treatment of any number of various disorders.

[0082] Referring now to Figure 6, a method 600 (e.g., performed by the device orientation engine 101 of Figures 1 and / or 2) is shown, in accordance with one or more embodiments. Figure 6 will be described with reference to Figures 1 and 7-12 for ease of explanation and brevity.

[0083] The method 600 addresses the need to understand and visualize whether a reference electrode (e.g., one of multiple electrodes 111 of the catheter 110) within the CS has moved due to the patient's breathing or heartbeat. Furthermore, when relying on the electrode 111 as a reference point within the CS, it is important that this electrode does not move; otherwise, the timing of atrial tachycardia (AT) measurements will be unreliable. Note that breathing and heartbeat occur throughout mapping and are not necessarily indicative of catheter 110 movement. Furthermore, in some cases, the device orientation engine 101 filters breathing and heartbeat to obtain an indication of movement on the axial insertion axis of the catheter 110. The device orientation engine 101 then enables repositioning of the catheter 110 to a baseline position, which avoids remapping and prolongs the procedure.

[0084] The method 600 begins at block 610, where the device orientation engine 101 establishes prerequisites. The prerequisites may include one or more inputs and / or assumptions. For example, the device orientation engine 101 aims to detect movement of a linear catheter (e.g., catheter 110) along an axial insertion axis into the CS. The inputs include the position of the electrodes 111. The device orientation engine 101 does not assume the use of a navigational diagnostic catheter and can be run without magnetic sensor input. The inputs also include a reference position of the catheter 110, relative to which movement is measured. The device orientation engine 101 then proceeds to movement calculations.

[0085] In block 620, the device orientation engine 101 obtains input data. The input data may include electrode positions per timestamp. The input data may also (or optionally) include reference positions for movement comparison.

[0086] In block 630, the device orientation engine 101 determines the movement of each electrode pair (e.g., begins determining and then calculating the movement of the catheter 110 along the axial axis). According to one embodiment, the movement of each electrode pair is calculated between a reference position and a current position (e.g., real-time position).

[0087] 7, a schematic diagram of a method 700 according to one or more embodiments is shown. Note that the device orientation engine 101 takes advantage of the natural curvature of the CS when performing vector determination. A set of two vectors is constructed for every adjacent electrode pair based on two positions of the catheter 110 (e.g., first position 710 and second position 720 shown). The set of two vectors is the first vector of the reference position

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[0096] In block 650, the device orientation engine 101 determines the total electrode movement. For example, once the movement is calculated for each pair of adjacent electrodes, the total electrode movement can be calculated based on the average movement of the three central pairs to remove the effects of noise. According to one embodiment, a median is determined by the device orientation engine 101 for all calculated movements of paired electrodes. The three closest movement measurements around the median are then calculated.

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[0099] In block 670, the device orientation engine 101 removes the overall effects of respiration and heartbeat. That is, because any calculated movement of the electrodes 111 may be affected by respiration and heartbeat motion (e.g., unrelated to baseline stability), the device orientation engine 101 implements an additional processing layer that removes these effects from any movement display. The device orientation engine 101 relies on the output of electrode movement to include low and high frequencies due to respiration and heartbeat, respectively. FIG. 8 shows a graph 800 of movement over time in accordance with one or more embodiments. The movement over time may be measured in millimeters or mm and may be considered a movement distance stream 810.

[0100] In sub-block 675, the device orientation engine 101 implements a peak detection (find peaks) function on the movement distance stream 810 to assign low-frequency peaks that represent respiration. According to one embodiment, the peak detection function involves defining a moving window (e.g., size of 361 samples) and calculating the maximum value for each step within the current window. FIG. 9 shows a graph 900 of peak detection according to one or more embodiments. For example, whenever a maximum value is located within the current window, its index is indicated as peak 920 (e.g., which may be said to be position 78) or peak index.

[0101] In sub-block 680, the device orientation engine 101 implements interpolation for each identified peak. Figure 10 shows a graph 1000 of an interpolated plot according to one or more embodiments. That is, for each peak, an interpolated value is determined using plotted values ​​(e.g., exemplary plotted values ​​1040) at a number of samples (e.g., 100 samples) before the peak index. For example, an interpolated value is then set for 100 samples before the peak index and 100 samples after the peak index. The interpolation results in low-frequency filtering.

[0102] In sub-block 685, the device orientation engine 101 applies a low-pass filter to smooth high frequencies and remove the effects of heartbeat using a cutoff frequency of 0.005. Figure 11 shows a graph 1100 of a movement display 1120 after applying a low-pass filter, according to one or more embodiments.

[0103] At block 690, the device orientation engine 101 provides output data. The output data includes a displacement indication (mm) for all input positions compared to the reference position. The output data can identify the displacement and alert the cardiac surgeon and medical personnel, provide one or more actions including position restoration, and provide information for repositioning. Note that the cardiac surgeon and medical personnel can then manually reposition the catheter 110.

[0104] 12 illustrates a visualization 1200 according to one or more embodiments. According to one embodiment, orientation engine 101 can implement pattern matching to account for any significant morphological shift in the electrical signals used to calculate the reference position. That is, once catheter 110 is repositioned to its initial position, orientation engine 101 ensures that catheter 110 recovers the same pattern of electrical signals (which may differ due to, for example, touching tissue). Thus, orientation engine 101 can provide correlation with the original signal pattern, as seen in visualization 1200.

[0105] More specifically, visualization 1200 shows image 1201, viewer 1202, and popup 1203. Image 1201 may be a three-dimensional image mapping of the CS vein in which the movement of catheter 110 is shown relative to reference point 1210 as catheter 110 moves between positions 1220 and 1230. That is, when repositioning catheter 110 using distance indications and signal correlation values, the mapping (e.g., image 1201) may continue with a stable, reliable baseline. Furthermore, IC pattern matching may be integrated or supplemented with image 1201. In this manner, viewer 1202 may be provided as a pattern matching viewer. IC pattern matching may be part of the movement calculation, part of the correlation, an aid to the overall process (e.g., with catheter movement), and / or a combination thereof. Popup 1203 provides a scale showing - / + position change (mm) for proximal 1250 and distal 1260 movement. Additionally, the amount of movement 1270 as well as the confidence 1280 are shown.

[0106] According to one or more embodiments, the device orientation engine 101 provides and utilizes the CS as an optimal reference point, since the temporal pattern of CS activation can aid in mapping. Analysis by the device orientation engine 101 of the temporal pattern of CS activation allows for rapid stratification or ranking of the most likely macro-reentrant AT, and the analysis also indicates the probable cause of focal AT. Thus, the device orientation engine 101 provides the technical effect and advantage of detecting CS catheter movement and analyzing such movement in a visual or graphical display to assist cardiac surgeons and medical personnel in repositioning the catheter 110 along the CS. Thus, the cardiac surgeon and medical personnel can reposition the catheter 110 to its original position or otherwise to a new baseline position to begin new mapping.

[0107] The flowcharts and block diagrams in the figures illustrate the structure, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of instructions, including one or more executable instructions for implementing the depicted logical function(s). In some alternative implementations, the functions shown in the blocks may occur out of the order shown in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flow diagrams, and combinations of blocks in the block diagrams and / or flow diagrams, may be implemented by a dedicated hardware-based system that performs the specified function or operation, or may be operated or executed by a combination of dedicated hardware and computer instructions.

[0108] While features and elements are described above in particular combinations, those skilled in the art will understand that each feature or element can be used alone or in combination with other features and elements. Additionally, the methods described herein may be implemented in a computer program, software, or firmware embodied in a computer-readable medium for execution on a computer or processor. As used herein, computer-readable medium should not be construed as being a transitory signal itself, such as an electric wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse passing through a fiber optic cable), or an electrical signal transmitted through a current line.

[0109] Examples of computer-readable media include electrical signals (transmitted over wired or wireless connections) and computer-readable storage media. Examples of computer-readable storage media include, but are not limited to, registers, cache memory, semiconductor memory devices, magnetic media such as internal hard disks and removable disks, magneto-optical media, optical media such as compact disks (CDs) and digital versatile disks (DVDs), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), and memory sticks. A processor together with software can be used to implement a radio frequency transceiver for use in a terminal, base station, or any host computer.

[0110] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used herein, the singular forms "a," "an," and "the" are intended to include the plural unless the context clearly dictates otherwise. It should be understood that the terms "comprise" and / or "comprising," as used herein, indicate the presence of stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0111] The description of different embodiments herein is provided for illustrative purposes, but is not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terms used herein are selected to best explain the principles, practical applications, or technical improvements of the embodiments compared to technologies found on the market, or to enable those skilled in the art to understand the embodiments disclosed herein.

[0112] [Embodiment] (1) A method comprising: determining, by a device orientation engine executed by one or more processors, a movement between each electrode group of the plurality of electrodes of the catheter to provide the plurality of movements; determining, by the device orientation engine, a total movement of the plurality of electrodes; removing standard components from the plurality of movements and the total movement by the device orientation engine; and outputting, by the device orientation engine, a representation of the catheter movement based on the plurality of movements with the standard component removed and the total movement. (2) The method of embodiment 1, wherein the device orientation engine utilizes the positions per timestamp for the electrodes of each electrode group and a reference position as inputs for determining the movement between each electrode group. (3) The method of embodiment 1, wherein the movement between each electrode group is determined based on at least one set of two vectors constructed for each electrode group. (4) The method of embodiment 3, wherein the movement between each electrode group is determined based on a third vector between the pair of two vectors of each electrode group. (5) The method of embodiment 1, wherein the total movement is based on the average movement of three central electrode pairs of the plurality of electrodes.

[0113] (6) The device orientation engine Determine the median of each movement between each electrode pair, selecting three moving measurements closest to the median to provide selected measurements; and 6. The method of claim 5, wherein an average value of the selected measurements is determined to provide the average shift. (7) The method of embodiment 1, wherein the standard component comprises respiratory motion or cardiac motion. (8) The method of claim 1, wherein the movement indication is for all input positions compared to a reference position. (9) The method of embodiment 1, wherein the movement indication is along the axial insertion axis of the catheter into the coronary sinus. (10) The method of embodiment 1, wherein the catheter is a straight catheter.

[0114] (11) A system comprising: a memory storing processor-executable code for a device orientation engine; one or more processors executing the processor executable code to provide the system with: determining, by the device orientation engine, a movement between each electrode group of a plurality of electrodes of a catheter to provide a plurality of movements; causing the device orientation engine to determine a total movement of the plurality of electrodes; causing the device orientation engine to remove standard components from the plurality of movements and the total movement; and one or more processors that cause the device orientation engine to output an indication of the catheter movement based on the multiple movements with the standard component removed and the total movement. (12) The system of embodiment 11, wherein the device orientation engine utilizes the position per timestamp for the electrodes of each electrode group and a reference position as input for determining the movement between each electrode group. (13) The system of embodiment 11, wherein the movement between each electrode group is determined based on at least one set of two vectors constructed for each electrode group. (14) The system of embodiment 13, wherein the movement between each electrode group is determined based on a third vector between the set of two vectors of each electrode group. (15) The system of embodiment 11, wherein the total movement is based on an average movement of three central electrode pairs of the plurality of electrodes.

[0115] (16) The processor executable code is configured to: determining the median of each movement between each electrode pair; selecting three moving measurements closest to the median to provide selected measurements; and 16. The system of claim 15, further comprising determining an average value of the selected measurements to provide the average shift. (17) The system of embodiment 11, wherein the standard component includes respiratory motion or cardiac motion. (18) The system of embodiment 11, wherein the movement indication is for all input positions compared to a reference position. (19) The system of embodiment 11, wherein the movement indication is along an axial insertion axis of the catheter into the coronary sinus. (20) The system of embodiment 11, wherein the catheter is a straight catheter.

Claims

1. 1. A system comprising: a memory storing processor-executable code for a device orientation engine; one or more processors executing the processor executable code to provide the system with: The device orientation engine calculates a movement based on the position data for each electrode group of the plurality of electrodes of the catheter for each time stamp as a position change of the same electrode group at different time points or a relative position change between different electrode groups, thereby providing a plurality of movements; extracting a plurality of measurements near a median from the movements of the plurality of electrode pairs belonging to the plurality of electrode groups by the device orientation engine, and determining a total movement as an average of the measurements; removing normal components from the plurality of movements and the total movement, the normal components being periodic components due to respiratory or cardiac movements, by the device orientation engine; and one or more processors that cause the device orientation engine to output an indication of the catheter movement based on the multiple movements with the standard component removed and the total movement.

2. The system of claim 1 , wherein the device orientation engine utilizes the positions per timestamp for the electrodes of each electrode group and a reference position as inputs for determining the movement between each electrode group.

3. The system of claim 1 , wherein the movement between each electrode group is determined based on at least one set of two vectors constructed for each electrode group.

4. The system of claim 3 , wherein the movement between each electrode group is determined based on a third vector between the set of two vectors of each electrode group.

5. The system of claim 1 , wherein the total movement is based on an average movement of three central electrode pairs of the plurality of electrodes.

6. The processor executable code may include: determining the median of each movement between each electrode pair; selecting three moving measurements closest to the median to provide selected measurements; and The system of claim 5 , further comprising determining an average of the selected measurements to provide the average shift.

7. The system of claim 1 , wherein the standard component comprises a respiratory motion or a cardiac motion.

8. The system of claim 1 , wherein the movement indication is for every input position compared to a reference position.

9. The system of claim 1 , wherein the motion indication is along an axial insertion axis of the catheter into the coronary sinus.

10. The system of claim 1 , wherein the catheter is a straight catheter.

11. 1. A method comprising: calculating, by a device orientation engine executed by one or more processors, a movement based on the position data for each electrode group of the plurality of electrodes of the catheter for each timestamp, as a position change of the same electrode group at different times or a relative position change between different electrode groups, to provide a plurality of movements; extracting a plurality of measurements near a median from the movements of the plurality of electrode pairs belonging to the plurality of electrode groups by the device orientation engine, and determining a total movement as an average of the measurements; removing, by the device orientation engine, from the plurality of movements and the total movement, normal components that are periodic components due to respiratory or cardiac movements; and outputting, by the device orientation engine, a representation of the catheter movement based on the plurality of movements with the standard component removed and the total movement.

12. The method of claim 11 , wherein the device orientation engine utilizes the positions per timestamp for the electrodes of each electrode group and a reference position as inputs for determining the movement between each electrode group.

13. The method of claim 11 , wherein the movement between each electrode group is determined based on at least one set of two vectors constructed for each electrode group.

14. The method of claim 13 , wherein the movement between each electrode group is determined based on a third vector between the set of two vectors for each electrode group.

15. The method of claim 11 , wherein the total movement is based on an average movement of three central electrode pairs of the plurality of electrodes.

16. the device orientation engine: Determine the median of each movement between each electrode pair, selecting three moving measurements closest to the median to provide selected measurements; and The method of claim 15 , further comprising determining an average of the selected measurements to provide the average shift.

17. The method of claim 11 , wherein the standard component comprises respiratory motion or cardiac motion.

18. The method of claim 11 , wherein the movement indication is for every input position compared to a reference position.

19. The method of claim 11 , wherein the motion indication is along an axial insertion axis of the catheter into the coronary sinus.

20. The method of claim 11 , wherein the catheter is a straight catheter.

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