Patent Description:
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 vein, and then guided into the chamber of the heart of concern. A typical ablation procedure involves the insertion of a catheter having a one or more electrodes at its distal end into a heart chamber. A reference electrode may be 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 between the catheter electrode(s) of the ablating catheter and an indifferent electrode (which may be one of the catheter electrodes), and current flows through the media between the electrodes, i.e., blood and tissue. The distribution of current may depend 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. In some applications, irreversible electroporation may be performed to ablate the tissue.

Electrophysiological (EP) cardiac mapping is a diagnostic medical procedure for identifying locations of cardiac dysfunction within a heart. Time-varying electro-cardiogram (ECG) signals are received by electrodes contacting points along the surface of a patient's heart. The signals are processed and different metrics regarding cardiac functions are computed from the processed (ECG) signals, which are then spatially mapped onto an image of the heart. The map is then outputted for a medical professional to analyze.

The analysis of cardiac signals sometimes involves synchronizing to the timing of the ECG signals. For example, <CIT> describes a method for analyzing signals, including sensing a time-varying intra-cardiac potential signal and finding a fit of the time-varying intra-cardiac potential signal to a predefined oscillating waveform. The method further includes estimating an annotation time of the signal responsive to the fit.

Electrophysiological (EP) cardiac mapping, or cardiac electro-anatomical mapping, is used to identify regions in the heart tissue that are dysfunctional. An intra-body probe, typically a catheter with multiple mapping electrodes disposed along the body of the catheter near the catheter distal end, is inserted into a cavity of the heart. Time varying electro-cardiogram (ECG) signals are recorded at multiple contact points between the mapping electrodes and the heart tissue. The multiple ECG electrodes are then moved to different contact positions with the heart tissue and the process is repeated. Then, metrics regarding cardiac function are computed from the local ECG signals, which are mapped spatially across the surface of the heart cavity. The mapping assists the medical professional to identify regions of heart dysfunction.

Electrical sources in the heart, such as the sinoatrial (SA) and atrioventricular (AV) nodes initiate electrical activity waves that propagate over the heart triggering the muscle tissue in the atria and ventricles to contract in a characteristic sinus rhythm. When the activity wave-front reaches the multiple mapping electrodes during each cardiac cycle, the characteristic ECG waveforms are detected at the multiple mapping electrodes. These waveforms are time-shifted due to the different arrival times of the same wave-front at the different multiple electrodes contacting the tissue at different spatial locations along the surface of the heart cavity.

The arrival times of the ECG waveforms detected at the multiple mapping electrodes can be used to map the propagation time and/or velocity of the activity wave across the heart. The mapping of the activity wave is performed with respect to a single time reference indicative of the cardiac cycle known herein as the reference annotation time.

The reference annotation time can be computed by processing the ECG signals obtained from a body surface (BS) electrode, or from an intra-cardiac (IC) reference electrode on an additional catheter and placed in contact with the surface of the cardiac chamber. Typically, the physician designates whether the reference annotation time is computed from a BS or IC channel, depending on the suspected pathology.

<CIT> discusses systems and methods for facilitating processing of cardiac information based on sensed electrical signals that include a processing unit configured to receive a set of electrical signals; receive an indication of a measurement location corresponding to each electrical signal of the set of electrical signals; and generate, based on at least one of an annotation waveform corresponding to each electrical signal of the set of electrical signals and a set of annotation mapping values, an annotation histogram.

There is provided in accordance with an embodiment of the present invention, a system to find local activation times of intracardiac electrogram (IEGM) signals according to independent claim <NUM>.

Alternative embodiments are defined in dependent claims <NUM>-<NUM>.

Further in accordance with another embodiment of the present invention it is provided a software product according to independent claim <NUM>.

One of the main challenges in electrophysiology (EP) is finding an accurate intracardiac electrogram (IEGM) signal annotation algorithm to select correct local activation times (LATs) from the IEGM signals, for example, to generate a LAT map. One method to automatically find the LATs includes finding the maximum negative slope of the signal within each window of interest (WOI) and assign the LAT to that point. The WOI in typically set by the system based on detecting the QRS complex of a signal captured by one or more body surface electrodes. Detecting the maximum negative slope does not provide a broad solution to the above problem as in some cases there may be many such slopes in the WOI and the wrong slope may be selected by the algorithm. Therefore, the physician has the option to manually change the computed LAT to a different point in the WOI.

Embodiments of the present invention solve the above problems by training an artificial neural network (ANN) using deep learning techniques to find LATs for corresponding IEGM signals. The ANN is trained using IEGM signals and corresponding LAT annotations manually annotated by annotation personnel (e.g., physicians or other medical professionals) provided by different EP laboratories (labs). The manual annotations may be correcting an automatically computed annotation or may be providing an initial annotation for an IEGM signal.

In some embodiments, during the training of the ANN, different LAT annotations are not provided an equal weight. For example, a loss function used to train the ANN may include weights, which may be associated with each respective IEGM signal and LAT annotation pair in order to weight the contribution of each pair in the training. In some embodiments, a binary cross-entropy (BCE) loss function may be used.

In some embodiments, the weight assigned to an IEGM signal and LAT annotation pair (e.g., for use in the loss function) may be computed responsibly to the LAT annotation experience level of the annotation person who manually determined that LAT annotation. In this way, the ANN may be trained by giving more weight to more experienced annotation personnel.

The LAT annotation experience levels of different annotation personnel may be estimated by searching a database including scientific literature publications to find the number of search matches (e.g. the number of scientific literature publications) for the different annotation personal in the realm of LAT annotation. For example, searching for "John Smith and LAT annotation" may provide <NUM> matches, while searching for "Tim Jones and LAT annotation" may provide <NUM> matches. In such a case, LAT annotations provided by Tim Jones are provided much more weight in the training than LAT annotations provided by John Smith. Any suitable database may be searched, for example, Google Scholar, Scopus or Web of Science.

Reference is now made to <FIG>, which is a pictorial illustration of a medical system <NUM> for performing catheterization procedures on a heart <NUM>, constructed and operative in accordance with an embodiment of the present disclosure.

The medical system <NUM> may be configured to evaluate electrical activity and perform ablative procedures on the heart <NUM> of a living subject. The system <NUM> comprises EP laboratory sub-systems <NUM> (only one shown for the sake of simplicity), which capture EP data. The medical system <NUM> also comprises a remote server <NUM>, e.g., a cloud computing device, to which EP data is sent for storage and/or processing by the EP laboratory sub-systems <NUM> over a network <NUM>. The EP data may be compressed in the EP laboratory sub-systems <NUM> and sent to the remote server <NUM> over the network <NUM> in compressed form.

One of the EP laboratory sub-systems <NUM> is now described in more detail below by way of example only. The different EP laboratory sub-systems <NUM> may comprises the same or different EP lab equipment to provide EP data to the remote server <NUM> for processing. The EP laboratory sub-system <NUM> of <FIG> comprises a catheter <NUM>, which is percutaneously inserted by an operator <NUM> through the patient's vascular system into a chamber or vascular structure of the heart <NUM>. The operator <NUM>, who is typically a physician, brings the catheter's distal end <NUM> into contact with the heart wall, for example, at an ablation target site. Electrical activation maps may be prepared, according to the methods disclosed in <CIT>, <CIT>, and <CIT>. One commercial product embodying elements of the system <NUM> is available as the CARTO® <NUM> System, available from Biosense Webster, Inc. , Irvine, CA. This system may be modified by those skilled in the art to embody the principles described herein.

Areas determined to be abnormal, for example by evaluation of the electrical activation maps, can be ablated by application of thermal energy, e.g., by passage of radiofrequency electrical current through wires in the catheter to one or more electrodes at the distal end <NUM>, which apply the radiofrequency energy to the myocardium. The energy is absorbed in the tissue, heating it to a point at which it permanently loses its electrical excitability. When successful, this procedure creates non-conducting lesions in the cardiac tissue, which disrupt the abnormal electrical pathway causing the arrhythmia. The principles of the disclosure can be applied to different heart chambers to diagnose and treat many different cardiac arrhythmias.

The catheter <NUM> typically comprises a handle <NUM>, having suitable controls on the handle to enable the operator <NUM> to steer, position and orient the distal end <NUM> of the catheter <NUM> as desired for the ablation. To aid the operator <NUM>, a distal portion of the catheter <NUM> contains position sensors (not shown) that provide signals to processing circuitry <NUM>, located in a console <NUM>. The processing circuitry <NUM> may fulfill several processing functions as described below.

Wire connections <NUM> may link the console <NUM> with body surface electrodes <NUM> and other components of a positioning sub-system for measuring location and orientation coordinates of the catheter <NUM>. The processing circuitry <NUM> or another processor (not shown) may be an element of the positioning subsystem. Catheter electrodes <NUM> and the body surface electrodes <NUM> may be used to measure tissue impedance at the ablation site as taught in <CIT>. Temperature sensors (not shown), typically a thermocouple or thermistor, may be mounted on ablation surfaces on the distal portion of the catheter <NUM> as described below.

The console <NUM> typically contains one or more ablation power generators <NUM>. The catheter <NUM> may be adapted to conduct ablative energy to the heart using any known ablation technique, e.g., radiofrequency energy, ultra-sound energy, irreversible electroporation and laser-produced light energy. Such methods are disclosed in <CIT> <CIT>, and<CIT>.

In one embodiment, the positioning subsystem comprises a magnetic position tracking arrangement that determines the position and orientation of the catheter <NUM> by generating magnetic fields in a predefined working volume and sensing these fields at the catheter, using field generating coils <NUM>. The positioning subsystem is described in <CIT>, and <CIT>.

As noted above, the catheter <NUM> is coupled to the console <NUM>, which enables the operator <NUM> to observe and regulate the functions of the catheter <NUM>. Console <NUM> includes the processing circuitry <NUM>, generally a computer with appropriate signal processing circuits. The processing circuitry <NUM> is coupled to drive a display <NUM> (e.g., a monitor). The signal processing circuits typically receive, amplify, filter and digitize signals from the catheter <NUM>, including signals generated by sensors such as electrical, temperature and contact force sensors, and location sensing electrodes <NUM> located distally in the catheter <NUM>. The digitized signals are received and used by the console <NUM> and the positioning system to compute the position and orientation of the catheter <NUM>, and to analyze the electrical signals from the electrodes. In some embodiments, the digitized signals are sent (and optionally compressed prior to sending) to the remote server <NUM> to compute the position and orientation data, and/or to analyze the electrical signals from the electrodes <NUM>, <NUM>, and/or to use the electrical signals and associated data to train an artificial neural network, described in more detail below.

In order to generate electroanatomic maps, the processing circuitry <NUM> typically comprises a mapping module including an electroanatomic map generator, an image registration program, an image or data analysis program and a graphical user interface configured to present graphical information on the display <NUM>. In some embodiments, some or all of the functionality of the mapping module is performed by the remote server <NUM>.

Typically, the system <NUM> includes other elements, which are not shown in the figures for the sake of simplicity. For example, the system <NUM> may include an electrocardiogram (ECG) monitor, coupled to receive signals from one or more of the body surface electrodes <NUM>, in order to provide an ECG synchronization signal to the console <NUM> or the remote server <NUM>. In some embodiments, some or all of the functionality of the ECG monitor is performed by the remote server <NUM>. As mentioned above, the system <NUM> typically also includes a reference position sensor, either on an externally-applied reference patch attached to the exterior of the subject's body, or on an internally-placed catheter, which is inserted into the heart <NUM> maintained in a fixed position relative to the heart <NUM>. Conventional pumps and lines for circulating liquids through the catheter <NUM> for cooling the ablation site may be provided. The system <NUM> and/or the remote server <NUM> may receive image data from an external imaging modality, such as an MRI unit or the like and includes image processors that can be incorporated in or invoked (e.g., by the processing circuitry <NUM> and/or the remote server <NUM>) for generating and displaying images.

In practice, some or all of the functions of the processing circuitry <NUM> may be combined in a single physical component or, alternatively, implemented using multiple physical components. These physical components may comprise hard-wired or programmable devices, or a combination of the two. In some embodiments, at least some of the functions of the processing circuitry <NUM> may be carried out by a programmable processor under the control of suitable software. This software may be downloaded to a device in electronic form, over a network, for example. Alternatively, or additionally, the software may be stored in tangible, non-transitory computer-readable storage media, such as optical, magnetic, or electronic memory.

Reference is now made to <FIG> is a flowchart <NUM> including steps in a method of operation of one of the electrophysiological laboratory sub-systems <NUM> in the system <NUM> of <FIG>. <FIG> is a view of displayed annotated IEGM signals <NUM> rendered using the system <NUM> of <FIG>.

Each EP laboratory sub-system <NUM> includes the catheter <NUM> configured to be inserted into at least one cardiac chamber of at least one living subject, and to capture respective IEGM signals <NUM> from the at least one cardiac chamber. Any suitable type of catheter or catheters <NUM> may be used in each EP laboratory sub-systems <NUM>. The processing circuitry <NUM> is configured to receive the IEGM signals <NUM> and render (block <NUM>) representations of the IEGM signals <NUM> to the display <NUM> and receive (block <NUM>) local activation time (LAT) annotations <NUM> of the corresponding IEGM signals <NUM> manually annotated by an annotation person of that EP laboratory sub-system <NUM>. The IEGM signals <NUM> may be first automatically annotated by an algorithm running on the processing circuitry <NUM>. The annotation person may then correct the automated annotation with a manual annotation by marking the local activation time annotations <NUM> on the displayed IEGM signals <NUM>. In some embodiments, the annotation person may view the displayed IEGM signals <NUM> on the display <NUM> (without the processing circuitry <NUM> computing an automated LAT annotation) and then mark the local activation time annotations <NUM> on the displayed IEGM signals <NUM>.

The processing circuitry <NUM> of the EP laboratory sub-system <NUM> is configured to provide (block <NUM>) the IEGM signals <NUM> and the corresponding local activation time annotations <NUM> to the remote server <NUM>. In some embodiments, the EP laboratory sub-systems <NUM> are configured to provide the IEGM signals <NUM> and corresponding local activation time annotations <NUM> to the remote server <NUM> over the network <NUM> (<FIG>) via suitable network interfaces.

Reference is now made to <FIG> and <FIG>. <FIG> is a block diagram view of the system <NUM> of <FIG>. <FIG> is a flowchart <NUM> including steps in a method of operation of the remote server <NUM> in the system <NUM> of <FIG>.

The remote server <NUM> includes processing circuitry <NUM>, a memory <NUM>, a data bus <NUM>, and a network interface <NUM>. The processing circuitry <NUM> is configured to run software to perform various signal processing and computation tasks, including an artificial neural network <NUM>, a training module <NUM>, and a mapping module <NUM>. The training module <NUM> is configured to train the artificial neural network <NUM> as described in more detail below with reference to <FIG>. The mapping module <NUM> is configured to generate EP maps responsively to cardiac signals and other data captured from a living subject as described in more detail with reference to <FIG>.

The memory <NUM> is configured to store data used by the processing circuitry <NUM>. The data bus <NUM> is configured to transfer data between the various elements of the remote server <NUM> for example, between the processing circuitry <NUM> and the network interface <NUM>.

The training module <NUM> running on the processing circuitry <NUM> is configured to receive (block <NUM>), from the EP laboratory sub-systems <NUM>, the IEGM signals <NUM> (captured in the respective EP laboratory sub-systems <NUM>) and corresponding local activation time annotations <NUM> of the IEGM signals <NUM> manually annotated by respective annotation personnel. In other words, the training module <NUM> is configured to receive IEGM signals <NUM> and corresponding local activation time annotations <NUM> manually annotated by one annotation person of one of the EP laboratory sub-systems <NUM>, and other IEGM signals <NUM> and corresponding local activation time annotations <NUM> manually annotated by another annotation person of another one of the EP laboratory sub-systems <NUM>, and so on.

The training module <NUM> running on the processing circuitry <NUM> is configured to train (block <NUM>) the artificial neural network <NUM> to find local activation times of IEGM signals responsively to training data including the IEGM signals <NUM> and the corresponding local activation time annotations <NUM>. The step of block <NUM> includes sub-steps of blocks <NUM>-<NUM> described in more detail below.

The training module <NUM> running on the processing circuitry <NUM> is configured to search (block <NUM>) a database of scientific literature publications (e.g., Google Scholar, Scopus, Web of Science) responsively to the respective annotation personnel (who supplied the local activation time annotations <NUM>) as search strings, yielding respective numbers of search matches indicative of the local activation time annotation experience level of the respective annotation personnel. In other words, the database is searched with different search strings for each of the annotation personnel (e.g., "J. Smith", "T. Jones", etc.) to yield a number of search matches (e.g., <NUM> matches for J. Smith and <NUM> matches for T. Jones, etc.) for each of the annotation personnel. In some cases, the search for a given annotation person may yield no search matches. The search may be performed using any suitable software script, e.g., using a web crawler such as pybliometrics <NUM>. <NUM> to access Scopus. The respective numbers of search matches may be respective numbers of the scientific literature publications matching the respective annotation personnel e.g., <NUM> publications for J. Smith and <NUM> publications for T. Jones, etc.).

In some embodiments, the training module <NUM> running on the processing circuitry <NUM> is configured to limit searching of the database to scientific literature publications describing local activation time annotation and/or IEGM and/or electrocardiogram annotation and/or EP annotation etc. Limiting the searching to one or more of the above is useful to prevent spurious results. For example, there may be a J. Smith who has published articles in Nuclear Physics and therefore his experience is irrelevant to the J. Smith who provided the local activation time annotations <NUM>.

The training module <NUM> running on the processing circuitry <NUM> is configured to compute (block <NUM>) weights for annotations performed by the respective annotation personnel responsively to the local activation time annotation experience level of the respective annotation personnel. For example, a weight is computed for annotations performed by J. Smith and another weight is computed for annotations performed by T. Jones, and so on.

In some embodiments, the training module <NUM> running on the processing circuitry <NUM> is configured to compute the weights for the annotations performed by the respective annotation personnel responsively to the respective numbers of search matches (e.g., numbers of the scientific literature publications) for the respective annotation personnel. For example, a weight is computed for annotations performed by J. Smith responsively to the <NUM> search matches (e.g., <NUM> publications) found in the database for J. Smith, and another weight is computed for annotations performed by T. Jones responsively to the <NUM> search matches (e.g., <NUM> publications) found in the database for T. Jones, and so on. The weights may be computed proportionally. For example, if there are N annotation personnel, and the jth annotation person has Pj publications found in the search, the weight Wi for the ith annotation person is equal to: <MAT>.

The training module <NUM> running on the processing circuitry <NUM> is configured to train (block <NUM>) the artificial neural network <NUM> to find local activation times of IEGM signals responsively to training data including the IEGM signals <NUM> (received from the EP laboratory sub-systems <NUM>) and the corresponding local activation time annotations <NUM> weighted according to respective computed weights of the respective annotation personnel who annotated respective local activation time annotations <NUM>. For example, the annotations provided by J. Smith are weighted according to the weight computed for J. Smith, and the annotations provided by T. Jones are weighted according to the weight computed for T. Jones and so on.

Reference is now made to <FIG> and <FIG>. <FIG> is a schematic view the artificial neural network <NUM> for use with the system <NUM> of <FIG>. <FIG> is a flowchart including sub-steps in the step of block <NUM> of the method of <FIG>.

A neural network is a network or circuit of neurons, or in a modern 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 <NUM> and <NUM>, or it could be -<NUM> and <NUM>.

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.

For completeness, 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. Apart from the electrical signaling, there are other forms of signaling that arise from neurotransmitter diffusion.

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 (NN), 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. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network. In more practical terms, neural networks are non-linear statistical data modeling or decision-making tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data.

In some embodiments, the artificial neural network <NUM> includes a fully connected neural network, e.g., a convolutional neural network. In other embodiments, the artificial neural network <NUM> may comprise any suitable ANN. The artificial neural network <NUM> may comprise software executed by the processing circuitry <NUM> (<FIG>) and/or hardware modules configured to perform the functions of the artificial neural network <NUM>.

The artificial neural network <NUM> includes an input layer <NUM> into which an input is received, and one or more hidden layers <NUM> which progressively process the input to an output layer <NUM> from which the output of the artificial neural network <NUM> is provided. The artificial neural network <NUM> may include layer weights between the layers <NUM>, <NUM>, <NUM> of the artificial neural network <NUM>. The artificial neural network <NUM> manipulates the data received at the input layer <NUM> according to the values of the various layer weights between the layers <NUM>, <NUM>, <NUM> of the artificial neural network <NUM>.

The layer weights of the artificial neural network <NUM> are updated during training of the artificial neural network <NUM> so that the artificial neural network <NUM> performs a data manipulation task that the artificial neural network <NUM> is trained to perform.

The number of layers in the artificial neural network <NUM> and the width of the layers may be configurable. As the number of layers and width of the layers increases so does the accuracy to which the artificial neural network <NUM> can manipulate data according to the task at hand. However, a larger number of layers, and wider layers, generally requires more training data, more training time and the training may not converge. By way of example, the input layer <NUM> may include <NUM> neurons (e.g., to compress a batch of <NUM> samples) and the output layer may also include <NUM> neurons.

Training the artificial neural network <NUM> is generally an iterative process. One method of training the artificial neural network <NUM> is now described below. The training module <NUM> running on the processing circuitry <NUM> (<FIG>) is configured to iteratively adjust (block <NUM>) parameters (e.g., layer weights) of the artificial neural network <NUM> to reduce a difference between an output of the artificial neural network <NUM> and the local activation time annotations <NUM> of the IEGM signals <NUM>.

In some embodiments, the training module <NUM> running on the processing circuitry <NUM> (<FIG>) is configured to: minimize a loss function which is a function of the output of the artificial neural network <NUM> and the local activation time annotations <NUM> of the IEGM signals <NUM> weighted according to respective ones of the computed weights (computed in the step of block <NUM> of <FIG>); and iteratively adjust the parameters (e.g., layer weights) of the artificial neural network <NUM> responsively to minimizing the loss function. In some embodiments, the loss function includes a binary cross entropy (BCE) loss function. org provides an example of a suitable BCE loss function.

Sub-steps of the step of block <NUM> are now described below.

The training module <NUM> running on the processing circuitry <NUM> of the processing circuitry <NUM> (<FIG>) is configured to input (block <NUM>) the IEGM signals <NUM> into the input layer <NUM> of the artificial neural network <NUM>. The training module <NUM> running on the processing circuitry <NUM> (<FIG>) is configured to compare (block <NUM>) the output of the artificial neural network <NUM> (e.g., the output of the output layer <NUM>) with the desired output, i.e., the corresponding local activation time annotations <NUM> of the IEGM signals <NUM>, for example, using a suitable loss function which takes into account the weights (computed for the respective annotation personnel) for the respective local activation time annotations <NUM>.

The output of the artificial neural network <NUM> includes various vectors corresponding with the IEGM signals <NUM> input into the artificial neural network <NUM>. Each of the output vectors includes components, each component with a floating-point value (for example, between <NUM> and <NUM>). Each of the desired outputs is expressed as a one-hot vector in which all the components of the vectors have zero values except for one of the components which has a value of one corresponding with the time value of the respective local activation time annotation <NUM>.

For example, if there is a set of vectors A, B, C output by the artificial neural network <NUM> and a corresponding set of vectors representing corresponding local activation time annotations A', B', and C', the training module <NUM> of the processing circuitry <NUM> (<FIG>) uses the loss function to compare A with A', B with B', C with C' and so on, based on the weights of the annotation personnel who annotated A', B' and C', respectively.

At a decision block <NUM>, the training module <NUM> running on the processing circuitry <NUM> (<FIG>) is configured to determine if the difference between the output of the artificial neural network <NUM> and desired output is small enough. If the difference between the output of the artificial neural network <NUM> and the desired output is small enough (branch <NUM>), the training module <NUM> running on the processing circuitry <NUM> (<FIG>) is configured to save (block <NUM>) the parameters (e.g., layer weights) of the artificial neural network <NUM> for future use.

If the difference is not small enough (branch <NUM>), the training module <NUM> running on the processing circuitry <NUM> (<FIG>) is configured to amend (block <NUM>) parameters (e.g., layer weights) of the artificial neural network <NUM> to reduce the difference between the output of the artificial neural network <NUM> and the desired output of the artificial neural network <NUM> according to the loss function. The difference being minimized in the above example is the overall difference between all the outputs of the artificial neural network <NUM> and all the desired outputs (e.g., local activation time annotations <NUM>) according to the loss function. The training module <NUM> running on the processing circuitry <NUM> (<FIG>) is configured to amend the parameters using any suitable optimization algorithm, for example, a gradient descent algorithm such as Adam Optimization. The steps of blocks <NUM>-<NUM> are then repeated.

Reference is now made to <FIG>, which is a flowchart <NUM> including steps in a method to process an intracardiac electrogram signal using the trained artificial neural network <NUM> of <FIG>. Reference is also made to <FIG>.

The mapping module <NUM> (or any other suitable module) running on the processing circuitry <NUM> is configured to receive (block <NUM>) an IEGM signal, from one of the EP laboratory sub-systems <NUM>. The mapping module <NUM> (or any other suitable module) running on the processing circuitry <NUM> is configured to apply (block <NUM>) the trained artificial neural network <NUM> to the received IEGM signal to provide an indication of a local activation time of the received IEGM signal. The output of the artificial neural network <NUM> may include a vector having components (e.g., <NUM> components), with each component having a floating value (e.g., a decimal value), for example, between <NUM> and <NUM>. The floating values represent respective probabilities that the respective vector components are the LAT value for the input IEGM signal. Therefore, the highest floating value is associated with the highest probability and therefore indicates the LAT value that should be used for the input IEGM signal. The mapping module <NUM> running on the processing circuitry <NUM> may be configured to receive the output of the artificial neural network <NUM> and find the highest floating value of the components of the vector output by the artificial neural network <NUM> and compute the LAT value for the received IEGM signal responsively to the position of the vector component with the highest floating value.

Reference is now made to <FIG>, which is a schematic view of a displayed electroanatomic map <NUM> rendered by the system <NUM> of <FIG>. Reference is also made to <FIG>. The mapping module <NUM> (or any other suitable module) running on the processing circuitry <NUM> is optionally configured to generate (block <NUM>) the electroanatomic map <NUM> responsively to the indication of the local activation time provided in the step of block <NUM> and other similar EP data. In some embodiments the processing circuitry <NUM> is configured to provide the electroanatomic map <NUM> to the EP laboratory sub-system <NUM> which provided the IEGM signals used to generate the electroanatomic map <NUM>. The mapping module <NUM> is optionally configured to provide (block <NUM>) the LAT found in the step of block <NUM> to the EP laboratory sub-system <NUM> which provided the IEGM signal for which the LAT was found.

Various features of the disclosure which are, for clarity, described in the contexts of separate embodiments may also be provided in combination in a single embodiment. Conversely, various features of the invention which are, for brevity, described in the context of a single embodiment may also be provided separately or in any suitable sub-combination.

Claim 1:
A system (<NUM>) to find local activation times of intracardiac electrogram (IEGM) signals, comprising a remote server (<NUM>) including processing circuitry (<NUM>) configured to:
receive (<NUM>), from electrophysiological laboratory sub-systems (<NUM>), first IEGM signals and corresponding local activation time annotations of the first IEGM signals manually annotated by respective annotation personnel;
compute (<NUM>) weights for annotations performed by respective ones of the annotation personnel responsively to a local activation time annotation experience level of the respective ones of the annotation personnel;
train (<NUM>) an artificial neural network (<NUM>) to find local activation times of IEGM signals responsively to the first IEGM signals and the corresponding local activation time annotations weighted according to respective ones of the computed weights of the respective ones of the annotation personnel who annotated respective ones of the local activation time annotations;
receive (<NUM>) a second IEGM signal; and
apply (<NUM>) the trained artificial neural network to the received second IEGM signal to provide an indication of a local activation time of the received second IEGM signal.