EP4229644A1 - Planification de traitement d'arythmie personnalisée - Google Patents

Planification de traitement d'arythmie personnalisée

Info

Publication number
EP4229644A1
EP4229644A1 EP21801994.1A EP21801994A EP4229644A1 EP 4229644 A1 EP4229644 A1 EP 4229644A1 EP 21801994 A EP21801994 A EP 21801994A EP 4229644 A1 EP4229644 A1 EP 4229644A1
Authority
EP
European Patent Office
Prior art keywords
patient
data
information
survival
patients
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP21801994.1A
Other languages
German (de)
English (en)
Inventor
Liat Tsoref
Shiran Eliyahu
Matityahu Amit
Ana KAUFMAN
Ronen Tal-Botzer
Iddo Lev
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Biosense Webster Israel Ltd
Original Assignee
Biosense Webster Israel Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Biosense Webster Israel Ltd filed Critical Biosense Webster Israel Ltd
Publication of EP4229644A1 publication Critical patent/EP4229644A1/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the present invention is related to artificial intelligence and machine learning associated with a system and method to determine need for ablation.
  • Atrial fibrillation also called AFib or AF
  • AFib AF
  • arrhythmia irregular heartbeat
  • FIG. 1 is a block diagram of an example system for remotely monitoring and communicating patient biometrics
  • FIG. 2 is a system diagram of an example of a computing environment in communication with network
  • FIG. 3 is a block diagram of an example device in which one or more features of the disclosure can be implemented;
  • FIG. 4 illustrates a graphical depiction of an artificial intelligence system incorporating the example device of FIG. 3;
  • FIG. 5 illustrates a method performed in the artificial intelligence system of FIG. 4
  • FIG. 6 illustrates an example of the probabilities of a naive Bayes calculation
  • FIG. 7 illustrates an exemplary decision tree
  • FIG. 8 illustrates an exemplary random forest classifier
  • FIG. 9 illustrates an exemplary logistic regression
  • FIG. 10 illustrates an exemplary support vector machine
  • FIG. 11 illustrated an exemplary linear regression model
  • FIG. 12 illustrates an exemplary K-means clustering
  • FIG. 13 illustrates an exemplary ensemble learning algorithm
  • FIG. 14 illustrates an exemplary neural network
  • FIG. 15 illustrates a hardware based neural network
  • FIGS. 16A through 16D show examples of cardiomyopathies with different etiologies
  • FIG. 17 is a diagram of an exemplary system in which one or more features of the disclosure subject matter can be implemented.
  • FIG. 18 illustrates the system of FIG. 4 adapted for the present description
  • FIG. 19 illustrates an exemplary survival decision tree including KM curves
  • FIG. 20 illustrates an exemplary utility curve within plot based on the survival decision tree and KM curves of FIG. 19.
  • a method is described to assess in advance which patients will not respond well to antiarrhythmic drugs and recommend to them undergoing ablation immediately and to provide an assessment of success between ablation and antiarrhythmic drugs.
  • FIG. 1 is a block diagram of an example system 100 for remotely monitoring and communicating patient biometrics (i.e., patient data).
  • the system 100 includes a patient biometric monitoring and processing apparatus 102 associated with a patient 104, a local computing device 106, a remote computing system 108, a first network 110 and a second network 120.
  • a monitoring and processing apparatus 102 may be an apparatus that is internal to the patient’s body (e.g., subcutaneously implantable).
  • the monitoring and processing apparatus 102 may be inserted into a patient via any applicable manner including orally injecting, surgical insertion via a vein or artery, an endoscopic procedure, or a laparoscopic procedure.
  • a monitoring and processing apparatus 102 may be an apparatus that is external to the patient.
  • the monitoring and processing apparatus 102 may include an attachable patch (e.g., that attaches to a patient’s skin).
  • the monitoring and processing apparatus 102 may also include a catheter with one or more electrodes, a probe, a blood pressure cuff, a weight scale, a bracelet or smart watch biometric tracker, a glucose monitor, a continuous positive airway pressure (CPAP) machine or virtually any device which may provide an input concerning the health or biometrics of the patient.
  • CPAP continuous positive airway pressure
  • a monitoring and processing apparatus 102 may include both components that are internal to the patient and components that are external to the patient.
  • Example systems may, however, may include a plurality of patient biometric monitoring and processing apparatuses.
  • a patient biometric monitoring and processing apparatus may be in communication with one or more other patient biometric monitoring and processing apparatuses. Additionally or alternatively, a patient biometric monitoring and processing apparatus may be in communication with the network 110.
  • One or more monitoring and processing apparatuses 102 may acquire patient biometric data (e.g., electrical signals, blood pressure, temperature, blood glucose level or other biometric data) and receive at least a portion of the patient biometric data representing the acquired patient biometrics and additional formation associated with acquired patient biometrics from one or more other monitoring and processing apparatuses 102.
  • the additional information may be, for example, diagnosis information and/or additional information obtained from an additional device such as a wearable device.
  • Each monitoring and processing apparatus 102 may process data, including its own acquired patient biometrics as well as data received from one or more other monitoring and processing apparatuses 102.
  • network 110 is an example of a short-range network (e.g., local area network (LAN), or personal area network (PAN)).
  • Information may be sent, via short-range network 110, between monitoring a processing apparatus 102 and local computing device 106 using any one of various short-range wireless communication protocols, such as Bluetooth, WiFi, Zigbee, Z-Wave, near field communications (NFC), ultraband, Zigbee, or infrared (IR).
  • Network 120 may be a wired network, a wireless network or include one or more wired and wireless networks.
  • a network 120 may be a long-range network (e.g., wide area network (WAN), the internet, or a cellular network,).
  • Information may be sent, via network 120 using any one of various long-range wireless communication protocols (e.g., TCP/IP, HTTP, 3G, 4G/LTE, or 5G/New Radio).
  • the patient monitoring and processing apparatus 102 may include a patient biometric sensor 112, a processor 114, a user input (UI) sensor 116, a memory 118, and a transmitter-receiver (i.e., transceiver) 122.
  • the patient monitoring and processing apparatus 102 may continually or periodically monitor, store, process and communicate, via network 110, any number of various patient biometrics.
  • patient biometrics include electrical signals (e.g., ECG signals and brain biometrics), blood pressure data, blood glucose data and temperature data.
  • the patient biometrics may be monitored and communicated for treatment across 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).
  • cardiovascular diseases e.g., arrhythmias, cardiomyopathy, and coronary artery disease
  • autoimmune diseases e.g., type I and type II diabetes.
  • Patient biometric sensor 112 may include, for example, one or more sensors configured to sense a type of biometric patient biometrics.
  • patient biometric sensor 112 may include an electrode configured to acquire electrical signals (e.g., heart signals, brain signals or other bioelectrical signals), a temperature sensor, a blood pressure sensor, a blood glucose sensor, a blood oxygen sensor, a pH sensor, an accelerometer and a microphone.
  • electrical signals e.g., heart signals, brain signals or other bioelectrical signals
  • a temperature sensor e.g., a blood pressure sensor, a blood glucose sensor, a blood oxygen sensor, a pH sensor, an accelerometer and a microphone.
  • patient biometric monitoring and processing apparatus 102 may be an ECG monitor for monitoring ECG signals of a heart.
  • the patient biometric sensor 112 of the ECG monitor may include one or more electrodes for acquiring ECG signals.
  • the ECG signals may be used for treatment of various cardiovascular diseases.
  • the patient biometric monitoring and processing apparatus 102 may be a continuous glucose monitor (CGM) for continuously monitoring blood glucose levels of a patient on a continual basis for treatment of various diseases, such as type I and type II diabetes.
  • the CGM may include a subcutaneously disposed electrode, which may monitor blood glucose levels from interstitial fluid of the patient.
  • the CGM may be, for example, a component of a closed-loop system in which the blood glucose data is sent to an insulin pump for calculated delivery of insulin without user intervention.
  • Transceiver 122 may include a separate transmitter and receiver. Alternatively, transceiver 122 may include a transmitter and receiver integrated into a single device.
  • Processor 114 may be configured to store patient data, such as patient biometric data in memory 118 acquired by patient biometric sensor 112, and communicate the patient data, across network 110, via a transmitter of transceiver 122. Data from one or more other monitoring and processing apparatus 102 may also be received by a receiver of transceiver 122, as described in more detail below.
  • the monitoring and processing apparatus 102 includes UI sensor 116 which may be, for example, a piezoelectric sensor or a capacitive sensor configured to receive a user input, such as a tapping or touching.
  • UI sensor 116 may be controlled to implement a capacitive coupling, in response to tapping or touching a surface of the monitoring and processing apparatus 102 by the patient 104.
  • 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 processor 114 may be configured to respond selectively to different tapping patterns of the capacitive sensor (e.g., a single tap or a double tap), which may be the UI sensor 116, such that different tasks of the patch (e.g., acquisition, storing, or transmission of data) may be activated based on the detected pattern.
  • different tasks of the patch e.g., acquisition, storing, or transmission of data
  • audible feedback may be given to the user from processing apparatus 102 when a gesture is detected.
  • the local computing device 106 of system 100 is in communication with the patient biometric monitoring and processing apparatus 102 and may be configured to act as a gateway to the remote computing system 108 through the second network 120.
  • the local computing device 106 may be, for example, a, smart phone, smartwatch, tablet, or other portable smart device configured to communicate with other devices via network 120.
  • the local computing device 106 may be a stationary or standalone device, such as a stationary base station including, for example, modem and/or router capability, a desktop or laptop computer using an executable program to communicate information between the processing apparatus 102 and the remote computing system 108 via the PC's radio module, or a USB dongle.
  • Patient biometrics may be communicated between the local computing device 106 and the patient biometric monitoring and processing apparatus 102 using a short-range wireless technology standard (e.g., Bluetooth, WiFi, ZigBee, Z-wave and other short-range wireless standards) via the short-range wireless network 110, such as a local area network (UAN) (e.g., a personal area network (PAN)).
  • a short-range wireless technology standard e.g., Bluetooth, WiFi, ZigBee, Z-wave and other short-range wireless standards
  • the local computing device 106 may also be configured to display the acquired patient electrical signals and information associated with the acquired patient electrical signals, as described in more detail below.
  • remote computing system 108 may be configured to receive at least one of the monitored patient biometrics and information associated with the monitored patient via network 120, which is a long-range network.
  • network 120 may be a wireless cellular network, and information may be communicated between the local computing device 106 and the remote computing system 108 via a wireless technology standard, such as any of the wireless technologies mentioned above.
  • the remote computing system 108 may be configured to provide (e.g., visually display and/or aurally provide) the at least one of the patient biometrics and the associated information to a healthcare professional (e.g., a physician).
  • FIG. 2 is a system diagram of an example of a computing environment 200 in communication with network 120.
  • the computing environment 200 is incorporated in a public cloud computing platform (such as Amazon Web Services or Microsoft Azure), a hybrid cloud computing platform (such as HP Enterprise OneSphere) or a private cloud computing platform.
  • a public cloud computing platform such as Amazon Web Services or Microsoft Azure
  • a hybrid cloud computing platform such as HP Enterprise OneSphere
  • computing environment 200 includes remote computing system 108 (hereinafter computer system), which is one example of a computing system upon which embodiments described herein may be implemented.
  • computer system hereinafter computer system
  • the remote computing system 108 may, via processors 220, which may include one or more processors, perform various functions. The functions may include analyzing monitored patient biometrics and the associated information and, according to physician- determined or algorithm driven thresholds and parameters, providing (e.g., via display 266) alerts, additional information, or instructions. As described in more detail below, the remote computing system 108 may be used to provide (e.g., via display 266) healthcare personnel (e.g., a physician) with a dashboard of patient information, such that such information may enable healthcare personnel to identify and prioritize patients having more critical needs than others.
  • healthcare personnel e.g., a physician
  • the computer system 210 may include a communication mechanism such as a bus 221 or other communication mechanism for communicating information within the computer system 210.
  • the computer system 210 further includes one or more processors
  • the processors 220 coupled with the bus 221 for processing the information.
  • the processors 220 may include one or more CPUs, GPUs, or any other processor known in the art.
  • the computer system 210 also includes a system memory 230 coupled to the bus
  • the system memory 230 may include computer readable storage media in the form of volatile and/or nonvolatile memory, such as read only system memory (ROM) 231 and/or random-access memory (RAM) 232.
  • the system memory RAM 232 may include other dynamic storage device(s) (e.g., dynamic RAM, static RAM, and synchronous DRAM).
  • the system memory ROM 231 may include other static storage device(s) (e.g., programmable ROM, erasable PROM, and electrically erasable PROM).
  • the system memory 230 may be used for storing temporary variables or other intermediate information during the execution of instructions by the processors 220.
  • a basic input/output system 233 may contain routines to transfer information between elements within computer system 210, such as during start-up, that may be stored in system memory ROM 231.
  • RAM 232 may comprise data and/or program modules that are immediately accessible to and/or presently being operated on by the processors 220.
  • System memory 230 may additionally include, for example, operating system 234, application programs 235, other program modules 236 and program data 237.
  • the illustrated computer system 210 also includes a disk controller 240 coupled to the bus 221 to control one or more storage devices for storing information and instructions, such as a magnetic hard disk 241 and a removable media drive 242 (e.g., floppy disk drive, compact disc drive, tape drive, and/or solid-state drive).
  • the storage devices may be added to the computer system 210 using an appropriate device interface (e.g., a small computer system interface (SCSI), integrated device electronics (IDE), Universal Serial Bus (USB), or FireWire).
  • SCSI small computer system interface
  • IDE integrated device electronics
  • USB Universal Serial Bus
  • FireWire FireWire
  • the computer system 210 may also include a display controller 265 coupled to the bus 221 to control a monitor or display 266, such as a cathode ray tube (CRT) or liquid crystal display (LCD), for displaying information to a computer user.
  • the illustrated computer system 210 includes a user input interface 260 and one or more input devices, such as a keyboard 262 and a pointing device 261, for interacting with a computer user and providing information to the processor 220.
  • the pointing device 261 for example, may be a mouse, a trackball, or a pointing stick for communicating direction information and command selections to the processor 220 and for controlling cursor movement on the display 266.
  • the display 266 may provide a touch screen interface that may allow input to supplement or replace the communication of direction information and command selections by the pointing device 261 and/or keyboard 262.
  • the computer system 210 may perform a portion or each of the functions and methods described herein in response to the processors 220 executing one or more sequences of one or more instructions contained in a memory, such as the system memory 230. Such instructions may be read into the system memory 230 from another computer readable medium, such as a hard disk 241 or a removable media drive 242.
  • the hard disk 241 may contain one or more data stores and data files used by embodiments described herein. Data store contents and data files may be encrypted to improve security.
  • the processors 220 may also be employed in a multi-processing arrangement to execute the one or more sequences of instructions contained in system memory 230.
  • hard-wired circuitry may be used in place of or in combination with software instructions. Thus, embodiments are not limited to any specific combination of hardware circuitry and software.
  • the computer system 210 may include at least one computer readable medium or memory for holding instructions programmed according to embodiments described herein and for containing data structures, tables, records, or other data described herein.
  • the term computer readable medium as used herein refers to any non-transitory, tangible medium that participates in providing instructions to the processor 220 for execution.
  • a computer readable medium may take many forms including, but not limited to, non-volatile media, volatile media, and transmission media.
  • Non-limiting examples of non-volatile media include optical disks, solid state drives, magnetic disks, and magneto-optical disks, such as hard disk 241 or removable media drive 242.
  • Non-limiting examples of volatile media include dynamic memory, such as system memory 230.
  • Non-limiting examples of transmission media include coaxial cables, copper wire, and fiber optics, including the wires that make up the bus 221. Transmission media may also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.
  • the computing environment 200 may further include the computer system 210 operating in a networked environment using logical connections to local computing device 106 and one or more other devices, such as a personal computer (laptop or desktop), mobile devices (e.g., patient mobile devices), a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to computer system 210.
  • computer system 210 may include modem 272 for establishing communications over a network 120, such as the Internet. Modem 272 may be connected to system bus 221 via network interface 270, or via another appropriate mechanism.
  • Network 120 may be any network or system generally known in the art, including the Internet, 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 computer system 610 and other computers (e.g., local computing device 106).
  • LAN local area network
  • WAN wide area network
  • MAN metropolitan area network
  • a direct connection or series of connections a cellular telephone network
  • cellular telephone network e.g., cellular telephone network
  • FIG. 3 is a block diagram of an example device 300 in which one or more features of the disclosure can be implemented.
  • the device 300 may be local computing device 106, for example.
  • the device 300 can include, for example, a computer, a gaming device, a handheld device, a set-top box, a television, a mobile phone, or a tablet computer.
  • the device 300 includes a processor 302, a memory 304, a storage device 306, one or more input devices 308, and one or more output devices 310.
  • the device 300 can also optionally include an input driver 312 and an output driver 314. It is understood that the device 300 can include additional components not shown in FIG. 3 including an artificial intelligence accelerator.
  • the processor 302 includes a central processing unit (CPU), a graphics processing unit (GPU), a CPU and GPU located on the same die, or one or more processor cores, wherein each processor core can be a CPU or a GPU.
  • the memory 304 is located on the same die as the processor 302, or is located separately from the processor 302.
  • the memory 304 includes a volatile or non-volatile memory, for example, random access memory (RAM), dynamic RAM, or a cache.
  • the storage device 306 includes a fixed or removable storage means, for example, a hard disk drive, a solid-state drive, an optical disk, or a flash drive.
  • the input devices 308 include, without limitation, a keyboard, a keypad, a touch screen, a touch pad, a detector, a microphone, an accelerometer, a gyroscope, a biometric scanner, or a network connection (e.g., a wireless local area network card for transmission and/or reception of wireless IEEE 802 signals).
  • the output devices 310 include, without limitation, a display, a speaker, a printer, a haptic feedback device, one or more lights, an antenna, or a network connection (e.g., a wireless local area network card for transmission and/or reception of wireless IEEE 802 signals).
  • a network connection e.g., a wireless local area network card for transmission and/or reception of wireless IEEE 802 signals.
  • the input driver 312 communicates with the processor 302 and the input devices 308, and permits the processor 302 to receive input from the input devices 308.
  • the output driver 314 communicates with the processor 302 and the output devices 310, and permits the processor 302 to send output to the output devices 310. It is noted that the input driver 312 and the output driver 314 are optional components, and that the device 300 will operate in the same manner if the input driver 312 and the output driver 314 are not present.
  • the output driver 316 includes an accelerated processing device (“APD”) 316 which is coupled to a display device 318.
  • the APD accepts compute commands and graphics rendering commands from processor 302, processes those compute and graphics rendering commands, and provides pixel output to display device 318 for display.
  • the APD 316 includes one or more parallel processing units to perform computations in accordance with a single-instruction-multiple-data (“SIMD”) paradigm.
  • SIMD single-instruction-multiple-data
  • the functionality described as being performed by the APD 316 is additionally or alternatively performed by other computing devices having similar capabilities that are not driven by ahost processor (e.g., processor 302) and provides graphical output to a display device 318.
  • ahost processor e.g., processor 302
  • any processing system that performs processing tasks in accordance with a SIMD paradigm may perform the functionality described herein.
  • computing systems that do not perform processing tasks in accordance with a SIMD paradigm performs the functionality described herein.
  • FIG. 4 illustrates a graphical depiction of an artificial intelligence system 200 incorporating the example device of FIG. 3.
  • System 400 includes data 410, a machine 420, a model 430, a plurality of outcomes 440 and underlying hardware 450.
  • System 400 operates by using the data 410 to train the machine 420 while building a model 430 to enable a plurality of outcomes 440 to be predicted.
  • the system 400 may operate with respect to hardware 450.
  • the data 410 may be related to hardware 450 and may originate with apparatus 102, for example.
  • the data 410 may be on-going data, or output data associated with hardware 450.
  • the machine 420 may operate as the controller or data collection associated with the hardware 450, or be associated therewith.
  • the model 430 may be configured to model the operation of hardware 450 and model the data 410 collected from hardware 450 in order to predict the outcome achieved by hardware 450.
  • hardware 450 may be configured to provide a certain desired outcome 440 from hardware 450.
  • FIG. 5 illustrates a method 500 performed in the artificial intelligence system of FIG. 4.
  • Method 500 includes collecting data from the hardware at step 510.
  • This data may include currently collected, historical or other data from the hardware.
  • this data may include measurements during a surgical procedure and may be associated with the outcome of the procedure.
  • the temperature of a heart may be collected and correlated with the outcome of a heart procedure.
  • method 500 includes training a machine on the hardware.
  • the training may include an analysis and correlation of the data collected in step 510.
  • the data of temperature and outcome may be trained to determine if a correlation or link exists between the temperature of the heart during the procedure and the outcome.
  • method 500 includes building a model on the data associated with the hardware. Building a model may include physical hardware or software modeling, algorithmic modeling, and the like, as will be described below. This modeling may seek to represent the data that has been collected and trained.
  • method 500 includes predicting the outcomes of the model associated with the hardware. This prediction of the outcome may be based on the trained model. For example, in the case of the heart, if the temperature during the procedure between 97.7 - 100.2 produces a positive result from the procedure, the outcome can be predicted in a given procedure based on the temperature of the heart during the procedure. While this model is rudimentary, it is provided for exemplary purposes and to increase understanding of the present invention.
  • the present system and method operate to train the machine, build the model, and predict outcomes using algorithms. These algorithms may be used to solve the trained model and predict outcomes associated with the hardware. These algorithms may be divided generally into classification, regression, and clustering algorithms.
  • a classification algorithm is used in the situation where the dependent variable, which is the variable being predicted, is divided into classes, and predicting a class, the dependent variable, for a given input.
  • a classification algorithm is used to predict an outcome, from a set number of fixed, predefined outcomes.
  • a classification algorithm may include naive Bayes algorithms, decision trees, random forest classifiers, logistic regressions, support vector machines and k nearest neighbors.
  • a naive Bayes algorithm follows the Bayes theorem, and follows a probabilistic approach. As would be understood, other probabilistic -based algorithms may also be used, and generally operate using similar probabilistic principles to those described below for the exemplary naive Bayes algorithm.
  • FIG. 6 illustrates an example of the probabilities of a naive Bayes calculation.
  • This naive Bayes algorithm, and Bayes algorithms generally, may be useful when needing to predict whether your input belongs to a given list of n classes or not.
  • the probabilistic approach may be used because the probabilities for all the n classes will be quite low.
  • a person playing golf which depends on factors including the weather outside shown in a first data set 610.
  • the first data set 610 illustrates the weather in a first column and an outcome of playing associated with that weather in a second column.
  • the frequency table 620 the frequencies with which certain events occur are generated.
  • the frequency of a person playing or not playing golf in each of the weather conditions is determined. From there, a likelihood table is compiled to generate initial probabilities. For example, the probability of the weather being overcast is 0.29 while the general probability of playing is 0.64.
  • the posterior probabilities may be generated from the likelihood table 630. These posterior probabilities may be configured to answer questions about weather conditions and whether golf is played in those weather conditions. For example, the probability of it being sunny outside and golf being played may be set forth by the Bayesian formula:
  • a decision tree is a flowchart-like tree structure where each external node denotes a test on an attribute and each branch represents the outcome of that test.
  • the leaf nodes contain the actual predicted labels.
  • the decision tree begins from the root of the tree with attribute values being compared until a leaf node is reached.
  • a decision tree can be used as a classifier when handling high dimensional data and when little time has been spent behind data preparation.
  • Decision trees may take the form of a simple decision tree, a linear decision tree, an algebraic decision tree, a deterministic decision tree, a randomized decision tree, a nondeterministic decision tree, and a quantum decision tree.
  • An exemplary decision tree is provided below in FIG. 7.
  • FIG. 7 illustrates a decision tree, along the same structure as the Bayes example above, in deciding whether to play golf.
  • the first node 710 examines the weather providing sunny 712, overcast 714, and rain 716 as the choices to progress down the decision tree. If the weather is sunny, the leg of the tree is followed to a second node 720 examining the temperature. The temperature at node 720 may be high 722 or normal 724, in this example. If the temperature at node 720 is high 722, then the predicted outcome of “No” 723 golf occurs. If the temperature at node 720 is normal 724, then the predicted outcome of “Yes” 725 golf occurs.
  • an outcome overcast 714 “Yes” 715 golf occurs.
  • an outcome of rain 716 results in the third node 730 (again) examining temperature. If the temperature at third node 730 is normal 732, then “Yes” 733 golf is played. If the temperature at third node 730 is low 734, then “No” 735 golf is played. [0076] From this decision tree, a golfer plays golf if the weather is overcast 715, in normal temperature sunny weather 725, and in normal temperature rainy weather 733, while the golfer does not play if there are sunny high temperatures 723 or low rainy temperatures 735.
  • a random forest classifier is a committee of decision trees, where each decision tree has been fed a subset of the attributes of data and predicts on the basis of that subset. The mode of the actual predicted values of the decision trees are considered to provide an ultimate random forest answer.
  • the random forest classifier generally, alleviates overfitting, which is present in a standalone decision tree, leading to a much more robust and accurate classifier.
  • FIG. 8 illustrates an exemplary random forest classifier for classifying the color of a garment.
  • the random forest classifier includes five decision trees 8101, 8IO2, 8IO3, 8IO4, and 8IO5 (collectively or generally referred to as decision trees 810).
  • Each of the trees is designed to classify the color of the garment.
  • a discussion of each of the trees and decisions made is not provided, as each individual tree generally operates as the decision tree of FIG. 7.
  • three (810i, 8IO2, 8IO4) of the five trees determines that the garment is blue, while one determines the garment is green (8IO3) and the remaining tree determines the garment is red (8IO5).
  • the random forest takes these actual predicted values of the five trees and calculates the mode of the actual predicted values to provide random forest answer that the garment is blue.
  • Logistic Regression is another algorithm for binary classification tasks. Logistic regression is based on the logistic function, also called the sigmoid function. This S-shaped curve can take any real-valued number and map it between 0 and 1 asymptotically approaching those limits.
  • the logistic model may be used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick. This can be extended to model several classes of events such as determining whether an image contains a cat, dog, lion, etc. Each object being detected in the image would be assigned a probability between 0 and 1 with the sum of the probabilities adding to one.
  • the log-odds (the logarithm of the odds) for the value labeled "1" is a linear combination of one or more independent variables ("predictors"); the independent variables can each be a binary variable (two classes, coded by an indicator variable) or a continuous variable (any real value).
  • the corresponding probability of the value labeled "1" can vary between 0 (certainly the value "0") and 1 (certainly the value " 1"), hence the labeling; the function that converts log-odds to probability is the logistic function, hence the name.
  • the unit of measurement for the log-odds scale is called a logit, from logistic unit, hence the alternative names.
  • Analogous models with a different sigmoid function instead of the logistic function can also be used, such as the probit model; the defining characteristic of the logistic model is that increasing one of the independent variables multiplicatively scales the odds of the given outcome at a constant rate, with each independent variable having its own parameter; for a binary dependent variable this generalizes the odds ratio.
  • the dependent variable has two levels (categorical). Outputs with more than two values are modeled by multinomial logistic regression and, if the multiple categories are ordered, by ordinal logistic regression (for example the proportional odds ordinal logistic model).
  • the logistic regression model itself simply models probability of output in terms of input and does not perform statistical classification (it is not a classifier), though it can be used to make a classifier, for instance by choosing a cutoff value and classifying inputs with probability greater than the cutoff as one class, below the cutoff as the other; this is a common way to make a binary classifier.
  • FIG. 9 illustrates an exemplary logistic regression.
  • This exemplary logistic regression enables the prediction of an outcome based on a set of variables. For example, based on a person’s grade point average, and outcome of being accepted to a school may be predicted. The past history of grade point averages and the relationship with acceptance enables the prediction to occur.
  • the logistic regression of FIG. 9 enables the analysis of the grade point average variable 920 to predict the outcome 910 defined by 0 to 1. At the low end 930 of the S- shaped curve, the grade point average 920 predicts an outcome 910 of not being accepted. While at the high end 940 of the S-shaped curve, the grade point average 920 predicts an outcome 910 of being accepted. Logistic regression may be used to predict house values, customer lifetime value in the insurance sector, etc.
  • a support vector machine may be used to sort the data with the margins between two classes as far apart as possible. This is called maximum margin separation.
  • the SVM may account for the support vectors while plotting the hyperplane, unlike linear regression which uses the entire dataset for that purpose.
  • FIG. 10 illustrates an exemplary support vector machine.
  • data may be classified into two different classes represented as squares 1010 and triangles 1020.
  • SVM 1000 operates by drawing a random hyperplane 1030. This hyperplane 1030 is monitored by comparing the distance (illustrated with lines 1040) between the hyperplane 1030 and the closest data points 1050 from each class. The closest data points 1050 to the hyperplane 1030 are known as support vectors.
  • the hyperplane 1030 is drawn based on these support vectors 1050 and an optimum hyperplane has a maximum distance from each of the support vectors 1050. The distance between the hyperplane 1030 and the support vectors 1050 is known as the margin.
  • SVM 1000 may be used to classify data by using a hyperplane 1030, such that the distance between the hyperplane 1030 and the support vectors 1050 is maximum. Such an SVM 1000 may be used to predict heart disease, for example.
  • K Nearest Neighbors refers to a set of algorithms that generally do not make assumptions on the underlying data distribution, and perform a reasonably short training phase. Generally, KNN uses many data points separated into several classes to predict the classification of a new sample point. Operationally, KNN specifies an integer N with a new sample. The N entries in the model of the system closest to the new sample are selected. The most common classification of these entries is determined, and that classification is assigned to the new sample. KNN generally requires the storage space to increase as the training set increases. This also means that the estimation time increases in proportion to the number of training points.
  • the output is a continuous quantity so regression algorithms may be used in cases where the target variable is a continuous variable.
  • FIG. 11 illustrates an exemplary linear regression model.
  • a predicted variable 1110 is modeled against a measured variable 1120.
  • a cluster of instances of the predicted variable 1110 and measured variable 1120 are plotted as data points 1130.
  • Data points 1130 are then fit with the best fit line 1140.
  • the best fit line 1140 is used in subsequent predicted, given a measured variable 1120, the line 1140 is used to predict the predicted variable 1110 for that instance.
  • Linear regression may be used to model and predict in a financial portfolio, salary forecasting, real estate and in traffic in arriving at estimated time of arrival.
  • Clustering algorithms may also be used to model and train on a data set.
  • the input is assigned into two or more clusters based on feature similarity.
  • Clustering algorithms generally learn the patterns and useful insights from data without any guidance. For example, clustering viewers into similar groups based on their interests, age, geography, etc. may be performed using unsupervised learning algorithms like K- means clustering.
  • K-means clustering generally is regarded as a simple unsupervised learning approach. In K-means clustering similar data points may be gathered together and bound in the form of a cluster. One method for binding the data points together is by calculating the centroid of the group of data points.
  • K-means clustering In determining effective clusters, in K-means clustering the distance between each point from the centroid of the cluster is evaluated. Depending on the distance between the data point and the centroid, the data is assigned to the closest cluster. The goal of clustering is to determine the intrinsic grouping in a set of unlabeled data.
  • the ‘K’ in K-means stands for the number of clusters formed. The number of clusters (basically the number of classes in which new instances of data may be classified) may be determined by the user. This determination may be performed using feedback and viewing the size of the clusters during training, for example.
  • K-means is used majorly in cases where the data set has points which are distinct and well separated, otherwise, if the clusters are not separated the modeling may render the clusters inaccurate. Also, K-means may be avoided in cases where the data set contains a high number of outliers or the data set is non-linear.
  • FIG. 12 illustrates a K-means clustering.
  • new cluster centroids formed, an iteration, or series of iterations may occur to enable the clusters to be minimized in size and the centroid of the optimal centroid determined. Then as new data points are measured, the new data points may be compared with the centroid and cluster to identify with that cluster.
  • Ensemble learning algorithms may be used. These algorithms use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Ensemble learning algorithms perform the task of searching through a hypothesis space to find a suitable hypothesis that will make good predictions with a particular problem. Even if the hypothesis space contains hypotheses that are very well- suited for a particular problem, it may be very difficult to find a good hypothesis. Ensemble algorithms combine multiple hypotheses to form a better hypothesis. The term ensemble is usually reserved for methods that generate multiple hypotheses using the same base learner. The broader term of multiple classifier systems also covers hybridization of hypotheses that are not induced by the same base learner.
  • Evaluating the prediction of an ensemble typically requires more computation than evaluating the prediction of a single model, so ensembles may be thought of as a way to compensate for poor learning algorithms by performing a lot of extra computation.
  • Fast algorithms such as decision trees are commonly used in ensemble methods, for example, random forests, although slower algorithms can benefit from ensemble techniques as well.
  • An ensemble is itself a supervised learning algorithm because it can be trained and then used to make predictions.
  • the trained ensemble therefore, represents a single hypothesis. This hypothesis, however, is not necessarily contained within the hypothesis space of the models from which it is built.
  • ensembles can be shown to have more flexibility in the functions they can represent. This flexibility can, in theory, enable them to over-fit the training data more than a single model would, but in practice, some ensemble techniques (especially bagging) tend to reduce problems related to over-fitting of the training data.
  • the number of component classifiers of an ensemble has a great impact on the accuracy of prediction.
  • a priori determining of ensemble size and the volume and velocity of big data streams make this even more crucial for online ensemble classifiers.
  • a theoretical framework suggests that there are an ideal number of component classifiers for an ensemble such that having more or less than this number of classifiers would deteriorate the accuracy.
  • the theoretical framework shows that using the same number of independent component classifiers as class labels gives the highest accuracy.
  • FIG. 13 illustrates an exemplary ensemble learning algorithm where bagging is being performed in parallel 1310 and boosting is being performed sequentially 1320.
  • a neural network is a network or circuit of neurons, or in a modem sense, an artificial neural network, composed of artificial neurons or nodes.
  • the connections of the biological neuron 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 0 and 1, or it could be -1 and 1.
  • These artificial networks may be used for predictive modeling, adaptive control and applications and can be trained via a dataset. Self-learning resulting from experience can occur within networks, which can derive conclusions from a complex and seemingly unrelated set of information.
  • a biological neural network is composed of a group or groups of chemically connected or functionally associated neurons.
  • a single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive. Connections, called synapses, are usually formed from axons to dendrites, though dendrodendritic synapses and other connections are possible.
  • connections called synapses, are usually formed from axons to dendrites, though dendrodendritic synapses and other connections are possible.
  • Artificial intelligence, cognitive modeling, and neural networks are information processing paradigms inspired by the way biological neural systems process data. Artificial intelligence and cognitive modeling try to simulate some properties of biological neural networks. In the artificial intelligence field, artificial neural networks have been applied successfully to speech recognition, image analysis and adaptive control, in order to construct software agents (in computer and video games) or autonomous robots.
  • a neural network in the case of artificial neurons called artificial neural network (ANN) or simulated neural network (SNN), is an interconnected group of natural or artificial neurons that uses a mathematical or computational model for information processing based on a connectionistic approach to computation.
  • an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network.
  • neural networks are non-linear statistical data modeling or decisionmaking tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data.
  • An artificial neural network involves a network of simple processing elements (artificial neurons) which can exhibit complex global behavior, determined by the connections between the processing elements and element parameters.
  • One classical type of artificial neural network is the recurrent Hopfield network.
  • 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 or task makes the design of such functions by hand impractical.
  • Neural networks can be used in different fields.
  • the tasks to which artificial neural networks are applied tend to fall within the following broad categories: function approximation, or regression analysis, 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.
  • ANNs 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, 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 user's interests emerging from pictures trained for object recognition.
  • FIG. 14 illustrates an exemplary neural network.
  • the neural network there is an input layer represented by a plurality of inputs, such as 14101 and 14102.
  • the inputs 1410i , 14102 are provided to a hidden layer depicted as including nodes 14201, 14202, 1420s, 14204. These nodes 14201, 1420s, 14203,14204 are combined to produce an output 1430 in an output layer.
  • the neural network performs simple processing via the hidden layer of simple processing elements, nodes 14201, 14202, 1420s, 14204, which can exhibit complex global behavior, determined by the connections between the processing elements and element parameters.
  • the neural network of FIG. 14 may be implemented in hardware. As depicted in FIG. 15 a hardware based neural network is depicted.
  • the heart which is comprised of atrial, ventricular, and excitatory conduction tissue, is electrically excited to beat in a synchronous, patterned fashion.
  • cardiac arrythmias abnormal regions of cardiac tissue do not follow the synchronous beating cycle associated with normally conductive tissue as in patients with normal sinus rhythm. Instead, the abnormal regions of cardiac tissue aberrantly conduct to adjacent tissue, thereby disrupting the cardiac cycle into an asynchronous cardiac rhythm.
  • SA sino-atrial
  • AV atrioventricular
  • Bundle of His the cardiac muscle tissue forming the walls of the ventricular and atrial cardiac chambers.
  • Cardiac arrhythmias may be of a multiwavelet reentrant type, characterized by multiple asynchronous loops of electrical impulses that are scattered about the atrial chamber and are often self -propagating.
  • cardiac arrhythmias may also have a focal origin, such as when an isolated region of tissue in an atrium fires autonomously in a rapid, repetitive fashion.
  • Ventricular tachycardia V-tach or VT) is a tachycardia, or fast heart rhythm that originates in one of the ventricles of the heart. This is a potentially life-threatening arrhythmia because it may lead to ventricular fibrillation and sudden death.
  • Atrial fibrillation occurs when the normal electrical impulses generated by the sinoatrial node are overwhelmed by disorganized electrical impulses that originate in the atria and pulmonary veins causing irregular impulses to be conducted to the ventricles.
  • An irregular heartbeat results and may last from minutes to weeks, or even years.
  • Atrial fibrillation (AF) is often a chronic condition that leads to a small increase in the risk of death often due to strokes. Risk increases with age. Approximately 8% of people over 80 having some amount of AF. Atrial fibrillation is often asymptomatic and is not in itself generally life-threatening, but it may result in palpitations, weakness, fainting, chest pain and congestive heart failure.
  • the first line of treatment for AF is medication that either slow the heart rate or revert the heart rhythm back to normal.
  • persons with AF are often given anticoagulants to protect them from the risk of stroke. The use of such anticoagulants comes with its own risk of internal bleeding.
  • medication is not sufficient, and their AF is deemed to be drug-refractory, i.e., untreatable with standard pharmacological interventions.
  • Synchronized electrical cardioversion may also be used to convert AF to a normal heart rhythm.
  • AF patients are treated by catheter ablation.
  • a catheter ablation-based treatment may include mapping the electrical properties of heart tissue, especially the endocardium and the heart volume, and selectively ablating cardiac tissue by application of energy.
  • Cardiac mapping for example, creating a map of electrical potentials (a voltage map) of the wave propagation along the heart tissue or a map of arrival times (a local time activation (LAT) map) to various tissue located points, may be used for detecting local heart tissue dysfunction
  • Ablations such as those based on cardiac mapping, can cease or modify the propagation of unwanted electrical signals from one portion of the heart to another.
  • the ablation process damages the unwanted electrical pathways by formation of non-conducting lesions.
  • mapping followed by ablation electrical activity at points within the heart is typically sensed and measured by advancing a catheter containing one or more electrical sensors (or electrodes) into the heart, and acquiring data at a multiplicity of points. These data are then utilized to select the endocardial target areas at which ablation is to be performed.
  • Cardiac ablation and other cardiac electrophysiological procedures have become increasingly complex as clinicians treat challenging conditions such as atrial fibrillation and ventricular tachycardia.
  • the treatment of complex arrhythmias can now rely on the use of three- dimensional (3D) mapping systems in order to reconstruct the anatomy of the heart chamber of interest.
  • CFAE Complex Fractionated Atrial Electrograms
  • the 3D maps can provide multiple pieces of information regarding the electrophysiological properties of the tissue that represent the anatomical and functional substrate of these challenging arrhythmias.
  • Cardiomyopathies with different etiologies ischemic, dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), arrhythmogenic right ventricular dysplasia (ARVD), left ventricular non-compaction (LVNC), etc.
  • DCM dilated cardiomyopathy
  • HCM hypertrophic cardiomyopathy
  • ARVD arrhythmogenic right ventricular dysplasia
  • LVNC left ventricular non-compaction
  • FIGS. 16A through 16D show examples of cardiomyopathies with different etiologies.
  • FIGs. 16A and 16B show an example rendering of a heart 1600 with post-ischemic Ventricular Tachycardia (VT) characterized by endo-epicardial low or intermediate voltage area 1602 in which signal conduction is slowed down.
  • VT Ventricular Tachycardia
  • the post-ischemic VT shown in FIG. 16A is characterized by an endo-epicardial low or intermediate voltage area in which signal conduction is slowed down.
  • FIG. 16A illustrates the bipolar signal amplitude (Bi) variance in the various sectors of the heart 1600.
  • FIG. 16A shows Bi ranges from 0.5 mV to 1.5 mV.
  • FIG. 16B illustrates the Shortex Complex Interval (SCI) variance in the various sectors of the heart.
  • SCI ranges from 15.0 msec to 171.00 msec with the SCI range of interest between 80 msec and 170 msec.
  • FIGs. 16C and 16D show an example rendering of a heart 1610 experiencing left ventricular non-compaction cardiomyopathy. More specifically, FIG. 16C shows an epicardial voltage map and FIG. 16D shows potential duration map (PDM). The three black circles in 1612 in FIGs. 16C and 16D are marked as abnormally prolonged potentials, e.g., potentials above 200 msec.
  • Abnormal tissue is generally characterized by low-voltage EGMs.
  • initial clinical experience in endo-epicardial mapping indicates that areas of low-voltage are not always present as the sole arrhythmogenic mechanism in such patients.
  • areas of low or medium voltage may exhibit EGM fragmentation and prolonged activities during sinus rhythm, which corresponds to the critical isthmus identified during sustained and organized ventricular arrhythmias, e.g., applies only to non-tolerated ventricular tachycardias.
  • EGM fragmentation and prolonged activities are observed in the regions showing a normal or nearnormal voltage amplitude (>1-1.5 mV).
  • the 3D mapping may be able to localize the arrhythmogenic substrate on the endocardial and/or epicardial layer of the right/left ventricle, which may vary in distribution according to the extension of the main disease.
  • the substrate linked to these cardiac conditions is related to the presence of fragmented and prolonged EGMs in the endocardial and/or epicardial layers of the ventricular chambers (right and left).
  • the 3D mapping system such as CARTO®3, is able to localize the potential arrhythmogenic substrate of the cardiomyopathy in terms of abnormal EGM detection.
  • Electrode catheters have been in common use in medical practice for many years. They are used to stimulate and map electrical activity in the heart and to ablate sites of aberrant electrical activity. In use, the electrode catheter is inserted into a major vein or artery, e.g., femoral artery, and then guided into the chamber of the heart of concern.
  • a typical ablation procedure involves the insertion of a catheter having at least one electrode at its distal end, into a heart chamber.
  • a reference electrode is provided, generally taped to the skin of the patient or by means of a second catheter that is positioned in or near the heart.
  • RF (radio frequency) current is applied to the tip electrode of the ablating catheter, and current flows through the media that surrounds it, i.e., blood and tissue, toward the reference electrode.
  • the distribution of current depends on the amount of electrode surface in contact with the tissue as compared to blood, which has a higher conductivity than the tissue. Heating of the tissue occurs due to its electrical resistance. The tissue is heated sufficiently to cause cellular destruction in the cardiac tissue resulting in formation of a lesion within the cardiac tissue which is electrically non-conductive. During this process, heating of the electrode also occurs as a result of conduction from the heated tissue to the electrode itself. If the electrode temperature becomes sufficiently high, possibly above 60 degrees C., a thin transparent coating of dehydrated blood protein can form on the surface of the electrode.
  • this dehydrated layer can become progressively thicker resulting in blood coagulation on the electrode surface. Because dehydrated biological material has a higher electrical resistance than endocardial tissue, impedance to the flow of electrical energy into the tissue also increases. If the impedance increases sufficiently, an impedance rise occurs, and the catheter must be removed from the body and the tip electrode cleaned.
  • FIG. 17 is a diagram of an exemplary system 1720 in which one or more features of the disclosure subject matter can be implemented. All or parts of system 1720 may be used to collect information for a training dataset and/or all or parts of system 1720 may be used to implement a trained model.
  • System 1720 may include components, such as a catheter 1740, that are configured to damage tissue areas of an intra-body organ. The catheter 1740 may also be further configured to obtain biometric data.
  • catheter 1740 is shown to be a point catheter, it will be understood that a catheter of any shape that includes one or more elements (e.g., electrodes) may be used to implement the embodiments disclosed herein.
  • System 1720 includes a probe 1721, having shafts that may be navigated by a physician 1730 into a body part, such as heart 1726, of a patient 1728 lying on a table 1729.
  • a physician 1730 may insert shaft 1722 through a sheath 1723, while manipulating the distal end of the shafts 1722 using a manipulator 1732 near the proximal end of the catheter 1740 and/or deflection from the sheath 1723.
  • catheter 1740 may be fitted at the distal end of shafts 1722.
  • Catheter 1740 may be inserted through sheath 1723 in a collapsed state and may be then expanded within heart 1726.
  • Cather 1740 may include at least one ablation electrode 1747 and a catheter needle 1748, as further disclosed herein.
  • catheter 1740 may be configured to ablate tissue areas of a cardiac chamber of heart 1726.
  • Inset 1745 shows catheter 1740 in an enlarged view, inside a cardiac chamber of heart 1726.
  • catheter 1740 may include at least one ablation electrode 1747 coupled onto the body of the catheter.
  • multiple elements may be connected via splines that form the shape of the catheter 1740.
  • One or more other elements may be provided and may be any elements configured to ablate or to obtain biometric data and may be electrodes, transducers, or one or more other elements.
  • the ablation electrodes such as electrode 1747, may be configured to provide energy to tissue areas of an intra-body organ such as heart 1726.
  • 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.
  • biometric data may include one or more of LATs, electrical activity, topology, bipolar mapping, dominant frequency, impedance, or the like.
  • the local activation time may be a point in time of a threshold activity corresponding to a local activation, calculated based on a normalized initial starting point.
  • Electrical activity may be any applicable electrical signals that may be measured based on one or more thresholds and may be sensed and/or augmented based on signal to noise ratios and/or other filters.
  • a topology may correspond to the physical structure of a body part or a portion of a body part and may correspond to changes in the physical structure relative to different parts of the body part or relative to different body parts.
  • a dominant frequency may be a frequency or a range of frequency that is prevalent at a portion of a body part and may be different in different portions of the same body part.
  • the dominant frequency of a pulmonary vein of a heart may be different than the dominant frequency of the right atrium of the same heart.
  • Impedance may be the resistance measurement at a given area of a body part.
  • Console 1724 may include a processor 1741, such as a general-purpose computer, with suitable front end and interface circuits 1738 for transmitting and receiving signals to and from catheter, as well as for controlling the other components of system 1720.
  • processor 1741 may be further configured to receive biometric data, such as electrical activity, and determine if a given tissue area conducts electricity.
  • the processor may be external to the console 1724 and may be located, for example, in the catheter, in an external device, in a mobile device, in a cloud-based device, or may be a standalone processor.
  • processor 1741 may include a general -purpose computer, which may be programmed in software to carry out the functions described herein.
  • the software may be downloaded to the general-purpose computer in electronic form, over a network, for example, or it may, alternatively or additionally, be provided and/or stored on non-transitory tangible media, such as magnetic, optical, or electronic memory.
  • non-transitory tangible media such as magnetic, optical, or electronic memory.
  • FIG. 17 may be modified to implement the embodiments disclosed herein.
  • system 1620 may include additional components, such as elements for sensing electrical activity, wired or wireless connectors, processing and display devices, or the like.
  • a display connected to a processor may be located at a remote location such as a separate hospital or in separate healthcare provider networks.
  • the system 1720 may be part of a surgical system that is configured to obtain anatomical and electrical measurements of a patient’s organ, such as a heart, and performing a cardiac ablation procedure.
  • a surgical system is the Carto® system sold by Biosense Webster.
  • the system 1720 may also, and optionally, obtain biometric data such as anatomical measurements of the patient’s heart using ultrasound, computed tomography (CT), magnetic resonance imaging (MRI) or other medical imaging techniques known in the art.
  • the system 1720 may obtain electrical measurements using catheters, electrocardiograms (EKGs) or other sensors that measure electrical properties of the heart.
  • the biometric data including anatomical and electrical measurements may then be stored in a memory 1742 of the mapping system 1720, as shown in FIG. 17.
  • the biometric data may be transmitted to the processor 1741 from the memory 1742.
  • the biometric data may be transmitted to a server 1760, which may be local or remote, using a network 1662.
  • Network 1762 may be any network or system generally known in the art such as 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 mapping system 1720 and the server 1760.
  • the network 1662 may be wired, wireless or a combination thereof. Wired connections may be implemented using Ethernet, Universal Serial Bus (USB), RJ-11 or any other wired connection generally known in the art. Wireless connections may be implemented using WiFi, WiMAX, and Bluetooth, infrared, cellular networks, satellite or any other wireless connection methodology generally known in the art. Additionally, several networks may work alone or in communication with each other to facilitate communication in the network 1762.
  • server 1762 may be implemented as a physical server. In other instances, server 1762 may be implemented as a virtual server a public cloud computing provider (e.g., Amazon Web Services (AWS) ®).
  • AWS Amazon Web Services
  • Control console 1724 may be connected, by a cable 1739, to body surface electrodes 1743, which may include adhesive skin patches that are affixed to the patient 1730.
  • the processor in conjunction with a current tracking module, may determine position coordinates of the catheter 1740 inside the body part (e.g., heart 1726) of a patient. The position coordinates may be based on impedances or electromagnetic fields measured between the body surface electrodes 1743 and the electrode 1748 or other electromagnetic components of the catheter 1740. Additionally, or alternatively, location pads may be located on the surface of bed 1729 and may be separate from the bed 1729.
  • Processor 1741 may include real-time noise reduction circuitry typically configured as a field programmable gate array (FPGA), followed by an analog-to-digital (A/D) ECG (electrocardiograph) or EMG (electromyogram) signal conversion integrated circuit.
  • the processor 1741 may 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.
  • Control console 1724 may also include an input/output (VO) communications interface that enables the control console to transfer signals from, and/or transfer signals to electrode 1747.
  • VO input/output
  • processor 1741 may facilitate the presentation of a body part rendering 1735 to physician 1730 on a display 1727, and store data representing the body part rendering 1735 in a memory 1742.
  • Memory 1742 may comprise any suitable volatile and/or nonvolatile memory, such as random-access memory or a hard disk drive.
  • medical professional 1730 may be able to manipulate a body part rendering 1735 using one or more input devices such as a touch pad, a mouse, a keyboard, a gesture recognition apparatus, or the like.
  • an input device may be used to change the position of catheter 1740 such that rendering 1735 is updated.
  • display 1727 may include a touchscreen that can be configured to accept inputs from medical professional 1730, in addition to presenting a body part rendering 1735.
  • the present description provides a system and method to determine the need for ablation therapy, i.e., effectively recommending therapy for cardiac arrhythmias.
  • surface ECGs may be utilized.
  • the surface ECG may take the form of a consumer wearable ECG.
  • a consumer wearable ECG may include watches, t-shirts, and other clothing, as well as devices adhered to a patient.
  • FIG. 18 illustrates the system of FIG. 4 adapted for the present description.
  • System 1800 includes data 410, a machine 420, a model 430, a plurality of outcomes 440 and underlying hardware 450.
  • System 1800 operates by using the data 410 to train the machine 420 while building a model 430 to enable a plurality of outcomes 440 to be predicted.
  • the system 1800 may operate with respect to hardware 450.
  • the data 410 may be related to hardware 450 and may originate with apparatus 102, for example.
  • the data 410 may be on-going data, or output data associated with hardware 450.
  • the machine 420 may operate as the controller or data collection associated with the hardware 450, or be associated therewith.
  • the model 430 may be configured to model the operation of hardware 450 and model the data 410 collected from hardware 450 in order to predict the outcome achieved by hardware 450.
  • hardware 450 may be configured to provide a certain desired outcome 440 from hardware 450.
  • data 410 may include information 1810.
  • This information 1810 may include patient records, prior examination information, drug protocol information, demographics, age and gender data and statistics, for example.
  • Information 1810 may be included within data 410.
  • Information 1810 may be augmented using external data collection apparati, such as surface ECG, including wearable ECGs, and MRI instruments for example.
  • information 1810 may be augmented to include mapping and modeling information 1830.
  • This mapping and modeling information may include a plurality of digital models, mapping data, and CARTO® data, for example. Use of mapping and modeling information may provide additional accuracy and precision to the data, as would be understood by those possessing an ordinary skill in the art.
  • training 420 may occur on the data 410, and a model 430 developed as described herein.
  • the outcomes 440 of the model may be based on outcome results of a large medical center database. This enables system 1800 to recommend the best fit therapy, using outcome 440, without the need to wait to see if drugs are effective or not, as such waiting may take significant time.
  • AFib AFib vascular endothelial tachycardia
  • Apparatus and methods are provided to estimate and recommend the optimal strategy for combining medication and operation for arrhythmia for a specific patient.
  • a decision tree-based algorithm for survival analysis is generated. The tree divides the study population into distinct groups with different Kaplan-Meier curves. By using the tree, a selection of the optimal strategy for a specific patient according to the characteristics at the decision tree's nodes may be performed.
  • AAD antiarrhythmic drug
  • Survival analysis is the phrase used to describe the analysis of data in the form of times from a well-defined time origin until the occurrence of some particular event or endpoint.
  • the time origin corresponds to the earlier of the beginning of the medication path or an operation/ablation procedure.
  • the analysis of time -related events is unique since the expected event, the recurrence of arrhythmia after a successful treatment, does not occur in all individuals.
  • Various statistical methods have been developed to describe and analyze time-related outcomes.
  • Kaplan-Meier curves are widely used in medical research for survival analysis. Kaplan-Meier curves are an estimation of the survivor function, which is the rate of event’s occurrence over time. In the description herein, Kaplan-Meier curves may be used to describe the rate of treatment failure for arrhythmia over time or the progress of the patient from one stage of arrhythmia condition (e.g., Paroxysmal AFIB) to a more severe stage of the disease (e.g., Persistent AFIB or Long lasting AFIB).
  • Kaplan- Meier curves can be calculated and drawn for different subgroups of the study recruited patients. Statistical tests, such as the log-rank test, can be used to compare Kaplan- Meier curves.
  • a Survival decision tree includes "Leaves”, each corresponding to some sub-group of the entire population. Each such leaf has several Kaplan- Meier (KM) curves, describing treatment results over time. One curve representing the "pure” strategy of giving only medication is provided. One curve representing the "pure” strategy of giving only an operation is provided. Additional curves , each representing a combined strategy, where the patient is given medication for the first X months, and then given an operation are also provided.
  • KM Kaplan- Meier
  • Additional parameters may be included.
  • the additional parameters include a set of decision rules in the upper decision nodes, characterizing subgroups of patients.
  • the trees can be created to maximize the difference between KM curves as explained.
  • FIG. 19 illustrates an exemplary survival decision tree 1900.
  • tree 1900 provides for a number of decisions 1910 with the leaves 1920 stemming from the decisions 1910.
  • a first decision node 19101 at the top level of the decision tree provides a question for the decision node regarding the age being less than 68 years old.
  • the left branch is taken if the patient age is less than 68 years old, or the right branch is selected if the patient age is not less than 68 years old.
  • a second decision node 19102 is encountered.
  • the second decision node 19 IO2 queries whether the LA diameter is less than 45.
  • the left branch is taken if the patient LA is less than 45, or the right branch is selected if the patient LA is not less than 45.
  • a third decision node 1910a is encountered.
  • Third decision node 1910s queries the gender of the patient.
  • the left branch is taken if the patient gender is female, or the right branch is selected if the patient gender is male. If the patient gender is female, the left branch provides a leaf 19201 including a set of KM graphs. If the patient gender is male, the right branch provides another leaf 19202 including a set of KM graphs.
  • Fourth decision node 19104 queries regarding hypertension. Again, as with the earlier described decision nodes 19101 , 19 IO2, 19103, based on the hypertension of the patient, the left branch is taken if the patient has hypertension, or the right branch is selected if the patient does not have hypertension. If the patient has hypertension, the left branch provides a leaf 19204 including a set of KM graphs. If the patient does not have hypertension, the right branch provides another leaf 1920s including a set of KM graphs.
  • the exemplary decision tree 1900 includes three levels and four decision nodes. Any amount of levels and decision nodes may be utilized based on the data and the correlation with the medical/surgery decision. Further, the leaves provide a series of KM curves that are based on the characteristics of the nodes and decision made to arrive at that respective leaf.
  • survival decision trees are built according to information theory, where each splitting rule is created to maximize the difference between groups from the top until the final leaves. Once a rule is created at the top of the tree, the rule is not considered and cannot be changed. This top down creation occurs even in situations where the optimal separation between groups may not be created at the final leaves.
  • An example of such a tree is the implementation of survival decision trees in the "rpart" package of R statistical programming language.
  • the survival curves may be designed to maximize, or provide an optimal separation between the KM survival curves for the different approaches over all leaves in the decision tree.
  • artificial intelligence focuses on the final leaves of the tree to effectively identify subgroups in which there are maximal differences between treatments approaches.
  • One option to perform this focus is to use an evolutionary algorithm adapted to construct an optimal survival decision tree. Evolutionary algorithms mimic the biological process of evolution with generations of solutions, selection of the best (fittest) solution, cross-over, mutations, and genetic drifts. Evolutionary algorithms provide a flexible framework to find the optimal survival tree that compares treatment approaches. One advantage includes massive search of the possible solutions space.
  • constructing a survival decision tree involves many considerations: which features are included in the tree, at what level the features are included, the cutoff values are chosen, and the like. Since there are many considerations, the number of possible trees is large and it is generally difficult to evaluate all the possible solutions. Evolutionary algorithms may consider a massive number of solutions in the process of algorithm evolution. Through a process that mimics evolution, the best solutions are evaluated, and an optimal solution can be found. Flexibility in the construction of possible solutions is also an advantage. [0158] Evolutionary algorithms may be fitted to solve problems with different constraints. In the present scenario, these algorithms build survival decision trees with different constraints. The main constraint is maximization of the difference between KM curves as explained.
  • Parameters that can be included in the creation of the Tree are predictors of successful arrhythmia approach. These parameters may include gender, Left Ventricular Ejection Fraction ⁇ 50, Sinus Rhythm Immediately Before Procedure, Stage of arrhythmia (e.g. Paroxysmal, Persistent or long lasting), Ablation approach (PVI vs PVI plus), Time since the arrhythmia was diagnosed, Average Length of PsAF AFIB Episodes, Structural Heart Disease, Other Arrhythmia Besides AF, Past Successful Cardioversion, Atrial Flutter or Tachycardia, Ventricular Tachycardia, Previous Ablation Procedure, Age, Obstructive Sleep Apnea, LA Diameter, Hypertension, and Pericardial Effusion, for example.
  • parameters taken out of surface ECG or cardiac imaging can be included for the creation of the decision tree. These parameters can include: average R-R interval, standard deviation of the R-R interval, grouping of QRS complexes, P wave morphology, and the like. Scar burden (% of scar tissue area vs healthy tissue area) as imaged in MRI, CT scans derived parameters such as left atrial volume, left atrial diameter, atrial wall thickness etc. Ultrasonic imaging of the heart - atrial volume, atrial diameter, atrial wall thickness, heart tissue contractility, electromechanical wave motion in the heart, and the like.
  • KM Difference Scores There are several known difference scores between two or more KM curves (henceforth "KM Difference Scores") including area between the KM curves, distance between the treatment failure rates at the final time point, the p-value of the log-rank test, and risk concordance index (Harrell's c-index).
  • each leaf has several KM curves, each corresponding to either a "pure” treatment strategy (only medication or only operation) or a combined treatment (first medication for a certain time period, and then operation).
  • a fixed set of treatment strategies may be used for the entire decision tree. For example, four strategies may be used, and all leaves have four KM curves: two for the pure strategies (only medication, only ablation), as well as two for mixed strategies: one-year medication and then ablation, and two years medication and then ablation. Each such strategy may be assigned a score using one of the KM Difference Scores mentioned above, over all the KM curves for that strategy (over all leaves).
  • the set of scores (one per strategy) is combined into one final score using some combination of aggregation operators including (but not limited to:) maximum over the scores, average of the scores, and/or weighted sum.
  • the maximum over the scores may be beneficial to identify situations in which most strategies do not exhibit significant differences between the KM curves over the various population sub-groups, but at least one strategy does exhibit a significant difference.
  • the average of the scores may enable optimization for maximal differences over all strategies and all subgroups.
  • the weighted sum may provide a generalization of the 2nd method (average), where certain strategies are given higher weight (e.g., medication is preferred to some degree over operation).
  • the set of treatment strategies itself can be fixed manually or automatically. Manually fixed may be designed to answer a certain research question(s). Automatically determined may be provided by the evolutionary algorithm over the entire search space of all possible strategies.
  • a generalized method may also be used.
  • the simplified method is a private case of the generalized method.
  • each leaf of the decision tree may have a different set of treatment strategies (not fixed for the entire tree).
  • the evolutionary algorithm finds the optimal tree as well as set of treatment strategies per leaf, according to the following calculation.
  • a "combination” is a selection of one treatment strategy per decision tree leaf. Each such strategy is associated with one KM curve. The "combination” gets a score using one of the KM Difference Scores mentioned above over the combination's KM curves.
  • Each "combination” is multiplied by a factor to obtain a "weighted combination". The factor represents how much overlap there is between the combination's strategies in terms of time-to-operation.
  • One edge case is where all the leaf strategies of the combination are the same. This is in fact the method mentioned above.
  • Another edge case is where at least two strategies of a combination do not overlap at all. This occurs when the combination contains one "pure” strategy from one leaf (only medication) with the opposite "pure” strategy from a different leaf (only operation). The other cases fall in between, where the percentage of time overlap between the combination's strategies is calculated. All the "weighted combinations" are combined using one of the ways mentioned for the method, such as max, average, weighted, for example.
  • FIG. 20 illustrates an exemplary utility curve 2050 within plot 2000.
  • Curve 2050 provides a series of points 2010, 2020, 2030, 2040, 2060, 2070 calculated based on a given leaf as described with respect to FIG. 19.
  • the horizontal axis 2045 represents the time-to-ablation in months.
  • the vertical axis 2055 represents the utility at a given instance based on the KM curves.
  • Each point 2010, 2020, 2030, 2040, 2060, 2070 is calculated for each treatment strategy (in the leaf) where the ablation is given after time t.
  • Each point 2010, 2020, 2030, 2040, 2060, 2070 of the utility curve has a point 2010, 2020, 2030, 2040, 2060, 2070 at: (t, RAUC) where RAUC is the relative area-under-curve of the KM curve corresponding to this treatment strategy in the leaf.
  • the points 2010, 2020, 2030, 2040, 2060, 2070 are connected according to a sigmoid regression function to created curve 2050.
  • Curve 2050 can be used by a physician to select the optimal treatment strategy considering
  • Examples of computer-readable storage media include, but are not limited to, a read only memory (ROM), a random-access memory (RAM), a register, cache memory, semiconductor memory devices, magnetic media such as internal hard disks and removable disks, magneto -optical media, and optical media such as CD-ROM disks, and digital versatile disks (DVDs).
  • ROM read only memory
  • RAM random-access memory
  • register cache memory
  • semiconductor memory devices magnetic media such as internal hard disks and removable disks, magneto -optical media, and optical media such as CD-ROM disks, and digital versatile disks (DVDs).
  • a processor in association with software may be used to implement a radio frequency transceiver for use in a WTRU, UE, terminal, base station, RNC, or any host computer.

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Public Health (AREA)
  • Primary Health Care (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Pathology (AREA)
  • Medicinal Chemistry (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Chemical & Material Sciences (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Surgery (AREA)
  • Urology & Nephrology (AREA)
  • Medical Treatment And Welfare Office Work (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)
  • Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
  • Electrotherapy Devices (AREA)
  • Paper (AREA)

Abstract

L'invention concerne un système et une méthode de prédiction du résultat d'une thérapie pour un patient souffrant d'arythmie cardiaque. Le système et la méthode consistent à entrer des données concernant l'arythmie dans une base de données, la base de données incluant des données sur d'autres patients souffrant d'arythmie et leur thérapie respective, à modéliser l'arythmie sur la base de l'entraînement et de la modélisation des autres données d'arythmie, et à prédire le résultat d'une thérapie pour le patient souffrant d'arythmie cardiaque. Le système et la méthode peuvent inclure les données d'entrée incluant des dossiers de patient, des informations sur des examens antérieurs, un protocole médicamenteux, des données démographiques, l'âge et le sexe, un ECG de surface, au moins une IRM, et des informations de modélisation. La modélisation peut inclure au moins l'un d'un modèle numérique, d'informations de mise en correspondance, et d'informations CARTO®.
EP21801994.1A 2020-10-13 2021-10-13 Planification de traitement d'arythmie personnalisée Pending EP4229644A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202063091174P 2020-10-13 2020-10-13
PCT/IB2021/059388 WO2022079623A1 (fr) 2020-10-13 2021-10-13 Planification de traitement d'arythmie personnalisée

Publications (1)

Publication Number Publication Date
EP4229644A1 true EP4229644A1 (fr) 2023-08-23

Family

ID=78500661

Family Applications (1)

Application Number Title Priority Date Filing Date
EP21801994.1A Pending EP4229644A1 (fr) 2020-10-13 2021-10-13 Planification de traitement d'arythmie personnalisée

Country Status (5)

Country Link
EP (1) EP4229644A1 (fr)
JP (1) JP2023544896A (fr)
CN (1) CN116348960A (fr)
IL (1) IL301724A (fr)
WO (1) WO2022079623A1 (fr)

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE60328477D1 (de) * 2002-04-16 2009-09-03 Univ California Verfahren zur prognose von nierenzellkarzinom und behandlungsauswahl mit carboanhydrase ix
US20190088373A1 (en) * 2017-09-15 2019-03-21 Alberto Sarmentero Automated Assistant For Remote Patient Tracking, Diagnosing, Alerting, And Prevention Of Heart Diseases, Cardio Warning Service/System
US20210137384A1 (en) * 2017-12-13 2021-05-13 Washington University System and method for determining segments for ablation

Also Published As

Publication number Publication date
IL301724A (en) 2023-05-01
JP2023544896A (ja) 2023-10-25
CN116348960A (zh) 2023-06-27
WO2022079623A1 (fr) 2022-04-21

Similar Documents

Publication Publication Date Title
US20210378579A1 (en) Local noise identification using coherent algorithm
EP3744282A2 (fr) Systèmes pour améliorer les procédures d'ablation cardiaque
US20210386355A1 (en) System and method to detect stable arrhythmia heartbeat and to calculate and detect cardiac mapping annotations
EP3933852A1 (fr) Efficacité de cartographie améliorée en suggérant l'emplacement de points cartographiques
US20220008126A1 (en) Optimized ablation for persistent atrial fibrillation
US20210369174A1 (en) Automatic detection of cardiac structures in cardiac mapping
CN114078574A (zh) 在心律失常的标测和治疗期间使用机器学习来改进工作流
EP4113528A1 (fr) Système et procédé pour déterminer l'emplacement d'un cathéter
EP3937182A1 (fr) Système et procédé pour déterminer l'emplacement d'un cathéter
US20210391082A1 (en) Detecting atrial fibrillation and atrial fibrillation termination
JP2021186675A (ja) 心臓マッピングにおける心構造の自動検出
US20220005198A1 (en) Automatic contiguity estimation of wide area circumferential ablation points
US20220061733A1 (en) Automatically identifying scar areas within organic tissue using multiple imaging modalities
EP4229644A1 (fr) Planification de traitement d'arythmie personnalisée
US20220338939A1 (en) System and method to determine the location of a catheter
WO2024009220A1 (fr) Système et procédé pour déterminer l'emplacement d'un cathéter
WO2022079621A1 (fr) Détection et réduction de bruit d'ecg intracardiaque
CN114068010A (zh) 利用机器学习算法来优化消融指数计算的用于治疗心律失常的装置

Legal Events

Date Code Title Description
STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: UNKNOWN

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE INTERNATIONAL PUBLICATION HAS BEEN MADE

PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: REQUEST FOR EXAMINATION WAS MADE

17P Request for examination filed

Effective date: 20230331

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

DAV Request for validation of the european patent (deleted)
DAX Request for extension of the european patent (deleted)