Late activation of cardiac signals

Abstract

In described examples, a method includes identifying, by a processor, a negative deflection of at least one unipolar signal representing cardiac electrical activity on a surface of interest during a respective interval of the at least one signal. The method also includes determining, by the processor, an activation time of a last identified negative deflection in the respective interval. The method further includes detecting, by the processor, an instance of late activation based on comparing the activation time to a time threshold.

Description

Technical Field

[0001] This technology involves the detection and analysis of late activation of cardiac signals. Background Technology

[0002] Normal electrical conduction in the heart allows electrical currents to propagate and stimulate the myocardium. Time-ordered stimulation of the myocardium enables efficient contraction of all four chambers of the heart, allowing selective blood flow through the lungs and systemic circulation. Ordered stimulation can become asynchronous and thus adversely affect the mechanical function of the heart. Various methods exist for collecting cardiac rhythm information, including invasive electrophysiological studies (e.g., invasive catheter mapping) and non-invasive methods (e.g., electrocardiography and non-invasive electroanatomical mapping). Activated cardiac mapping can be used to highlight late-activated areas of the heart, which can lead to irregular rhythms and asynchronous contractions. Summary of the Invention

[0003] The technology disclosed herein relates to the detection and analysis of late activation of cardiac signals.

[0004] In the described example, one method includes having a processor identify a negative deflection of at least one unipolar signal representing cardiac electrical activity on a surface of interest during a corresponding interval of the at least one signal. The method also includes having the processor determine the activation time of the last identified negative deflection within the corresponding interval. Furthermore, the method includes having the processor detect instances of late activation based on a comparison of that activation time with a time threshold.

[0005] In another example, a system includes surface electrodes adapted for placement on the outer surface of a patient's body to non-invasively measure electrophysiological signals. A computing device is configured to store data and execute machine-readable instructions. The data may include electrophysiological data representing cardiac electrophysiological activity at multiple points across at least one spatial region on the surface of the patient's heart. This electrophysiological data can be reconstructed based on the non-invasively measured electrophysiological signals for the at least one spatial region of the heart including the multiple points. These instructions are executable to perform the methods described above.

[0006] In yet another example, one or more non-transitory computer-readable media have instructions that, when executed by a processor, are programmed to perform a method. The method includes:

[0007] Identify negative deflections of electrophysiological signals representing cardiac electrical activity on the surface of interest during the corresponding time interval;

[0008] Reduce the far-field component in this electrophysiological signal;

[0009] Determine the activation time of the last identified negative deflection in each of the corresponding electrophysiological signals; and

[0010] This involves detecting instances of late activation of each electrophysiological signal within the corresponding electrophysiological signal by comparing the activation time with a time threshold.

[0011] Details of one or more aspects of this disclosure are illustrated in the following drawings and description. Other features, objects, and advantages of the technology described in this disclosure will become apparent from the description, drawings, and claims. Attached Figure Description

[0012] Figure 1 This is a flowchart illustrating an exemplary method for detecting late activation of cardiac signals.

[0013] Figure 2 This is a graph illustrating an exemplary cardiac waveform under late activation conditions.

[0014] Figure 3 This is a graph illustrating another exemplary cardiac waveform under late activation conditions.

[0015] Figure 4 This is a graphical map showing an example of an activation map.

[0016] Figure 5 This is a graphical mapping that shows an example of ST elevation mapping.

[0017] Figure 6 This is a graphical mapping that shows an example of voltage mapping.

[0018] Figure 7 This is a graphical map showing an example of late-activation partition mapping of one or more areas of the heart.

[0019] Figure 8 An example of a monitoring system is depicted. Detailed Implementation

[0020] This disclosure relates to detecting late activation in cardiac signals, such as that which can be further used to identify one or more regions (e.g., partitions) in the heart exhibiting late activation.

[0021] Figure 1 An example of a method 100 for detecting late activation in cardiac signals is depicted. Although for the purpose of illustrative simplicity, Figure 1 The exemplary method is shown and described as being performed sequentially, but it should be understood and appreciated that the exemplary method is not limited to the order shown, as some actions may occur in different orders, multiple times, and / or simultaneously with the actions shown and described herein in other examples. Additionally, optionally, some actions may be omitted from method 100. At least some portions of method 100 may be implemented by hardware, software, or a combination of hardware and / or software, such as those described herein (e.g., see [link to relevant documentation]). Figure 8(system).

[0022] exist Figure 1 In the example, at 102, method 100 includes acquiring an electrophysiological (EP) signal of the heart. In the example, the electrophysiological signal acquired at 102 is a unipolar signal (such as a unipolar electrocardiogram) across a surface of interest. In the example, the EP signal corresponds to a signal reconstructed onto the surface of interest based on electrical signals measured non-invasively by a sensor arrangement, such as one distributed across the patient's chest cavity. For example, the processor may solve an inverse problem to compute estimates of the cardiac signal reconstructed at points (e.g., nodes) across the distribution of the surface of interest. The processor may solve the inverse problem based on geometrical data and electrical data representing the non-invasive electrophysiological signal. The electrical data may be acquired from a non-transitory memory storing the signal. Alternatively or additionally, the electrophysiological signal may be acquired in real-time (or near real-time) from a monitoring system having inputs coupled to sensors suitable for measuring EP signals from a body surface. For example, the surface of interest is the cardiac envelope, such as the epicardial surface of a patient's heart, an endocardial surface, a combination of epicardial and endocardial surfaces, or another three-dimensional geometric surface (e.g., a sphere or a model of the heart).

[0023] At position 104, the method includes processing the signal from the surface of interest to reduce the influence of far-field signal components on the corresponding signal, which effectively enhances the local signal components of the corresponding signal on the surface of interest. In one example, the processing implemented to reduce far-field components is globally applied to all corresponding signals during one or more time intervals. In another example, the processing implemented to reduce far-field components is selectively applied, thereby reducing the far-field components of the reconstructed signal from one or more spatial regions of the surface of interest.

[0024] As an example, a surface of interest (e.g., the entire heart or one or more chambers of the heart) can be divided into multiple distinct zones. Each zone can be assigned a value representing the susceptibility of that zone to far-field components. Thus, a susceptibility value can be determined for each spatial zone across the surface of interest. The susceptibility value can be a binary value (e.g., 0 or 1; on / off) to indicate whether processing is employed to reduce the far-field component of the signal in each spatial zone within the corresponding spatial zone. Alternatively or additionally, the value can be determined as a discrete value or a value along a continuous scale to control (or change) the amount by which the far-field signal component is reduced for each spatial zone within the corresponding spatial zone. These values ​​can be automatically assigned to the corresponding spatial zones (e.g., via instructions executed by a processor) (such as based on prior information or medical imaging data), or manually assigned to the corresponding spatial zones in response to user input specifying values ​​for those zones. Zones identified as containing dense scar tissue tend to experience large far-field components. It is also known that the right ventricular outflow tract (RVOT) and the apex of the left ventricle experience large far-field components. Therefore, each region in the area where processing is implemented to reduce far-field components can be assigned a value indicating its susceptibility to far-field components. The assigned susceptibility values ​​can be stored in memory and programmatically linked to each of the corresponding spatial regions. Thus, at 104, the stored susceptibility values ​​can be used to selectively reduce the far-field components of the signal (e.g., the unipolar reconstructed EP signal) across each corresponding region of the surface of interest.

[0025] As a further example, far-field signal component reduction (at 104) is achieved by applying a surface Laplacian operator to the acquired signal (e.g., to a unipolar reconstructed EP signal on the surface of interest). The surface Laplacian operator can be as follows:

[0026]

[0027] In Oostendorp et al.'s "The Surface Laplacian of the Potential: Theory and Application",

[0028] The application of the surface Laplacian operator to electrical data, including the acquired signal (at 102) for the surface of interest, is described in IEEE Transactions on Biomedical Engineering, Vol. 43, No. 4, (April 1996), pp. 394-405.

[0029] At 106, the method includes detecting a negative deflection of a corresponding signal (e.g., a signal component with a negative slope) on the surface of interest during one or more time intervals of the corresponding signal. Each interval of the corresponding signal may include one or more negative deflections. The negative deflection of the signal can be determined regardless of whether the far-field component of the corresponding signal has been reduced. That is, the reduction of the far-field component (at 104) may be applied only to some regions, all regions, or omitted entirely from method 100.

[0030] In another example, the corresponding negative deflection (detected at 106) is compared to an amplitude threshold to confirm whether the deflection represents actual activation. In this way, negative deflections that do not at least meet the amplitude threshold are discarded (e.g., discarded to remove deflections as noise). The amplitude threshold can have a fixed value or a value that varies spatially across the surface of interest. For example, the value of the amplitude threshold across the surface of interest depends on the region's susceptibility to far-field components. Therefore, the amplitude threshold can vary spatially across the surface of interest, such as by changing the amplitude threshold according to the assigned susceptibility value assigned to each region in the corresponding region. For example, the amplitude threshold for a given spatial region can be inversely proportional to the susceptibility value assigned to that given region. In this way, spatial regions experiencing larger far-field components have lower amplitude thresholds, while spatial regions experiencing smaller far-field components can have higher amplitude thresholds.

[0031] At point 108, a late activation time is determined for each interval of the corresponding signal across the surface, as the latest negative deflection for each corresponding interval. Therefore, with the amplitude threshold applied (as described above), negative deflections corresponding to noise have been removed to provide a remaining set of one or more negative deflections, from which the latest negative deflection is selected as the late activation containing each signal in the corresponding signal. The late activation time is determined based on the latest negative deflection of each signal in each interval (the latest signal component with a negative slope), regardless of whether the negative slope contains the maximum negative derivative. This method has better accuracy in identifying late activation partitions compared to existing methods that use the maximum negative derivative. In the example, the activation time can be determined as the midpoint of the latest negative deflection, which can be the midpoint of the amplitude or time of the corresponding signal component. In another example, the activation time can be determined as the point along the latest negative deflection with the maximum negative slope (-dV / dt).

[0032] Figure 2 and Figure 3 Exemplary cardiac waveforms 200 and 300 are depicted respectively, and corresponding instances of late activations 202 and 302 have been identified for these cardiac waveforms.

[0033] In some examples, a time threshold may also be applied to each of the late activations identified at 108 locations to filter out (e.g., remove or flag) late activation times determined for corresponding signals across the surface of interest that do not meet the time threshold. For example, if a corresponding late activation time is less than the time threshold, then that late activation time does not constitute a late activation. Those late activation times that meet the time threshold can be classified (e.g., flagged by data stored in memory) as confirmed late activations. The time threshold can be fixed or variable (e.g., in response to user input setting the time threshold to a desired time value). Thus, a minimum time threshold can be set to help ensure that late activations represent late activations for diseased tissue.

[0034] As an example, to define a time threshold, the activation time of known healthy (e.g., without heart disease) control subjects is calculated (using the methods described herein). For example, the 95th percentile of the Gaussian distribution of the most recent activation time (using the earliest activation on the cardiac surface as a reference time, time = 0) in control subjects without heart disease is approximately 80 milliseconds. Therefore, this value (e.g., 80 ms) can be used as a time threshold to identify pathological late activation and late activation partitions as described herein. In other examples, time thresholds may be determined according to other methods. Exemplary time thresholds are respectively in... Figure 2 and Figure 3 The values ​​at 204 and 304 are shown. Since instances of late activation at 202 and 302 occur after thresholds 204 and 304, late activation can represent effective late activation.

[0035] A maximum time (e.g., a time upper limit or threshold) can also be set to remove (filter out) certain negative deflections that may indicate erroneous late activation (e.g., during cardiac tissue repolarization). As described herein, each of the corresponding signals across the surface of interest is an interval of a cardiac waveform with Q, R, S, and T wave components. In the example, the method may therefore include detecting only negative deflections of the corresponding signal that occur before the time upper limit of the signal interval (negative deflection determined for the corresponding interval at 106, or the most recent negative deflection determined at at least 108). The time upper limit may represent the start of a signal feature. For example, a time upper limit for a given interval of the corresponding signal is set to indicate the start of a T wave in the given interval. Thus, the upper limit for each corresponding signal may vary depending on the interval. In this way, negative deflections that may occur during cardiac repolarization are excluded for the purpose of identifying late activation. Exemplary upper limits corresponding to the start of the T wave are respectively at... Figure 2 and Figure 3 Each figure in the diagram shows the late activations at positions 206 and 306. The identified late activations were confirmed because the instances of late activations 202 and 302 occurred before their respective upper limits 206 and 306 determined for each corresponding waveform 200 and 300.

[0036] At 110, the method includes identifying one or more late-activation partitions (e.g., spatial regions across the surface of interest), which correspond to spatial regions having one or more points in a corresponding region exhibiting one or more late activations. A late-activation partition may correspond to a spatial region across the surface of interest that exhibits late activation (determined at 108) over a certain time interval. In the example, a minimum number of late activations may be defined to classify a corresponding region as a late-activation partition. In the example, a late-activation partition may be constructed from a set of one or more points distributed across the surface of interest that are determined to exhibit late activation (at 108). A consecutive set of such points may be defined as belonging to the same late-activation partition.

[0037] At 112, method 100 includes calculating (or measuring) one or more parameters for each identified late-activation partition. As mentioned, each late-activation partition may include one or more points distributed across a surface of interest (e.g., the cardiac envelope). For example, parameters calculated at 112 for each late-activation partition may include the mean activation time for the corresponding partition, the mean voltage for the corresponding partition, voltage rise or fall for the corresponding partition (e.g., signal rise or fall of the ST portion of a quantified unipolar signal), and / or the spatial area (e.g., size) of late activation in the corresponding partition. The values ​​of one or more such parameters may then be used for potential risk stratification purposes. Additionally or alternatively, the values ​​of one or more such parameters may be used to quantify the disease state of a region of the heart based on at least one instance of late activation detected in that region. In some examples, the method may also include controlling the delivery of treatment (e.g., ablation, resynchronization, pacing, etc.) to regions (e.g., late-activation partitions) exhibiting late activation on the surface of interest. This control may be implemented by a processor that provides machine-readable instructions to a treatment delivery system or specifies parameters to a user for applying treatment at one or more designated locations on the heart.

[0038] As a further example, the method may also include generating a graphical map based on late-activation partitions identified across the surface of interest (at 110). This graphical map can be provided to a display to visualize one or more electrocardiogram recording maps and other electrical information derived from the reconstructed electrical signals, including any parameters measured or calculated for each late-activation partition within the late-activation partitions. For example, each late-activation partition may be displayed on a graphical map of the heart. In such a map, each late-activation partition may be rendered according to a color scale based on the late-activation time (or one or more other parameters calculated for the corresponding partition at 112). Alternatively, the late-activation time (and / or other parameters) at each point across the late-activation partitions may be mapped independently across the surface of the heart to provide a higher level of granularity.

[0039] Figure 4 , Figure 5 , Figure 6 and Figure 7 An example of a graph mapping that can be generated is depicted. Figure 4 An example of activation mapping 400 is depicted, with the late activation region shown at 402.

[0040] Figure 5 An example of an ST elevation map 500 is depicted. For example, the ST elevation map 500 shows the ST segment elevation across the cardiac surface. The ST elevation at corresponding points (e.g., nodes) on the cardiac surface within one or more corresponding cardiac cycles can be calculated, and this ST elevation can be visualized on the map, such as... Figure 5 As depicted in the text.

[0041] Figure 6 An example of voltage mapping 600 is depicted. For example, the voltage potential can be determined based on reconstructed and / or invasively measured electrocardiogram signals and rendered on the surface of the heart within one or more cardiac cycles.

[0042] Figure 7 An example of a late-activation partition map 700 depicting one or more regions of the heart. Figure 7 In the example, a threshold can be applied to the calculated activation time to display late activation regions 702 (or more regions) whose activation time exceeds the threshold. As described herein, the threshold can be user-programmable, such as in response to user input setting the threshold. The threshold can be changed to display corresponding changes in the mapping 700, such as visualizing one or more late activation regions across the cardiac surface based on the selected threshold. For example, one or more thresholds can thus be used to identify corresponding distinct late activation regions.

[0043] Figure 8 An example of a system 800 that can be used to identify late activations in a patient's heart is depicted. In some examples, system 800 may generate output data 824 that can be used to render a graphical map (e.g., a map on a cardiac model) 844 and / or display processed electrical signals on a display 842. System 800 may also provide information in other formats to provide the user with guidance representing detected late activations and / or derived from detected late activations.

[0044] As disclosed herein, System 800 has applications throughout various stages of patient care. As an example, the system can be used as part of a patient screening process, such as a patient risk stratification process (e.g., part of a diagnostic and / or treatment planning procedure). Additionally, System 800 can be used as part of a treatment procedure, such as determining parameters for delivering treatment to a patient (e.g., the delivery location, amount, and type of treatment for one or more treatment systems 808). System 800 can also be used to perform post-treatment assessments of patients.

[0045] exist Figure 8 In one example, system 800 includes a sensor array 814 having one or more surface electrodes for measuring a patient's electrical activity. As an example, sensor array 814 may correspond to an arrangement of surface sensors distributed over a portion of the patient's torso (e.g., the chest cavity) for measuring electrical activity associated with the patient's heart. Examples of non-invasive surface devices that can be used as sensor array 814 are shown and described in U.S. Patent 9,655,561 and International Publication WO 2010 / 054352. Other arrangements and numbers of sensing electrodes may be used as sensor array 814, configured according to application requirements. For example, the array may be a reduced set of electrodes that does not cover the entire torso of the patient and is designed to measure electrical activity for a specific purpose (e.g., an electrode array specifically designed for analyzing atrial fibrillation and / or ventricular fibrillation) and / or for monitoring predetermined spatial areas of the heart. In some examples, system 800 includes one or more sensors, which may also be located on device 806 adapted for insertion into patient body 804 and adapted for invasive measurement of electrophysiological signals.

[0046] In each of these exemplary methods for acquiring patient electrical information (including invasive sensing, non-invasive sensing, or a combination of invasive and non-invasive sensing), sensor array 814 provides the sensed electrophysiological information to a corresponding measurement system 816. Measurement system 816 may include a corresponding control 818 configured to provide electrophysiological measurement data 820 describing the electrical activity (e.g., ECG signal) detected by the sensors in sensor array 814. For example, signal processing circuitry of measurement system 816 may convert the measured analog signal into a corresponding digital electrophysiological signal represented in electrophysiological measurement data 820.

[0047] The controller 818 can also be configured to control the data acquisition process for measuring electrical activity and providing measurement data 820 (e.g., at a predefined sampling rate). In some examples, the controller 818 may control the acquisition of measurement data 820 separately from the operation of the treatment system 808 (if implemented), such as in response to user input. In other examples, measurement data 820 may be acquired simultaneously and synchronously with the delivery of treatment via the treatment system to detect electrical activity of the heart 802 in response to the application of a given treatment (e.g., according to treatment parameters). For example, the temporal relationship between the corresponding measurement data 820 and the treatment system 808 may be indexed using appropriate timestamps to facilitate evaluation and analysis of the treatment system.

[0048] For example, a catheter with one or more treatment delivery devices 806 attached thereto can be inserted into the body 804 to contact the patient's heart 802 in an endocardial or epicardial manner. Various types and configurations of the treatment delivery devices 806 can be utilized, varying depending on the type and procedure of treatment. For example, the treatment device 806 can be configured to deliver electrotherapy, chemotherapy, sound therapy, heat therapy, or any combination thereof.

[0049] As a further example, the treatment delivery device 806 may include one or more electrodes located at the tip of an ablation catheter, the electrodes being configured to generate heat for ablation of tissue in response to an electrical signal (e.g., radiofrequency energy) supplied by the treatment system 808. In other examples, the treatment delivery device 806 may be configured to deliver cooling to perform ablation (e.g., cryoablation), deliver chemicals (e.g., drugs), ultrasound ablation, high-frequency radiofrequency ablation, pulsed field ablation, non-invasive ablation, or combinations thereof. In yet another example, the treatment delivery device 806 may include one or more electrodes located at the tip of a pacing catheter to deliver electrical stimulation, such as for pacing the heart, in response to an electrical signal (e.g., a pacing current pulse) supplied by the treatment system 808. Other types of treatment may also be delivered via the treatment system 808 and the invasive treatment delivery device 806 positioned within the body.

[0050] As a further example, the treatment system 808 may be located outside the patient's body 804 and configured to control treatment being delivered by the device 806. For example, the treatment system 808 includes a control system (e.g., hardware and / or software) 810 that can transmit (e.g., supply) electrical signals via a conductive link electrically connected between the delivery device (e.g., one or more electrodes) 806 and the treatment system 808. The control system 810 can control signal parameters (e.g., current, voltage, repetition rate, trigger delay, sense trigger amplitude) supplied to the device 806 for delivering treatment (e.g., ablation, stimulation, etc.) to one or more locations on the heart 802 via the electrodes of the treatment device 806. The control system 810 can set treatment parameters and apply stimulation based on automatic, manual (e.g., user input), or a combination of automatic and manual (e.g., semi-automatic control). One or more sensors (not shown) of the device 806 can also transmit sensor information back to the treatment system 808. The position of device 806 relative to the heart 802 can be determined and tracked intraoperatively via imaging modalities (e.g., fluoroscopy, X-ray), mapping system 812, direct visualization, positioning system, etc. Therefore, the position of device 806 and treatment parameters can be combined to determine the corresponding treatment delivery parameters.

[0051] The mapping system 812 is programmed to combine electrophysiological measurement data 820 corresponding to the sensed surface electrical activity of the heart 802 to generate corresponding output data 824. As described herein, the output data 824 may represent or characterize electrophysiological signals on a surface of interest (e.g., on a body surface, on the heart 802, or on the cardiac envelope within the heart). The mapping system 812 may further generate output data 824 to represent information derived from the measured electrophysiological signals, including representations of late activation of cardiac signals on the surface of interest, as disclosed herein.

[0052] For example, system 800 includes electrogram reconstruction 832, which is programmed to compute an inverse solution based on electrophysiological measurement data 820 and geometric data 822 and provide a corresponding reconstructed electrogram across the surface of interest. For example, the geometric data 822 may correspond to a mathematical model, such as a general model or a model constructed based on image data obtained for the patient (e.g., via imaging modalities such as CT, MRI, biplane X-rays, etc.), and provide spatial coordinates for the patient's heart 802 and electrodes on a sensor array. Therefore, the reconstructed electrogram may correspond to electrocardiographic recording activity across the surface of interest (such as the cardiac envelope) and may be static (three-dimensional at a given moment) and / or dynamic (e.g., a time-varying four-dimensional mapping). Examples of inverse algorithms that can be implemented via EGM reconstruction 832 include those disclosed in U.S. Patents 7,983,743 and 6,772,004. Therefore, EGM reconstruction 832 is programmed to reconstruct the body surface electrical activity measured via sensor array 814 onto a large number of locations (e.g., more than 1,000 locations, such as about 2,000 locations or more) on the surface of the heart of interest. In other examples, mapping system 812 may calculate electrical activity on subregions of the heart based on invasive measurements of electrical activity, such as via basket catheters or other forms of measurement probes (e.g., on or attached to device 806)).

[0053] As a further example, the mapping system 812 includes a late activation detector 826 programmed to detect instances of late cardiac activation across the surface of interest. In this example, the late activation detector 826 determines instances of late activation across the surface of interest using reconstructed electrophysiological signals (e.g., unipolar EP signals) calculated by EGM reconstruction 832. In other examples, the late activation detector 826 determines instances of late activation based on other electrophysiological signal measurements.

[0054] For example, the late activation detector 826 is programmed to perform Figure 1 Method 100 identifies instances of late activation across the surface of interest. Therefore, in Figure 8In the example, the late activation detector 826 includes a far-field reduction function 828 and a late activation thresholding function 830. The far-field reduction function 828 is programmed to reduce the influence of far-field signal components on the electrophysiological signal, as described herein. The late activation detector 826 is programmed to detect negative deflections in one or more intervals of the corresponding electrophysiological signal. The thresholding function 830 can be programmed to apply one or more time thresholds and / or amplitude thresholds, as described herein. The late activation detector 826 can determine instances of late activation across the surface of interest based on the applied far-field reduction and / or thresholding. As described herein, each of the far-field reduction function 828 and / or thresholding function 830 can be applied globally to all signals or spatially variable in a selected signal across the surface of interest (e.g., based on susceptibility to far-field components).

[0055] The mapping system 812 includes an output generator 836 to provide output data 824, thereby visualizing a graphical mapping or other information of one or more intervals of an electrophysiological signal on a display 842. About Figure 4 , Figure 5 , Figure 6 and Figure 7 Examples of output displays that can be provided by output generator 836 are disclosed. Thus, output generator 836 generates output data 824 to display a graphical representation based on identified instances of late activation, such as including a late activation map across a surface of interest and / or parameters derived for one or more late activation partitions. In some examples, output generator 836 may be further configured to provide risk stratification for patients (e.g., heart failure, sudden cardiac death) based on information generated by late activation detector 826. For example, output generator 836 may classify the type of treatment or protocol (e.g., resynchronization pacing, ablation, or implantable cardioverter-defibrillator) in response to the detection of instances of late activation and / or parameters derived from late activation data.

[0056] Output generator 836 can also generate output data 824 to display other types of information, such as time-domain plots, frequency-domain plots, etc. In some examples, output generator 836 maps computed late activation data onto the geometric surface of a cardiac model. As disclosed herein, these mappings can be computed based on electrical data acquired non-invasively via one or more electrodes in a sensor array 814 distributed on the surface of the patient's body 804.

[0057] Because the measurement system 816 can simultaneously measure the electrical activity of a predetermined area of ​​the trunk or the entire trunk (e.g., a sensor array 814 comprising multiple electrodes covering the entire thoracic cavity of the patient's body 804), the resulting output data (e.g., ECG signals and / or electrocardiogram mappings) 824 can also represent simultaneous ECG data in a temporally and spatially consistent manner. Therefore, instances of late activation can be identified over one or more time intervals across the entire cardiac surface. The time interval for calculating the output data / mapping can be selected based on user input, such as via GUI 840. Additionally or alternatively, the selected interval can be synchronized with the application of treatment by the treatment system 808. Parameters associated with the graphical representation (corresponding to the output visualization of the calculated mapping, such as including the selected time interval, the type of information to be presented in the visualization, etc.) can be selected in response to user input via GUI 840.

[0058] Additionally, if treatment system 808 is included in system 800, the treatment system may utilize output data 824. For example, the implemented control may be fully automated control, semi-automated control (partially automated and responsive to user input), or manual control based on output data 824. In some examples, control system 810 of treatment system 808 may utilize the output data to control one or more treatment parameters. For example, control 810 may control the delivery of ablation treatment to a cardiac site (e.g., epicardium or endocardium) based on late activation data determined by function 830, as disclosed herein. For example, the delivery of treatment may be automatically terminated in response to the detection of a lack of late activation in the heart (e.g., atrium) after a certain period of time. In other examples, an individual user may view a mapping generated on a display to manually control the treatment system based on visualized information. Other types of treatments and devices may also be controlled based on output data 824.

[0059] Certain techniques of this disclosure are described in the following clauses.

[0060] Clause 1: A method comprising: identifying by a processor a negative deflection of at least one signal representing cardiac electrophysiological activity on a surface of interest during a corresponding interval of the at least one signal; determining by the processor an activation time of the last identified negative deflection in the corresponding interval; and detecting instances of late activation by the processor based on a comparison of the activation time with a time threshold.

[0061] Clause 2: The method according to Clause 1, wherein the at least one signal includes a reconstructed cardiac signal representing the cardiac electrophysiological activity on the surface of interest, the reconstructed cardiac signal being reconstructed by a processor from a non-invasive electrophysiological signal measured for at least the corresponding interval to a point distributed across the surface of interest.

[0062] Clause 3: The method according to Clause 2, wherein the surface of interest includes the cardiac pericardium within the patient's body, and the non-invasive electrophysiological signal is measured on the outer surface of the patient's body.

[0063] Clause 4: The method described in Clause 2 or 3 further includes processing the reconstructed cardiac signal to reduce the far-field component of the signal.

[0064] Clause 5: The method according to Clause 4, wherein processing the reconstructed cardiac signal includes applying a surface Laplacian operator to the reconstructed cardiac signal to reduce the far-field component in the corresponding signal and thereby enhance the local signal component.

[0065] Clause 6: The method according to Clause 4 or 5, wherein processing the reconstructed cardiac signal includes selectively reducing the far-field component of the reconstructed cardiac signal for one or more regions in the surface of interest that are identified as having susceptibility to an increase in the corresponding far-field component.

[0066] Clause 7: The method described in Clause 6 further includes determining, based on imaging data and / or tissue characteristics, the susceptibility to the increase in the far-field component for a corresponding region across the surface of interest.

[0067] Clause 8: The method according to any one of Clauses 1 to 7 further includes comparing the time of the late-activated instance with an upper limit of the start time of a representative signal feature for the corresponding interval.

[0068] Clause 9: As described in Clause 8, wherein the upper limit of the time for the corresponding interval represents the start of the T wave.

[0069] Clause 10: The method according to any one of Clauses 1 to 9 further includes comparing the corresponding negative deflection with an amplitude threshold and discarding negative deflections that do not at least satisfy the amplitude threshold.

[0070] Clause 11: The method according to Clause 10, wherein the amplitude threshold has a value that varies spatially across the surface of interest.

[0071] Clause 12: The method according to any one of Clauses 1 to 11 further includes identifying one or more regions on the surface of interest that exhibit late activation based on corresponding instances of late activation detected across the surface of interest.

[0072] Clause 13: The method according to any one of Clauses 1 to 11 further includes quantifying the disease state of the region of the heart based on at least one instance of late activation of the region of the heart detected.

[0073] Clause 14: The method according to any one of Clauses 1 to 11 further includes controlling the delivery of treatment to one or more regions of the heart based on instances of late activation detected across the surface of interest.

[0074] Clause 15: The method according to any one of Clauses 1 to 11 further includes generating a graphical map based on instances of late activation detected across the surface of interest.

[0075] Clause 16: One or more non-transitory computer-readable media having instructions, which, when executed by a processor, are programmed to perform a method comprising the steps of: identifying a negative deflection of an electrophysiological signal representing cardiac electrophysiological activity on a surface of interest during a corresponding time interval; reducing the far-field component of the electrophysiological signal; determining the activation time of the last identified negative deflection in each of the corresponding electrophysiological signals; and detecting instances of late activation of each of the corresponding electrophysiological signals based on a comparison of the activation time with a time threshold.

[0076] It should be understood that the various aspects disclosed herein can be combined in combinations different from those specifically presented in the specification and drawings. It should also be understood that, depending on the example, certain actions or events of any of the processes or methods described herein may be performed in a different order, or may be completely added, combined, or omitted (e.g., performing these techniques may not require all the described actions or events). Furthermore, although for clarity some aspects of this disclosure are described as being performed by a single module or unit, it should be understood that the techniques of this disclosure can be performed by combinations of units or modules associated with, for example, a medical device.

[0077] In one or more examples, the described techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functionality may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include non-transitory computer-readable media, which correspond to tangible media such as data storage media (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and is accessible by a computer).

[0078] The instructions can be executed by one or more processors, such as one or more digital signal processors (DSPs), general-purpose microprocessors, application-specific integrated circuits (ASICs), field-programmable arrays (FPGAs), or other equivalent integrated or discrete logic circuit systems. Therefore, as used herein, the term "processor" can refer to any of the foregoing structures or any other physical structures suitable for implementing the described techniques. Furthermore, this technique can be fully implemented in one or more circuit or logic elements.

[0079] The examples described above are merely examples. It is impossible to describe every conceivable combination of structures, components, or methods, but those skilled in the art will understand that many other combinations and arrangements are possible. Therefore, this invention is intended to cover all such alternatives, modifications, and variations that fall within the scope of this application, which includes the appended claims. If the disclosure or claims refer to an element “a,” “an,” “first,” or “another,” or its equivalent, it should be interpreted as including one or more such elements, neither requiring nor excluding two or more such elements. As used herein, the term “comprising” means including but not limited to, and the term “including” means including but not limited to. The term “based on” means at least partially based on.

Claims

1. A monitoring system, comprising: Surface electrodes, which are adapted to be placed on the outer surface of a patient's body to non-invasively measure electrophysiological signals; and A computing device configured to store data and execute machine-readable instructions, the data including electrophysiological data representing cardiac electrophysiological activity at multiple points across at least one spatial region of the patient's cardiac surface, the electrophysiological data being reconstructed based on the non-invasively measured electrophysiological signals for the at least one spatial region of the heart including the multiple points, the instructions being executable to perform a method including the following steps: Identify a negative deflection of at least one signal representing cardiac electrophysiological activity on a surface of interest during a corresponding interval of the at least one signal; Determine the activation time of the last identified negative deflection in the corresponding interval; as well as Late activation instances are detected by comparing the activation time with a time threshold.

2. The system of claim 1, wherein the at least one signal comprises a reconstructed cardiac signal representing the cardiac electrophysiological activity on the surface of interest, the reconstructed cardiac signal being reconstructed from points distributed across the surface of interest based on non-invasive electrophysiological signals measured for at least the corresponding intervals.

3. The system of claim 2, wherein the instructions are further programmed to process the reconstructed cardiac signal to reduce the far-field component in the corresponding signal.

4. The system of claim 3, wherein the instructions for processing the reconstructed cardiac signal are further programmed to apply a surface Laplacian operator to the reconstructed cardiac signal to reduce the far-field component in the corresponding signal and thereby enhance the local signal component.

5. The system of claim 3, wherein the instructions for processing the reconstructed cardiac signal are further programmed to selectively reduce the far-field component of the reconstructed cardiac signal for one or more regions in the surface of interest that are identified as having susceptibility to an increase in the corresponding far-field component.

6. The system of claim 5, wherein the instructions are further programmed to determine susceptibility to the increase in the far-field component in a corresponding space across the surface of interest, based on imaging data and / or tissue characteristics.

7. The system according to any one of claims 1 to 6, wherein the instructions are further programmed to compare the time of the late-activated instance with an upper limit of the start time of a representative signal feature for the corresponding interval.

8. The system of claim 7, wherein the upper time limit for the corresponding interval represents the start of the T-wave.

9. The system according to any one of claims 1 to 6, wherein the instructions are further programmed to compare a corresponding negative deflection with an amplitude threshold and discard negative deflections that do not at least satisfy the amplitude threshold.

10. The system of claim 9, wherein the amplitude threshold has a value that varies spatially across the surface of interest.

11. The system according to any one of claims 1 to 6, wherein the instructions are further programmed to identify one or more regions exhibiting late activation on the surface of interest based on corresponding instances of late activation detected across the surface of interest.

12. The system according to any one of claims 1 to 6, wherein the instructions are further programmed to quantify the disease state of the region of the heart based on detecting at least one instance of late activation of the region of the heart.

13. The system according to any one of claims 1 to 6, wherein the instructions are further programmed to control the delivery of treatment to the heart based on instances of late activation detected across the surface of interest.

14. The system according to any one of claims 1 to 6, wherein the instructions are further programmed to generate a graph map based on the instances of late activation detected across the surface of interest.

15. The system according to any one of claims 1 to 6, further comprising a treatment system, wherein the computing device is configured to control the treatment system based on one or more detected instances of late activation.

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