Patent Description:
The present invention relates generally to intrabody medical instruments, and particularly to intrabody cardiac electrocardiogram (ECG) sensing.

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

Various methods exist for such iECG signal processing. For example, <CIT> describes an automatic method of determining local activation time (LAT) of four or more multi-channel cardiac electrogram signals which include a Ventricular channel, a mapping channel and a plurality of reference channels.

Another example is "<NPL>, which describes an analog signal processing IC for the low-power heart rhythm analysis, featuring three identical, but independent intra-ECG readout channels, each comprising an analog QRS feature extractor for low-power consumption and fast diagnosis of the electrocardiogram.

In <CIT> there is described catheterization of the heart that is carried out by inserting a probe having electrodes into a heart of a living subject, recording a bipolar electrogram and a unipolar electrogram from one of the electrodes at a location in the heart, and defining a window of interest wherein a rate of change in a potential of the bipolar electrogram exceeds a predetermined value.

The invention is defined by appended claim <NUM>.

Intra-cardiac probe-based (e.g., catheter-based) cardiac diagnostic and therapeutic systems may measure multiple intra-cardiac signals, such as electrocardiograms (ECG), during an invasive procedure. Such systems may acquire the multiple intra-cardiac signals using electrodes (also referred to hereinafter as "distal electrodes") that are fitted at the distal end of the probe. The measured signals may be used to provide a physician with visual cardiac information such as <NUM>-D mapping of the source of pathological electrical patterns within the heart of the patient, and to support corrective medical procedures such as ablation.

The measured signals are typically weak, with a low Signal to Noise Ratio (SNR). Moreover, the galvanic connection of some of the electrodes with tissue may be poor or non-existent. On the other hand, many electrodes are used, and, hence, there may be some redundancy in the data that the system receives from the electrodes.

Embodiments of the present invention that are disclosed herein provide intra-cardiac probe-based electro-anatomical measurement and analysis systems that use statistical characteristics of the signals that the distal electrodes collect, to improve the quality and reliability of the collected data.

In the description hereinbelow we will refer to annotation value of Local Activation Time (LAT).

In some embodiments according to the present invention, a processor extracts annotation values i.e. , the LAT) of the signals, and then calculates statistical characteristics of the LAT values of a group of signals that are acquired by a corresponding group of electrodes (which may comprise all or some of the electrodes). In an embodiment, the statistical characteristics comprise the mean of the LAT values of the group of signals (e.g., ẋ=Σx/n); in other embodiments the characteristics further comprise the standard deviation (e.g., o=√(T(x-ẋ)<NUM>/n)) of the group. The processor then uses statistical methods to determine, for each one of the group of signals, whether annotation values of signals are valid values, or values that should be ignored.

In another embodiment, the statistical characteristics comprise the quartiles of the group of LAT values. The processor calculates the first and the third quartiles Q1, Q3, and then ignores all values that are lower than Q1 or higher than Q3 (a first quartile (Q1) is defined as the middle number between the smallest number and the median of a data set; a third quartile (Q3) is the middle value between the median and the highest value of the data set). Alternatively, the processor may define the measure of deviation of the LAT values in terms of any other suitable percentile (or multiple percentiles) of the LAT values. Further alternatively, any other suitable process that discards outlier LAT values can be used.

The technique disclosed hereinabove assumes that, devoid of noise and irregular galvanic connections, the electrodes of the group exhibit similar annotation values. Typically, the annotation values acquired by electrodes that are remote from each other may vary substantially. In addition, signals from each electrode may be annotated periodically, with each heartbeat ("cardiac cycle"), and annotation values derived from cardiac cycles that are temporally remote from each other may vary. In an embodiment, the group of signals is inter-related. In some embodiments, a tracking system measures the geometrical location of the electrodes, and the group comprises annotation values derived from neighboring electrodes only ("spatially related," i.e., electrodes that are located no more than a predefined distance from one another). In other embodiments the group comprises annotation values derived from neighboring cardiac cycles only ("temporally related," i.e., cardiac cycles that all occur within no more than a predefined time duration); and, in an embodiment, the group comprises values that are both spatially and temporally related (will be referred to, in short, as "related values").

The processor, after calculating the statistical characteristics of the group of related LAT values, omits LAT values that are statistically deviant in the group, e.g., substantially different from the mean value of the group of values (the group of the remaining LAT values will be referred to as the group of valid LAT values). Thus, LAT values that correspond to poorly connected electrodes, or to electrodes that are subject to extreme noise, may be eliminated from the group of valid LAT values.

In embodiments, to determine whether a LAT value is statistically deviant from the mean LAT of a group of signals, the processor measures the deviation of annotated LAT valued from the mean of the group of LAT values. In an embodiment, the measure of the deviation is the Standard Score of the value (defined as the difference between the value and the mean, divided by the standard deviation), which is compared to preset limits. For example, values that are larger than the mean by more than <NUM> standard deviations (standard score = <NUM>), or lower than the mean by more than <NUM> standard deviations (standard score = - <NUM>) may be considered statistically deviant and thus omitted. In another embodiment, the processor omits values that are lower than the first quartile or higher than the third quartile.

In embodiments of the present invention, the processor mitigates the variance in LAT values of spatially related electrodes due to the different time delays of cardiac signal propagation within the heart. According to embodiments, the processor corrects the LAT annotation acquired by a given electrode, by compensating for the displacement of the given electrode relative to the other electrodes, so as to cancel the difference in propagation delay.

In summary, according to embodiments of the present invention, the quality and reliability of a group of annotation values of spatially and/or temporally related inter-cardiac signals may be improved by calculating statistical characteristics of the annotation values, comparing the annotation values to the group mean, and omitting from the group of valid values, values that are remote from the mean. In embodiments, prior to statistical characteristics calculation, the group of annotation values is first be modified to correct for propagation delays of the signals.

<FIG> is a schematic, pictorial illustration of an electro-anatomical system <NUM> for multi-channel measurement of intra-cardiac ECG signals, in accordance with an embodiment of the present invention. In some embodiments, system <NUM> is used for electro-anatomical mapping of a heart.

<FIG> depicts a physician <NUM> using an electro-anatomical catheter <NUM> to perform an electro-anatomical mapping of a heart <NUM> of a patient <NUM>. Catheter <NUM> comprises, at its distal end, one or more arms <NUM>, which may be mechanically flexible, to each of which are coupled one or more distal electrodes <NUM>. As would be appreciated, although <FIG> depicts a catheter with five arms, other types of catheters may be used in alternative embodiments according to the present invention. The electrodes are coupled, through an interface <NUM>, to a processor <NUM>.

During the electro-anatomical mapping procedure, a tracking system is used to track the intra-cardiac locations of distal electrodes <NUM>, so that each of the acquired electrophysiological signals may be associated with a known intra-cardiac location. An example of tracking system is Active Current Location (ACL), which is described in <CIT>. In the ACL system, a processor estimates the respective locations of the distal electrodes based on impedances measured between each of distal electrodes <NUM> and a plurality of surface electrodes <NUM> that are coupled to the skin of patient <NUM>. (For ease of illustration, only one surface-electrode is shown in <FIG>. ) The processor may then associate any electrophysiological signal received from distal electrodes <NUM> with the location at which the signal was acquired.

In some embodiments, multiple distal electrodes <NUM> acquire intra-cardiac ECG signals from tissue of a cardiac chamber of heart <NUM>. The processor comprises a signal acquisition circuitry <NUM> that is coupled to receive the intra-cardiac signals from interface <NUM>, a memory <NUM> to store data and/or instructions, and a processing unit <NUM> (e.g., a CPU or other processor).

Signal acquisition circuitry <NUM> digitizes the intra-cardiac signals so as to produce multiple digital signals. The Acquisition Circuitry then conveys the digitized signals to processing unit <NUM>, included in processor <NUM>.

Among other tasks, processing unit <NUM> is configured to extract annotation parameters from the signals, calculate statistical characteristics such as mean value of the annotated parameters for groups of neighboring signals that are likely to be similar (in the current context, neighboring signals refers to signals from electrodes located close to each other ("spatially related"), and/or to annotation values extracted from cardiac cycles that are close to each other in time ("temporally related")).

The processing unit is further configured, after calculating the statistical characteristics, to drop (i.e., omit) annotation values that are likely to be invalid from the group (such as annotation from electrodes with poor galvanic connection, or subject to an intense temporal noise). The remaining annotation values will be referred to hereinbelow as "valid annotation values.

The processing unit visualizes the valid annotation values, i.e., the annotation values excluding the statistically deviant annotation values that have been omitted, to a user. In some embodiments, processing unit <NUM> visualizes the valid annotation values, for example, by overlaying them on an electro-anatomical map <NUM> of the heart and displaying the map to physician <NUM> on a screen <NUM>. Alternatively, processing unit <NUM> may visualize the valid annotation values (after omitting the invalid annotation values) in any other suitable way.

The example illustration shown in <FIG> is chosen purely for the sake of conceptual clarity. In alternative embodiments of the present invention, for example, position measurements can also be done by applying a voltage gradient between pairs of surface electrodes <NUM> and measuring, with distal electrodes <NUM>, the resulting potentials (i.e., using the CARTO®<NUM> technology produced by Biosense-Webster, Irvine, California). Thus, embodiments of the present invention apply to any position sensing method.

Other types of catheters, such as the Lasso® Catheter (produced by Biosense-Webster), or a basket catheter, may equivalently be employed. Contact sensors may be fitted at the distal end of electro-anatomical catheter <NUM>. Other types of electrodes, such as those used for ablation, may be utilized in a similar way on distal electrodes <NUM> to acquire intra-cardiac electrophysiological signals.

<FIG> mainly shows 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 are omitted, as well as elements for filtering, digitizing, protecting circuitry, and others.

In an optional embodiment, a read-out application-specific integrated circuit (ASIC) is used for measuring the intra-cardiac ECG signals. The various elements for routing signal acquisition circuitry <NUM> may be implemented in hardware, e.g., using one or more discrete components, such as field-programmable gate arrays (FPGAs) or ASICs. In some embodiments, some elements of signal acquisition circuitry <NUM> and/or processing unit <NUM> may be implemented in software, or by using a combination of software and hardware elements.

Processing unit <NUM> typically comprises a general-purpose processor with software programmed to carry out the functions described herein. The software may be downloaded in electronic form, over a network, for example, or it may, alternatively or additionally, be provided and/or stored on non-transitory tangible media, such as magnetic, optical, or electronic memory.

Related Annotation Values are derived from spatially related electrodes (e.g., electrodes that are geometrically close to each other, i.e., located no more than a predefined distance from one another) and/or from temporally related signals (e.g., values extracted from cardiac cycles that are close to each other, i.e., occur within no more than a predefined time duration). More precisely, related annotation values are annotation values for which the combined distance, comprising the geometrical distance between the electrodes and the temporal distance between the cardiac cycles, is below some predefined threshold.

<FIG> is a diagram <NUM> that schematically illustrates acquisition of signals by multiple electrodes at multiple cardiac cycles. A horizontal axis <NUM> shows the cardiac cycle (each vertical line is one cardiac cycle), and a vertical axis <NUM> shows the distance of the electrode from a reference point (only one spatial dimension is shown; as would be appreciated, two or three dimensions may be used in practice, but are not shown, for clarity). According to the example embodiment illustrated in <FIG>, there are electrodes in all horizontal lines, and LAT annotation values are registered for all intersections of horizontal and vertical lines (each intersection will be referred hereinbelow to as a LAT-point).

Curves <NUM> are equi-LAT lines, showing the location of the indicated LAT values, and the electrodes are likely to measure, at the corresponding cardiac cycles, values interpolated from the neighboring equi-LAT curves. For example, the expected registered value of LAT-point <NUM> (which is vertically half-way between equi-LAT lines <NUM> and <NUM>) is <NUM>, whereas the expected registered value of LAT-point <NUM> is <NUM>.

As can be seen, the LAT values of neighboring vertical lines and of neighboring horizontal lines are similar. Circle <NUM> represents a group of related LAT values <NUM>, that are close to each other in terms of geometrical (vertical) and temporal (horizontal) distances.

The example illustration shown in <FIG> is simplified and shown purely for the sake of conceptual clarity. In alternative embodiments, for example, the distance between the electrodes is not uniform, and, the group of related signals may not be a circle.

<FIG> is a flow chart <NUM> that schematically illustrates a first method for enhancing the reliability of annotation values, according to examples. The method of <FIG> is not encompassed by the wording of the claims, but is considered as useful for understanding the invention. The flow is executed by processing unit <NUM> (<FIG>). The flow starts at a Recording Signals step <NUM>, wherein the processing unit records ECG signals monitored by electrodes <NUM> and acquired by acquisition circuitry <NUM> (<FIG>). Next, at an Extracting Annotation Values step <NUM>, the processing unit calculates the annotation values for each electrode and each cardiac cycle.

The processing unit then enters a Getting Electrode Location step <NUM>, wherein the location of the electrodes is acquired (e.g., using the ACL technique), and the spatial location of each electrode is registered, and then enters a Selecting Group step <NUM>.

In step <NUM>, the processing unit selects a group of related annotation values. As described hereinabove, the group comprises annotation values that are likely to be similar, from spatially and/or temporally related signals.

Next, in a Calculating Mean and SD step <NUM>, the processing unit calculates the average and standard deviation for all annotation values of the group. In the present context, any suitable type of mean can be used, such as an arithmetic mean, a geometric mean, a median, a Root Mean Square (RMS) value, a center of mass, or any other.

The processing unit then, repeatedly for each annotation value of the group, sequentially enters steps <NUM>, <NUM>, and either step <NUM> or step <NUM>. In a Calculating Standard Score step <NUM>, the processing unit calculates the standard score of the annotation value (e.g., by dividing the difference between the annotation value and the mean by the standard deviation). In a Comparing Standard Score step <NUM> the processing unit compares the standard score calculated in step <NUM> to preset limits. In a Dropping Value step <NUM>, which is entered if the standard score exceeds a preset limit, the processing unit drops the statistically deviant annotation value; and, in an Adding Value step <NUM>, which is entered if the standard score is within the preset limits, the processing unit adds the annotation value to a group of valid annotation values.

The processor repeats the sequence of steps <NUM>, <NUM> and either step <NUM> or step <NUM> for all annotation values of the group. The flow chart may then repeat (from step <NUM>) for other groups of related electrodes.

When the flow ends, groups of valid annotation values replace the original groups, with better reliability, as extreme values (for example, from electrodes with poor galvanic connection) are omitted.

<FIG> is a flow chart <NUM> that schematically illustrates a second method for enhancing the reliability of annotation values, according to examples. The method of <FIG> is not encompassed by the wording of the claims, but is considered as useful for understanding the invention. The method illustrated in <FIG> differs from the method illustrated in <FIG> only in the statistical characteristics and the selection of omitted values. Hence, steps <NUM> to <NUM> illustrated in <FIG> are identical, respectively, to steps <NUM> to <NUM> of <FIG>, except for steps <NUM> and <NUM>, which are different from steps <NUM>, <NUM> of <FIG>, and will be described hereinbelow.

In a Calculating Quartiles step <NUM>, processing unit <NUM> (<FIG>) calculates the first and the third quartiles (Q1 and Q3) of the group of LAT values (Q1 is defined as the middle number between the smallest number and the median of the group of LAT values; Q3 is the middle value between the median and the highest value of group of LAT values).

In a Comparing Annotation Value step <NUM>, the processing units compares the annotated LAT value to Q1 and to Q3. If the value is smaller than Q1 or higher than Q3, the processing unit will enter Dropping Annotation Value step <NUM>, wherein if the value is between Q1 and Q3, will enter Adding Value step <NUM>.

The example flow charts shown in <FIG>, <FIG> are chosen purely for the sake of conceptual clarity. In alternative embodiments, for example, annotation values may be extracted when the signal is acquired (rather than after the signal is recorded). In an embodiment, the selection of the signals of the group may be done by the physician; in other embodiments the processing unit will select the group, according to an area and/or a time range that the physician indicates.

In some embodiments, step <NUM> (<NUM> in <FIG>) is not needed - the processing unit will, in step <NUM> (<NUM>), drop extreme values from the group, and when the flow is completed only the good values will remain. In other embodiments, all annotation values are initially marked as invalid, and step <NUM> (<NUM>) is not needed.

In some embodiments, other statistical characteristics that are used, different than those described above; for example, in an embodiment, octiles rather than quartiles may be used, and the processing unit may omit values lower than the first octile or higher than the last octile. Further alternatively, any other suitable percentile can be used.

Any other suitable statistical methods to detect and omit extreme values may be used in alternative embodiments.

The technique described above is improved by correcting the extracted LAT values, prior to statistical characteristics calculation, for expected changes in value due to different spatial positions of the electrodes. The wave through the heart is assumed to travel at a given speed (e.g., <NUM>/s). Using the known positions of the electrodes acquiring the signals, theoretical differences in LAT are applied when calculating the mean.

<FIG> is a flow chart <NUM> that schematically illustrates an improved method for enhancing the reliability of annotation values, according to examples. The processor of the present invention is configured to implement the method illustrated by <FIG>, while the method as such is not encompassed by the wording of the claims. The flow is executed by processing unit <NUM>.

The flow starts at a Recording Signals step <NUM>, followed by a Calculating Annotation Values step <NUM>, a Getting Electrodes Location step <NUM> and a Selecting Group step <NUM>, which may be identical, respectively, to steps <NUM>, <NUM>, <NUM> and <NUM> (<FIG>).

Next, the processing unit enters a Correcting LAT Value step <NUM>, wherein, for each LAT value of the group, the processing unit calculates and applies an estimated correction according to the spatial position of the electrode and the assumed wave travel speed. After step <NUM>, the flow reverts to <FIG>, at Calculating Mean and SD step <NUM>.

Thus, an estimate of the deviation that is caused by propagation delay is removed from the group, further enhancing the reliability of the annotation signals.

The example flow chart shown in <FIG> is chosen purely for the sake of conceptual clarity. In alternative embodiments, for example, the correction for anticipated signal delay can be integrated in the Calculating Mean and SD step. In other embodiments, the correction is done before the groups are selected (and, thus, groups may comprise a larger number of related LAT values).

Claim 1:
A system (<NUM>), comprising:
signal acquisition circuitry (<NUM>), which is configured to receive multiple intra-cardiac signals acquired by multiple electrodes (<NUM>) of an intra-cardiac probe (<NUM>) in a heart of a patient; and
a processor (<NUM>), which is configured to:
extract multiple local activation time (LAT) values from the intra-cardiac signals;
estimate the respective intra-cardiac locations of the electrodes based on signals received from a tracking system;
select a group of the intra-cardiac signals;
correct one or more of the LAT values in a given intra-cardiac signal, acquired by a given electrode in the group, for expected changes in value due to different spatial positions of the electrodes by applying theoretical differences in the LAT values based on an assumed wave travel speed through the heart;
identify in the group one or more LAT values that are statistically deviant in the group by more than a predefined measure of deviation; and
visualize the LAT values to a user, excluding the statistically deviant LAT values.