Annotation of slow electrophysiological (EP) cardiac pathways associated with ventricular tachycardia (VT)

Abstract

To evaluate electrical propagation in a heart.SOLUTION: A method for evaluation of electrical propagation in the heart includes receiving a pacing signal applied to a heart of a patient, the pacing signal including a normal sequence and shorter abnormal pacing stimuli. A responsive cardiac signal is received, which is sensed by electrodes at a location in the heart and on a body surface of the patient. A model response is found and annotated from evoked potentials caused by the normal pacing stimuli. A correlation is made between the model responses along the different signal sections to find and calculate a normal time delay and a decremental time delay between the pacing stimuli and respectively resulting evoked potentials at a tissue location. A time difference is calculated, between the normal time delay and the decremental time delay. An EP map of at least a portion of the heart is presented to a user, with a graphical indication of the time difference presented at the tissue location.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention generally relates to electrophysiological (EP) signals, and more particularly to a method for evaluating electrical propagation within the heart.

Background Art

[0002] Annotation of electrophysiological signals for determining local activation time (LAT) has been previously proposed in the patent literature. For example, U.S. Patent No. 9,662,178 describes various embodiments of a system and method for identifying a patient's arrhythmogenic circuit. In one embodiment, the method includes obtaining data from electrograms recorded at various locations of the heart while performing programmed ventricular pacing using an extrastimulus, obtaining a reduction value at at least two different locations of the heart using the recorded electrograms, generating at least a portion of a reduction map using the reduction value, and identifying an arrhythmogenic circuit based on an electrogram having significant reduction characteristics.

[0003] As another example, U.S. Patent Application Publication No. 2018 / 0089825 describes a method for identifying an isthmus in a three-dimensional map of a heart chamber by a processing unit configured to perform: a) a correlation step between a set of stimulation points in the heart chamber, wherein each stimulation point is represented by a set of signals obtained after an electrocardiography (ECG) examination excluding ventricular beats; b) an identification step of a watershed line based on the correlation result and the 3D coordinates of the stimulation points in the 3D map; and c) a determination step of an isthmus based on a 3D corridor substantially crossing the watershed line.

[0004] International Publication No. 2018 / 073722 describes a computer implementation method and computer program product for identifying ventricular arrhythmogenic substrates in myocardial scar or fibrous tissue. Multiple mapping points acquired from a patient are stored in a signal acquisition unit, and the mapping points include an ECG signal, an electrographic (EGM) signal, and the 3D location of the EGM signal. The method includes, for a reference mapping point, a) detecting each beat present in one recorded ECG signal and identifying a target beat from the detected beats; b) identifying the major EGM wave associated with the identified target beat; c) identifying the start and end time landmarks of the major EGM wave that provide a primary drawn EGM signal and measuring the voltage amplitude of the primary drawn EGM signal; d) performing further analysis of the primary drawn EGM; and e) creating a cardiac conduction channel map and propagation map based on the results of tagging performed during the analysis. [Overview of the project] [Means for solving the problem]

[0005] Embodiments of the present invention provide a method comprising receiving a pacing signal applied to a patient's heart, the pacing signal comprising (i) a sequence of normal pacing stimuli at sinus rhythm intervals, and (ii) one or more abnormal pacing stimuli at abnormal intervals shorter than sinus rhythm intervals. Electrodes located within the heart and electrodes on the patient's body surface receive the cardiac signal sensed in response to the pacing signal. A model response is found and annotated from the evoked potentials caused by the normal pacing stimuli. Correlations are made between model responses along different signal sections to find annotations for a first evoked potential caused by the normal pacing stimuli and annotations for a second evoked potential caused by one or more abnormal pacing stimuli, according to the index in the signal section that received the highest scoring in the model response. A first time delay and a second time delay are calculated. A time difference is calculated between the normal time delay and the reduced time delay. An EP map of at least a portion of the heart, with a graphical representation of the time difference presented at the tissue location, is presented to the user.

[0006] In some embodiments, correlating with a model response involves weighting the correlation according to the derivatives of one or more signals.

[0007] In some embodiments, estimating a first time delay and a second time delay includes annotating the estimated first and second delays with bipolar signals.

[0008] In embodiments, the method further includes receiving electrocardiogram (ECG) signals from external electrodes of the patient, and using the ECG signals to estimate whether a stable contraction occurred in response to a predefined number of normal pacing stimuli and a first abnormal pacing stimulus. Portions of the cardiac signal in which each ECG does not indicate a stable contraction are discarded.

[0009] In some embodiments, estimating stable contractions includes estimating the repeatability of evoked potentials in a bipolar signal. In other embodiments, estimating stable contractions includes smoothing a bipolar signal and detecting evoked potentials in the smoothed bipolar signal.

[0010] In the embodiment, estimating one or more correlations includes identifying the far field from the near-field signal.

[0011] In some embodiments, presenting an EP map involves adjusting the GUI scale to define the minimum positive value of the time difference displayed on the EP map.

[0012] In other embodiments, presenting an EP map involves overlaying artificial icons, which are graphically coded to indicate time difference values, onto the EP map.

[0013] In yet another embodiment, presenting an EP map further includes indicating to the user the occurrence of abnormal ventricular activity.

[0014] In the embodiment, indicating the occurrence of abnormal ventricular activity includes using at least one of color and surface morphology on the EP map.

[0015] In the embodiment, receiving cardiac signals includes receiving unipolar and bipolar electrophoresis obtained using a catheter.

[0016] In some embodiments, estimating the highest scoring correlation involves training a machine learning model to estimate the highest scoring correlation based on a pre-specified metric.

[0017] According to another embodiment of the present invention, a system is further provided which includes an interface and a processor. The interface is configured to receive a pacing signal applied to the patient's heart, the pacing signal including (i) a sequence of normal pacing stimuli at sinus rhythm intervals and (ii) one or more abnormal pacing stimuli at abnormal intervals shorter than sinus rhythm intervals. In response to the pacing signal, the interface is further configured to receive cardiac signals sensed by electrodes at locations within the heart and electrodes on the patient's body surface. The processor is configured to (a) find and annotate model responses from evoked potentials caused by normal pacing stimuli, (b) correlate model responses along different signal sections to find annotations for a first evoked potential caused by a normal pacing stimulus and annotations for a second evoked potential caused by one or more abnormal pacing stimuli, according to the index in the signal section that received the highest scoring in the model response, (c) calculate a first time delay and a second time delay, (d) calculate the time difference between a normal time delay and a reduced time delay, and (e) present to the user an EP map of at least a portion of the heart, with a graphical representation of the time difference presented at the tissue location. [Brief explanation of the drawing]

[0018] A more complete understanding of the disclosure can be obtained by reading the following detailed description of embodiments in conjunction with the drawings. [Figure 1] This is a schematic diagram of a catheter-based electrophysiological (EP) mapping system according to an exemplary embodiment of the present invention. [Figure 2] An exemplary graph of a bipolar potential diagram acquired using the system of Figure 1, having decrement-evoked potentials (DeEPs) annotated on the bipolar signal, is shown according to an exemplary embodiment of the present invention. [Figure 3]Schematic depictive volume rendering of an EP map of the ventricles showing the tissue location and size of DeEP that can cause ventricular tachycardia (VT) according to an exemplary embodiment of the present invention. [Figure 4] Flowchart schematically showing a method and algorithm for annotation of DeEP in a bipolar electrogram according to an exemplary embodiment of the present invention. **Embodiments for Carrying Out the Invention**

[0019] **Overview** Cardiac arrhythmias (irregular heartbeats), such as ventricular tachycardia (VT) or atrial tachycardia, are cardiac rhythm disorders caused by abnormal electrical signals within the heart chambers. For example, VT can be caused by abnormal electrical signals within the lower chambers (ventricles) of the heart. VT can be caused by local electrophysiological (EP) conduction defects in ventricular tissue, such as scar tissue. For example, in order to find and treat such arrhythmia sites, such as by ablation, the ventricles can be paced and EP mapped using a catheter to identify abnormal tissue locations (e.g., locations showing delayed evoked potentials) that may be causing VT.

[0020] Specifically, EP mapping can be performed in support of a treatment approach called "scar homogenization," which has been found to be useful for ablating scar tissue across the entire area of a scar. The motivation behind ablation therapy is to target poorly connected ventricular tissue fibers that survive within the resulting scar. These bundles are thought to generate EP pathways that exhibit slow conduction (scar isthmus), which is thought to be the cause of VT. To achieve this goal, EP mapping of scar tissue, followed by scar homogenization, so far appears to be the optimal procedural endpoint for eliminating VT.

[0021] To perform EP mapping of the pathways and circuits leading to tachycardia, tachycardia is usually initiated during an EP study. However, VT is often non-inducible or hemodynamically unstable, so substrate mapping is often necessary. In substrate mapping, the properties of tissue in atrial rhythm or ventricular pacing are related to the arrhythmogenicity of the tissue.

[0022] Embodiments of the present invention described below provide a method and system for automatically identifying and analyzing delayed evoked potentials while using only substrate mapping to pace a heart chamber such as a ventricle. In the disclosed embodiments, an automated Decremental Evoked Potential (DeEP) detection method is used to automatically detect, analyze, annotate, calculate, and present abnormally delayed evoked potentials in an electrogram (e.g., a bipolar electrogram) indicative of a scar isthmus.

[0023] DeEP mapping is a substrate mapping technique. The basis of this method is that ventricular tissue showing decremental conduction with decremental extra stimuli seems to be specific to abnormal VT circuits. DeEP information is typically presented as a DeEP data layer superimposed on an EP map of the ventricle.

[0024] In the disclosed technique, the ventricle is paced with a short sequence of normal sinus rhythm pulses. The short sequence ends with one or more pulses (typically up to three) having shorter inter-pulse intervals. A common notation defines this as pacing with a train of S1 paces (not always, but usually having a cycle length of 600 milliseconds) followed by one or more decremental extra stimuli (S2, S3, etc.) delivered immediately (e.g., 20 milliseconds later) after a period during which a new action potential cannot normally be initiated (commonly called the refractory period), thereby stimulating the arrhythmogenic response being sought in abnormal tissue.

[0025] In the context of this explanation, the expression “normal sinus rhythm pulse” includes pulses equidistant from a predefined variation. For example, a sequence of pulses with an interval of 600 ± 6 milliseconds is included in the definition. More generally, a sequence of pulses with any given equidistant interval (e.g., less than 600 milliseconds, such as 500 milliseconds or 700 milliseconds) up to a predefined variation is hereafter considered a “normal sinus rhythm pulse.” Typically, the predefined variation is limited to the order of 1% of a given equidistant interval. Each decreasing additional stimulus is defined with respect to the above equidistant interval.

[0026] The sequence described above simulates the initial beat (early ventricular contraction, PVC) to evoke delayed potentials in arrhythmogenic tissue. In response to a normal sinus rhythm pacing pulse, one evoked potential occurs with a normal time delay after stimulation. This normal delay between the pacing pulse and the resulting evoked potential is hereafter referred to as the "first time delay." This part of the EP mapping method is sufficient to characterize normal tissue.

[0027] A short sequence ends with one or more short intervals (i.e., decreasing) of pacing stimuli. For example, if sinus rhythm pacing is performed over a period of 0.6 seconds, one or more short interval pacing stimuli are applied at 0.4-second intervals following the sinus rhythm stimulation. The terms “pulse” and “stimulus” are used interchangeably herein.

[0028] In response to a reduced additional stimulus, evoked potentials may be delayed (compared to the first time delay). Regions where a meaningful relative delay (e.g., greater than 10 milliseconds) exists are considered specific to abnormal VT circuits. Generally, early pulsations (premature ventricular contractions, PVCs) are relatively common. Abnormal VT circuits can only be initiated and maintained when early pulsations encounter arrhythmogenic tissue, such as tissue that exhibits slower conduction as a result of early pulsations. When this occurs, a delay greater than the first time delay of these evoked potentials compared to the corresponding additional stimulus pacing pulse is hereafter referred to as the "second time delay."

[0029] The processor analyzes signals resulting from acquisitions over time at various tissue locations on the cardiac surface, such as bipolar signals acquired by a mapping catheter, and detects delayed evoked potentials that occur as a result of one or more short-interval pacing pulses. The processor annotates the evoked potentials having a first time delay and a second time delay, calculates the time difference between the second time delay and the first time delay, and this time difference is hereafter referred to as the "DeEP interval".

[0030] In some embodiments, the following steps are performed at each mapped tissue location to estimate a first time delay and a second time delay. 1. Generate one or more decreasing (i.e., additional stimulus) paces that potentially trigger delayed evoked potentials. 2. For each evoked potential, determine a candidate annotation for the time of the delayed evoked potential. For example, the candidate time to be annotated could be the center of a short signal section. In exemplary embodiments, the annotation of each evoked potential should be at a similar morphological location (e.g., the same deflection of the response) to maximize the reliability of the DeEP calculation. In one embodiment, the disclosed annotation process comprises three steps: (i) extraction of a model response from the response to the S1 pace, which is an evoked potential that has a high correlation with other S1 pace evoked potentials in the EGM, particularly the last 3-4 responses after the S1 pace; (ii) annotation of the model response, e.g., at the center of mass, or preferably at the last deflection of the response; and (iii) annotation of other evoked potentials, including responses to additional stimuli, based on the scoring calculated in the model response. In exemplary embodiments, the processor calculates scoring along each signal section of the pacing signal (between paces, or between the last pace and 350-400 milliseconds later) based on the derivative-weighted correlation with the model response of each section. In each signal section, the point (e.g., index) that receives the highest scoring is selected as the annotation for that interval. After annotating all intervals within the EGM, the algorithm removes annotations whose scoring falls below a predefined threshold (poor morphological fit to the model response). In addition, the disclosed automated algorithm checks the stability of the last three evoked potentials after S1 by calculating the standard deviation of response timing (distance of annotation from pace). 3. The first and second delays are calculated using the distance between the selected annotation and the relative position at each interval. The simplest relative position is the pace before each response. In another embodiment, the annotation may be considered a marking of the local near-field response, while the relative position may be a marking of the far-field response (the distance is then measured between the near-field and far-field responses after the pace).

[0031] More generally, a processor can estimate the degree of correlation by any other method known in the art, such as by using machine learning, define a scoring system, and find the correlation with the highest scoring system. For example, the scoring system can be based on a pre-specified metric such as L1.

[0032] In an exemplary embodiment, the DeEP interval (i.e., time difference) value is graphically coded (for example, longer DeEP intervals are marked by larger tags (e.g., spherical icons)).

[0033] In an exemplary embodiment, a GUI is provided to assist a physician in determining how well a given DeEP interval indicates the location of the scar isthmus causing VT. The GUI includes an adjustable scale that the physician can use to show only DeEPs longer than a (positive) threshold on the map.

[0034] Typically, a processor is programmed with software that includes specific algorithms that enable it to perform each of the processor-related processes and functions outlined above.

[0035] By applying an automated DeEP annotation algorithm to map the tissue location of localized cardiac areas (e.g., ventricles) that induce VT, the disclosed invasive cardiac diagnostic method may improve the safety and value of diagnostic catheterization procedures.

[0036] System Description Figure 1 is a schematic diagram of a catheter-based electrophysiological (EP) mapping system 21 according to an exemplary embodiment of the present invention. Figure 1 illustrates a physician 27 using an electroanatomical mapping catheter 29 to perform electroanatomical mapping of the heart 23 of a patient 25. The mapping catheter 29 includes one or more arms 20 at its distal end, each of which is connected to a bipolar electrode 22 including adjacent electrodes 22a and 22b.

[0037] During the mapping procedure, the position of the electrodes 22 is tracked while they are located within the patient's heart 23. For this purpose, an electrical signal is transmitted between the electrodes 22 and the external electrodes 24. For example, three external electrodes 24 may be connected to the patient's chest, or another three external electrodes may be connected to the patient's back. (Only one external electrode is shown in Figure 1 for illustrative purposes.)

[0038] Based on the signals and considering the known locations of the electrodes 24 on the patient's body, the processor 28 calculates the estimated location of each electrode 22 within the patient's heart. Each electrophysiological data, such as bipolar electrophoresis traces, is additionally acquired from the tissue of the heart 23 using the electrodes 22. Thus, the processor can associate any given signal received from the electrodes 22, such as bipolar EP signals, with the location from which the signal was acquired. The processor 28 receives the resulting signals via the electrical interface 35 and uses the information contained in these signals to construct electrophysiological maps 31 and ECG traces 40, which are then presented on the display 26.

[0039] The processor 28 typically comprises a general-purpose computer having software programmed to perform the functions described herein. The software can be downloaded to the computer in electronic form, for example, over a network, or alternatively or additionally, it can be provided and / or stored on a non-temporary physical medium such as magnetic memory, optical memory, or electronic memory. In particular, the processor 28 implements a dedicated algorithm disclosed herein, which is included in Figure 4, enabling the processor 28 to perform the steps of the disclosure as further described below.

[0040] The example shown in Figure 1 is selected solely for the purpose of illustrating the concept. Other types of electrophysiological sensing catheter geometry may be employed, such as the Lasso® catheter (manufactured by Biosense Webster Inc., Irvine, California). Additionally, a contact sensor may be attached to the distal end of the mapping catheter 29 to transmit data indicating the physical quality of electrode contact with tissue. In the exemplary embodiment, measurements from one or more electrodes 22 may be discarded if they indicate poor physical contact quality, while measurements from other electrodes may be considered valid if they indicate sufficient contact quality.

[0041] Annotation of slow EP cardiac pathways associated with VT Figure 2 shows exemplary graphs of bipolar potential diagrams 200 acquired using the system 21 of Figure 1, with a (224) decrementing evoked potential (DeEP) annotated on the bipolar signal, according to an exemplary embodiment of the present invention. Graphs (i) and (ii) show, respectively, short sinus rhythm pacing stimulus sections 207 within an overall s1 pace section 206 containing a sequence of normal equidistant (205) pacing stimuli 202, and the resulting evoked potentials 204. One of the sections 207 has been found to contain the aforementioned model response 256 with annotation 214. The model response 256 is selected because its entire 207 section has the highest correlation with the other 207 sections. In the embodiment, the processor starts about 70 milliseconds after pace 202 and cuts the model response 256 from its section 207 according to a duration of about 170–200 milliseconds. In another embodiment, this is done by a processor that annotates intervals (interval 205 in section 207 holding response 256) by using a different annotation algorithm (for example, an algorithm created by Biosense Webster to identify local activity in intracardiac signals) and searching for "quiet zones" around the annotations (where responses begin and end).

[0042] The final pacing stimulus 208 is applied after a shorter time interval 209 than normal sinus rhythm, and therefore may decrease and induce a delayed evoked potential 210. In graphs (i) and (ii), the interval 205 is 0.6 seconds and the short interval 209 is only 0.35 seconds.

[0043] A short sequence of normal sinus rhythm pacing stimuli 202 (in graphs (i) and (ii), section 206 contains seven periods), which typically yields a stable cardiac response and has approximately equal time delays 212 between each S1 pacing stimulus 202 and the resulting annotated (214) evoked potential 204. Consequently, a clearly defined “first time delay” can be derived, for example, using the last time delay 212, or by averaging the most recent five delays 212 (before the pacing stimulus 208). In graphs (i) and (ii), the first time delays are 147 milliseconds and 210 milliseconds, respectively.

[0044] In embodiments, the disclosed algorithm includes checking the stability of the three last S1-induced potentials 204 by calculating the standard deviation of the response timing (i.e., the delay of the annotation from pace 212). If the standard deviation is less than a given threshold, the response timing is considered well-defined and useful, and the processor selects the average or most recent of the three as the delay 212 for use in subsequent calculations of the DeEP.

[0045] As described above, short-interval stimuli 208 can produce different cardiac responses, such as evoked potentials having a “second time delay” 222 indicating abnormal tissue. This is seen in block 216, which includes a portion 215 of the S2 evoked potential (having the annotation 224 which is derived as described below).

[0046] To find annotations 214 and 224, the processor 28 performs the following scan correlation process.

[0047] Between each section 207, 209, or the last 208 pace and 350-400 milliseconds later (216), the processor correlates each S1 block (having its evoked potential 204) with the extracted model response of this 200 block. The scan is started for each section 207 / 209 / 216, and the annotation index of the model response 214 (selected as the center of mass at 256, or preferably at the last deflection) is aligned to the beginning of the section, and the aligned index is moved by the index "i" in Equation 1 below, and the result is calculated using Equation 1 at each such index i on the block's time axis. A typical time step between consecutive indices is 1 millisecond.

[0048] The processor selected the index with the highest scoring as the interval time annotation (214 in S1 and 224 in S2), where the processor calculated the highest scoring using Equation 1.

[0049] Annotation point 224 is also estimated using the disclosed derivative-weighted-correlation annotation scoring method. As seen in inset 230, the model response 256 is correlated with the S2 signal section 215, and the index with the highest scoring is selected by the processor 28 as annotation 224. The correlation process between the model response 256 and the decreasing signal section 215 is illustrated in inset 230 using a dotted arrow 218.

[0050] As seen in graphs (i) and (ii), evoked potentials can be characterized by strong fluctuations in the signal (large derivatives). In embodiments, the correlation is weighted by the magnitude of the derivative to improve scoring accuracy.

[0051] During the derivative-weighted-correlation process, also referred to herein as “scanning,” the processor 28 calculates a scoring which is a combination of correlation with favorability toward high derivatives, i.e., at each point of scanning, the processor 28 calculates the following equation:

[0052]

number

[0053] In some embodiments, the processor uses equation 1 (or a similar equation) of the algorithm to check the degree of correlation between the sinus-tuned bipolar signal sections 207 and uses the correlation treatment to select annotation 214 in the S1 block that yields the highest scoring in order to receive the model response 256.

[0054] Next, the processor 28 selects the annotation point 224 with the highest derivative-weighted-correlation scoring among the candidate annotation points 220 of the defined section 215.

[0055] When an evoked potential suspected to be VT is annotated (224), the delay time 222 can be calculated. The DeEP interval is defined as the time difference between delay 2 and 1 at the tissue location. Equation 2: DeEP interval = [Delay 222] - [Delay 212]

[0056] Typically, for it to be clinically significant, the time difference should well exceed several milliseconds. A time difference (i.e., DeEP interval) exceeding 40 milliseconds, as shown in Figure 2, is a strong indication of the tissue location causing VT. Physicians can adjust the GUI to show only those considered clinically "significant" DeEP intervals by adjusting the DeEp threshold (always a positive value), as shown in Figure 3.

[0057] Equation 1 is provided as an example. Other scoring formulas may also be used to provide the necessary selection of the best annotation.

[0058] Figure 3 is a schematic descriptive volume rendering of a ventricular electrophysiological (EP) map 300 showing the tissue location and size of DeEPs that can cause ventricular tachycardia (VT) according to an embodiment of the present invention.

[0059] In the illustrated example, the EP map 300 is a voltage map overlaid with ball icons 302 to show the locations of cardiac tissue exhibiting DeEP intervals that indicate VT.

[0060] In Figure 3, the potential risk of VT from a tissue location is defined as the DeEP interval value at that location. As can be seen, cardiac tissue locations exhibiting a larger DeEP interval are marked with a larger tag (e.g., ball icon) 302.

[0061] As described above, the physician can adjust the GUI scale 304 to show only DeEP intervals that exceed a user-specified positive threshold of 306. Optionally, the user can set a maximum DeEP interval value of 308.

[0062] Based on layers of information at the DeEP interval, physicians such as physician27 can carefully plan and perform selective ablation while minimizing the risk to the patient.

[0063] Automatic detection method for DEEP Figure 4 is a schematic flowchart illustrating a method and algorithm for the annotation of DeEP 210 in a bipolar potential diagram 200 (S1-214 and S2-224) according to an exemplary embodiment of the present invention. According to the presented embodiment, the algorithm performs a process in which, in the pacing step 400, the system 21 applies a pacing signal, the pacing signal taking the form of a sequence of a normal pacing pulse followed by one or more short pacing pulses.

[0064] In the EP data reception step 402, the processor 28 receives the pacing signal, the bipolar signal (e.g., waveform) from the catheter 29, and the respective ECG signals from the body surface (BS) electrodes. First, in the pacing verification step 403, the processor checks whether there are valid s1 and s2 pacing signals. If not, in the signal drop step 450, the processor drops the batch of signals.

[0065] Next, in the stable contraction verification step 404, the processor 28 estimates from the body surface ECG signal whether stable myocardial contractions occurred in response to sinus rhythm pacing and additional stimulus pacing. If not, in the signal drop step 450, the processor processes a batch of signals. By checking contractions, the processor typically checks: 1. that there are stable contractions of the heart in response to a predefined number of normal pacing s1 pacing s1 pacings (typically 3-4), particularly in the last four intervals after s1 pacing; and 2. that there is a response to the first additional stimulus. In embodiments, estimating stable contractions includes estimating the repetition in time of evoked potentials in the bipolar signal (e.g., stability of time interval 205).

[0066] In the noise thresholding step 405, the processor checks the amplitude of the received bipolar signal block against a noise threshold. In this step, the processor checks which channels of the bipolar EGM data have passed a predetermined noise threshold. This is done, for example, by checking the maximum amplitude at each interval, such as intervals 205 and 209. However, other criteria may be used, such as checking the SNR of the peak amplitude. The minimum threshold may be selected based on known characteristics of the EGM or may be given as an option for user input. EGMs that have passed the noise threshold proceed to the annotation step.

[0067] Next is the model response extraction step 406, in which the processor 28 finds the model response 256 as one of the evoked potential signals 204 that has the highest correlation among the signals 204. The processor annotates the model response 256 at the center of mass or identifies the delayed potential to select the last deflection using an annotation algorithm fabricated by Biosense Webster (Example).

[0068] Next, the processor 28 correlates the model response 256 with the normal evoked potential (204) and the remainder of the reduced signal section 215, and in the signal annotation step 408, annotates the time index on the signal that has the highest correlation with the annotation 214 or 224, respectively.

[0069] Using the timing of the stimulus 202 and the annotation 214, in the first delay calculation step 410, the processor 28 calculates a first time delay. This first time delay can be calculated as the average of several calculated delays 212.

[0070] Using the stimulation timing 208 and the selected annotation 224, the processor 28 calculates a second time delay 222 in a second delay calculation 412 that potentially indicates the tissue location causing VT.

[0071] In the DeEP interval calculation step 414, the processor 28 calculates the DeEP interval using equation 2.

[0072] Finally, as shown in Figure 3, in step 416 of laying out the DeEP interval, the processor 28 indicates the DeEP interval on the EP map.

[0073] In one embodiment, between steps 406-408, the processor annotates the most recent near-field response within the segment (for example, by identifying delayed potentials using an annotation algorithm created by Biosense Webster). In another embodiment, the processor identifies the far-field response of the segment by, for example, marking the first meaningful fluctuation within a unipolar intracardiac channel. In that embodiment, the delay is calculated between the near-field and far-field responses (not the pace-forming response).

[0074] The illustrative flowchart shown in Figure 4 is selected solely for the purpose of clarifying the concept. This embodiment may also include additional algorithmic steps, such as simultaneously receiving multiple bipolar and ECG signals, and receiving an indication of the degree of physical contact between the electrodes and the tissue being diagnosed from a contact force sensor. These steps and other possible steps have been intentionally omitted from the disclosure herein in order to provide a more simplified flowchart.

[0075] While the embodiments described herein primarily address cardiac diagnostic applications, the methods and systems described herein can also be used for other medical applications.

[0076] The embodiments described above are illustrative examples, and it will be understood that the present invention is not limited to those specifically illustrated and described above. Rather, the scope of the present invention includes both combinations and partial combinations of the various features described herein, as well as variations and modifications thereof not disclosed in the prior art, which would be conceived by those skilled in the art upon reading the foregoing description.

[0077] [Implementation Method] (1) A method for evaluating electrical propagation within the heart, Receiving a pacing signal applied to a patient's heart, wherein the pacing signal includes (i) a sequence of normal pacing stimuli at sinus rhythm intervals, and (ii) one or more abnormal pacing stimuli at abnormal intervals shorter than the sinus rhythm interval. In response to the pacing signal, the system receives cardiac signals sensed by electrodes located within the heart and electrodes on the patient's body surface. Finding and annotating model responses from evoked potentials caused by the aforementioned normal pacing stimulus, Correlating the model responses along different signal sections to find annotations for a first evoked potential caused by the normal pacing stimulus, and annotations for a second evoked potential caused by one or more abnormal pacing stimuli, according to the index in the signal section that received the highest scoring in the model response, Calculate the first time delay and the second time delay, Calculating the time difference between normal time delay and reduced time delay, A method comprising presenting to a user an EP map of at least a portion of the heart, which includes a graphical representation of the time difference presented at the tissue location. (2) The method according to Embodiment 1, wherein the correlation with the model response is weighted according to the derivatives of the one or more signals. (3) The method according to Embodiment 1, wherein estimating the first time delay and the second time delay includes annotating the estimated first and second delays with bipolar signals. (4) The method according to Embodiment 1, comprising: receiving each electrocardiogram (ECG) signal from an external electrode of the patient; using the ECG signals to estimate whether a stable contraction occurred in response to a predefined number of normal pacing stimuli and the first abnormal pacing stimuli; and discarding portions of the cardiac signal in which each ECG does not indicate a stable contraction. (5) The method according to Embodiment 4, wherein estimating the stable contraction includes estimating the repeatability of the evoked potentials in the bipolar signal.

[0078] (6) The method according to Embodiment 4, wherein estimating the stable contraction includes smoothing the bipolar signal and detecting the evoked potential in the smoothed bipolar signal. (7) The method according to Embodiment 4, wherein estimating one or more correlations includes distinguishing the far field from the near field signal. (8) The method according to Embodiment 1, wherein presenting the EP map includes adjusting the scale of the GUI to define the minimum positive value of the time difference displayed on the EP map. (9) The method according to Embodiment 1, wherein presenting the EP map includes overlaying artificial icons, which are graphically coded to indicate the time difference values, onto the EP map. (10) The method according to Embodiment 1, further comprising presenting the EP map to indicate the occurrence of abnormal ventricular activity to the user.

[0079] (11) The method according to Embodiment 10, wherein indicating the occurrence of the abnormal ventricular activity includes using at least one of color and surface morphology on the EP map. (12) The method according to Embodiment 1, wherein receiving the cardiac signal includes receiving unipolar and bipolar electrophoresis obtained using a catheter. (13) The method according to Embodiment 1, wherein estimating the highest scoring correlation includes training a machine learning model to estimate the highest scoring correlation based on a prespecified metric. (14) A system for evaluating electrical propagation within the heart, It is an interface, A pacing signal applied to a patient's heart, wherein the pacing signal includes (i) a sequence of normal pacing stimuli at sinus rhythm intervals, and (ii) one or more abnormal pacing stimuli at abnormal intervals shorter than the sinus rhythm interval. An interface configured to receive cardiac signals sensed by electrodes located within the heart and electrodes on the patient's body surface in response to the pacing signal, It is a processor, Finding and annotating model responses from evoked potentials caused by the aforementioned normal pacing stimulus, Correlating the model responses along different signal sections to find annotations for a first evoked potential caused by the normal pacing stimulus, and annotations for a second evoked potential caused by one or more abnormal pacing stimuli, according to the index in the signal section that received the highest scoring in the model response, Calculate the first time delay and the second time delay, Calculating the time difference between normal time delay and reduced time delay, A system comprising a processor configured to present to a user an EP map of at least a portion of the heart, which includes a graphical display of the time difference presented at the tissue location. (15) The system according to embodiment 14, wherein the processor is configured to correlate the model response by weighting the correlation according to the derivatives of the one or more signals.

[0080] (16) The system according to Embodiment 14, wherein the processor is configured to estimate the first time delay and the second time delay by annotating the estimated bipolar signals of the first delay and the second delay. (17) The system according to Embodiment 14, wherein the interface is further configured to receive electrocardiogram (ECG) signals from external electrodes of the patient, and the processor is configured to use the ECG signals to estimate whether a stable contraction has occurred in response to a predefined number of normal pacing stimuli and the first abnormal pacing stimuli, and to discard portions of the cardiac signal in which the respective ECGs do not indicate a stable contraction. (18) The system according to embodiment 17, wherein the processor is configured to estimate the stable contraction by estimating the repeatability of the evoked potentials in the bipolar signal. (19) The system according to embodiment 17, wherein the processor is configured to estimate the stable contraction by smoothing the bipolar signal and detecting the evoked potentials in the smoothed bipolar signal. (20) The system according to embodiment 17, wherein the processor is configured to estimate one or more correlations by identifying a far field from a near field signal.

[0081] (21) The system according to embodiment 14, wherein the processor is configured to present the EP map by adjusting the scale of the GUI to define the minimum positive value of the time difference to be displayed on the EP map. (22) The system according to embodiment 14, wherein the processor is configured to present the EP map by superimposing artificial icons, which are graphically coded to represent the time difference values, onto the EP map. (23) The system according to embodiment 14, wherein the processor is configured to present the EP map by indicating the occurrence of abnormal ventricular activity to the user. (24) The system according to embodiment 23, wherein the processor is configured to indicate the occurrence of the abnormal ventricular activity by using at least one of color and surface morphology on the EP map. (25) The system according to embodiment 14, wherein the interface is configured to receive the cardiac signal by receiving unipolar and bipolar electrophoresis acquired using a catheter.

[0082] (26) The system according to embodiment 14, wherein the processor is configured to estimate the highest scoring correlation by training a machine learning model to estimate the highest scoring correlation based on a pre-specified metric.

Claims

1. A system for evaluating electrical propagation within the heart, It is an interface, A pacing signal applied to a patient's heart, wherein the pacing signal includes (i) a sequence of normal pacing stimuli at sinus rhythm intervals, and (ii) one or more abnormal pacing stimuli at abnormal intervals shorter than the sinus rhythm intervals. An interface configured to receive cardiac signals sensed by electrodes located within the heart and electrodes on the patient's body surface in response to the pacing signal, It is a processor, Finding and annotating model responses from evoked potentials caused by the aforementioned normal pacing stimulus, Correlating the model responses along different signal sections to find annotations for a first evoked potential caused by the normal pacing stimulus, and annotations for a second evoked potential caused by one or more abnormal pacing stimuli, according to the index in the signal section that received the highest scoring in the model response, Calculate the first time delay and the second time delay, Calculating the time difference between normal time delay and reduced time delay, A system comprising a processor configured to present to a user an EP map of at least a portion of the heart, which includes a graphical representation of the time difference presented at a location within the heart.

2. The system according to claim 1, wherein the processor is configured to correlate the model responses by weighting the correlation between the model responses according to the derivatives of the one or more signals.

3. The system according to claim 1, wherein the processor is configured to estimate the first time delay and the second time delay by annotating the estimated bipolar signals of the first time delay and the second time delay.

4. The system according to claim 1, wherein the interface is further configured to receive electrocardiogram (ECG) signals from external electrodes of the patient, and the processor is configured to use the ECG signals to estimate whether a stable contraction occurred in response to a predefined number of normal pacing stimuli and one or more abnormal pacing stimuli, and to discard portions of the cardiac signals in which each ECG does not indicate a stable contraction.

5. The system according to claim 4, wherein the processor is configured to estimate the stable contraction by estimating the repeatability of evoked potentials in the cardiac signal.

6. The system according to claim 4, wherein the processor is configured to estimate the stable contraction by smoothing the cardiac signal and detecting the evoked potentials in the smoothed cardiac signal.

7. The system according to claim 4, wherein the processor is configured to estimate one or more correlations by identifying a far-field signal from a near-field signal.

8. The system according to claim 1, wherein the processor is configured to present the EP map by adjusting the scale of the GUI to define the minimum positive value of the time difference to be displayed on the EP map.

9. The system according to claim 1, wherein the processor is configured to present the EP map by overlaying an artificial icon, which is graphically coded to represent the value of the time difference, onto the EP map.

10. The system according to claim 1, wherein the processor is configured to present the EP map by indicating the occurrence of abnormal ventricular activity to the user.

11. The system according to claim 10, wherein the processor is configured to indicate the occurrence of the abnormal ventricular activity by using at least one of color and surface morphology on the EP map.

12. The system according to claim 1, wherein the interface is configured to receive the cardiac signal by receiving unipolar and bipolar electrophoresis obtained using a catheter.

13. The system according to claim 1, wherein the processor is configured to estimate the highest scoring correlation by training a machine learning model to estimate the highest scoring correlation based on a pre-specified metric.

14. A method for operating a system for evaluating electrical propagation within the heart, The aforementioned system comprises an interface and a processor, The interface receives a pacing signal applied to the patient's heart, wherein the pacing signal includes (i) a sequence of normal pacing stimuli at sinus rhythm intervals, and (ii) one or more abnormal pacing stimuli at abnormal intervals shorter than the sinus rhythm intervals. The interface receives cardiac signals in response to the pacing signal, which are sensed by electrodes located within the heart and electrodes on the patient's body surface. The processor finds and annotates a model response from the evoked potentials caused by the normal pacing stimulus, The processor correlates the model responses along different signal sections to find annotations for first evoked potentials caused by the normal pacing stimulus and annotations for second evoked potentials caused by one or more abnormal pacing stimuli, according to the index in the signal section that received the highest scoring in the model response. The processor calculates the first time delay and the second time delay, The aforementioned processor calculates the time difference between the normal time delay and the reduced time delay, A method of operating the system, comprising the processor presenting to the user an EP map of at least a portion of the heart, which includes a graphical representation of the time difference presented at a location within the heart.

15. A method of operating the system according to claim 14, wherein the correlation between the model responses by the processor includes the processor weighting the correlation between the model responses according to the derivatives of one or more signals.

16. The method of operating the system according to claim 14, wherein the processor estimating the first time delay and the second time delay includes the processor annotating the estimated bipolar signals of the first time delay and the second time delay.

17. A method of operating the system according to claim 14, comprising: the interface receiving respective electrocardiogram (ECG) signals from external electrodes of the patient; the processor using the ECG signals to estimate whether a stable contraction occurred in response to a predefined number of normal pacing stimuli and the one or more abnormal pacing stimuli; and the processor discarding portions of the cardiac signals in which the respective ECGs do not indicate a stable contraction.

18. A method of operating the system according to claim 17, wherein the processor estimating the stable contraction includes the processor estimating the repeatability of evoked potentials in the cardiac signal.

19. A method of operating the system according to claim 17, wherein the processor estimates the stable contraction by smoothing the cardiac signal and the processor detects the evoked potential in the smoothed cardiac signal.

20. A method of operating the system according to claim 17, wherein the processor estimating one or more correlations includes the processor identifying a far-field signal from a near-field signal.

21. The method of operating the system according to claim 14, wherein the processor presenting the EP map includes the processor adjusting the scale of the GUI to define the smallest positive value of the time difference displayed on the EP map.

22. The method of operating the system according to claim 14, wherein the processor presenting the EP map includes the processor overlaying artificial icons, which are graphically coded to indicate the time difference values, onto the EP map.

23. A method of operating the system according to claim 14, further comprising the processor presenting the EP map, the processor indicating to the user the occurrence of abnormal ventricular activity.

24. A method of operating the system according to claim 23, wherein the processor indicates the occurrence of the abnormal ventricular activity by using at least one of color and surface morphology on the EP map.

25. The method of operating the system according to claim 14, wherein the interface receiving the cardiac signal includes the interface receiving unipolar and bipolar electrophoresis obtained using a catheter.

26. A method of operating the system according to claim 14, wherein the processor estimating the highest scoring correlation includes training a machine learning model to estimate the highest scoring correlation based on a pre-specified metric.

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