Method for finding abnormal excitation in intracardiac electrograms

Abstract

The present invention is entitled "Method for finding abnormal activations in intracardiac electrograms." Methods, devices, and systems for medical procedures are disclosed herein, and the methods, devices, and systems include detecting points of an intracardiac region that exhibit abnormal activations such as local abnormal ventricular activations (LAVA). The points that exhibit such abnormal activations can be referred to as seed points, which are identified during a first step of the methods disclosed herein. One or more inputs such as unipolar and bipolar mapping channels, body surface ECG, past activations, neighboring points, etc. can be used to identify the seed points during the first step, which prioritizes high specificity over sensitivity. During a second step, which prioritizes high sensitivity, the electrical activations of neighboring points near the seed points are analyzed to determine whether the activations are similar (e.g., have a similar timing) to the abnormal activation corresponding to the corresponding seed point.

Description

Technical Field

[0001] This application provides systems, apparatus, and methods for detecting abnormal intracardiac activity. Background Technology

[0002] Medical conditions such as cardiac arrhythmias (e.g., atrial fibrillation (AF)) are typically diagnosed and treated via in vivo procedures. For example, ablation is used to perform pulmonary vein electrical isolation (PVI) from the left atrium (LA) body for the treatment of AF. Such in vivo procedures rely on detection of the area of ​​interest within the body's organs, such as the heart.

[0003] Detecting abnormal or targeted electrical activity in a region of the heart can identify areas of the heart to be ablated, thus preventing the spread of abnormal or targeted electrical activity within the heart and reducing the likelihood of cardiac conditions such as arrhythmias. Summary of the Invention

[0004] This document discloses methods, apparatus, and systems for medical procedures, including the detection of points in intracardiac regions exhibiting aberrant excitation such as regional aberrant ventricular activation (LAVA). Points exhibiting such aberrant excitation may be referred to as seed points, which are identified during a first step of the methods disclosed herein. Seed points may be identified using one or more inputs such as unipolar and bipolar mapping channels, surface ECG, past excitations, neighboring points, etc., during the first step, which prioritizes high specificity over sensitivity. During a second step, which prioritizes high sensitivity, the electrical excitations of neighboring points near the seed point are analyzed to determine whether the excitations are similar to (e.g., have a similar timing) the aberrant excitations corresponding to the respective seed point. Attached Figure Description

[0005] A more detailed understanding can be obtained by referring to the accompanying drawings and giving examples, in which:

[0006] Figure 1 A diagram of an exemplary system that can implement one or more features of the subject matter of this disclosure;

[0007] Figure 2 It is a process used to identify abnormal arousal;

[0008] Figure 3 It is a diagram used to receive intracardiac input to determine abnormal activity;

[0009] Figure 4 It is a diagram used to apply intracardiac input to determine abnormal activity;

[0010] Figure 5 It is a diagram used to determine abnormal activity based on intracardiac input;

[0011] Figure 6It is a diagram used to identify adjacent abnormal activities;

[0012] Figure 7A This is a diagram illustrating seed points used to identify anomalous activity;

[0013] Figure 7B This is a diagram used to identify adjacent points of abnormal activity;

[0014] Figure 8 This is a diagram illustrating seed points used to identify anomalous activity;

[0015] Figure 9 It is a diagram used to identify neighboring points of anomalous activity based on seed points; and

[0016] Figure 10 These are experimental results based on the techniques implemented according to the topics disclosed in this article. Detailed Implementation

[0017] Identifying complex electrocardiogram (ECG) excitations (such as regional anomalous ventricular activations (LAVA), fragmented and / or late potentials) with high sensitivity and specificity can be challenging. Techniques such as feature extraction and dynamic thresholding can be used to identify such complex ECG excitations and can be based on one or more inputs and / or features. However, such techniques may sacrifice specificity (i.e., true negative rate) in an attempt to improve sensitivity (i.e., true positive rate).

[0018] According to an exemplary embodiment of the present invention, a catheter may be inserted into the intracardiac cavity of a patient's heart. The catheter may include one or more electrodes that can provide electrical activity of a region of the intracardiac cavity in contact with the electrodes. Seed points corresponding to aberrant activity can be identified with high specificity. Subsequently, neighboring points of the seed point can be identified with high sensitivity. One or more complex ECG activations, such as LAVA, can be determined based on the seed point and neighboring points. Notably, the techniques disclosed herein can be implemented to increase the sensitivity of the results without sacrificing specificity.

[0019] According to an exemplary embodiment of the invention, in a first step, abnormal excitation of endocardial or epicardial tissue is identified with high specificity, as further disclosed herein. In a second step following the identification of abnormal excitation, points adjacent to the identified abnormal excitation are evaluated to determine whether they contain excitation at a time similar to that of the identified abnormal excitation. If it is determined that one or more points adjacent to the identified abnormal excitation contain excitation at a time similar to that of the identified abnormal excitation, then such one or more adjacent points are also marked as having abnormal excitation.

[0020] Figure 1This is an illustration of an exemplary mapping system 20 capable of implementing one or more features of the disclosed subject matter. The mapping system 20 may include means, such as catheter 40, configured to obtain electrical activity data according to an exemplary embodiment of the invention. While catheter 40 is shown as having a basket-shaped shape, it should be understood that catheters of any shape, including one or more elements (e.g., electrodes), can be used to implement the exemplary embodiments disclosed herein. The mapping system 20 includes a probe 21 having an axis 22 that can be navigated by a medical professional 30 to a body part of a patient 28 lying on a table 29, such as the heart 26. Figure 1 As shown, a medical professional 30 can insert the shaft 22 through the sheath 23 while manipulating the distal end of the shaft 22 using a manipulator 32 near the proximal end of the catheter and / or by deflection from the sheath 23. As shown in illustration 25, the catheter 40 can be fitted at the distal end of the shaft 22. The catheter 40 can be inserted through the sheath 23 in a collapsed state and can then be deployed within the heart 26.

[0021] According to an exemplary embodiment of the invention, the catheter 40 may be configured to collect electrical activity within the intracardiac cavity of the heart 26. Illustration 45 shows the catheter 40 within the intracardiac cavity of the heart 26 in an enlarged view. As shown, the catheter 40 may include an array of elements (e.g., electrodes 48) coupled to teeth forming the shape of the catheter 40. The elements (e.g., electrodes 48) may be any element configured to collect electrical activity and may be electrodes, transducers, or one or more other elements. It should be understood that although one catheter 40 is shown, multiple catheters may be used to collect electrical activity from organs within the body, such as the heart 26.

[0022] According to the exemplary embodiments disclosed herein, electrical activity can be any suitable electrical signal that can be measured based on one or more thresholds and sensed and / or amplified based on signal-to-noise ratio and / or other filters. The catheter (such as catheter 40) can also be configured to sense additional biometric data besides electrical activity. Data collected by catheter 40 may include one or more of the following: local activation time (LAT), topology, bipolar mapping, unipolar mapping, surface electrode-based mapping, dominant frequency, impedance, etc. Furthermore, catheter 40 can be used to obtain spatial information about organs within the body. Local activation time can be a time point corresponding to a threshold activity of local activation calculated based on a normalized initial starting point. Topology can correspond to the physical structure of a body part or a portion of a body part, and can correspond to variations in the physical structure relative to different parts of the body part or relative to different body parts. Dominant frequency can be a frequency or frequency range that is prevalent in a part of the body part and can differ in different parts of the same body part. For example, the dominant frequency of the pulmonary veins of the heart may differ from the dominant frequency of the right atrium of the same heart. Impedance can be a resistance measurement at a given area of ​​a body part, and can be calculated as an independent value based on frequency and / or in combination with additional considerations such as blood concentration.

[0023] like Figure 1 As shown, probe 21 and catheter 40 can be connected to console 24. Console 24 may include processor 41 (such as a general-purpose computer) having suitable front-end and interface circuitry 38 for transmitting and receiving signals to and from catheter 40, and for controlling other components of mapping system 20. In some exemplary embodiments of the invention, processor 41 may also be configured to receive electrical activity data, allocate point clusters at different times, and provide visual indications from a first point cluster to an associated second point cluster. According to an exemplary embodiment of the invention, rendering data can be used to provide a medical professional 30 with renderings of one or more body parts (e.g., body part rendering 35) on display 27. According to an exemplary embodiment of the invention, processor 41 may be located external to console 24 and may be located, for example, in a catheter, an external device, a mobile device, a cloud-based device, or may be a stand-alone processor.

[0024] As described above, processor 41 may include a general-purpose computer that can be programmed with software to perform the functions described herein. The software may be downloaded to the general-purpose computer electronically, for example, via a network, or alternatively or additionally set and / or stored on a non-transitory tangible medium, such as magnetic storage, optical storage, or electronic storage. Figure 1The exemplary configurations shown can be modified to implement the embodiments disclosed herein. The disclosed exemplary embodiments can be applied similarly using other system components and settings. Additionally, the mapping system 20 may include additional components such as elements for sensing biometric patient data, wired or wireless connectors, processing and display devices, etc.

[0025] According to an exemplary embodiment of the invention, the display connected to the processor (e.g., processor 41) may be located in a remote location, such as a separate hospital or within a separate healthcare provider network. Additionally, the mapping system 20 may be part of a surgical system configured to acquire anatomical and electrical measurements of a patient's organs, such as the heart, and to perform cardiac ablation procedures. An example of such a surgical system is sold by Biosense Webster. system.

[0026] Mapping system 20 can also, and optionally, use ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), or other medical imaging techniques known in the art to obtain biometric data, such as anatomical measurements of the patient's heart. Mapping system 20 can use catheters, electrocardiograms (ECG), or other sensors that measure the electrical properties of the heart to obtain electrical measurements. Figure 1 As shown, biometric data, including anatomical and electrophysiological measurements, can then be stored in the local memory 42 of the mapping system 20. Notably, memory 42 can simultaneously store biometric data from multiple different modalities. The biometric data can be transferred from memory 42 to processor 41. Alternatively or otherwise, biometric data can be transferred to a server 60, which may be local or remote, using network 62.

[0027] Network 62 can be any network or system known in the art, such as an intranet, local area network (LAN), wide area network (WAN), metropolitan area network (MAN), direct connection or a series of connections, cellular telephone network, or any other network or medium capable of facilitating communication between the measurement system 20 and the server 60. Network 62 can be wired, wireless, or a combination thereof. Wired connections can be implemented using Ethernet, Universal Serial Bus (USB), RJ-11, or any other wired connection known in the art. Wireless connections can be implemented using Wi-Fi, WiMAX and Bluetooth, infrared, cellular networks, satellite, or any other wireless connection method known in the art. Additionally, several networks can operate independently or communicate with each other to facilitate communication within network 62.

[0028] In some cases, server 60 can be implemented as a physical server. In other cases, server 60 can be implemented as a public cloud computing provider (e.g., Amazon Web Services). () virtual server.

[0029] The console 24 can be connected via cable 39 to a body surface electrode 43, which may include an adhesive skin patch attached to the patient 28. The processor 41, in conjunction with a current tracking module, can determine the positional coordinates of the catheter 40 within a body part of the patient (e.g., the heart 26). These positional coordinates may be based on impedance or electromagnetic field measured between electrodes 43 and 48 or other electromagnetic components of the catheter 40.

[0030] Processor 41 may include real-time noise reduction circuitry, typically configured as a field-programmable gate array (FPGA), followed by an analog-to-digital (A / D) ECG (electrocardiogram) or EMG (electromyography) signal conversion integrated circuit. Processor 41 may pass signals from the A / D ECG or EMG circuitry to another processor and / or may be programmed to perform one or more functions disclosed herein.

[0031] The console 24 may also include an input / output (I / O) communication interface that enables the console to transmit signals from and / or to the electrodes 48 and 43. Based on the signals received from the electrodes 48 and / or 43, the processor 41 may generate rendering data that enables a display (such as display 27) to render body parts (such as body part rendering 35) and biometric data of various modalities as part of body part rendering 35.

[0032] During the procedure, processor 41 may facilitate the rendering of body part rendering 35 (including one or more clusters of points active at a given time). Processor 41 may identify one or more clusters at a given time and one or more other related or unrelated clusters at subsequent times. Processor 41 may also determine propagation paths based on two or more related clusters of points and provide visual indications of the propagation paths accordingly. Electrical activity may be stored in memory 42, and processor 41 may access the electrical activity stored in memory 42 to determine clusters of points and corresponding propagation paths. Propagation paths may be provided to medical professional 30 on display 27.

[0033] Memory 42 may include any suitable volatile and / or non-volatile memory, such as random access memory or a hard disk drive. In some exemplary embodiments of the invention, the medical professional 30 may be able to manipulate the body part rendering 35 using one or more input devices, such as a touchpad, mouse, keyboard, gesture recognition device, etc. In another exemplary embodiment of the invention, the display 27 may include a touchscreen that can be configured to accept input from the medical professional 30 in addition to displaying the body part rendering 35 (including propagation paths).

[0034] Figure 2 A process 200 for identifying and labeling aberrant excitation points according to an exemplary embodiment of the present invention is illustrated. As described herein, seed points are points in the intracardiac cavity that exhibit aberrant excitation. At step 210 of process 200, such seed points are identified with high sensitivity, thereby emphasizing the true positive rate when identifying such seed points. As understood in the art, when seed points are identified at step 210, the receiver operating characteristic (ROC) curve is adjusted to be biased towards specificity. Subsequently, at steps 220-240, even higher sensitivity is emphasized by identifying adjacent points that exhibit excitation at times similar to the aberrant excitation of the corresponding seed point, as further described herein. Notably, by identifying adjacent points at steps 220-240, the techniques disclosed herein effectively expand the area around the identified seed point by identifying aberrant excitation corresponding to adjacent points. When adjacent points are identified at steps 220-240, the ROC curve is adjusted to be biased towards sensitivity. The result of process 200 is the identification of abnormally excited regions in endocardial or epicardial tissue with a high level of specificity (e.g., through step 210) and a high level of sensitivity (e.g., through steps 220-240).

[0035] exist Figure 2 In step 210 of process 200, one or more anomalous excitation points are identified at a high specificity level. The anomalous excitation points identified in step 210 may be referred to as seed points to distinguish them from neighboring points that are also identified as having anomalous excitation, as further disclosed herein.

[0036] Figure 3 The diagram illustrates input 310 (e.g., input information) that can be applied to determine abnormal activation points from the intracardiac cavity. Input 310 may include, but is not limited to, bipolar ECG of the mapping channel, distal and / or proximal unipolar ECG of the mapping channel, multi-lead (e.g., 12-lead) surface (BS) ECG, special information, and adjacent point information, etc. It can be processed by a processor (such as...) Figure 1The processor 41) receives one or more of the inputs 310. In addition, user input 320 may be provided to the processor (such as processor 41) and may include location information of the area to be excluded when abnormal excitation is identified, and may include limit and / or minimum voltage and maximum voltage.

[0037] like Figure 3 As shown, the processor can execute one or more techniques 330 (e.g., analysis modules) based on input 310 and / or user input 320. The one or more techniques 330 (e.g., analysis modules) may include QRS detection, wavefront algorithms, fragmentation detection, LAVA logic, etc. The application of one or more techniques 330 can provide, but may also include, a deterministic electrical activity 340 that may include LAT values ​​and may also include a determination of whether a point exhibits LAVA.

[0038] Figure 4 Provided information about Figure 3 Additional information for the application input 310. When in Figure 2 When determining the seed point at step 210, the following can be applied: Figure 4 The QRS detection 410, wavefront excitation 420, and fragmentation detection 430, such as Figure 8 Further details are provided in the text.

[0039] Figure 4 The QRS detection 410 may include a preprocessing step 412 and a QRS detection logic step 414. The QRS detection 410 may be based on data from a catheter (such as...) Figure 1 The electrodes of the catheter 40 receive electrical activity relative to baseline electrical activity determined using one or more BS electrodes attached outside the patient's body. For example, a processor (such as processor 41) may receive baseline electrical activity from multiple (e.g., twelve) BS electrodes and may extract a QRS signal based on comparing the electrical activity received from the electrodes of the catheter with the baseline electrical activity.

[0040] During preprocessing step 412, electrical activity measurements can be collected over a given time period (e.g., 150 ms) around the reference annotation. High-pass and / or low-pass filters can be applied to the received electrical activity. As an example, a high-pass filter can be applied with a threshold of 0.5 Hz, and a low-pass filter can be applied with a threshold of 120 Hz.

[0041] During QRS detection step 414, the QRS signal can be determined based on the change in voltage over time of the received electrical activity provided during preprocessing step 412. An additional low-pass filter can be applied using median measurements within a time window (e.g., 21 ms). Voltages with inter-peak measurements greater than a given voltage (e.g., 4 mV) can be identified, and additional electrical activity can be observed within additional time periods (e.g., 20 ms before and after the peak). QRS detection logic step 414 can provide intervals for the start and end of the QRS signal (StartOfQrs, EndOfQrs). Notably, these intervals can be used to determine whether electrical activity at a point on the intracardiac surface exhibits anomalous excitation (i.e., is a seed point), as further disclosed herein.

[0042] like Figure 4 As shown, wavefront excitation 420 can be determined based on the original unipolar ECG signal and / or bipolar ECG signal. During preprocessing step 422, a high-pass filter can be applied using the median measurement of a given time window (e.g., a 121ms window) through a finite impulse response (FIR) filter at a given frequency (10Hz). The preprocessing input can be provided to wavefront logic 424, and feature extraction step 426 can be applied to the output of wavefront logic 424. Feature extraction step 426 can provide one or more of the following: unipolar derivative, unipolar activity duration, unipolar amplitude, unipolar duration over amplitude, and / or bipolar amplitude. The output of feature extraction step 426 can be applied to fuzzy logic step 428, which produces fuzzy fractions as a set of (timestamp, fuzzy fraction) pairs for a given point on the intracardiac surface. Notably, the set of (timestamp, fuzzy fraction) pairs can be used to determine whether the electrical activity at a point on the intracardiac surface exhibits anomalous excitation (i.e., is a seed point), as further disclosed herein. The fuzziness score can be calculated based on features including the height, weight, and / or slope of the negative deflection for bipolar signals and features including the height, weight, and / or slope of the negative deflection for unipolar signals. Each such feature can influence the fuzziness score according to different fuzzy membership functions. The iterative fuzziness scores of each feature can be multiplied to calculate the final fuzziness score for a given point.

[0043] like Figure 4As shown, fragmentation detection 430 can be determined based on the raw unipolar ECG signal and / or bipolar ECG signal. During fragmentation window detection step 432, a fragmentation window of electrical activity at a point on the intracardiac surface can be determined. The fragmentation window can be determined based on the raw unipolar ECG signal and / or bipolar ECG signal and can be determined based on any applicable technique used for detecting the fragmentation window. Such techniques may include, but are not limited to, applying one or more steps, such as preprocessing, differentiation and squaring, moving window integration, thresholding, postprocessing, evaluation with the nonlinear energy operator (NELO), Gaussian low-pass filtering, or combinations thereof. The output of fragmented window detection step 432 provides the start and end points of the fragmented window (e.g., the interval between (StartOfFractionation, EndOfFractionation)). Notably, the interval between the start and end points of the fragmented window can be used to determine whether electrical activity at a point on the intracardiac surface exhibits anomalous excitation (i.e., is a seed point), as further disclosed herein. When comparing fragmented windows, a specific percentage of intersection between the two points being compared may be required for them to be considered similar.

[0044] like Figure 5 As shown, processors (such as Figure 1 The processor 41) can determine whether electrical activity at a point on the intracardiac surface is abnormal based on one or more of fuzzy fractions, temporal consistency, and location consistency. For example... Figure 5 As shown, for each union of distal and proximal wavefront excitations 510 (including excitations 512, 514, 516, 518, 520, and 522), the processor can determine the fuzziness fraction, whether there is temporal consistency between adjacent beats, and whether there is positional consistency between adjacent points. A threshold fuzziness fraction (e.g., 65) can be applied such that the calculated fuzziness fraction at a given point on the intracardiac surface corresponds to anomalous electrical activity if it exceeds the threshold fuzziness fraction.

[0045] According to an exemplary embodiment of the invention, if the detected electrical excitation is present in one or more previous cycles, temporal consistency for that excitation may exist. The presence of the detected electrical excitation in one or more previous cycles indicates that the detected electrical excitation is generated by the heart and is not noise. If the electrical excitation is present in a previous cycle, temporal consistency can be detected, wherein there is a tolerance of up to 1% deviation for the previous cycle, a tolerance of up to 2% deviation for the two cycles preceding a given electrical excitation, etc. For clarity, if the electrical activity detected during a given cycle is also present at the same time in a previous cycle, temporal consistency may exist, wherein there is a tolerance deviation of 1% time deviation in the previous cycle. Similarly, if the electrical activity detected during a given cycle is also present at the same time in the two cycles preceding the given cycle, temporal consistency may exist, wherein there is a tolerance amount of 2% time deviation in the two cycles preceding the given cycle. Figure 5 As shown, excitations 512, 514, 518, and 522 can exhibit temporal consistency.

[0046] exist Figure 2 At step 220 of process 200, the electrical activity of neighboring points within a given threshold distance (e.g., 12 mm) of the seed point identified in step 210 can be identified. At step 230, it can be determined that one or more of the neighboring points exhibit anomalous electrical activity similar to that of the corresponding seed point identified in step 210. The neighboring points exhibiting anomalous electrical activity can be determined based on the same process applied at step 210 and disclosed herein. At step 240, the neighboring points exhibiting anomalous activity can be marked as anomalous neighbors.

[0047] According to an exemplary embodiment of the invention, neighboring points up to a given threshold distance (e.g., 12 mm) can be identified at step 220. This given threshold distance can be a constant or user-defined. The given threshold distance can be the Euclidean distance between two points. Alternatively, the given threshold distance can be, for example, the shortest path between two points on the intracardiac surface determined by the Dijkstra algorithm.

[0048] According to an exemplary implementation, regions or points can be excluded to avoid being considered seed points and / or anomalously adjacent points. Such regions or points can be excluded based on user input, such as in cases where the user has marked anatomical locations to be excluded and / or points near anatomical locations. Such excluded points may be part of the bundle of His, which comprises broad, rapidly conducting muscle fibers that carry the heartbeat through an insulating fibrous ring into the upper part of the interventricular septum.

[0049] like Figure 5As shown, a positional consistency indicator can be determined for adjacent points of a seed point. Positional consistency provides an indication that anomalous electrical activity exhibited at the seed point also exists at adjacent points. Notably, for adjacent points near a seed point, anomalous electrical activity can be expected to be exhibited at a similar time to that at the seed point. Positional consistency can be determined based on the distance between the seed point and adjacent points divided by the wavefront velocity plus a certain percentage (e.g., 1%) of the cycle length of a given cycle (i.e., similar time = (distance / wavefront velocity) + 1%CL), such that positional consistency exists if adjacent points exhibit electrical excitation at a time similar to that of the seed point. As indicated by the formula provided above, and in general, the tolerance (e.g., 1%CL) used to consider two excitation times as similar times can be a function of CL, such that a shorter CL allows for smaller deviations, and a longer CL allows for larger deviations. Especially when the actual wavefront velocity is unknown, a default wavefront velocity of 0.5 mm / ms can be applied. A tolerance of a certain percentage (e.g., 1%) of CL can account for deviations in the wavefront velocity. For example, the wavefront velocity in healthy tissue can be 0.9 mm / ms, while the wavefront velocity in scar tissue can be 0.1 mm / ms. Figure 5 As shown, excitations 512, 518, and 520 can exhibit positional consistency, such that at least two excitations at similar times exist within adjacent points of the seed point. Notably, positional consistency can indicate that anomalous electrical activity at the seed point is confirmed by electrical activity at adjacent points. According to one embodiment, the wavefront velocity can be 1 mm / ms. According to one embodiment, the similar time can be a predetermined value or a user-provided value. Adjacent points with electrical excitations at similar times to the seed point can be considered anomalous adjacent points, such that the seed point and anomalous adjacent points can correspond to regions of complex ECG excitations (e.g., LAVA, fragmentation, late potential, etc.).

[0050] According to an exemplary embodiment, in order to consider the excitation times of two or more points as similar times, a threshold can be applied to the center or beginning of the signal to compare the start or center of the excitation window. In the case of fragmented ECG signals, the excitation time can be a window rather than a single point. Furthermore, the duration of excitation may also need to be similar when considering similar excitation times. According to an embodiment, the tolerance for excitation times considered similar can be a function of interpeak voltage, such that interpeak bipolar voltage corresponds to healthy tissue with a relatively high wavefront velocity, as disclosed herein. Similarly, low interpeak bipolar voltage corresponds to unhealthy tissue (such as scar tissue) with a relatively low wavefront velocity, as disclosed herein. Thus, for interpeak voltage regions, the excitation time can propagate faster between adjacent points, and conversely, it can propagate more slowly between adjacent points in low interpeak voltage regions.

[0051] According to an exemplary implementation, when neighboring points with anomalous activity are identified (e.g., Figure 2 During steps 220-240 of process 200, the number of radio components used to find neighboring points can be limited to a relatively large number, but the ROC can be adjusted to have a lower sensitivity and higher specificity for points further away from the seed point found in step 210. It should be understood that at any point on the ROC curve, the time of a given excitation at a neighboring point still needs to be at a similar time to that at the corresponding seed point, as disclosed herein.

[0052] like Figure 6 As shown, it can be based on Figure 2 The process 200 removes far-field-affected neighboring points by determining neighboring points at step 230. Far-field-affected neighboring points can be determined by detecting neighboring points within a given radius (e.g., 15 mm) from the seed point at 610. For each neighboring point within the given radius, the strongest negative deflection amplitude below the QRS can also be determined at 610. An upper limit (e.g., the 90th percentile) of the strongest –dV / dt among the neighbors can be determined at 620, where the strongest –dV / dt is below the QRS. At 630, the median of the LAT values ​​of the strongest –dV / dt below the QRS can be identified, and at 640, this median can be assumed to be the possible far-field LAT value. At 650, the median distance of the neighboring points that contributes to the LAT value determined at 640 can be stored as a far-field distance, such that the far-field distance is the median distance of neighboring points having a certain percentage (e.g., 10%) of the strongest negative deflection amplitude.

[0053] Figure 7A Showing based on Figure 2 The illustration of the identified seed point 710 with high specificity determined by step 210 of process 200. Figure 7B Showing based on Figure 2 The process 200 steps 220-240 determine the relationship with Figure 7A The illustration shows the identified neighboring points 720 that have electrical excitation at a similar time to the seed point 710. It is worth noting that the seed point 710 is a LAVA point with high specificity, and the neighboring points 720 allow for increased sensitivity and electrical excitation at a similar time to the corresponding seed point 710.

[0054] Figure 8 Showing the use of based Figure 2 Step 210 of process 200 determines the variants 810, 820, and 830 of the seed point. When identifying the seed point, conditions can be applied for the latest variant in time (i.e., from variants 810, 820, and 830) to be satisfied. Variants 810, 820, and 830 can utilize... Figure 4The outputs of QRS detection 410, wavefront excitation 420 and fragmentation detection 430 are used as inputs.

[0055] At variant 810, if conditions 812 and 814 are met, a seed point can be identified. As shown in the figure, if corresponding electrical activity is exhibited at 812 before or after the QRS, and if the reference exists... Figure 5 The temporal consistency discussed, and if the fuzzy fraction corresponding to electrical activity at 814 is greater than a threshold fuzzy fraction (e.g., 65), a seed point can be identified at variant 810. Seed points identified based on variant 810 can, for example, have a specificity of 96% and a positive predictive value (PPV) of 72%.

[0056] At variant 820, a seed point can be identified if conditions 822 and 824 are met. At 822, it can be determined whether the electrical excitation at a given point is within a fragmentation window (e.g., whether at least two wavefront candidates exist within the fragmentation window). At 824, the seed point can be determined based on the last excitation within the fragmentation window that has an ambiguity score greater than a threshold ambiguity score (e.g., 65), and if no such excitation exists, the seed point can be determined based on the strongest –dV / dt within the fragmentation window. Seed points identified based on variant 820 can, for example, have 94% specificity and 71% PPV.

[0057] At variant 830, a seed point can be identified if conditions 832 and 834 are met. At 832, it can be determined whether the electrical excitation at a given seed point is below the QRS and outside the fragmentation window. At 834, a seed point can be identified if, as disclosed herein, the electrical excitation at a given point is time-consistent, if the electrical excitation is positionally consistent with adjacent points, if the ambiguity score is above a threshold ambiguity score (e.g., 65), if the slope amplitude between the positive amplitude and the adjacent negative amplitude of the ECG signal (i.e., the negative deflection amplitude) is greater than a given voltage (e.g., 30 μV), and if the electrical excitation at a given point is not a possible far-field effect determined, for example, by ((far-field distance / wavefront velocity) + 1% CL). Seed points identified based on variant 830 can, for example, have 99% specificity and 70% PPV.

[0058] Figure 9 Showing the use of based Figure 2 Steps 220-240 of process 200 determine the process 900 for identifying anomalous neighboring points. It is worth noting that process 900 allows for increased sensitivity. At step 910 of process 900, points within a given radius (e.g., 12 mm) from the seed point are identified. At step 920, it is determined whether there is positional consistency, such as relative to the seed point and each of these points identified in step 910, exists. Figure 5For clarity, the distance between each neighboring point and the seed point can be divided by the wavefront velocity (e.g., 0.5 mm / ms). Variance can be added to the resulting time (e.g., 1% of CL). At step 930, the fuzziness fraction of points where excitation exists within the time determined by step 920 (i.e., where positional consistency exists) can be determined. At step 930, points whose fuzziness fraction exceeds a threshold fuzziness fraction (e.g., 65) can be identified. At step 940, the slope amplitude of the electrical activity of points whose fuzziness fraction exceeds the threshold fuzziness fraction can be identified. Points with slope amplitudes exceeding the threshold slope amplitude (e.g., 30 μV) can be identified as anomalous neighbors. As described at step 950, if there are more than one excitation satisfying steps 910-940 at a given point, the latest excitation among such excitations can be applied when identifying anomalous neighbors.

[0059] Figure 10 Showing according to Figures 3-9 application Figure 2 The experimental results of process 200. Figure 10 The results are based on fifteen unique cases from the dataset and an analysis of a total of 41,953 points according to the topics disclosed in this paper. As shown in Table 1010, a mean sensitivity of 75% (77% after QRS, 75% before / below QRS) was observed across the entire dataset. A mean specificity of 83% (100% after QRS, 83% before / after QRS) was observed across the entire dataset. As stated, the results in Table 1010 are provided assuming physicians are interested in LAVA in the region of interpeak bipolar amplitude up to 1.5 mV. For the dataset, LAT accuracy up to 20 ms was observed in 85% (80% after QRS, 88% before / below QRS) of true positives. Figure 1020 shows the LAT accuracy in true positives. Figure 1030 shows the LAT accuracy in false positives. Figure 1040 shows the LAT accuracy in false negatives.

[0060] Any of the functions and methods described herein can be implemented in a general-purpose computer, processor, or processor core. By way of example, suitable processors include general-purpose processors, special-purpose processors, conventional processors, digital signal processors (DSPs), multiple microprocessors, one or more microprocessors associated with a DSP core, controllers, microcontrollers, application-specific integrated circuits (ASICs), field-programmable gate array (FPGA) circuits, any other type of integrated circuit (IC), and / or state machines. Such processors can be manufactured by configuring the manufacturing process using the results of processing hardware description language (HDL) instructions and other intermediate data, including netlists (such instructions can be stored on a computer-readable medium). The result of this processing can be a maskwork, which is subsequently used in a semiconductor manufacturing process to manufacture processors implementing the features of this disclosure.

[0061] Any of the functions and methods described herein may be implemented in computer programs, software, or firmware incorporated into a non-transitory computer-readable storage medium for execution by a general-purpose computer or processor. Examples of non-transitory computer-readable storage media include read-only memory (ROM), random access memory (RAM), registers, cache memory, semiconductor memory devices, magnetic media (e.g., internal hard disks and removable disks), magneto-optical media, and optical media (e.g., CD-ROMs and DVDs).

[0062] It should be understood that many variations are possible based on the disclosure herein. Although features and elements have been described above in specific combinations, each feature or element may be used alone without other features and elements, or in various combinations with or without other features and elements.

Claims

1. A computer-implemented method for identifying abnormal excitation in an intracardiac electrocardiogram, the method comprising: Receive input information; Seed points with anomalous excitation at the first time point are identified, wherein when the seed points are identified, the receiver operating characteristic curve is adjusted to improve specificity; Identify at least one neighboring point within a threshold distance of the seed point; Determine that the neighboring points exhibit arousal at a time similar to the first time, wherein the arousal is an indication of the positional consistency of the neighboring points; as well as Based on the determination that the neighboring points exhibited excitement at a time similar to the first time, the neighboring points were identified as anomalous neighboring points, thereby increasing sensitivity.

2. The method according to claim 1, wherein, The received input information includes one or more of the following: bipolar ECG from the mapping channel, distal and proximal unipolar ECG from the mapping channel, lead surface ECG, and intracardiac spatial information.

3. The method according to claim 2, further comprising: The input information is provided to one or more analysis modules, which include one or more of the following: a QRS detection module, a wavefront activation module, a fragmentation detection module, and a Local Anomalous Ventricular Activation (LAVA) logic module.

4. The method of claim 3, wherein the seed point is identified based on the output of the one or more analysis modules.

5. The method according to any one of claims 3-4, wherein the QRS detection module comprises one or more of the following: a preprocessing step and a QRS detection logic step.

6. The method according to claim 5, wherein the QRS detection logic step outputs the start and end of the QRS.

7. The method according to any one of claims 3-4, wherein the wavefront excitation module comprises one or more of the following: a preprocessing step, a wavefront logic step, a feature extraction step, and a fuzzy logic step.

8. The method according to any one of claims 3-4, wherein the wavefront excitation module outputs one or more sets of timestamps and fuzzy scores.

9. The method according to any one of claims 3-4, wherein the fragmentation detection module output includes an interval between the start and end of fragmentation.

10. The method according to any one of claims 1-4, wherein the seed point is determined based on one or more of fuzzy scores, temporal consistency, and positional consistency, and wherein the neighboring points are determined further based on one or more of fuzzy scores and temporal consistency.

11. The method of claim 10, wherein the time consistency is based on identifying the anomalous excitation in which the previous jump is within a deviation tolerance based on the cycle length.

12. The method of claim 11, wherein the positional consistency is based on the distance between the seed point and the adjacent points divided by the wavefront velocity.

13. The method of claim 12, wherein the positional consistency is further based on the deviation tolerance based on the cycle length.

14. The method according to any one of claims 1-4, further comprising: Identify far-field distance.

15. The method of claim 14, further comprising: Determine at least one of the neighboring points to be within the far-field distance, and identify the neighboring points within the far-field distance as far-field neighbors.

16. The method according to any one of claims 1-4, wherein the seed point is determined based on one or more of QRS time, time consistency, fuzziness fraction, fragmentation period, and slope magnitude.

17. The method according to any one of claims 1-4, wherein the at least one adjacent point is within 12 mm of the seed point.

18. The method according to any one of claims 1-4, wherein identifying the neighboring point as an abnormal neighboring point is also based on one or more of the slope magnitudes.

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