Real-time adjustment of annotation points

Abstract

To provide intra-body cardiac electrocardiogram (ECG) sensing and visualizing.SOLUTION: A system includes signal acquisition circuitry and a processing unit. The signal acquisition circuitry is configured to receive multiple intra-cardiac signals acquired by multiple electrodes of an intra-cardiac probe in a heart of a patient. The processing unit is configured to select a group of the intra-cardiac signals, extract the most likely annotation value from each of the intra-cardiac signals in the group, in accordance with a likelihood criterion, identify in the group an intra-cardiac signal whose most likely annotation value is statistically deviant in the group by more than a predefined measure of deviation, extract, from the intra-cardiac signal having the statistically deviant annotation value, at least the second most likely annotation value in accordance with the likelihood criterion, and, in response to a statistical deviation of the second most likely annotation value, select a valid annotation value for the corresponding intra-cardiac signal.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention generally relates to medical treatments and medical devices within the body, and more specifically, to the detection and visualization of electrocardiogram (ECG) of the heart within the body.

Background Art

[0002] When measuring and annotating internal-electrocardiogram (iECG) signals generated by a plurality of electrodes, it may be desirable to process the signals (e.g., by a computer) in order to reduce the embedded noise.

[0003] There are various methods for such iECG signal processing. For example, U.S. Patent Application Publication No. 2016 / 0089048 describes an automatic method for determining the local activation time (LAT) of four or more multi-channel electrocardiogram signals, including ventricular channels, mapping channels, and a plurality of reference channels.

[0004] Another example is U.S. Patent Application Publication No. 2017 / 0311833, which describes a system for guiding catheter treatment by diagnosing arrhythmias and measuring, classifying, analyzing, and mapping the spatial electrophysiological (EP) patterns within the body.

[0005] Yet another example is U.S. Patent Application Publication No. 2017 / 0042436, which describes a system and method for automatically integrating measurements taken over multiple heartbeats into a single heart map.

Summary of the Invention

Means for Solving the Problems

[0006] One embodiment of the present invention described herein provides a system comprising a signal acquisition circuit mechanism and a processing unit. The signal acquisition circuit mechanism is configured to receive multiple intracardiac signals acquired by multiple electrodes of an intracardiac probe within a patient's heart. The processing unit is configured to select a group of intracardiac signals, extract the most likely annotation value from each of the intracardiac signals in the group according to a likelihood criterion, identify the intracardiac signals in the group whose most likely annotation value statistically deviates within the group by a predetermined scale of deviation, extract at least the second most likely annotation value from the intracardiac signals having the statistically deviant annotation value according to a likelihood criterion, and select a valid annotation value for the corresponding intracardiac signal in response to the statistical deviation of the second most likely annotation value.

[0007] In some embodiments, the processing unit is configured to define the measure of the deviation by converting it to a standard score of the annotation value. In the disclosed embodiments, the processing unit is configured to calculate the deviation of the annotation value over intracardiac signals acquired by a selected subset of spatially related electrodes located within a predetermined distance from each other within the heart.

[0008] In an exemplary embodiment, the processing unit is further configured to identify a group of alternative annotation values ​​by reducing the likelihood rank of at least the most likely statistically deviant annotation value, and to select a valid annotation value in response to the statistical deviation and the likelihood rank of the alternative annotation values. In another embodiment, the annotation value includes local excitation time (LAT).

[0009] In some embodiments, the processing unit is configured to extract the most likely annotation value in a given intracardiac signal by finding the extreme value of the intracardiac signal during the cardiac cycle, and to extract the second most likely annotation value by finding the second highest local extreme value of the intracardiac signal. In other embodiments, the processing unit is configured to extract the most likely annotation value in a given intracardiac signal by finding the extreme derivative of the intracardiac signal during the cardiac cycle, and to extract the second most likely annotation value by finding the second highest local extreme value of the derivative.

[0010] According to one embodiment of the present invention, a method is provided which includes receiving multiple intracardiac signals acquired by multiple electrodes of an intracardiac probe within a patient's heart. A group of intracardiac signals is selected. The most likely annotation value for each is extracted from each of the intracardiac signals in the group according to a likelihood criterion. Intracardiac signals whose most likely annotation value statistically deviates within the group by a predetermined scale of deviation are identified within the group. At least a second most likely annotation value is extracted from the intracardiac signal having the most likely annotation value that statistically deviates according to a likelihood criterion. In response to the statistical deviation of the second most likely annotation value, a valid annotation value for the corresponding intracardiac signal is selected.

[0011] According to one embodiment of the present invention, a method for obtaining effective local excitation time for intracardiac electrocardiogram signals is further provided. This method includes obtaining a group of digitized signals representing intracardiac electrocardiogram (ECG) signals, and extracting a first best estimate of effective local excitation time from the group of ECG signals. Statistical properties are calculated for the group of ECG signals. Based on the group statistical properties, a standard score is calculated for each signal in the group of ECG signals. The standard score for each signal is compared to a predetermined limit value. The first best estimate of effective local excitation time is replaced with the local excitation time of the signal having a standard score within the predetermined limit value.

[0012] This invention will be more fully understood by considering the following "Modes for Carrying Out the Invention" in conjunction with the drawings. [Brief explanation of the drawing]

[0013] [Figure 1] This is a schematic diagram of an electroanatomical system for multi-channel measurement of intracardiac ECG signals according to one embodiment of the present invention. [Figure 2] This figure schematically illustrates signal acquisition by a spatially related group of electrodes during a single heart cycle, according to one embodiment of the present invention. [Figure 3] This flowchart provides a schematic example of a method for improving the reliability of annotation values ​​according to one embodiment of the present invention. [Figure 4] This flowchart schematically illustrates a method for improving the reliability of annotation values ​​for spatially related LAT value groups, according to one embodiment of the present invention. [Modes for carrying out the invention]

[0014] Overview Intracardiac probe-based (e.g., catheter-based) cardiac diagnostic and therapeutic systems can measure multiple intracardiac signals, such as electrocardiograms (ECGs), during invasive procedures. Such systems can acquire multiple intracardiac signals using electrodes attached to the distal end of the probe (hereinafter also referred to as "distal electrodes"). The measured signals are typically analyzed, and local excitation time (LAT) values ​​are annotated, which can be used to provide physicians with visual cardiac information, such as a 3D mapping of the sources of pathological electrical patterns within the patient's heart, to support corrective medical procedures such as ablation.

[0015] The measured signals are typically weak and have a low signal-to-noise ratio (SNR). Furthermore, pathological electrocardiograms, such as those caused by atrial flutter or atrial fibrillation, can exhibit multiple peaks in the cardiac cycle, which complicates the measurement of LAT values. On the other hand, many electrodes are used, and therefore, some redundancy may exist in the data the system receives from the electrodes.

[0016] Embodiments of the present invention disclosed herein provide intracardiac probe-based electroanatomical measurement and analysis systems and methods that utilize the statistical characteristics of signals collected by distal electrodes to improve the quality and reliability of the collected data. This method is high-speed and may therefore be performed in real time, for example, during invasive procedures.

[0017] The following description refers to annotation values ​​for local excitation time (LAT), but in various embodiments of the present invention, other suitable signal parameters may be used with necessary modifications. Thus, the term “annotation value” includes other parameters, as well as parameters related to LAT.

[0018] According to the embodiment, the most likely LAT annotation value of a measured intracardiac signal can be estimated by analyzing the intracardiac signal using pre-defined criteria. Several LAT estimation criteria can be used, including, but not limited to, the maximum signal voltage event, the minimum signal voltage event, the maximum rate of change of a positive voltage gradient event, and the maximum rate of change of a negative voltage gradient event. The LAT estimate is then set to the event occurrence time according to the selected criteria. The voltage or gradient value at the event is referred to herein as the “y value”.

[0019] As mentioned above, due to noise and / or cardiac pathology, such LAT estimates may misidentify local extrema (maximum or minimum value according to the criteria) rather than the desired extrema. In these cases, the processor reanalyzes the signal using the same criteria, but can achieve a more accurate LAT estimate by searching for second, third, fourth, or other extrema. (To avoid confusion of minimum / maximum / high / low, terms such as "best LAT estimate," "second best," and "third best" are used below. For maximum voltage or maximum dv / dt events, the best estimate is the highest voltage or highest dv / dt, the second best is the second highest local maximum, etc. Similarly, for minimum voltage or minimum dv / dt events, the best estimate is the minimum voltage or minimum dv / dt, the second best estimate is the second lowest local minimum, etc.)

[0020] The above LAT estimate is sometimes called the "LAT annotation value."

[0021] The techniques disclosed herein assume that signals acquired from physically adjacent electrodes ("adjacent electrodes") and / or temporally adjacent heartbeats, free from noise and irregular galvanic connections, will exhibit similar annotation values. Signals extracted from adjacent electrodes and LAT values ​​annotated from such signals are referred to as "spatially related," while signals extracted from temporally adjacent heartbeats and LAT values ​​annotated from such signals are referred to as "temporally related." Spatially and / or temporally related signals and annotation values ​​are collectively referred to as "related."

[0022] Embodiments of the present invention utilize the expected similarity of related signals to enhance the reliability of visualized LAT values.

[0023] In some embodiments, the processor annotates the LAT value of a group of related intracardiac electrophysiological signals using, for example, one of the four criteria described above. The processor then calculates the statistical characteristics of the group and uses a measure of deviation in response to the statistical characteristics to determine which signals deviate substantially from the LAT value of the group (e.g., signals whose LAT value is not within a preset distance from the group mean).

[0024] According to one embodiment, for signals that deviate substantially from the group value, the processor determines to re-evaluate the LAT value using the second best estimate, the third best estimate, etc. until the processor finds an LAT value that does not deviate substantially from the group value. In one embodiment, the statistical characteristics include the mean of the LAT values of the group (e.g.,

[0025]

Number 1

[0026]

Number 2

[0027] In summary, according to embodiments of the present invention, the quality and reliability of a group of annotation values of spatially related intracardiac signals visualized by a user can be improved in real time by calculating the statistical characteristics of the annotation values and recycling LAT values that deviate substantially from the group value using the next best estimate.

[0028] Description of the System Figure 1 is a schematic diagram of an electroanatomical system 21 for multi-channel measurement of intracardiac ECG signals according to one embodiment of the present invention. In some embodiments, the system 21 is used for electroanatomical mapping of the heart.

[0029] Figure 1 illustrates a physician 22 performing electroanatomical mapping of a patient's heart 24 using an electroanatomical catheter 23. The catheter 23 has one or more arms 26 at its distal end that may be mechanically flexible, with one or more distal electrodes 27 coupled to each of those arms. As understood, Figure 1 depicts a catheter having five arms, but other types of catheters may be used in alternative embodiments according to the present invention. The electrodes are coupled to a processor 34 via an interface 32.

[0030] During electroanatomical mapping procedures, a tracking system can be used to track the intracardiac position of the distal electrode 27, thereby associating each acquired electrophysiological signal with a known intracardiac position. An example of a tracking system is Active Current Location (ACL), which is described in U.S. Patent No. 8,456,182. In the ACL system, the processor estimates the position of each distal electrode based on the impedance measured between each distal electrode 27 and a plurality of surface electrodes 28 connected to the patient's skin 25 (for ease of illustration, only one surface electrode is shown in Figure 1). The processor can then associate any electrophysiological signal received from the distal electrode 27 with the position from which the signal was acquired.

[0031] In some embodiments, multiple distal electrodes 27 acquire intracardiac ECG signals from ventricular tissue of the heart 24. The processor includes a signal acquisition circuit mechanism 36 coupled to receive intracardiac signals from an interface 32, a memory 38 for storing data and / or instructions, and a processing unit 42 (e.g., a CPU or other processor).

[0032] The signal acquisition circuit mechanism 36 generates multiple digital signals by digitizing intracardiac signals. The acquisition circuit mechanism then transmits the digitized signals to the processing unit 42 included in the processor 28.

[0033] Among other tasks, the processing unit 42 is configured to extract annotation parameters such as local excitation time ("LAT") from the signal according to a criterion selected from a group of LAT estimation criteria, including the maximum ECG voltage, the minimum ECG voltage, the maximum positive rate of change of the ECG voltage, and the maximum negative rate of change of the ECG voltage. When the processing unit estimates LAT, for example, according to the maximum ECG voltage criterion, the LAT value is equal when the Y value of the ECG signal is at its maximum value (with respect to each cardiac cycle). Similarly, the estimated LAT may be equal when the Y value is at its minimum value, when the first derivative of the Y value is at its maximum value, or when the first derivative is at its minimum value.

[0034] The processing unit 42 is further configured to calculate statistical properties such as the mean of annotated parameters of a group of potentially similar adjacent signals (in the current context, adjacent signals refer to signals from electrodes located in close proximity to each other ("spatially related")).

[0035] According to the embodiment, the acquired ECG signal may be noisy due to poor galvanic connection, induction noise from various sources, noise from the signal acquisition circuit 36, or any other source. In addition, pathological ECG signals, such as those associated with atrial flutter and right bundle branch block, may exhibit multiple voltage peaks and / or slopes, as well as multiple dv / dt peaks and slopes, for each cardiac cycle. By comparing the LAT values ​​of each signal in a group, the processing unit may determine that an LAT value that deviates significantly from the group's value may be erroneous. An LAT value that is less likely to be erroneous (e.g., the best estimated LAT value) is hereafter referred to as the effective LAT value.

[0036] In one embodiment, with respect to any of the LAT estimation criteria, the processing unit may extract a set of alternative LAT values ​​with falling likelihood, in addition to the most likely value (corresponding to the absolute maximum or minimum). For example, with respect to the maximum voltage criterion, the processing unit may be configured to find the local maximum value of the ECG signal, and then annotate the second-best LAT estimate from the second highest maximum value, the third-best estimate from the third highest maximum value, and so on. In a similar manner, the processing unit may construct a set of alternative LAT values ​​with falling likelihood for any of the four criteria by finding the local maximum or minimum value of the voltage and the first derivative of the voltage of the ECG signal.

[0037] If the processing unit 42 determines that the LAT value is invalid, the processing unit will start with the second-best estimate and try alternative LAT values, moving on to less likely LAT values ​​until a valid value (e.g., a LAT value that is not likely to be wrong) is found (for example, if the processing unit does not find any valid value, the processing unit may, for example, degrade the signal or use the best estimate that is likely to be wrong).

[0038] In some embodiments, the processing unit 42 uses valid annotation values ​​to construct, for example, an electroanatomical map 50 of the heart and displays this map 50 to the physician 22 on a screen 52 in real time. Alternatively, the processing unit 42 may present valid annotation values ​​in any other preferred manner.

[0039] The examples shown in Figure 1 are selected solely for the purpose of illustrating the concept. In alternative embodiments of the present invention, for example, position measurement may also be performed by applying a voltage with a gradient between a pair of surface electrodes 28 and measuring the resulting potential using a distal electrode 27 (for example, using the CARTO® 4 technology from Biosense-Webster, Irvine, California). Thus, embodiments of the present invention are applicable to any position detection method.

[0040] Other types of catheters, such as Lasso® Catheter (manufactured by Biosense-Webster) or basket catheters, may be used as equivalent. A contact sensor may be attached to the distal end of the electroanatomical catheter 23. Other types of electrodes, such as those used for ablation, may be similarly used on the distal electrode 27 to acquire intracardiac electrophysiological signals.

[0041] Figure 1 primarily shows the parts relevant to embodiments of the present invention. Other system elements, such as external ECG recording electrodes and their connections, are omitted. Various ECG recording system elements, along with filtering, digitization, protection circuit mechanisms, and other elements, are omitted.

[0042] In any embodiment, an application-specific integrated circuit (ASIC) is used to measure the intracardiac ECG signal. Various elements routing the signal acquisition circuit mechanism 36 may be implemented in hardware, for example, using one or more individual components, a field-programmable gate array (FPGA), or an ASIC. In some embodiments, some elements of the signal acquisition circuit mechanism 36 and / or the processing unit 42 may be implemented in software, or using a combination of software and hardware elements.

[0043] The processing unit 42 typically includes a general-purpose processor using software programmed to perform the functions described herein. The software may be downloaded electronically, for example, via a network, or the software may be provided and / or stored on a non-temporary tangible medium such as magnetic memory, optical memory, or electronic memory.

[0044] Figure 2 is a schematic illustration of signal acquisition by a spatially related group of electrodes during a single heart cycle according to one embodiment of the present invention. The horizontal axis represents time, and the vertical axis represents voltage level. Four signals are illustrated: the first electrode signal 202, the second electrode signal 204, the third electrode signal 206, and the fourth electrode signal 208. Each signal shows an R peak followed by a T peak.

[0045] The processing unit 42 (Figure 1) employs a maximum voltage criterion to annotate the LAT values ​​of the signals to the following values: value 210 corresponding to the R peak of signal 208, value 212 corresponding to the R peak of signal 202, and value 214 corresponding to the R peak of signal 206. Due to noise and / or pathology for signal 204, the voltage of the T peak is higher than the voltage of the R peak, and therefore the initial LAT annotation (best LAT estimate) for signal 204 is the LAT value 216 corresponding to the peak of the T signal. However, the second best estimate of the LAT of signal 204 (i.e., the second best local maximum) is the R peak represented by the LAT value 218.

[0046] According to the exemplary embodiment shown in Figure 2, the LAT value 216 deviates substantially from the group's LAT value. Therefore, the processing unit 42 attempts alternative values ​​starting from the second-best estimate. Since the LAT value 218 does not deviate from the group's LAT value, the most likely annotation value, LAT value 218, is the valid LAT value.

[0047] Therefore, the processing unit can improve the reliability of annotated LAT values ​​by using a relatively fast method that can be done in real time to check the LAT values ​​associated with local maximums that are lower than the absolute maximum when the best LAT estimate deviates significantly from the group's LAT values.

[0048] The examples shown in Figure 2 are selected solely for the purpose of illustrating the concept. In alternative embodiments of the present invention, for example, more spatially relevant signals may be used, the number of local maximums may exceed two, and other LAT estimation criteria may be used.

[0049] Figure 3 is a flowchart 300 illustrating a schematic method for improving the reliability of annotation values ​​according to one embodiment of the present invention. This method is performed by a processing unit 42 (Figure 1).

[0050] This method begins with a signal acquisition step 302 in which a processing unit acquires multiple intracardiac ECG signals. The processing unit then selects a group of relevant signals from the acquired signals in a group selection step 304. Such selection can be performed, for example, using CARTO® 4 technology or by other preferred technology.

[0051] Next, in the criterion selection step 304, the processing unit selects a LAT estimation criterion. The selected criterion may be, for example, one of the maximum voltage, minimum voltage, maximum dv / dt, and minimum (most negative) dv / dt. The selection may be instructed by the user or selected by any other means.

[0052] Using the criteria selected in step 304, the processing unit extracts the LAT values ​​for all group signals in the group annotation value extraction step 306 by finding the best LAT estimate corresponding to the selected criteria. For example, in Figure 2, the processor may look for a group of signals (210-218) having LAT values ​​210, 212, 214, 216, and 218, and considering these values ​​against the selected likelihood criteria, select LAT value 218 (from Figure 2) as the "best LAT estimate" (or LAT value that is unlikely to be wrong) in step 306 (Figure 3), which is a "valid annotation value" that can be graphed.

[0053] It should be noted that, depending on the results of the statistical tests used in loops 310–314, 316–320, or 324–328, the processor may replace the best LAT estimate from process 306 with the best estimate from the respective statistical test loops 316–320 (i.e., replacing the first best estimate from process 306 with the second best estimate from process 322) or with the best estimate from loops 324–328 (replacing the first best estimate from process 306 with the third best estimate from process 330).

[0054] Next, in the statistical characteristics calculation step 308, the processing unit calculates one or more parameters, such as the mean and standard deviation of the group of LAT values.

[0055] After calculating group statistics in step 308, the processing unit starts a loop (steps 310-314) in which the LAT annotation of each signal is checked against the group's statistical characteristics (from step 308), and recalculated if the LAT value deviates significantly from the group LAT value. That is, the processor checks for LAT values ​​that "statistically deviate" beyond a predetermined scale of statistical deviation of the group statistical characteristics obtained in step 308. The loop (steps 310-314) starts in next signal selection step 310, in which the processing unit selects the next signal from the list of group signals (e.g., signals 210-218 in Figure 2), and then calculates the standard score in standard score calculation step 312, which calculates the standard score of the signal for the group statistical characteristics (i.e., the processor calculates the number of standard deviations between the signal's LAT value and the group mean).

[0056] Next, in the limit value comparison step 314, the processing unit checks whether the standard score of the LAT signal is within a preset limit value. If the LAT value is within the preset limit value, for example, 0.25, the processing unit proceeds to step 315, which checks all signals to be considered. In step 315, if more signals are to be considered, the processing unit returns to step 310 to check the next signal in the group. However, if no more signals should be considered, the flowchart terminates (or can be rerun for a different group of signals).

[0057] Steps 310–314 enable the processor to identify any intracardiac signal within a group of intracardiac signals that has an annotation value that is "statistically deviant." For example, an annotation value that deviates from the group mean by a default scale of statistical standard deviation.

[0058] In step 314, if the signal's LAT value is not within a preset limit, the processing unit enters the second-best LAT extraction step 316, calculates the second-best LAT value (as described above in relation to step 312), calculates the standard score of the second-best LAT in the standard score calculation step 318, and compares the standard score with the preset limit in the limit value comparison step 320.

[0059] In step 320, if the standard score is within a predetermined limit, the second-best estimate is better than the first. The processing unit then enters step 322, replacing the best with the second-best, replacing the best estimated LAT (from step 306) in the group of signals with the second-best, and then enters step 310 again to check the next signal. However, if the standard score is not within a predetermined limit in step 320, the processing unit assumes the second-best estimate is wrong and tries the third-best estimate. In step 324, the processing unit extracts the third-best value. In step 326, the processing unit calculates the standard score for the third-best LAT estimate and checks in step 328 whether the standard score is within a predetermined limit.

[0060] In step 328, if the standard score is within a predetermined limit, the third-best estimate is better than the first-best estimate in step 306 (and the second-best estimate in step 316), and the processing unit enters step 330, replacing the best with the third-best, replacing the best estimate LAT (from step 306) in the group of signals with the third-best, and then enters step 310 again to check the next signal. However, in step 328, if the standard score of the third-best estimate is not within the limit, the best estimate LAT should not be changed, and the processing unit enters step 310 again to check the next signal.

[0061] As described above, the flowchart terminates at step 315 if there are no more signals in the current group being considered. At this stage, the processing unit has annotated LAT values ​​for all signals in the group. The processing unit may then re-enter the flowchart for another group of related signals, or perform other tasks beyond the scope of this disclosure.

[0062] The exemplary flowchart shown in Figure 3 is selected solely for the purpose of illustrating the concept. In alternative embodiments, for example, the flowchart may include checking the fourth and / or even less likely best estimate. In other embodiments, only the second-best estimate is checked. In some embodiments, less likely estimates for the entire group are calculated and stored as part of step 306, and therefore steps 312, 318, and 326 are redundant. In some embodiments, some or all of the steps may be performed simultaneously. Several typical alternative flowcharts are illustrated with reference to Figure 4.

[0063] Figure 4 is a flowchart 400 illustrating a schematic method for improving the reliability of annotation values ​​for spatially related LAT value groups according to one embodiment of the present invention. This method is performed for each member of a group by a processing unit 42 (Figure 1) after the statistical properties of the group have been calculated (for example, after step 308 in Figure 3; however, note that Figure 4 replaces the further steps in Figure 3, from steps 310 to 330).

[0064] Unlike the exemplary embodiment in Figure 3, according to the method shown in Figure 4, the processing unit generates a new group of valid LAT signals while inspecting the group of signals, which can then be made visible to the user.

[0065] The flowchart begins with the best LAT acquisition step 402, in which the processing unit stores the best LAT value and corresponding Y value from the routines described in relation to the flowchart in Figure 3. For example, in step 306, in Figure 3, we can assume that the best LAT and corresponding Y value were extracted when the processor calculated the group annotation value.

[0066] Next, in the standard score calculation step 404, the processing unit calculates the number of standard deviations between the signal's LAT value and the group mean, and proceeds to the limit value comparison step 406 to check whether the standard score of the LAT signal is within a predetermined limit value.

[0067] If the standard score of the LAT value is not within a pre-set limit, the processing unit enters the next permitted iteration check step 408. When the processing unit first enters step 408, it starts the first iteration, and each time the processing unit enters step 408 again, the number of iterations increases. In some embodiments, there may be a limit on the number of iterations. For example, the processor may skip a less likely estimate after the third best estimate (thus the next iteration is not permitted in step 408 after three iterations). In another embodiment, the processing unit may skip further iterations if there are no more local maximum or minimum values ​​(according to a selected criterion).

[0068] If the next iteration is permitted in step 408, the processing unit enters the next best LAT calculation step 410, where the processing unit calculates the next best LAT and the corresponding Y value.

[0069] According to the exemplary embodiment shown in Figure 4, in order to qualify a valid LAT value, the local maximum (or minimum, according to a selected criterion) should be sufficient for the best estimate. For example, if the criterion is maximum dv / dt, the absolute maximum is 25 mV / s and the second local maximum (second-best estimate) is 1 mV / s, and the processing unit can reject the second-best estimate, even though the best estimate may deviate substantially from the group mean.

[0070] The processing unit performs this test in the Y-value ratio comparison step 412, which is executed after step 410. The processing unit divides the second-best estimated Y-value (1 mV / sec in the example above) by the Y-value of the best estimate (25 mV / sec) and compares the result (0.04) to a preset threshold. If the ratio is greater than the threshold, the processing unit proceeds to the standard score calculation step 414 to calculate the standard score of the current LAT estimate for group statistics and enters the limit value comparison step 416 to check whether the standard score of the next best LAT signal is within the preset limit value. If the standard score is not within the limit value, the processing unit returns to step 408 to attempt the next best estimate.

[0071] According to the exemplary embodiment shown in Figure 4, the processing unit may be in drop mode, and invalid LAT values ​​are not visualized (as opposed to visualizing the best estimate, which is usually and probably incorrect). In step 408, if no further iterations are allowed, or if the ratio is too low in step 412, the current best LAT estimate is considered invalid, and there are no valid LAT values ​​for the signal. The processing unit enters drop mode check step 418. If drop mode is on, the process ends (and no valid values ​​are in the group of valid LAT values, and therefore the LAT of the current signal is not visualized). In step 418, if drop mode is off, the processing unit enters step 420 to return to best estimate, changing the current LAT estimate to the best LAT stored in step 402.

[0072] In step 406, if the best LAT is within the limit, or in step 416, if a lower best estimate is within the limit, and following step 420, the processing unit enters step 422, adding the LAT to the valid LATs, adding the current LAT estimate to the group of valid LAT estimates, and the flowchart ends (or typically, re-running the next signal in the group).

[0073] To ensure understanding, the illustrative flowchart shown in Figure 4 is selected solely for the purpose of clarifying the concept. In alternative embodiments of the present invention, for example, in one embodiment where the drop mode does not exist, invalid values ​​are never visualized, while in another embodiment, invalid values ​​are always visualized. In yet another embodiment, the processing unit may decide whether to visualize or hide invalid LAT values ​​according to a less restrictive test, for example, if its standard score deviates from the group mean by a threshold greater than the threshold used in steps 406 and 416. In some embodiments, invalid values ​​are visualized but clearly marked.

[0074] 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 herein. Rather, the scope of the present invention includes both combinations and partial combinations of the various features described above, as well as variations and modifications thereof that a person skilled in the art would likely conceive upon reading the foregoing description, but which are not disclosed in the prior art.

[0075] [Implementation Method] (1) A system, A signal acquisition circuit mechanism configured to receive multiple intracardiac signals acquired by multiple electrodes of an intracardiac probe within the patient's heart, A processing unit, The group of intracardiac signals is acquired, From each of the intracardiac signals within the group, the most likely annotation value is extracted according to the likelihood criterion. Identify within the group the intracardiac signal whose most likely annotation value is statistically deviating beyond a predetermined scale of deviation within the group, From the intracardiac signal having the most likely annotation value that is statistically deviant, at least the second most likely annotation value is extracted according to the likelihood criterion. A system comprising: a processing unit configured to select a valid annotation value for the corresponding intracardiac signal in response to the statistical deviation of the second most likely annotation value. (2) The system according to Embodiment 1, wherein the processing unit is configured to convert the scale of the deviation into a standard score of the annotation value and define it. (3) The system according to Embodiment 1, wherein the processing unit is configured to calculate the deviation of the annotation value over intracardiac signals obtained by a selected subset of spatially related electrodes located within a predetermined distance from one another within the heart. (4) The system according to Embodiment 1, wherein the processing unit is further configured to identify a group of alternative annotation values ​​by reducing the likelihood rank with respect to at least the most likely statistically deviant annotation value, and to select the valid annotation value in response to the statistical deviation and the likelihood rank of the alternative annotation value. (5) The system according to Embodiment 1, wherein the annotation value includes local excitation time (LAT).

[0076] (6) The system according to Embodiment 1, wherein the processing unit is configured to extract the most likely annotation value in a given intracardiac signal by finding the extreme value of a given intracardiac signal within a cardiac cycle, and to extract the second most likely annotation value by finding the second highest local extreme value of the intracardiac signal. (7) The system according to Embodiment 1, wherein the processing unit is configured to extract the most likely annotation value in a given intracardiac signal by finding the extreme derivative of a given intracardiac signal within a cardiac cycle, and to extract the second most likely annotation value by finding the second highest local extreme of the derivative. (8) A method, Receiving multiple intracardiac signals acquired by multiple electrodes of an intracardiac probe within the patient's heart, Selecting the group of intracardiac signals, According to the likelihood criteria, the most probable annotation value is extracted from each of the intracardiac signals within the group, Identifying within the group the intracardiac signal whose most likely annotation value exceeds a predetermined scale of deviation and statistically deviates within the group, From the intracardiac signal having the most likely annotation value that is statistically deviant, at least the second most likely annotation value is extracted according to the likelihood criterion. A method comprising selecting a corresponding valid annotation value for the intracardiac signal in response to the statistical deviation of the second most likely annotation value. (9) The method of Embodiment 8, comprising defining the scale of the deviation by converting it to a standard score of the annotation value. (10) The method of Embodiment 8, wherein identifying the most likely statistically deviant annotation value involves calculating the deviation of the annotation value across intracardiac signals obtained by a selected subset of spatially related electrodes located within a predetermined distance from one another within the heart.

[0077] (11) The method of Embodiment 8, wherein extracting at least the second most likely annotation value and selecting the valid annotation value includes identifying a group of alternative annotation values ​​by reducing the likelihood rank for at least the most likely statistically deviant annotation value, and selecting the valid annotation value in response to the statistical deviation and the likelihood rank of the alternative annotation value. (12) The method according to embodiment 8, wherein the annotation value includes local excitation time (LAT). (13) The method according to Embodiment 8, wherein extracting the most likely annotation value in a given intracardiac signal includes finding an extremum of the given intracardiac signal within a cardiac cycle, and extracting the second most likely annotation value includes finding the second highest local extremum of the intracardiac signal. (14) The method according to Embodiment 8, wherein extracting the most likely annotation value in a given intracardiac signal includes finding the extreme derivative of the given intracardiac signal within a cardiac cycle, and extracting the second most likely annotation value includes finding the second highest local extreme of the derivative. (15) A method for obtaining an effective local excitation time for an intracardiac electrocardiogram signal, wherein the method is A process for acquiring a group of digitized signals representing intracardiac electrocardiogram (ECG) signals, A step of extracting a first best estimate of the effective local excitation time from the group of ECG signals, A step of calculating the statistical characteristics of the group of ECG signals, A step of calculating a standard score for each signal within the group of ECG signals based on the statistical characteristics of the group, A step of comparing the standard score of each signal with a pre-set limit value, A method comprising the step of replacing the first best estimate of the effective local excitation time with the local excitation time of the signal having a standard score within the pre-set limit.

Claims

1. 1. A system comprising: signal acquisition circuitry configured to receive a plurality of intracardiac signals acquired within the patient's heart by a plurality of electrodes of an intracardiac probe; A processing unit comprising: acquiring the group of intracardiac signals; extracting a respective most likely annotation value from each of the intracardiac signals in the group according to a likelihood criterion; identifying intracardiac signals within the group whose most likely annotation values ​​deviate statistically within the group by more than a predetermined measure of deviation; extracting at least a second most likely annotation value from the intracardiac signals having the most likely annotation value that statistically deviates according to the likelihood criterion; a processing unit configured to select a valid annotation value for the corresponding intracardiac signal in response to a statistical deviation of the second-most likely annotation value.

2. The system of claim 1 , wherein the processing unit is configured to define the measure of the deviation in terms of a standard score for the annotation value.

3. 2. The system of claim 1, wherein the processing unit is configured to calculate a deviation of the annotation value across intracardiac signals acquired by a selected subset of spatially related electrodes located within a predetermined distance from each other within the heart.

4. 2. The system of claim 1, wherein the processing unit is further configured to identify a group of alternative annotation values ​​by decreasing a likelihood rank for at least the statistically most likely deviant annotation value, and to select the valid annotation value in response to the statistical deviation and the likelihood rank of the alternative annotation value.

5. The system of claim 1 , wherein the annotation value comprises a local excitation time (LAT).

6. 2. The system of claim 1, wherein the processing unit is configured to extract the most likely annotation value in a given intracardiac signal by finding an extremum of the given intracardiac signal within a cardiac cycle, and to extract the second most likely annotation value by finding a second-highest local extremum of the intracardiac signal.

7. 2. The system of claim 1, wherein the processing unit is configured to extract the most likely annotation value in a given intracardiac signal by finding an extremum derivative of the given intracardiac signal within a cardiac cycle, and to extract the second most likely annotation value by finding a second-highest local extremum of the derivative.

8. 1. A method comprising: receiving a plurality of intracardiac signals acquired by a plurality of electrodes of an intracardiac probe within the patient's heart; selecting the group of intracardiac signals; extracting a respective most likely annotation value from each of the intracardiac signals in the group according to a likelihood criterion; identifying intracardiac signals within the group whose most likely annotation values ​​deviate statistically within the group by more than a predetermined measure of deviation; extracting at least a second most likely annotation value from the intracardiac signals having the most likely annotation value that statistically deviates according to the likelihood criterion; and selecting a valid annotation value for the corresponding intracardiac signal in response to a statistical deviation of the second most likely annotation value.

9. The method of claim 8 , comprising defining the measure of the deviation in terms of a standard score for the annotation value.

10. 9. The method of claim 8, wherein identifying the most likely statistically deviant annotation value comprises calculating the deviation of the annotation value across intracardiac signals acquired by a selected subset of spatially related electrodes located within a predetermined distance from each other within the heart.

11. 9. The method of claim 8, wherein extracting at least the second most likely annotation value and selecting the valid annotation value comprises: identifying a group of alternative annotation values ​​by decreasing a likelihood rank for at least the statistically deviant most likely annotation value; and selecting the valid annotation value in response to the statistical deviation and the likelihood ranks of the alternative annotation values.

12. The method of claim 8 , wherein the annotation value comprises a local excitation time (LAT).

13. 9. The method of claim 8, wherein extracting the most likely annotation value in a given intracardiac signal comprises finding an extremum of the given intracardiac signal within a cardiac cycle, and extracting the second most likely annotation value comprises finding a second highest local extremum of the intracardiac signal.

14. 9. The method of claim 8, wherein extracting the most likely annotation value in a given intracardiac signal comprises finding an extremum derivative of the given intracardiac signal within a cardiac cycle, and extracting the second most likely annotation value comprises finding a second highest local extremum of the derivative.

15. 1. A method for obtaining effective local activation times of an intracardiac electrocardiogram signal, said method comprising: obtaining a group of digitized signals representing intracardiac electrocardiogram (ECG) signals; extracting a first best estimate of a valid local activation time from said group of ECG signals; calculating statistical properties of the group of ECG signals; calculating a standard score for each signal in the group of ECG signals based on statistical characteristics of the group; comparing said standard score for each signal with a preset threshold; and replacing the first best estimate of a valid local excitation time with the local excitation time of the signal having a standard score within the preset limits.

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