Patent Description:
Currently, cardiac electrophysiology systems are used to map or visualize real-time calculated positions and orientations of a catheter within a patient's heart. In some cases, the cardiac electrophysiology systems utilize a reference point in time to visualize an activation wave on the heart, from beat to beat and/or for each point taken during a mapping process. Further, the cardiac electrophysiology systems can track time about the reference point.

Generally, the CS vein is an excellent reference point for the cardiac electrophysiology systems because the CS vein is between an atrium and a ventricle (e.g., meaning that a catheter can monitor both atria and ventricle activity). The CS vein is also optimal for reference point as the CS vein is a stable location to place the catheter and expect the catheter to maintain a same position throughout mapping. As cardiac electrophysiology systems rely on electrodes of a catheter as reference points within the CS, it is important that the electrodes do not move. When the reference electrodes within the CS move, it essentially hinders the accuracy of all measurements and further mapping should not take place. In particular, if the electrodes move, a timing of atrial tachycardia (AT) measurements or ventricular tachycardia (VT) measurements become unreliable and must be re-mapped, which prolongs the procedure.

There are presently no techniques that improve electrode/catheter stability and/or account for movement thereof. Such a technique may be beneficial for cardiac electrophysiology systems.

<CIT> discloses a method of detecting dislodgement of a navigational reference for a localization system includes securing a reference catheter, including at least one reference localization element, at an initial reference location within a localization field. The positions of one or more of the reference localization elements are monitored for a perceived displacement that suggests that the reference catheter has become dislodged from the initial reference location (e.g., a displacement above a certain threshold, such as about <NUM>). The direction of this perceived displacement may then be further analyzed (e.g., compared to a predicted or most likely direction of displacement) to determine whether there has been an actual dislodgement of the reference catheter, and, if so, an appropriate signal (e.g., an audible or visual warning) may be generated. Upon dislodgement, guidance may be provided to aid the practitioner in restoring the reference catheter to its initial location.

According to the invention, a system as claimed in claim <NUM> is provided. Embodiments are provided in the dependent claims.

Disclosed herein is a method and system for signal processing. More particularly, the present invention relates to a signal analysis of movements of a reference electrode of a catheter in coronary sinus (CS) vein.

For example, a device orientation engine is a processor executable code or software that is necessarily rooted in process operations by, and in processing hardware of, medical device equipment. According to an exemplary embodiment, device orientation engine can include machine learning/artificial intelligence (ML/AI) algorithms. The device orientation engine tracks movements of a CS catheter within the CS while mapping the CS. For instance, the device orientation engine tracks movements of the CS catheter along an axial axis of the CS, which may affect a stability of a reference of a map, and tracks deviations from an acquired reference position at a beginning of mapping.

The technical effects and benefits of the device orientation engine include providing cardiac physicians and medical personnel a visual representation of an original catheter position in relation to a displaced catheter position following inadvertent catheter movements. Thus, the device orientation engine particularly utilizes and transforms medical device equipment to enable/implement CS catheter displacement estimations that are otherwise not currently available or currently performed by cardiac electrophysiology systems.

According to one or more embodiments, the device orientation engine being executed by one or more processors implements a method.

<FIG> is a diagram of a system <NUM> (e.g., medical device equipment) in which one or more features of the subject matter herein can be implemented according to one or more embodiments. All or part of the system <NUM> can be used to collect information (e.g., biometric data and/or a training dataset) and/or used to implement a device orientation engine <NUM>, as described herein.

The system <NUM>, as illustrated, includes a probe <NUM> with a catheter <NUM> (including at least one electrode <NUM>), a shaft <NUM>, a sheath <NUM>, and a manipulator <NUM>. The system <NUM>, as illustrated, also includes a physician <NUM> (or a medical professional, clinician, technician, or the like), a heart <NUM>, a patient <NUM>, and a bed <NUM> (or a table). Note that insets <NUM> and <NUM> show the heart <NUM> and the catheter <NUM> in greater detail. The system <NUM> also, as illustrated, includes a console <NUM> (including one or more processors <NUM> and memories <NUM>) and a display <NUM>. Note further each element and/or item of the system <NUM> is representative of one or more of that element and/or that item. The example of the system <NUM> shown in <FIG> can be modified to implement the embodiments disclosed herein. The disclosed embodiments can similarly be applied using other system components and settings. Additionally, the system <NUM> can include additional components, such as elements for sensing electrical activity, wired or wireless connectors, processing and display devices, or the like.

The system <NUM> can be utilized to detect, diagnose, and/or treat cardiac conditions (e.g., using the device orientation engine <NUM>). Cardiac conditions, such as cardiac arrhythmias, persist as common and dangerous medical ailments, especially in the aging population. For instance, the system <NUM> can be part of a surgical system (e.g., Carto® system sold by Biosense Webster) that is configured to obtain biometric data (e.g., anatomical and electrical measurements of a patient's organ, such as the heart <NUM>) and perform a cardiac ablation procedure.

In patients (e.g., the patient <NUM>) with normal sinus rhythm (NSR), the heart (e.g., the heart <NUM>), which includes atrial, ventricular, and excitatory conduction tissue, is electrically excited to beat in a synchronous, patterned fashion. Note that this electrical excitement can be detected as intracardiac electrocardiogram (IC ECG) data or the like.

In patients (e.g., the patient <NUM>) with a cardiac arrhythmia (e.g., atrial fibrillation or aFib), abnormal regions of cardiac tissue do not follow a synchronous beating cycle associated with normally conductive tissue, which is in contrast to patients with NSR. Instead, the abnormal regions of cardiac tissue aberrantly conduct to adjacent tissue, thereby disrupting the cardiac cycle into an asynchronous cardiac rhythm. Note that this asynchronous cardiac rhythm can also be detected as the IC ECG data. Such abnormal conduction has been previously known to occur at various regions of the heart <NUM>, for example, in the region of the sino-atrial (SA) node, along the conduction pathways of the atrioventricular (AV) node, or in the cardiac muscle tissue forming the walls of the ventricular and atrial cardiac chambers.

In support of the system <NUM> detecting, diagnosing, and/or treating cardiac conditions, the probe <NUM> can be navigated by the physician <NUM> into the heart <NUM> of the patient <NUM> lying on the bed <NUM>. For instance, the physician <NUM> can insert the shaft <NUM> through the sheath <NUM>, while manipulating a distal end of the shaft <NUM> using the manipulator <NUM> near the proximal end of the catheter <NUM> and/or deflection from the sheath <NUM>. As shown in an inset <NUM>, the catheter <NUM> can be fitted at the distal end of the shaft <NUM>. The catheter <NUM> can be inserted through the sheath <NUM> in a collapsed state and can be then expanded within the heart <NUM>.

The catheter <NUM>, which can include the at least one electrode <NUM> and a catheter needle coupled onto a body thereof, can be configured to obtain biometric data, such as electrical signals of an intra-body organ (e.g., the heart <NUM>), and/or to ablate tissue areas of thereof (e.g., a cardiac chamber of the heart <NUM>). Note that the electrodes <NUM> are representative of any like elements, such as tracking coils, piezoelectric transducer, electrodes, or combination of elements configured to ablate the tissue areas or to obtain the biometric data. According to one or more embodiments, the catheter <NUM> can include one or more position sensors that used are to determine trajectory information. The trajectory information can be used to infer motion characteristics, such as the contractility of the tissue.

Biometric data (e.g., patient biometrics, patient data, or patient biometric data) can include one or more of local time activations (LATs), electrical activity, topology, bipolar mapping, dominant frequency, impedance, or the like. The LAT can be a point in time of a threshold activity corresponding to a local activation, calculated based on a normalized initial starting point. Electrical activity can be any applicable electrical signals that can be measured based on one or more thresholds and can be sensed and/or augmented based on signal to noise ratios and/or other filters. A topology can correspond to the physical structure of a body part or a portion of a body part and can correspond to changes in the physical structure relative to different parts of the body part or relative to different body parts. A dominant frequency can be a frequency or a range of frequency that is prevalent at a portion of a body part and can be different in different portions of the same body part. For example, the dominant frequency of a PV of a heart can be different than the dominant frequency of the right atrium of the same heart. Impedance can be the resistance measurement at a given area of a body part.

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, historical data, brain biometrics, blood pressure data, ultrasound signals, radio signals, audio signals, a two- or three-dimensional image data, blood glucose data, and temperature data. The biometrics data can be used, generally, to monitor, diagnosis, and treatment any number of various diseases, such as cardiovascular diseases (e.g., arrhythmias, cardiomyopathy, and coronary artery disease) and autoimmune diseases (e.g., type I and type II diabetes). Note that BS ECG data can include data and signals collected from electrodes on a surface of a patient, IC ECG data can include data and signals collected from electrodes within the patient, and ablation data can include data and signals collected from tissue that has been ablated. Further, BS ECG data, IC ECG data, and ablation data, along with catheter electrode position data, can be derived from one or more procedure recordings.

For example, the catheter <NUM> can use the electrodes <NUM> to implement intravascular ultrasound and/or MRI catheterization to image the heart <NUM> (e.g., obtain and process the biometric data). Inset <NUM> shows the catheter <NUM> in an enlarged view, inside a cardiac chamber of the heart <NUM>. Although the catheter <NUM> is shown to be a point catheter, it will be understood that any shape that includes one or more electrodes <NUM> can be used to implement the embodiments disclosed herein.

Examples of the catheter <NUM> include, but are not limited to, a linear catheter with multiple electrodes, a balloon catheter including electrodes dispersed on multiple spines that shape the balloon, a lasso or loop catheter with multiple electrodes, or any other applicable shape. Linear catheters can be fully or partially elastic such that it can twist, bend, and or otherwise change its shape based on received signal and/or based on application of an external force (e.g., cardiac tissue) on the linear catheter. The balloon catheter can be designed such that when deployed into a patient's body, its electrodes can be held in intimate contact against an endocardial surface. As an example, a balloon catheter can be inserted into a lumen, such as a pulmonary vein (PV). The balloon catheter can be inserted into the PV in a deflated state, such that the balloon catheter does not occupy its maximum volume while being inserted into the PV. The balloon catheter can expand while inside the PV, such that those electrodes on the balloon catheter are in contact with an entire circular section of the PV. Such contact with an entire circular section of the PV, or any other lumen, can enable efficient imaging and/or ablation.

The probe <NUM> and other items of the system <NUM> can be connected to the console <NUM>. The console <NUM> can include any computing device, which can employ ML/AI algorithms of the device orientation engine <NUM>. According to an embodiment, the console <NUM> includes the one or more processors <NUM> (any computing hardware) and the memory <NUM> (any non-transitory tangible media), where the one or more processors <NUM> execute computer instructions with respect the device orientation engine <NUM> and the memory <NUM> stores these instructions for execution by the one or more processors <NUM>. For instance, the console <NUM> can be configured to receive and process the biometric data and determine if a given tissue area conducts electricity. In some embodiments, the console <NUM> can be further programmed by the device orientation engine <NUM> (in software) to carry out the functions of determining a movement between each electrode group of a catheter to provide movements; determining a total movement of electrodes of the catheter; removing a standard component from the movements and the total movement; and outputting a movement indication for the catheter based on the movements and the total movement with the standard component. According to one or more embodiments, the device orientation engine <NUM> can be external to the console <NUM> and can be located, for example, in the catheter <NUM>, in an external device, in a mobile device, in a cloud-based device, or can be a standalone processor. In this regard, the device orientation engine <NUM> can be transferable/downloaded in electronic form, over a network.

In an example, the console <NUM> can be any computing device, as noted herein, including software (e.g., the device orientation engine <NUM>) and/or hardware (e.g., the processor <NUM> and the memory <NUM>), such as a general-purpose computer, with suitable front end and interface circuits for transmitting and receiving signals to and from the probe <NUM>, as well as for controlling the other components of the system <NUM>. For example, the front end and interface circuits include input/output (I/O) communication interfaces that enables the console <NUM> to receive signals from and/or transfer signals to the at least one electrode <NUM>. The console <NUM> can include real-time noise reduction circuitry typically configured as a field programmable gate array (FPGA), followed by an analog-to-digital (A/D) ECG or electrocardiograph/electromyogram (EMG) signal conversion integrated circuit. The console <NUM> can pass the signal from an A/D ECG or EMG circuit to another processor and/or can be programmed to perform one or more functions disclosed herein.

The display <NUM>, which can be any electronic device for the visual presentation of the biometric data, is connected to the console <NUM>. According to an embodiment, during a procedure, the console <NUM> can facilitate on the display <NUM> a presentation of a body part rendering to the physician <NUM> and store data representing the body part rendering in the memory <NUM>. For instance, maps depicting motion characteristics can be rendered/constructed based on the trajectory information sampled at a sufficient number of points in the heart <NUM>. As an example, the display <NUM> can include a touchscreen that can be configured to accept inputs from the physician <NUM>, in addition to presenting the body part rendering.

In some embodiments, the physician <NUM> can manipulate the elements of the system <NUM> and/or the body part rendering using one or more input devices, such as a touch pad, a mouse, a keyboard, a gesture recognition apparatus, or the like. For example, an input device can be used to change a position of the catheter <NUM>, such that rendering is updated. Note that the display <NUM> can be located at a same location or a remote location, such as a separate hospital or in separate healthcare provider networks.

According to one or more embodiments, the system <NUM> can also obtain the biometric data using ultrasound, computed tomography (CT), MRI, or other medical imaging techniques utilizing the catheter <NUM> or other medical equipment. For instance, the system <NUM> can obtain ECG data and/or anatomical and electrical measurements of the heart <NUM> (e.g., the biometric data) using one or more catheters <NUM> or other sensors. More particularly, the console <NUM> can be connected, by a cable, to BS electrodes, which include adhesive skin patches affixed to the patient <NUM>. The BS electrodes can procure/generate the biometric data in the form of the BS ECG data. For instance, the processor <NUM> can determine position coordinates of the catheter <NUM> inside the body part (e.g., the heart <NUM>) of the patient <NUM>. The position coordinates may be based on impedances or electromagnetic fields measured between the body surface electrodes and the electrode <NUM> of the catheter <NUM> or other electromagnetic components. Additionally, or alternatively, location pads may be located on a surface of the bed <NUM> and may be separate from the bed <NUM>. The biometric data can be transmitted to the console <NUM> and stored in the memory <NUM>. Alternatively, or in addition, the biometric data may be transmitted to a server, which may be local or remote, using a network as further described herein.

According to one or more embodiments, the catheter <NUM> may be configured to ablate tissue areas of a cardiac chamber of the heart <NUM>. Inset <NUM> shows the catheter <NUM> in an enlarged view, inside a cardiac chamber of the heart <NUM>. For instance, ablation electrodes, such as the at least one electrode <NUM>, may be configured to provide energy to tissue areas of an intra-body organ (e.g., the heart <NUM>). The energy may be thermal energy and may cause damage to the tissue area starting from the surface of the tissue area and extending into the thickness of the tissue area. The biometric data with respect to ablation procedures (e.g., ablation tissues, ablation locations, etc.) can be considered ablation data.

Turning now to <FIG>, a diagram of a system <NUM> in which one or more features of the disclosure subject matter can be implemented is illustrated according to one or more embodiments. The system <NUM> includes, in relation to a patient <NUM> (e.g., an example of the patient <NUM> of <FIG>), an apparatus <NUM>, a local computing device <NUM>, a remote computing system <NUM>, a first network <NUM>, and a second network <NUM>. Further, the apparatus <NUM> can include a biometric sensor <NUM> (e.g., an example of the catheter <NUM> of <FIG>), a processor <NUM>, a user input (UI) sensor <NUM>, a memory <NUM>, and a transceiver <NUM>. Note that the device orientation engine <NUM> of <FIG> is reused in <FIG> for ease of explanation and brevity.

According to an embodiment, the apparatus <NUM> can be an example of the system <NUM> of <FIG>, where the apparatus <NUM> can include both components that are internal to the patient and components that are external to the patient. According to an embodiment, the apparatus <NUM> can be an apparatus that is external to the patient <NUM> that includes an attachable patch (e.g., that attaches to a patient's skin). According to another embodiment, the apparatus <NUM> can be internal to a body of the patient <NUM> (e.g., subcutaneously implantable), where the apparatus <NUM> can be inserted into the patient <NUM> via any applicable manner including orally injecting, surgical insertion via a vein or artery, an endoscopic procedure, or a laparoscopic procedure. According to an embodiment, while a single apparatus <NUM> is shown in <FIG>, example systems may include a plurality of apparatuses.

Accordingly, the apparatus <NUM>, the local computing device <NUM>, and/or the remote computing system <NUM> can be programed to execute computer instructions with respect the device orientation engine <NUM>. As an example, the memory <NUM> stores these instructions for execution by the processor <NUM> so that the apparatus <NUM> can receive and process the biometric data via the biometric sensor <NUM>. In this way, the processor <NUM> and the memory <NUM> are representative of processors and memories of the local computing device <NUM> and/or the remote computing system <NUM>.

The apparatus <NUM>, local computing device <NUM>, and/or the remote computing system <NUM> can be any combination of software and/or hardware that individually or collectively store, execute, and implement the device orientation engine <NUM> and functions thereof. Further, the apparatus <NUM>, local computing device <NUM>, and/or the remote computing system <NUM> can be an electronic, computer framework comprising and/or employing any number and combination of computing device and networks utilizing various communication technologies, as described herein. The apparatus <NUM>, local computing device <NUM>, and/or the remote computing system <NUM> can be easily scalable, extensible, and modular, with the ability to change to different services or reconfigure some features independently of others.

The networks <NUM> and <NUM> can be a wired network, a wireless network, or include one or more wired and wireless networks. According to an embodiment, the network <NUM> is an example of a short-range network (e.g., local area network (LAN), or personal area network (PAN)). Information can be sent, via the network <NUM>, between the apparatus <NUM> and the local computing device <NUM> using any one of various short-range wireless communication protocols, such as Bluetooth, Wi-Fi, Zigbee, Z-Wave, near field communications (NFC), ultra-band, Zigbee, or infrared (IR). Further, the network <NUM> 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 the local computing device <NUM> and the remote computing system <NUM>. Information can be sent, via the network <NUM>, using any one of various long-range wireless communication protocols (e.g., TCP/IP, HTTP, <NUM>, <NUM>/LTE, or <NUM>/New Radio). Note that for either network <NUM> and <NUM> wired connections can be implemented using Ethernet, Universal Serial Bus (USB), RJ-<NUM> or any other wired connection and wireless connections can be implemented using Wi-Fi, WiMAX, and Bluetooth, infrared, cellular networks, satellite or any other wireless connection methodology.

In operation, the apparatus <NUM> can continually or periodically obtain, monitor, store, process, and communicate via network <NUM> the biometric data associated with the patient <NUM>. Further, the apparatus <NUM>, local computing device <NUM>, and/ the remote computing system <NUM> are in communication through the networks <NUM> and <NUM> (e.g., the local computing device <NUM> can be configured as a gateway between the apparatus <NUM> and the remote computing system <NUM>). For instance, the apparatus <NUM> can be an example of the system <NUM> of <FIG> configured to communicate with the local computing device <NUM> via the network <NUM>. The local computing device <NUM> can be, for example, a stationary/standalone device, a base station, a desktop/laptop computer, a smart phone, a smartwatch, a tablet, or other device configured to communicate with other devices via networks <NUM> and <NUM>. The remote computing system <NUM>, implemented as a physical server on or connected to the network <NUM> or as a virtual server in a public cloud computing provider (e.g., Amazon Web Services (AWS) ®) of the network <NUM>, can be configured to communicate with the local computing device <NUM> via the network <NUM>. Thus, the biometric data associated with the patient <NUM> can be communicated throughout the system <NUM>.

Elements of the apparatus <NUM> are now described. The biometric sensor <NUM> may include, for example, one or more transducers configured to convert one or more environmental conditions into an electrical signal, such that different types of biometric data are observed/obtained/acquired. For example, the biometric sensor <NUM> can include one or more of an electrode (e.g., the electrode <NUM> of <FIG>), a temperature sensor (e.g., thermocouple), a blood pressure sensor, a blood glucose sensor, a blood oxygen sensor, a pH sensor, an accelerometer, and a microphone.

The processor <NUM>, in executing the device orientation engine <NUM>, can be configured to receive, process, and manage the biometric data acquired by the biometric sensor <NUM>, and communicate the biometric data to the memory <NUM> for storage and/or across the network <NUM> via the transceiver <NUM>. Biometric data from one or more other apparatuses <NUM> can also be received by the processor <NUM> through the transceiver <NUM>. Also, as described in more detail herein, the processor <NUM> may be configured to respond selectively to different tapping patterns (e.g., a single tap or a double tap) received from the UI sensor <NUM>, such that different tasks of a patch (e.g., acquisition, storing, or transmission of data) can be activated based on the detected pattern. In some embodiments, the processor <NUM> can generate audible feedback with respect to detecting a gesture.

The UI sensor <NUM> includes, for example, a piezoelectric sensor or a capacitive sensor configured to receive a user input, such as a tapping or touching. For example, the UI sensor <NUM> can be controlled to implement a capacitive coupling, in response to tapping or touching a surface of the apparatus <NUM> by the patient <NUM>. Gesture recognition may be implemented via any one of various capacitive types, such as resistive capacitive, surface capacitive, projected capacitive, surface acoustic wave, piezoelectric and infra-red touching. Capacitive sensors may be disposed at a small area or over a length of the surface, such that the tapping or touching of the surface activates the monitoring device.

The memory <NUM> is any non-transitory tangible media, such as magnetic, optical, or electronic memory (e.g., any suitable volatile and/or non-volatile memory, such as random-access memory or a hard disk drive). The memory <NUM> stores the computer instructions for execution by the processor <NUM>.

The transceiver <NUM> may include a separate transmitter and a separate receiver. Alternatively, the transceiver <NUM> may include a transmitter and receiver integrated into a single device.

In operation, the apparatus <NUM>, utilizing the device orientation engine <NUM>, observes/obtains the biometric data of the patient <NUM> via the biometric sensor <NUM>, stores the biometric data in the memory, and shares this biometric data across the system <NUM> via the transceiver <NUM>. The device orientation engine <NUM> can then utilize models, neural networks, ML, and/or AI to provide cardiac physicians and medical personnel a visual representation of an original catheter position in relation to a displaced catheter position following inadvertent catheter movements.

Turning now to <FIG>, a method <NUM> (e.g., performed by the device orientation engine <NUM> of <FIG> and/or of <FIG>) is illustrated according to one or more embodiments. The method <NUM> addresses a need for reliable measurements and mapping by providing a multi-step signal analysis of electrical signals, representing movements of a reference electrode of the catheter <NUM> in a CS vein, that enable an improved understanding an electrophysiology with more precision. In this example, the catheter <NUM> is a linear catheter with a plurality of electrodes <NUM>. The plurality of electrodes <NUM> of the catheter <NUM> can be grouped (such as paired) and can provide position information over time.

The method begins at block <NUM>, where the device orientation engine <NUM> determines a movement between each electrode group of the plurality of electrodes <NUM> of the catheter <NUM> to provide a plurality of movements. The electrode group can include two or more electrodes, such as pair, triplets, etc. The device orientation engine <NUM> utilizes the noted position information (e.g., positions per timestamp for electrodes <NUM> of each electrode group) and a reference position as inputs to determine the movement between each electrode group. According to an embodiment, the movement between each electrode group is determined based on at least a set of two vectors constructed for each electrode group and on a third vector between the set of two vectors for each electrode group.

At block <NUM>, the device orientation engine <NUM> determines a total movement of the plurality of electrodes <NUM>. The total movement can be based on an average movement of three median electrode pairs of the plurality of electrodes <NUM>. For instance, the device orientation engine <NUM> determines a median value for each movement between each electrode pair, selects three movement measurements nearest to the median value to provide selected measurements, and determines an average value for the selected measurements to provide the average movement.

At block <NUM>, the device orientation engine <NUM> removes a standard component from the plurality of movements and the total movement. The standard component comprises respiration movement and/or heartbeat movement (e.g., that can include gating, compensation, and/or the like).

At block <NUM>, the device orientation engine <NUM> outputs a visualization <NUM> that includes movement indication for the catheter <NUM> based on the plurality of movements and the total movement with the standard component removed. The movement indication can be for every input position compared against a reference position and/or can be along an axial insertion axis of the catheter <NUM> into the CS.

The technical effects and benefits of the method <NUM> include enabling the cardiac physician to experience the visualization <NUM>. The visualization <NUM> includes graphics enabling a correcting of axial movements along the CS, which affect the signals and timing of measurements (e.g., the plurality of movements and the total movement). More particularly, as shown in <FIG>, the visualization <NUM> can indicate where the catheter <NUM> was originally (e.g., a baseline position), in relation to inadvertent catheter movements of the catheter <NUM>, utilizing axial vector calculations to show a direction of the inadvertent catheter movements, relative to the baseline position. The measurements provided can be in any unit of length, such as millimeters.

That is, using the method <NUM>, when the device orientation engine <NUM> detects catheter <NUM> movement along the CS, a dialogue box (e.g., the visualization <NUM>) may provide an alert. The alert may indicate a threshold or delta threshold to show how far the catheter <NUM> moved (e.g., proximally or distally) from the baseline position. In this way, the dialogue box may show the baseline position versus the real-time position, particularly for the axial movement of the catheter <NUM> along the CS. With such alerts, the physician can decide whether to return the catheter <NUM> to the baseline position, using the delta value as a guide. Accordingly, the visualization <NUM> provides enhanced capability for tracking a position of the catheter <NUM>, distinguishing between lateral and axial movements, and correcting inadvertent catheter movement along the CS.

<FIG> illustrates a graphical depiction of an AI system <NUM> according to one or more embodiments. The AI system <NUM> includes data <NUM> (e.g., biometric data), a machine <NUM>, a model <NUM>, an outcome <NUM>, and (underlying) hardware <NUM>. The description of <FIG> is made with reference to <FIG> for ease of understanding where appropriate. For example, the machine <NUM>, the model <NUM>, and the hardware <NUM> can represent aspects of the device orientation engine <NUM> of <FIG> (e.g., the ML/AI algorithms therein), while the hardware <NUM> can also represent the catheter <NUM> of <FIG>, the console <NUM> of <FIG>, and/o the apparatus <NUM> of <FIG>. In general, the ML/AI algorithms of the AI system <NUM> (e.g., as implemented by the device orientation engine <NUM> of <FIG>) operate with respect to the hardware <NUM>, using the data <NUM>, to train the machine <NUM>, build the model <NUM>, and predict the outcomes <NUM>.

For instance, the machine <NUM> operates as the controller or data collection associated with the hardware <NUM> and/or is associated therewith. The data <NUM> (e.g., the biometric data as described herein) can be on-going data or output data associated with the hardware <NUM>. The data <NUM> can also include currently collected data, historical data, or other data from the hardware <NUM>; can include measurements during a surgical procedure and may be associated with an outcome of the surgical procedure; can include a temperature of the heart <NUM> of <FIG> collected and correlated with an outcome of a heart procedure; and can be related to the hardware <NUM>. The data <NUM> can be divided by the machine <NUM> into one or more subsets.

Further, the machine <NUM> trains, such as with respect to the hardware <NUM>. This training can also include an analysis and correlation of the data <NUM> collected. For example, in the case of the heart, the data <NUM> of temperature and outcome may be trained to determine if a correlation or link exists between the temperature of the heart <NUM> of <FIG> during the heart procedure and the outcome. In accordance with another embodiment, training the machine <NUM> can include self-training by the device orientation engine <NUM> of <FIG> utilizing the one or more subsets. In this regard, the device orientation engine <NUM> of <FIG> learns to detect case classifications on a point by point basis.

Moreover, the model <NUM> is built on the data <NUM> associated with the hardware <NUM>. Building the model <NUM> can include physical hardware or software modeling, algorithmic modeling, and/or the like that seeks to represent the data <NUM> (or subsets thereof) that has been collected and trained. In some aspects, building of the model <NUM> is part of self-training operations by the machine <NUM>. The model <NUM> can be configured to model the operation of hardware <NUM> and model the data <NUM> collected from the hardware <NUM> to predict the outcome <NUM> achieved by the hardware <NUM>. Predicting the outcomes <NUM> (of the model <NUM> associated with the hardware <NUM>) can utilize a trained model <NUM>. For example and to increase understanding of the disclosure, in the case of the heart, if the temperature during the procedure that is between <NUM> degrees Celsius and <NUM> degrees Celsius (i.e., <NUM> degrees Fahrenheit and <NUM> degrees Fahrenheit) produces a positive result from the heart procedure, the outcome <NUM> can be predicted in a given procedure using these temperatures. Thus, using the outcome <NUM> that is predicted, the machine <NUM>, the model <NUM>, and the hardware <NUM> can be configured accordingly.

Thus, for the AI system <NUM> to operate with respect to the hardware <NUM>, using the data <NUM>, to train the machine <NUM>, build the model <NUM>, and predict the outcomes <NUM>, the ML/AI algorithms therein can include neural networks. In general, a neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network (ANN), composed of artificial neurons or nodes or cells.

For example, an ANN involves a network of processing elements (artificial neurons) which can exhibit complex global behavior, determined by the connections between the processing elements and element parameters. These connections of the network or circuit of neurons are modeled as weights. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. Inputs are modified by a weight and summed using a linear combination. An activation function may control the amplitude of the output. For example, an acceptable range of output is usually between <NUM> and <NUM>, or it could be -<NUM> and <NUM>. In most cases, the ANN is an adaptive system that changes its structure based on external or internal information that flows through the network.

In more practical terms, neural networks are non-linear 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. Thus, ANNs may be used for predictive modeling and adaptive control applications, while being trained via a dataset. Note that self-learning resulting from experience can occur within ANNs, which can derive conclusions from a complex and seemingly unrelated set of information. The utility of artificial neural network models lies in the fact that they can be used to infer a function from observations and also to use it. Unsupervised neural networks can also be used to learn representations of the input that capture the salient characteristics of the input distribution, and more recently, deep learning algorithms, which can implicitly learn the distribution function of the observed data. Learning in neural networks is particularly useful in applications where the complexity of the data (e.g., the biometric data) or task (e.g., monitoring, diagnosing, and treating any number of various diseases) makes the design of such functions by hand impractical.

Neural networks can be used in different fields. Thus, for the AI system <NUM>, the ML/AI algorithms therein can include neural networks that are divided generally according to tasks to which they are applied. These divisions tend to fall within the following categories: regression analysis (e.g., function approximation) including time series prediction and modeling; classification including pattern and sequence recognition; novelty 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, "KDD"), visualization and e-mail spam filtering. For example, it is possible to create a semantic profile of patient biometric data emerging from medical procedures.

According to one or more embodiments, the neural network can implement a long short-term memory neural network architecture, a convolutional neural network (CNN) architecture, or other the like. The neural network can be configurable with respect to a number of layers, a number of connections (e.g., encoder/decoder connections), a regularization technique (e.g., dropout); and an optimization feature.

The long short-term memory neural network architecture includes feedback connections and can process single data points (e.g., such as images), along with entire sequences of data (e.g., such as speech or video). A unit of the long short-term memory neural network architecture can be composed of a cell, an input gate, an output gate, and a forget gate, where the cell remembers values over arbitrary time intervals and the gates regulate a flow of information into and out of the cell.

The CNN architecture is a shared-weight architecture with translation invariance characteristics where each neuron in one layer is connected to all neurons in the next layer. The regularization technique of the CNN architecture can take advantage of the hierarchical pattern in data and assemble more complex patterns using smaller and simpler patterns. If the neural network implements the CNN architecture, other configurable aspects of the architecture can include a number of filters at each stage, kernel size, a number of kernels per layer.

Turning now to <FIG>, an example of a neural network <NUM> and a block diagram of a method <NUM> performed in the neural network <NUM> are shown according to one or more embodiments. The neural network <NUM> operates to support implementation of the ML/AI algorithms (e.g., as implemented by the device orientation engine <NUM> of <FIG>) described herein. The neural network <NUM> can be implemented in hardware, such as the machine <NUM> and/or the hardware <NUM> of <FIG>. As indicated herein, the description of <FIG> is made with reference to <FIG> for ease of understanding where appropriate.

In an example operation, the device orientation engine <NUM> of <FIG> includes collecting the data <NUM> from the hardware <NUM>. In the neural network <NUM>, an input layer <NUM> is represented by a plurality of inputs (e.g., inputs <NUM> and <NUM> of <FIG>). With respect to block <NUM> of the method <NUM>, the input layer <NUM> receives the inputs <NUM> and <NUM>. The inputs <NUM> and <NUM> can include biometric data. For example, the collecting of the data <NUM> can be an aggregation of biometric data (e.g., BS ECG data, IC ECG data, and ablation data, along with catheter electrode position data), from one or more procedure recordings of the hardware <NUM> into a dataset (as represented by the data <NUM>).

At block <NUM> of the method <NUM>, the neural network <NUM> encodes the inputs <NUM> and <NUM> utilizing any portion of the data <NUM> (e.g., the dataset and predictions produced by the AI system <NUM>) to produce a latent representation or data coding. The latent representation includes one or more intermediary data representations derived from the plurality of inputs. According to one or more embodiments, the latent representation is generated by an element-wise activation function (e.g., a sigmoid function or a rectified linear unit) of the device orientation engine <NUM> of <FIG>. As shown in <FIG>, the inputs <NUM> and <NUM> are provided to a hidden layer <NUM> depicted as including nodes <NUM>, <NUM>, <NUM>, and <NUM>. The neural network <NUM> performs the processing via the hidden layer <NUM> of the nodes <NUM>, <NUM>, <NUM>, and <NUM> to exhibit complex global behavior, determined by the connections between the processing elements and element parameters. Thus, the transition between layers <NUM> and <NUM> can be considered an encoder stage that takes the inputs <NUM> and <NUM> and transfers it to a deep neural network (within the hidden layer <NUM>) to learn some smaller representation of the input (e.g., a resulting the latent representation).

The deep neural network can be a CNN, a long short-term memory neural network, a fully connected neural network, or combination thereof. The inputs <NUM> and <NUM> can be intracardiac ECG, body surface ECG, or intracardiac ECG and body surface ECG. This encoding provides a dimensionality reduction of the inputs <NUM> and <NUM>. Dimensionality reduction is a process of reducing the number of random variables (of the inputs <NUM> and <NUM>) under consideration by obtaining a set of principal variables. For instance, dimensionality reduction can be a feature extraction that transforms data (e.g., the inputs <NUM> and <NUM>) from a high-dimensional space (e.g., more than <NUM> dimensions) to a lower-dimensional space (e.g., <NUM>-<NUM> dimensions). The technical effects and benefits of dimensionality reduction include reducing time and storage space requirements for the data <NUM>, improving visualization of the data <NUM>, and improving parameter interpretation for ML. This data transformation can be linear or nonlinear. The operations of receiving (block <NUM>) and encoding (block <NUM>) can be considered a data preparation portion of the multi-step data manipulation by the device orientation engine <NUM>.

At block <NUM> of the method <NUM>, the neural network <NUM> decodes the latent representation. The decoding stage takes the encoder output (e.g., the resulting the latent representation) and attempts to reconstruct some form of the inputs <NUM> and <NUM> using another deep neural network. In this regard, the nodes <NUM>, <NUM>, <NUM>, and <NUM> are combined to produce in the output layer <NUM> an output <NUM>, as shown in block <NUM> of the method <NUM>. That is, the output layer <NUM> reconstructs the inputs <NUM> and <NUM> on a reduced dimension but without the signal interferences, signal artifacts, and signal noise. Examples of the output <NUM> include cleaned biometric data (e.g., clean/ denoised version of IC ECG data or the like). The technical effects and benefits of the cleaned biometric data include enabling more accurate monitor, diagnosis, and treatment any number of various diseases.

Turning now to <FIG>, a method <NUM> (e.g., performed by the device orientation engine <NUM> of <FIG> and/or of <FIG>) is illustrated according to one or more embodiments. <FIG> is described with reference to <FIG> and <FIG> for ease or understanding and brevity.

The method <NUM> addresses a need for understanding and visualizing whether reference electrodes (e.g., of the plurality of electrodes <NUM> of the catheter <NUM>) within the CS have moved due to a patient's respiration or heart beats. Further, when relying on the electrodes <NUM> as reference points within the CS, it is important that the electrodes do not move, otherwise the timing of the atrial tachycardia (AT) measurements are unreliable. Note that respiration and heart beats occur all through mapping and are not necessarily a sign of movement of the catheter <NUM>. Moreover, in some cases, the device orientation engine <NUM> filters respiration and heart beats to obtain an indication of movements that are on the axial insertion axis of the catheter <NUM>. In turn, the device orientation engine <NUM> allows relocation of the catheter <NUM> to a baseline position, which avoids re-mapping and prevents prolonging ay procedures.

The method <NUM> begins at block <NUM>, where the device orientation engine <NUM> establishes prerequisites. Prerequisites can include one or more inputs and/or assumptions. For example, the device orientation engine <NUM> aims to detect movement of a linear catheter (e.g., the catheter <NUM>), along an axial insertion axis of into the CS. An input includes positions of the electrodes <NUM>. The device orientation engine <NUM> does not assume a use of a navigational diagnostic catheter and can be activated without a magnetic sensor input. An input also includes a reference position of the catheter <NUM> against which movements are measured. Next, the device orientation engine <NUM> proceeds with a movement calculation.

At block <NUM>, the device orientation engine <NUM> acquires input data. Input data can include a position of an electrode per timestamp. Input data can also include (or in the alternative) a reference position for movement comparison.

At block <NUM>, the device orientation engine <NUM> determines a movement of individual pairs of electrodes (e.g., to begin the determination of a movement of the catheter <NUM> along the axial axis is then calculated). According to an embodiment, the movement of each pair of electrodes is calculated between a reference position and a current position (e.g., a real-time position).

Turning now to <FIG>, a diagram <NUM> of a vector determination is illustrated according to one or more embodiments. Note that the device orientation engine <NUM>, when making the vector determination, leverages of natural curvature of the CS. A set of two vectors are constructed for every adjacent pair of electrodes, based on the two locations (e.g., a first location <NUM> and a second location <NUM>, as shown) of the catheter <NUM>. The set of two vectors includes a first vector ui for the reference position and a second vector vi for the current position. An additional vector wi is calculated between ui and vi. The device orientation engine <NUM> determines a magnitude of vector ui ({x<NUM>, y<NUM>, z<NUM>}, {x<NUM>, y<NUM>, z<NUM>}) according to Equation <NUM> and determines an axial movement (measured in millimeters or mm) of each pair of electrodes using a dot product according to Equation <NUM>. <MAT> <MAT>.

At block <NUM>, the device orientation engine <NUM> determines a total electrode movement. For example, once the movements have been calculated for each pair of adjacent electrodes, the movement of all electrodes can be calculated based on the average movement of the three median pairs, to remove effects of noise. According to an embodiment, a median value is determined by the device orientation engine <NUM> for all calculated movements of the paired electrodes. Then, the three nearest movement measurements Δi near the median value are selected by the device orientation engine <NUM>. The device orientation engine <NUM>, next, calculates an average value for the selected measurements according to Equation <NUM>.

At block <NUM>, the device orientation engine <NUM> removes total respiration and heart beat effects. That is, because any calculated movements of the electrodes <NUM> can be affected by respiration and heartbeat movements (e.g., which are irrelevant to reference stability), the device orientation engine <NUM> implements an additional layer of processing remove these effects from any movement indications. The device orientation engine <NUM> relies on an output of an electrodes movement to include a low frequency and a high frequency due to respiration and heartbeat, respectively. <FIG> illustrates a graph <NUM> of movement over time according to one or more embodiments. Movement over time can be measured in millimeters or mm and can be considered a movement distances stream <NUM>.

At sub-block <NUM>, the device orientation engine <NUM> implements a find peaks function on the movement distances stream <NUM> to allocate the peaks of the low frequency, representing of respiration. According to an embodiment, the find peaks function includes defining a moving window (e.g., a size of <NUM> samples) and calculating a maximum value for each step in a current window. <FIG> illustrates a graph <NUM> of peaks detection according to one or more embodiments. For instance, wherever the maximum value is located within the current window, that index is then indicated as a peak <NUM> (e.g., can be said to be at position <NUM>) or peak index.

At sub-block <NUM>, the device orientation engine <NUM> implements an interpolation around each identified peak. <FIG> illustrates a graph <NUM> of a plot after interpolation according to one or more embodiments. That is, per each peak, an interpolation value is determined using a plot value (e.g., example plot value <NUM>) at a number of samples (e.g., <NUM> samples) before the peak index. For example, the interpolation value is then set for <NUM> samples before and <NUM> samples after the peak index. The interpolation results in a filtering of the low frequency.

At sub-block <NUM>, the device orientation engine <NUM> applies a low pass filter to smooth the high frequency and remove the heartbeat effect, using a cutoff frequency of <NUM>. <FIG> illustrates a graph <NUM> of a movement indication <NUM> after utilizing a low pass filter according to one or more embodiments.

At block <NUM>, the device orientation engine <NUM> provides output data. The output data includes a movement indication (mm) for every input position, compared against the reference position. The output data can identify movements, alert cardiac physicians and medical personnel, provide one or more actions including regain position, and provide information to reposition. Note that cardiac physicians and medical personnel can then manual reposition the catheter <NUM>.

<FIG> illustrates a visualization <NUM> according to one or more embodiments. According to an embodiment, the orientation engine <NUM> can implement pattern matching, accommodating any significance of movement on a morphology of the electrical signals, which is used to calculate the reference positions. That is, once the catheter <NUM> has been repositioned to an initial position, the orientation engine <NUM> ensures that the catheter <NUM> regains a same pattern of electrical signals (e.g., which could be different due to a tissue touched). For that reason, the orientation engine <NUM> can provide correlation with an original signal pattern, as seen in the visualization <NUM>.

More particularly, the visualization <NUM> shows an image <NUM>, a viewer <NUM>, and a pop-up <NUM>. The image <NUM> can be a three dimensional image mapping of a CS vein, where movements of the catheter <NUM> are shown with respect to a reference point <NUM> as the catheter <NUM> moves between positions <NUM> and <NUM>. That is, when repositioning the catheter <NUM> using a distance indication and a signals correlation value, a mapping (e.g., the image <NUM>) can be continued with high confidence of a stable reference. Further, IC pattern matching can be integrated or supplemented to the image <NUM>. In this way, the viewer <NUM> can be provided as a pattern matching viewer. The IC pattern matching can be part of a calculation of movement, part of a correlation, supplementary to a whole process (e.g., when you have a movement of the catheter), and/or combination thereof. The pop-up <NUM> provides a scale indicating a -/+ in the position change (mm) with respect to proximal <NUM> and distal <NUM> movements. Further, an amount of movement <NUM> is shown, as well as a confidence <NUM>.

According to one or more embodiments, the device orientation engine <NUM> provides leverages the CS as an optimal reference point because temporal patterns of CS activations can be helpful in mapping. The analysis by the device orientation engine <NUM> of the temporal patterns of the CS activations provides a rapid stratification, or ordering, of most likely macro-reentrant ATs, and the analysis also points toward the likely origin of focal ATs. Therefore, the device orientation engine <NUM> provides the technical effect and benefits of detecting CS catheter movement and analyzing such movement with a visual or graphical representation, to assist the cardiac physicians and medical personnel in repositioning the catheter <NUM> along the CS. Thus, the cardiac physicians and medical personnel could either reposition the catheter <NUM> to the original position or otherwise to a new baseline position, to start a new mapping.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the disclosure.

Although features and elements are described above in particular combinations, one of ordinary skill in the art will appreciate that each feature or element can be used alone or in any combination with the other features and elements. In addition, the methods described herein may be implemented in a computer program, software, or firmware incorporated in a computer-readable medium for execution by a computer or processor. A computer readable medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

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, a register, cache memory, semiconductor memory devices, magnetic media such as internal hard disks and removable disks, magneto-optical media, optical media such as compact disks (CD) and digital versatile disks (DVDs), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), and a memory stick. A processor in association with software may be used to implement a radio frequency transceiver for use in a terminal, base station, or any host computer.

It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one more other features, integers, steps, operations, element components, and/or groups thereof.

Claim 1:
A system (<NUM>) comprising:
a memory (<NUM>) storing processor executable code for a device orientation engine (<NUM>); and
one or more processors (<NUM>) configured for executing the processor executable code to cause the system to:
determine, by the device orientation engine, a movement of each electrode pair of a plurality of electrodes (<NUM>) of a catheter (<NUM>) to provide a plurality of movements, by constructing a set of two vectors for every adjacent pair of electrodes, one <MAT>
for a reference position and another vector for a current position of the catheter, calculating a third vector <MAT>
between the first and second vectors, and determining an axial movement of each pair of electrodes based upon the three vectors, wherein the device orientation engine utilizes positions per timestamp for electrodes of each electrode pair and a reference position as inputs for determining the movement between each electrode pair, and wherein the axial movement is determined by determining a magnitude, ∥ui∥, of the vector for the reference position according to the equation <MAT> and determining the axial movement, Δi, according to the equation <MAT>
determine, by the device orientation engine, a total movement of the plurality of electrodes, wherein the total movement is based on an average movement of three median electrode pairs of the plurality of electrodes;
remove, by the device orientation engine, a standard component from the plurality of movements and the total movement; and
output, by the device orientation engine, a movement indication for the catheter based on the plurality of movements and the total movement with the standard component removed.
wherein the total movement is determined by:
determining
a median value for each movement between each electrode pair;
selecting three movement measurements nearest to the median value to provide selected measurements; and
determining an average value for the selected measurements to provide the average movement.