Patent Application
20220095985
The present disclosure relates to electrophysiology systems and methods for processing cardiac electrical signals.
Use of minimally invasive procedures, such as catheter ablation, to treat a variety of heart conditions, such as supraventricular and ventricular arrhythmias, is becoming increasingly more prevalent. Such procedures involve the mapping of electrical activity in the heart (e.g., based on cardiac signals), such as at various locations on the endocardium surface (“cardiac mapping”), to identify the site of origin of the arrhythmia followed by a targeted ablation of the site. To perform such cardiac mapping, a catheter with one or more electrodes can be inserted into the patient's heart chamber.
Conventional three-dimensional (3D) mapping techniques include contact mapping, non-contact mapping, and a combination of contact and non-contact mapping. In both contact and non-contact mapping, one or more catheters are advanced into the heart. With some catheters, once in the chamber, the catheter may be deployed to assume a 3D shape. In contact mapping, physiological signals resulting from the electrical activity of the heart are acquired with one or more electrodes located at the catheter distal tip after determining that the tip is in stable and steady contact with the endocardium surface of a particular heart chamber. In non-contact-based mapping systems, using the signals detected by the non-contact electrodes and information on chamber anatomy and relative electrode location, the system provides physiological information regarding the endocardium of the heart chamber. Location and electrical activity are usually measured sequentially on a point-by-point basis at about 50 to 200 points on the internal surface of the heart to construct an electro-anatomical depiction of the heart. The generated map may then serve as the basis for deciding on a therapeutic course of action, for example, tissue ablation, to alter the propagation of the heart's electrical activity and to restore normal heart rhythm.
In many conventional mapping systems, the clinician visually inspects or examines the captured electrograms (EGMs), which increases examination time and cost. During an automatic electro-anatomical mapping process, however, approximately 6,000 to 20,000 intracardiac electrograms (EGMs) may be captured, which does not lend itself to being manually inspected in full by a clinician (e.g., a physician) for a diagnostic assessment, EGM categorization, and/or the like. Typically mapping systems extract scalar values from each EGM to construct voltage, activation, or other map types to depict overall patterns of activity within the heart. While maps reduce the need to inspect the captured EGMs, they also condense the often complex and useful information in the EGMs. Further, maps may be misleading due to electrical artifacts or inappropriate selection of features such as activation times. Additionally, due to the complex nature of conventional techniques, cardiac maps often are not suitable for accurate and efficient interpretation.
As recited in examples, Example 1 is a system for processing cardiac information. The system comprising: a processing unit configured to: receive a plurality of cardiac electrical signals collected from a plurality of electrodes disposed within a cardiac chamber, wherein the plurality of cardiac electrical signals are acquired over a cardiac beat having a cycle length; calculate an importance metric for each of the plurality of the cardiac electrical signals, wherein the importance metric represents a contribution of a respective cardiac electrical signal to an overall duty cycle of the cardiac beat as a function of the cycle length; and facilitate presentation, on a display device, a graphical representation of selected cardiac electrical signals, wherein each of the selected cardiac electrical signals meets a selection criteria based on a respective importance metric.
Example 2 is the system of Example 1, wherein each of the plurality of cardiac electrical signals includes an intra-cardiac electrogram (EGM).
Example 3 is the system of Example 1 or 2, wherein the processing unit is further configured to: generate a plurality of activation waveforms based on the plurality of cardiac electrical signals.
Example 4 is the system of Example 3, wherein the processing unit is further configured to: identify, for each of the plurality of electrical signals, a deflection, the deflection comprising a deviation from a signal baseline, wherein each of the plurality of activation waveforms is generated based on a respective identified deflection, wherein the activation waveform comprises activation waveform values corresponding to probabilities that the identified deflections represent activations of cardiac tissue.
Example 5 is the system of Example 3, wherein the importance metric is calculated based on an activation metric and a novelty metric, wherein the activation metric represents a first contribution of the respective cardiac electrical signal to an activation zone, and wherein the novelty metric represents a second contribution of the respective cardiac electrical signal to outside the activation zone.
Example 6 is the system of Example 5, wherein the activation metric is calculated based on an average value of the activation waveform values.
Example 7 is the system of Example 6, wherein the activation metric is calculated by normalizing the average value of the activation waveform values.
Example 8 is the system of Example 5, further comprising:
selecting one or more activation waveforms based on the activation metrics; and determining an activation zone waveform based on the selected one or more activation waveforms, the activation zone waveform representing the activation zone.
Example 9 is the system of Example 8, wherein each of the selected one or more activation waveforms has an activation metric greater than a predetermined activation metric threshold.
Example 10 is the system of Example 8, wherein the novelty metric is calculated using weight factors based on the activation zone waveform, wherein a first weight factor is corresponding to a first activation zone value and a second weight factor is corresponding to a second activation zone value, and wherein the first weight factor is greater than the second weight factor with the first activation zone value smaller than the second activation zone value.
Example 11 is the system of any one of Examples 5-10, wherein the importance metric is determined by applying a non-linear function to the activation metric and the novelty metric.
Example 12 is the system of any one of Examples 1-11, wherein the selection criteria comprises the respective importance metric greater than a predetermined threshold.
Example 13 is a method of processing cardiac information, the method comprising: receiving a plurality of cardiac electrical signals collected from a plurality of electrodes disposed within a cardiac chamber, wherein the plurality of cardiac electrical signals are acquired over a cardiac beat having a cycle length; calculating an importance metric for each of the plurality of the cardiac electrical signals, wherein the importance metric represents a contribution of a respective cardiac electrical signal to an overall duty cycle of the cardiac beat as a function of the cycle length; and facilitating presentation, on a display device, a graphical representation of selected cardiac electrical signals, wherein each of the selected cardiac electrical signals meets a selection criteria based on a respective importance metric.
Example 14 is the method of Example 13, further comprising: generating a plurality of activation waveforms based on the plurality of cardiac electrical signals, wherein each of the plurality of activation waveforms is generated based on deflections of the plurality of cardiac electrical signals from a signal baseline, wherein the activation waveform comprises activation waveform values corresponding to probabilities that the identified deflections represent activations of cardiac tissue.
Example 15 is the method of Example 13 or 14, wherein the importance metric is calculated based on an activation metric and a novelty metric, wherein the activation metric represents a first contribution of the respective cardiac electrical signal to an activation zone, wherein the novelty metric represents a second contribution of the respective cardiac electrical signal to outside the activation zone, and wherein the activation metric and the activation zone are determined based on the activation waveform values.
Example 16 is a system for processing cardiac information, the system comprising: a processing unit configured to: receive a plurality of cardiac electrical signals collected from a plurality of electrodes disposed within a cardiac chamber, wherein the plurality of cardiac electrical signals are acquired over a cardiac beat having a cycle length; calculate an importance metric for each of the plurality of the cardiac electrical signals, wherein the importance metric represents a contribution of a respective cardiac electrical signal to an overall duty cycle of the cardiac beat as a function of the cycle length; and facilitate presentation, on a display device, a graphical representation of selected cardiac electrical signals, wherein each of the selected cardiac electrical signals meets a selection criteria based on a respective importance metric.
Example 17 is the system of Example 16, wherein each of the plurality of cardiac electrical signals includes an intra-cardiac electrogram (EGM).
Example 18 is the system of Example 16, wherein the processing unit is further configured to: generate a plurality of activation waveforms based on the plurality of cardiac electrical signals.
Example 19 is the system of Example 18, wherein the processing unit is further configured to: identify, for each of the plurality of electrical signals, a deflection, the deflection comprising a deviation from a signal baseline, wherein each of the plurality of activation waveforms is generated based on a respective identified deflection, wherein the activation waveform comprises activation waveform values corresponding to probabilities that the identified deflections represent activations of cardiac tissue.
Example 20 is the system of Example 18, wherein the importance metric is calculated based on an activation metric and a novelty metric, wherein the activation metric represents a first contribution of the respective cardiac electrical signal to an activation zone, and wherein the novelty metric represents a second contribution of the respective cardiac electrical signal to outside the activation zone.
Example 21 is the system of Example 20, wherein the activation metric is calculated based on an average value of the activation waveform values.
Example 22 is the system of Example 21, wherein the activation metric is calculated by normalizing the average value of the activation waveform values.
Example 23 is the system of Example 20, further comprising: selecting one or more activation waveforms based on the activation metrics; and determining an activation zone waveform based on the selected one or more activation waveforms, the activation zone waveform representing the activation zone.
Example 24 is the system of Example 23, wherein each of the selected one or more activation waveforms has an activation metric greater than a predetermined activation metric threshold.
Example 25 is the system of Example 23, wherein the novelty metric is calculated using weight factors based on the activation zone waveform, wherein a first weight factor is corresponding to a first activation zone value and a second weight factor is corresponding to a second activation zone value, and wherein the first weight factor is greater than the second weight factor with the first activation zone value smaller than the second activation zone value.
Example 26 is the system of Example 20, wherein the importance metric is determined by applying a non-linear function to the activation metric and the novelty metric.
Example 27 is the system of Example 16, wherein the selection criteria comprises the respective importance metric greater than a predetermined threshold.
Example 28 is a method of processing cardiac information, the method comprising: receiving a plurality of cardiac electrical signals collected from a plurality of electrodes disposed within a cardiac chamber, wherein the plurality of cardiac electrical signals are acquired over a cardiac beat having a cycle length; calculating an importance metric for each of the plurality of the cardiac electrical signals, wherein the importance metric represents a contribution of a respective cardiac electrical signal to an overall duty cycle of the cardiac beat as a function of the cycle length; and facilitating presentation, on a display device, a graphical representation of selected cardiac electrical signals, wherein each of the selected cardiac electrical signals meets a selection criteria based on a respective importance metric.
Example 29 is the method of Example 28, further comprising: generating a plurality of activation waveforms based on the plurality of cardiac electrical signals, wherein each of the plurality of activation waveforms is generated based on deflections of the plurality of cardiac electrical signals from a signal baseline, wherein the activation waveform comprises activation waveform values corresponding to probabilities that the identified deflections represent activations of cardiac tissue.
Example 30 is the method of Example 29, wherein the importance metric is calculated based on an activation metric and a novelty metric, wherein the activation metric represents a first contribution of the respective cardiac electrical signal to an activation zone, wherein the novelty metric represents a second contribution of the respective cardiac electrical signal to outside the activation zone, and wherein the activation metric and the activation zone are determined based on the activation waveform values.
Example 31 is the method of Example 28, wherein each of the plurality of cardiac electrical signals includes an intra-cardiac electrogram (EGM).
Example 32 is the method of Example 30, wherein the activation metric is calculated based on an average value of the activation waveform values.
Example 33 is the method of Example 32, wherein the activation metric is calculated by normalizing the average value of the activation waveform values.
Example 34 is the method of Example 30, further comprising:
selecting one or more activation waveforms based on the activation metrics; and determining an activation zone waveform based on the selected one or more activation waveforms, the activation zone waveform representing the activation zone.
Example 35 is the method of Example 34, wherein each of the selected one or more activation waveforms has an activation metric greater than a predetermined activation metric threshold.
While multiple embodiments are disclosed, still other embodiments of the present invention will become apparent to those skilled in the art from the following detailed description, which shows and describes illustrative embodiments of the invention. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not restrictive.
While the invention is amenable to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and are described in detail below. The intention, however, is not to limit the invention to the particular embodiments described. On the contrary, the invention is intended to cover all modifications, equivalents, and alternatives falling within the scope of the invention as defined by the appended claims.
As the terms are used herein with respect to measurements (e.g., dimensions, characteristics, attributes, components, etc.), and ranges thereof, of tangible things (e.g., products, inventory, etc.) and/or intangible things (e.g., data, electronic representations of currency, accounts, information, portions of things (e.g., percentages, fractions), calculations, data models, dynamic system models, algorithms, parameters, etc.), “about” and “approximately” may be used, interchangeably, to refer to a measurement that includes the stated measurement and that also includes any measurements that are reasonably close to the stated measurement, but that may differ by a reasonably small amount such as will be understood, and readily ascertained, by individuals having ordinary skill in the relevant arts to be attributable to measurement error; differences in measurement and/or manufacturing equipment calibration; human error in reading and/or setting measurements; adjustments made to optimize performance and/or structural parameters in view of other measurements (e.g., measurements associated with other things); particular implementation scenarios; imprecise adjustment and/or manipulation of things, settings, and/or measurements by a person, a computing device, and/or a machine; system tolerances; control loops; machine-learning; foreseeable variations (e.g., statistically insignificant variations, chaotic variations, system and/or model instabilities, etc.); preferences; and/or the like.
Although illustrative methods may be represented by one or more drawings (e.g., flow diagrams, communication flows, etc.), the drawings should not be interpreted as implying any requirement of, or particular order among or between, various steps disclosed herein. However, some embodiments may require certain steps and/or certain orders between certain steps, as may be explicitly described herein and/or as may be understood from the nature of the steps themselves (e.g., the performance of some steps may depend on the outcome of a previous step). Additionally, a “set,” “subset,” or “group” of items (e.g., inputs, algorithms, data values, etc.) may include one or more items, and, similarly, a subset or subgroup of items may include one or more items. A “plurality” means more than one.
As used herein, the term “based on” is not meant to be restrictive, but rather indicates that a determination, identification, prediction, calculation, and/or the like, is performed by using, at least, the term following “based on” as an input. For example, predicting an outcome based on a particular piece of information may additionally, or alternatively, base the same determination on another piece of information.
Embodiments of the present disclosure facilitate assessing importance metrics for signals collected from electrodes from the perspectives of contributing to cardiac beats. In embodiments, an importance metric represents a contribution to a duty cycle of a cardiac beat having a cycle length, where a duty cycle represents a percent of activation during the cycle length. Embodiments of the present disclosure facilitate assessing importance metrics of signals collected from electrodes based on activation waveforms generated from the cardiac electrical signals. An activation waveform, or referred to as an annotation waveform, is a set of activation waveform values and may include, for example, a set of discrete activation waveform values (e.g., a set of activation waveform values, a set of activation time annotations, etc.), a function defining an activation waveform curve, and/or the like. In some embodiments, each data point of an activation waveform represents the per-sample “probability” of tissue activation. In some embodiments, the cardiac electrical signals and/or the activation waveform may be displayed, used to present in an activation propagation map, used to facilitate diagnoses, used to facilitate classification (e.g., importance metrics) of cardiac electrical signals, and/or the like. To perform aspects of embodiments of the methods described herein, the cardiac electrical signals may be obtained from a mapping catheter (e.g., associated with a mapping system), which may be used in conjunction with other equipment typically used in an electrophysiology lab, e.g., a recording system, a coronary sinus (CS) catheter or other reference catheter, an ablation catheter, a memory device (e.g., a local memory, a cloud server, etc.), a communication component, a medical device (e.g., an implantable medical device, an external medical device, a telemetry device, etc.), and/or the like.
As the term is used herein, a sensed cardiac electrical signal may refer to one or more sensed signals. Each cardiac electrical signal may include intracardiac electrograms (EGMs) sensed within a patient's heart and may include any number of features that may be ascertained by aspects of an electrophysiology system. A cardiac electric signal, also referred to as an electrical signal, may be an electrogram (EGM), a filtered EGM, a set of absolute values of an EGM, values of peaks of an EGM at peak locations, a combination of these, and/or the like. For example, a cardiac electrical signal may be represented as a set of ordered values (e.g., the amplitude of each sample point may be a value in the set), and a specified percentile and/or multiplier thereof, may be used to define a signal baseline.
Examples of cardiac electrical signal features include, but are not limited to, activation times, activations, activation waveforms, filtered activation waveforms, minimum voltage values, maximum voltages values, maximum negative time-derivatives of voltages, instantaneous potentials, voltage amplitudes, dominant frequencies, peak-to-peak voltages, and/or the like. A cardiac electrical signal feature may refer to one or more features extracted from one or more cardiac electrical signals, derived from one or more features that are extracted from one or more cardiac electrical signals, and/or the like. In embodiments, the cardiac electrical signal feature may include a importance metric. Additionally, a representation of a cardiac electrical signal feature may represent one or more cardiac electrical signal features, an interpolation of a number of cardiac electrical signal features, and/or the like. In some cases, a representation of the cardiac electrical signal is on a cardiac and/or a surface map.
Each cardiac signal also may be associated with a set of respective position coordinates that corresponds to the location at which the cardiac electrical signal was sensed. Each of the respective position coordinates for the sensed cardiac signals may include three-dimensional Cartesian coordinates, polar coordinates, and/or the like. In some cases, other coordinate systems can be used. In some embodiments, an arbitrary origin is used and the respective position coordinates refer to positions in space relative to the arbitrary origin. Since, in some embodiments, the cardiac signals may be sensed on the cardiac surfaces, the respective position coordinates may be on the endocardial surface, epicardial surface, in the mid-myocardium of the patient's heart, and/or in the vicinity of one of one of these.
At each of the locations to which the catheter 110 is moved, the catheter's multiple electrodes acquire signals resulting from the electrical activity in the heart. Consequently, reconstructing and presenting to a user (such as a doctor and/or technician) physiological data pertaining to the heart's electrical activity may be based on information acquired at multiple locations, thereby providing a more accurate and faithful reconstruction of physiological behavior of the endocardium surface. The acquisition of signals at multiple catheter locations in the heart chamber enables the catheter to effectively act as a “mega-catheter” whose effective number of electrodes and electrode span is proportional to the product of the number of locations in which signal acquisition is performed and the number of electrodes the catheter has.
To enhance the quality of the reconstructed physiological information at the endocardium surface, in some embodiments the catheter 110 is moved to more than three locations (for example, more than 5, 10, or even 50 locations) within the heart chamber. Further, the spatial range over which the catheter is moved may be larger than one third (⅓) of the diameter of the heart cavity (for example, larger than 35%, 40%, 50% or even 60% of the diameter of the heart cavity). Additionally, in some embodiments the reconstructed physiological information is computed based on signals measured over several heart beats, either at a single catheter location within the heart chamber or over several locations. In circumstances where the reconstructed physiological information is based on multiple measurements over several heart beats, the measurements may be synchronized with one another so that the measurements are performed at approximately the same phase of the heart cycle. The signal measurements over multiple beats may be synchronized based on features detected from physiological data such as surface electrocardiograms (ECGs) and/or intracardiac electrograms (EGMs).
The electrophysiology system 100 further includes a processing unit 120 which performs several of the operations pertaining to the mapping procedure, including the reconstruction procedure to determine the physiological information at the endocardium surface (e.g., as described above) and/or within a heart chamber. The processing unit 120 also may perform a catheter registration procedure. The processing unit 120 also may generate a 3D grid used to aggregate the information captured by the catheter 110 and to facilitate display of portions of that information.
The location of the catheter 110 inserted into the heart chamber can be determined using a conventional sensing and tracking system 180 that provides the 3D spatial coordinates of the catheter and/or its multiple electrodes with respect to the catheter's coordinate system as established by the sensing and tracking system. These 3D spatial locations may be used in building the 3D grid. Embodiments of the system 100 may use a hybrid location technology that combines impedance location with magnetic location technology. This combination may enable the system 100 to accurately track catheters that are connected to the system 100. Magnetic location technology uses magnetic fields generated by a localization generator positioned under the patient table to track catheters with magnetic sensors. Impedance location technology may be used to track catheters that may not be equipped with a magnetic location sensor, which may be used with surface ECG patches.
In some embodiments, to perform a mapping procedure and reconstruct physiological information on the endocardium surface, the processing unit 120 may align the coordinate system of the catheter 110 with the endocardium surface's coordinate system. The processing unit 120 (or some other processing component of the system 100) may determine a coordinate system transformation function that transforms the 3D spatial coordinates of the catheter's locations into coordinates expressed in terms of the endocardium surface's coordinate system, and/or vice-versa. In some cases, such a transformation may not be necessary, as some embodiments of the 3D grid may be used to capture contact and non-contact EGMs, and select mapping values based on statistical distributions associated with nodes of the 3D grid. The processing unit 120 also may perform post-processing operations on the physiological information to extract and display useful features of the information to the operator of the system 100 and/or other persons (e.g., a physician).
According to embodiments, the signals acquired by the multiple electrodes of catheter 110 are passed to the processing unit 120 via an electrical module 140, which may include, for example, a signal conditioning component. The electrical module 140 receives the signals communicated from the catheter 110 and performs signal enhancement operations on the signals before they are forwarded to the processing unit 120. The electrical module 140 may include signal conditioning hardware, software, and/or firmware that may be used to amplify, filter and/or sample intracardiac potential measured by one or more electrodes. The intracardiac signals typically have a maximum amplitude of 60 mV, with a mean of a few millivolts.
In some embodiments, the signals are filtered by a bandpass filter with a frequency range (e.g., 0.5-500 Hz) and sampled with analog to digital converters (e.g., with 15-bit resolution at 1 kHz). To avoid interference with electrical equipment in the room, the signals may be filtered to remove the frequency corresponding to the power supply (e.g., 60 Hz). Other types of signal processing operations such as spectral equalization, automatic gain control, etc. may also take place. In some implementations, the intracardiac signals may be unipolar signals measured relative to a reference (which may be a virtual reference). In such implementations, the reference can be, for example, a coronary sinus catheter or Wilson's Central Terminal (WCT), from which the signal processing operations may compute differences to generate multipolar signals (e.g., bipolar signals, tripolar signals, etc.). In some other implementations, the signals may be processed (e.g., filtered, sampled, etc.) before and/or after generating the multipolar signals. The resultant processed signals are forwarded by the electrical module 140 to the processing unit 120 for further processing.
As further shown in
In some embodiments, the processing unit 120 may be configured to automatically improve the accuracy of its algorithms by using one or more artificial intelligence techniques (e.g., machine learning models, deep learning models), classifiers, and/or the like. In some embodiments, for example, the processing unit may use one or more supervised and/or unsupervised techniques such as, for example, support vector machines (SVMs), k-nearest neighbor techniques, neural networks, convolutional neural networks, recurrent neural networks, and/or the like. In some embodiments, classifiers may be trained and/or adapted using feedback information from a user, other metrics, and/or the like.
The illustrative electrophysiology system 100 shown in
As depicted in
The accepted electrical signals are received by an activation waveform generator 214 that is configured to extract at least one annotation feature from each of the electrical signals, in cases in which the electrical signal includes an annotation feature to extract. In some embodiments, the at least one annotation feature includes at least one value corresponding to at least one activation metric. The at least one feature may include at least one event, where the at least one event includes the at least one value corresponding to the at least one metric and/or at least one corresponding time (a corresponding time does not necessarily exist for each activation feature). In some embodiments, the at least one metric may include, for example, an activation time, minimum voltage value, maximum voltage value, maximum negative time-derivative of voltage, an instantaneous potential, a voltage amplitude, a dominant frequency, a peak-to-peak voltage, an activation duration, and/or the like. In some embodiments, the activation waveform generator 214 may be configured to detect activations and to generate an activation waveform. In some cases, the waveform generator 214 can use any one of activation waveform embodiments, for example, including those described in U.S. Patent Publication 2018/0296113, entitled “ANNOTATION WAVEFORM,” the disclosure of which is hereby expressly incorporated herein by reference.
As illustrated in
The illustrative processing unit 200 shown in
Additionally, the processing unit 200 may (alone and/or in combination with other components of the system 100 depicted in
According to embodiments, various components of the electrophysiology system 100, illustrated in
In some embodiments, a computing device includes a bus that, directly and/or indirectly, couples the following devices: a processor, a memory, an input/output (I/O) port, an I/O component, and a power supply. Any number of additional components, different components, and/or combinations of components may also be included in the computing device. The bus represents what may be one or more busses (such as, for example, an address bus, data bus, or combination thereof). Similarly, in some embodiments, the computing device may include a number of processors, a number of memory components, a number of I/O ports, a number of I/O components, and/or a number of power supplies. Additionally, any number of these components, or combinations thereof, may be distributed and/or duplicated across a number of computing devices.
In some embodiments, memory (e.g., the storage device 160 depicted in
Computer-executable instructions may include, for example, computer code, machine-useable instructions, and the like such as, for example, program components capable of being executed by one or more processors associated with a computing device. Examples of such program components include the acceptor 212, the waveform generator 214, the metric analyzer 216, and the representation engine 220. Program components may be programmed using any number of different programming environments, including various languages, development kits, frameworks, and/or the like. Some or all of the functionality contemplated herein may also, or alternatively, be implemented in hardware and/or firmware.
The data repository 206 may be implemented using any one of the configurations described below. A data repository may include random access memories, flat files, XML files, and/or one or more database management systems (DBMS) executing on one or more database servers or a data center. A database management system may be a relational (RDBMS), hierarchical (HDBMS), multidimensional (MDBMS), object oriented (ODBMS or OODBMS) or object relational (ORDBMS) database management system, and the like. The data repository may be, for example, a single relational database. In some cases, the data repository may include a plurality of databases that can exchange and aggregate data by data integration process or software application. In an exemplary embodiment, at least part of the data repository 206 may be hosted in a cloud data center. In some cases, a data repository may be hosted on a single computer, a server, a storage device, a cloud server, or the like. In some other cases, a data repository may be hosted on a series of networked computers, servers, or devices. In some cases, a data repository may be hosted on tiers of data storage devices including local, regional, and central.
The system calculates an importance metric for each of the plurality of cardiac electrical signals (320A). In embodiments, the importance metric represents a contribution of a respective cardiac electrical signal to an overall duty cycle of the cardiac beat as a function of the cycle length. In some cases, the contribution to an overall duty cycle includes a contribution to the main activation period of a cardiac beat, also referred to as an activation metric. In some cases, the main activation period of a cardiac beat is determined based on one or more cardiac electrical signals that are main contributors to the activation of the cardiac beat (e.g., signals contributing to more than 60% of the beat's duty cycle). In embodiments, the contribution to an overall duty cycle includes a contribution outside the main activation period of the cardiac beat, also referred to as a novelty metric. The novelty metric can represent the uniqueness of signals collected by one or more electrodes.
The electrophysiology system may select cardiac electrical signals based on the importance metrics (330A). In some embodiments, the system can select cardiac electrical signals based on one or more criteria, where at least one of the criteria uses the importance metric as a parameter. In one embodiment, at least one of the criteria is a respective importance metric equal to or greater than a predetermined threshold. In one embodiment, at least one of the criteria aggregates the importance metric with at least one other characteristics of the cardiac electric signal. The system can generate a representation of selected cardiac electrical signals (340A). In one embodiment, the representation is a graphical representation.
In one embodiment, the electrophysiology system identifies a deflection for each of the plurality of electrical signals, where the deflection is a deviation from a signal baseline. Each of the plurality of activation waveforms is generated based on one or more identified deflections of an electrical signal. The activation waveform includes values corresponding to probabilities that the identified deflections represent activations of cardiac tissue. The system may also calculate an activation metric for each of the cardiac electrical signals (320B). In some cases, the activation metric is calculated based on the corresponding activation waveform. In one example, the activation metric can be calculated based on the activation waveform values. In some embodiments, the activation metric represents a contribution of a cardiac electrical signal within an activation zone that is explained in more detail below.
In one implementation, the system further normalizes the arithmetic average values, where the normalized values are referred to as normalized average values, of the activation waveforms of the plurality cardiac electrical signals. In one example, the largest arithmetic average value is set to 1 by a multiplier and the other arithmetic average values are normalized using the same multiplier.
Referring back to
AZ(t)=max(∀ AWS(t)|MetricA>0.6) (1),
where AZ(t) is the activation zone as a function of time, AWS(t) is the activation waveforms of the selected electrical signals, and MetricA is the activation metric of an electrical signal.
In some embodiments, the system calculates a novelty metric for each of the plurality of cardiac electrical signals (335B). In some embodiments, the novelty metric is calculated based on each activation waveform outside of the activation zone. In some embodiments, the activation zone is used as inversed weight factors (i.e., higher activation values corresponding to lower weight factors) to determine the novelty metric. In one example, a novelty waveform is generated using equation (2) below:
NW
CS(t)=(1−AZ(t))×AWCS(t) (2),
where NWCS(t) is the novelty waveform of a cardiac electrical signal as a function of time, AZ(t) is the activation zone as a function of time, and AWLS(t) is the activation waveform of the cardiac electrical signal. In one embodiment, a novelty metric is determined based on the novelty waveform. In one case, the novelty metric is set to the maximum value of the novelty waveform.
In some embodiments, the electrophysiology system calculates an importance metric for each of the plurality of cardiac electrical signals based on the corresponding activation metric and novelty metric (340B). In one embodiment, the importance metric is determined using equation (3) below:
MetricI=f(MetricA, MetricN) (3),
where MetricI is the importance metric, MetricA is the activation metric, MetricN is the novelty metric, and f( ) is a function. In some cases, the function f( ) is a linear function. In some cases, the function f( ) is a non-linear function. In some cases, the function f( ) is an error function. In one example, the importance metric is determined using equation (4) below:
where MetricI is the importance metric, MetricA is the activation metric, MetricN is the novelty metric, and erf( ) is an error function.
In some embodiments, the system selects the cardiac electrical signals based on the calculated importance metric (350B). In one example, a cardiac electrical signal is selected if its calculated importance metric is greater than a predetermined threshold.
Various modifications and additions can be made to the exemplary embodiments discussed without departing from the scope of the present invention. For example, while the embodiments described above refer to particular features, the scope of this invention also includes embodiments having different combinations of features and embodiments that do not include all of the described features. Accordingly, the scope of the present invention is intended to embrace all such alternatives, modifications, and variations as fall within the scope of the claims, together with all equivalents thereof.
This application claims priority to Provisional Application No. 63/085,671, filed Sep. 30, 2020, which is herein incorporated by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63085671 | Sep 2020 | US |
