Patent Description:
Mapping and imaging of the electrical signals in the heart is typically based on combining local activation time (LAT), as indicated by a catheter's ECG signals, with the spatial position of the signals. Such a method is used in the CARTO® <NUM> System, produced by Biosense Webster of Diamond Bar, Ca.

Existing activity measurement algorithms are based on processing of bipolar signals which are inherently immune to far field interference. Historically, LAT is determined by the time difference between the reference annotation and the earliest activity in the bipolar signal in the mapping catheter channels. However, determining earliest activations in bipolar signals presents an ambiguity in LAT measurements since the earliest activation is a composition of the approaching fields from the two unipolar signals and does not necessarily correspond to the onset of the activity wave.

<CIT> describes a medical device and associated method which discriminate near-field and far-field events by sensing a bipolar signal and a unipolar signal at a tissue site, detecting an event in response to one of the bipolar and unipolar signals, and comparing an event feature determined from the bipolar signal to an event feature determined from the unipolar signal.

The present disclosure will be more fully understood from the following detailed description of the embodiments thereof, taken together with the drawings, in which:.

Unlike bipolar signals the onset of the activity in a unipolar signal is clearly marked by a sharp downward deflection in the signal, but unlike bipolar signals the unipolar is sensitive to far field activations.

Embodiments of the present invention use a wavefront annotation algorithm which acts to combine the properties of the two types of ECG signals - a bipolar signal together with one of its associated unipolar signals - to generate accurate signal annotations. The inventors have verified that the algorithm provides accurate annotations which are immune to far field interferences.

The wavefront annotation algorithm provides automatic and reliable detection of annotation points that enable acquisition and annotation of numerous LAT points in a relatively short time.

An embodiment of the present invention comprises receiving a bipolar signal from a pair of electrodes in proximity to a myocardium of a human subject. A unipolar signal is also received from a selected one of the pair of electrodes, and a local unipolar minimum derivative of the unipolar signal is computed. A time of occurrence of the unipolar minimum derivative is also computed. A bipolar derivative of the bipolar signal is computed, and a ratio of the bipolar derivative to the local unipolar minimum derivative is evaluated.

When the ratio is greater than a preset threshold ratio value, the time of occurrence (of the unipolar minimum derivative) is identified as a time of activation of the myocardium.

<FIG> is a schematic block diagram of a wavefront annotation algorithm, according to an embodiment of the present invention. The algorithm inputs consist of a single bipolar signal and one of its unipolar signals, which are typically provided to a processor <NUM> operating the algorithm, following a low pass filter with a cut-off of <NUM> and a power rejection filter. More detail of the operation of processor <NUM> is provided with reference to <FIG> below. The polarity of the unipolar signal is assumed to be known (i.e. it is derived from either a positive or a negative electrode). The processor may be a stand-alone processor, and/or a general purpose processor that is typically operating a computer. The algorithm comprises a number of stages, summarized here.

A pre-processing stage <NUM> includes removal of baseline wander, low pass filtering and any order of differentiation. The removal of baseline wander includes removal of an additive low frequency signal that is an artifact and originates from various reasons such as mechanical catheter movement or respiration. This low frequency signal can alter the estimated derivative of the signals and therefore is typically removed.

A feature extraction stage <NUM> uses the post-processed signals and extracts features for every candidate annotation.

A first annotation detector stage <NUM> performs eliminations of candidate annotations based on a subset of features.

Next, in a pair elimination stage <NUM> candidate annotations that pass the required feature threshold, but are insignificant relative to another very close activation may be discarded.

Finally, in a second annotation detector stage <NUM> a score is given to each candidate annotation based on its feature values. Only candidate annotations that surpass the score thresholds are considered as valid annotations, and the timing and features of these are used by the processor in further operations of the processor, such as generating a map of the candidate annotations.

The elements of the algorithm are described in more detail below.

The core of the algorithm relies on three basic observations:.

<FIG> is an example of activity as measured by the bipolar signal and the unipolar positive electrode signal, according to an embodiment of the present invention. A graph <NUM> shows the bipolar signal; a graph <NUM> shows the unipolar signal. The sharp downward deflection on the left, in a region "A", is a near field activity which is concurrent in the unipolar and the bipolar signals. As shown in a region "B" during far field ventricular activation the unipolar signal changes, however, the bipolar activity is negligible. Embodiments of the present invention use multiple features of the signal similar to those exemplified above to assist in separating between local and far field activations. For example, in region A the unipolar amplitude and its rate are similar to the bipolar signal, while in region B the unipolar signal amplitude is much larger and its rate is much faster than the bipolar signal.

The following description describes the elements of the algorithm illustrated in <FIG>.

The purpose of these pre-processing and feature extraction stages is to remove and attenuate interferences in the unipolar and bipolar signals while maintaining and emphasizing those features of the signal that are used in subsequent stages. While for simplicity the actions described herein are assumed to occur in stages <NUM> and <NUM>, it will be understood that at least some of these actions may occur in other stages of the algorithm. A characteristic that we want to retain is the morphology of activations, since it reflects slope changes. Characteristics that are typically discarded are the baseline-wander that acts as an additive signal that can corrupt the slope measurements and also high frequency noise. Stages <NUM> and <NUM> are divided into four sub-stages:.

The Unipolar pre-processing stage consists of applying the following steps in series:.

The derivative of step <NUM> is used as an input to a unipolar annotation detector-(Phase I) in first annotation detector stage <NUM> (<FIG>). The additional filtered signal output of step <NUM> is used for feature extraction stage <NUM> of the algorithm.

The bipolar pre-processing stage consists of applying the following steps in series:.

The final output of the bipolar preprocessing stage (the bipolar derivative) is used as an input to the unipolar annotation detector-(Phase I) referred to above (<FIG>).

Intra-cardiac (IC) signals may contain additive baseline wander signals arising from movement of the catheter, movement of the subject and respiration that changes the interface with the tissue (see <FIG> and its description below). These motion artifacts contain mostly low frequency components. However, the near field activity signal may also contain significant energy in these spectral bands. Therefore the conventional approach of removal by high pass IIR or FIR filter is problematic and can cause distortion and morphology changes to the IC signals. Consequently, the selected approach that we use is based on estimation of the baseline wander (<FIG>) and its subtraction from the signal.

<FIG> is a graph illustrating baseline wander removal, according to an embodiment of the present invention. A unipolar signal <NUM> is originally contaminated by a low frequency artifact, contributing to the baseline wander. The purpose of the baseline estimation is to calculate the baseline which is then subtracted from the signal. In the figure a calculated baseline <NUM> has been overlaid on the unipolar signal. Baseline wander rejection is important since baseline wander can add noise to the estimate of the unipolar derivatives, and thus may affect the annotation detection.

The estimation of the baseline wander, and its subtraction from the original, is accomplished by removal of the near field activity using a series of two filters as is illustrated in <FIG>.

<FIG> is a block diagram of a baseline wander removal system, according to an embodiment of the present invention. A median filter <NUM>, typically having a window of <NUM>, is designed to remove the activities from the raw signal while an LPF <NUM>, which in one embodiment is an <NUM> taps FIR Hanning filter with a typical cut-off of approximately <NUM>, is designed to smooth out edges resulting from the median filter. Finally the baseline estimate is subtracted from the raw signal, by a process of negation <NUM> then summation <NUM>, resulting in a signal free of baseline wander.

<FIG> is a graph of two Gaussian filters, according to an embodiment of the present invention. The detection of sharp deflection points in the signal is based on the velocity of the signal, therefore a derivative approach is used. However, derivative functions act as a high pass filter, thus enhancing high frequency noise. Therefore, we use a smoothing function to decrease the noise in the derivative estimation. The smoothing function that we use are normalized zero mean Gaussian functions, comprising a unipolar Gaussian function <NUM> and a bipolar Gaussian function <NUM>, illustrated in <FIG>. These unipolar and bipolar Gaussian filters have <NUM>% of the energy in time windows of ±<NUM> and ±<NUM> respectively. Thus activations or approaching far fields at distances larger than these values are virtually ignored and do not affect the derivative value.

Reference is now made to <FIG>. <FIG> is a schematic block diagram of Annotation Detector-I stage <NUM>; <FIG> is a graph of unipolar and bipolar signals, and their derivatives; <FIG> has graphs illustrating a first rejection phase of the annotation algorithm; and <FIG> has graphs illustrating local and far field candidate annotations, according to embodiments of the present invention.

Referring to <FIG>, Table I below gives parameters used in the detector, and corresponding acronyms in the block diagram.

<FIG> shows an example of a bipolar slope of zero around a unipolar annotation. The graphs show a unipolar distal signal <NUM>, its derivative <NUM>, its local activation (A) as well as a bipolar signal <NUM>, and its derivative <NUM>. Notice that at the unipolar deflection point (A) the bipolar derivative is almost zero and it is not indicative of the large change in bipolar amplitude.

<FIG> has graphs illustrating a first rejection stage of the annotation algorithm of <FIG>. A top graph <NUM> shows the unipolar signal and a bottom graph <NUM> shows its smoothed derivative. Black dots <NUM> are minima values in the derivative signal below a threshold value and will be further considered as possible annotation points while grey dots <NUM> mark minima value above the threshold that will be rejected.

<FIG> illustrates separation between local (A) and far field (B) candidate annotations using the bipolar and unipolar derivative ratio feature described herein. The figure shows unipolar <NUM> and bipolar <NUM> signals, and unipolar <NUM> and bipolar <NUM> derivatives. In local activation, unipolar derivative changes are accompanied by a bipolar derivative change as illustrated by a <NUM> activity window <NUM>. However, this is not the case in the far field derived deflection (B), as illustrated by window <NUM>, thus the ratio between the change in the bipolar and unipolar slope for the far field case will be below the required ratio threshold.

Returning to <FIG>, the inputs for the annotation detector-I block are the relevant unipolar signal derivative under test, its polarity and its smoothed bipolar derivative. The outputs of the block are the annotation indexes and their slope value (the unipolar derivative value at the annotation index). The slope value acts as the score of the annotation.

In an embodiment of the invention the deflection points in the downslopes of the unipolar signal are detected, in blocks <NUM> and <NUM>, by finding the minima points below a threshold (typically -<NUM> mv/ms), see also <FIG>. Activities typically satisfy this condition in addition to two others:.

#<NUM> and #<NUM> are evaluated in blocks <NUM> and <NUM>, and in a decision <NUM>.

Referring to <FIG>, the bipolar derivative value (S-bip) is computed differently for positive and negative electrodes. In a disclosed embodiment, for a positive electrode it is the minimal value within a <NUM> time window, and for a negative electrode it is the negative value of the maximal value within that time window. The reason for using a time window and not the derivative at the annotation point is that in certain pathologies and/or orientations (of the catheter and the wave propagation direction) the bipolar signal at a given point can be small or even zero since the time delay of activities between unipolar activations can cancel out (<FIG>). The value is calculated differently for positive and negative electrodes since the tip activity at the positive electrode is registered as a downslope in the bipolar signal, while activity at the negative electrode is registered as an upslope in the bipolar signal.

The ratio between the unipolar and the bipolar derivatives may also be used as a classification criterion since this criterion can distinguish between near field and far field activity. In near field activity at least some of the downslope activity is typically represented in the bipolar signal, while in far field cases the bipolar signal may only have residual activity.

The pair elimination stage of the algorithm is responsible for merging two annotations that arise from a single activity. This split phenomena can occur when for some reason the downward slope of a near field activity contains a momentary upslope, either from activity recorded in the other electrode or from far field activity that influences one electrode more than the other. The momentary upslope will cause two minima in the derivative of the signal, and if these are strong enough they result in two annotations. In order to exclude these cases we evaluate the change in the signal due to the upslope.

All annotation pairs in the same unipolar signal that are not too far apart (typically less than <NUM>) are analyzed for a split. The segment between the two candidate annotations in the unipolar derivative signal is analyzed for upsloping. When the upsloping amplitude is considered significant the two annotations are maintained. If not, the annotation with a smaller downslope is discarded.

<FIG> is a graph <NUM> illustrating merging of candidate annotations, and how rejection criteria are used, according to an embodiment of the present invention. The graph shows a unipolar derivative signal and two possible annotations (circles, marked A[i] and A[i+<NUM>]).

The purpose of pair elimination block <NUM> is to decide whether the upsloping amplitude change (marked with a vertical double-headed arrow) between the smallest derivative amplitude and the peak P between the two possible annotations is significant or not. If the change is considered significant both annotations are maintained, otherwise the weaker activation - A[i] is discarded.

Thus, for an annotation A[i] to be discarded the relative change to the peak amplitude (P) between any adjacent candidates annotation with a stronger slope within the <NUM> time windows A[i+<NUM>] is considered. If the peak is significantly higher this point will not be rejected. In mathematical terms, in one embodiment, if the value of (P-A[i])/(<NUM>-A[i]) is lower than <NUM> the annotation A[i] is discarded. , annotation A[i] is rejected if one or more annotations in the <NUM> time window follow the above rule.

The candidate annotations that passed the earlier phases are revaluated in this block using additional features and metrics. Only annotations that pass this block and that also pass a user bipolar voltage controlled threshold are considered valid annotations. For each annotation multiple features are computed. Each feature value is given a fuzzy score ranging from zero to one, corresponding to a confidence value for the feature. Finally, all scores are combined together and their value is tested against a global score threshold. Those annotations that pass the global score threshold, i.e., that have a high confidence value, are considered valid annotations and those that do not, i.e., that have a low confidence value, are rejected.

The fuzzy functions described herein are examples of such functions that are used in one embodiment of the present invention. However, other such fuzzy functions or other probabilistic terms/functions will be apparent to those having ordinary skill in the art, and all such functions are assumed to be included within the scope of the present invention. In addition, for a specific requirement multiple fuzzy scores may be used (for example -fuzzy functions that highlight strong or small bipolar signals etc..

All fuzzy functions are bounded between <NUM> and <NUM>.

<FIG> is a graph <NUM> of a unipolar derivative fuzzy function, according to an embodiment of the present invention. The graph provides a score f(s<NUM>) assigned to the derivative, where the derivative value is herein termed s<NUM>. As shown in the graph, values of the derivative below -<NUM> receive a score of <NUM>, and values larger than -<NUM> decrease linearly such that a <NUM> score is reached at a slope of - <NUM>. Derivative values smaller than -<NUM> receive a score of zero.

The unipolar derivative s<NUM> is used in both detector stages, but unlike the first stage where it has a dichotomy threshold of <NUM> mv/ms, here its value is used to provide the score f(s<NUM>). The higher the score the more probable that this is a valid annotation according to this feature alone.

<FIG> shows graphs illustrating unipolar signal segmentation, according to an embodiment of the present invention. The segmentation is described further below. A unipolar signal <NUM> and its derivative <NUM> are illustrated around a candidate annotation time index <NUM> (black dot). A dotted horizontal line <NUM> representing a threshold marks a search segment (typically approximately ±<NUM>) in both directions. In one embodiment the segment value is defined as <NUM>% of the absolute maximum unipolar derivative value at the annotation point. Segments A, B mark the time intervals within the search window where the signal derivative is below the threshold. A final segment in this example can be either segment A or, if certain conditions (described hereinbelow) are met, it can be the joint segment starting from onset of A to the end of B.

A feature that we derive from the unipolar signal is the duration s<NUM> of the downslope segment around the candidate annotation. The aim is to detect the unipolar downslope from its initial descent until it starts to upslope. The motivation is to inspect features of the signals in that segment, such as properties of duration, amplitude, and their relationship, and to use them as a basis for a classifier. The inventors considered several methods for this task, all of which worked well for the obvious cases of a single slope, but the method described herein was selected since it works well on complicated cases having slope trend changes and local peaks within the slope segment.

Referring to <FIG>, the segmentation is based on analyzing the unipolar derivative via the following steps:.

The duration determined from the above steps, herein termed s<NUM>, is then assigned a score f(s<NUM>) using the fuzzy function described below with reference to <FIG>.

<FIG> is a graph <NUM> of a unipolar duration fuzzy function, according to an embodiment of the present invention. Very short slopes of less than <NUM> are unlikely to originate from real activation; very long activations are probably far field events. In addition, the unipolar duration for local valid activation cannot be too short and cannot be too long. The above observations are encapsulated in the fuzzy function of <FIG>, which provides the score f(s<NUM>). The function points <NUM>, <NUM> are: {<NUM>,<NUM>}. {<NUM>,<NUM>} and the slopes are <NUM> and -<NUM> respectively.

<FIG> is a graph <NUM> of a unipolar amplitude fuzzy function, according to an embodiment of the present invention. The unipolar amplitude is the amplitude of the unipolar signal (herein termed s<NUM>) in the detected activity segment (peak-to-peak) duration s<NUM>. In one embodiment the fuzzy function slope intersects points <NUM>, <NUM>: {<NUM>,<NUM>},{<NUM>,<NUM>}. The score derived from the fuzzy function, f(s<NUM>), is high the higher the amplitude of the signal. , for high scores, and high amplitudes, the more likely it is that the signal originates from a local activation, unless the far field signals have a large amplitude.

<FIG> is a graph <NUM> of a unipolar duration to amplitude ratio fuzzy function, according to an embodiment of the present invention. The unipolar duration to amplitude ratio excludes high ratio values since the longer the activity and the smaller the amplitude, the more likely that this is a false annotation. In one embodiment the equation of the fuzzy function line is <MAT>.

<FIG> is a graph <NUM> of a bipolar amplitude fuzzy function, according to an embodiment of the present invention. The bipolar amplitude within the unipolar activity segment (peak-to-peak), s<NUM>, is also used for scoring the likelihood of the candidate annotations. The higher the value, the more likely that this is a true activation.

An equation for the fuzzy function is: <MAT>.

The amplitude is calculated on the baseline rejected bipolar smoothed signal after low pass of Gaussian and anti-aliasing filter.

As described above, each feature receives a score and the scores are used together in generating a global score. The idea is that features can support one another in inclusion or exclusion of an annotation. In one embodiment the score method which we used is defined as follows: <MAT> where GS is the global score.

The value of GS should pass a specific threshold, for example <NUM>, for the annotation to be considered as valid.

It will be apparent to those skilled in the art that global scores, different from those exemplified above but having an equivalent outcome, can be used in embodiments of the present invention. Such global scores can include substantially any combination of weighted average of individual scores, and/or dot products of individual scores. Such global scores can also include a composition of scores based on a subset of fuzzy features.

In some embodiments a final stage of the algorithm is designed to provide the user the ability to eliminate annotations that were detected if they have a low bipolar amplitude. The required amplitude threshold is controlled by the user. The bipolar amplitude filtering compares the bipolar amplitude of each annotation that surpassed the post processing stage with a threshold. Only annotations having a bipolar amplitude that exceeds the threshold are passed to the system. (If a user desires to skip this stage she/he may set the threshold to zero, thus eliminating the rule of this stage.

The bipolar amplitude of each annotation is defined by measuring the peak-to-peak amplitude, baseline removed, <NUM> bipolar signal in a <NUM> window centered around the annotation time (maximum unipolar velocity point). In one embodiment a system default value of bipolar amplitude threshold is set to <NUM> micro Volts.

This bipolar amplitude is different from the fuzzy controlled bipolar amplitude (described above), since this bipolar amplitude is determined on a fixed interval. The fuzzy classifier uses a dynamic segment of the unipolar activation and therefore in some embodiments the dynamic segment may be more meaningful as a classifier. In addition this classifier is used as a dichotomic user controlled threshold.

All annotations that pass the fuzzy score and the bipolar user controlled bipolar amplitude are considered valid annotations that may be used by the processor.

In one embodiment each annotation should have the following features:.

In addition trace files may be provided, to include.

<FIG> is a schematic illustration of an invasive medical procedure using an apparatus <NUM>, according to an embodiment of the present invention. The procedure is performed by a medical professional <NUM>, and, by way of example, the procedure in the description hereinbelow is assumed to comprise acquisition of ECG signals from a heart <NUM> of a human patient <NUM>.

In order to acquire the signals, professional <NUM> inserts a probe <NUM> into a sheath <NUM> that has been pre-positioned in a lumen of the patient. Sheath <NUM> is positioned so that a distal end <NUM> of the probe may enter the heart of the patient, after exiting a distal end <NUM> of the sheath, and contact tissue of the heart.

Probe <NUM> may comprise any type of catheter that can be inserted into the heart of the patient, and that can be tracked, typically using a magnetic tracking system and/or an impedance measuring system. For example, probe <NUM> may comprise a lasso catheter, a shaft-like catheter, or a pentaRay catheter, produced by Biosense Webster of Diamond Bar, CA, or catheters generally similar to these catheters. Biosense Webster also produces a magnetic tracking system and an impedance measuring system that may be used in embodiments of the present invention.

Probe <NUM> comprises at least two electrodes <NUM>, which are used to acquire the ECG signals used by processor <NUM> in performing the algorithms described herein.

Apparatus <NUM> is controlled by processor <NUM> (<FIG>), and the processor may comprise real-time noise reduction circuitry <NUM>, typically configured as a field programmable gate array (FPGA), followed by an analog-to-digital (A/D) signal conversion integrated circuit <NUM>. The processor can pass the signal from A/D circuit <NUM> to another processor and can be programmed to perform the algorithms disclosed herein.

Processor <NUM> is located in an operating console <NUM> of the apparatus. Console <NUM> comprises controls <NUM> which are used by professional <NUM> to communicate with the processor. During the procedure, processor <NUM> communicates with an ECG module <NUM> in a module bank <NUM>, in order to acquire ECG signals as well as to perform the algorithms disclosed herein.

ECG module <NUM> receives ECG signals from electrode <NUM>. In one embodiment the signals are transferred, in module <NUM>, through a low noise pre-amplifier <NUM>, and via a band pass filter <NUM>, to a main amplifier <NUM>. Module <NUM> also comprises an analog to digital converter (ADC) <NUM>, which transfers digitized values of the ECG signals to processor <NUM>, for implementation by the processor of the algorithms described herein. Typically, processor <NUM> controls the operation of pre-amplifier <NUM>, filter <NUM>, amplifier <NUM>, and ADC <NUM>.

Thus, ECG module <NUM> enables processor <NUM> to acquire and analyze EP (electrophysiological) signals received by electrode <NUM>, including the ECG signals referred to herein. The signals are typically presented to professional <NUM> as voltage-time graphs, which are updated in real time, on a display screen <NUM>.

The software for processor <NUM> and module bank <NUM> may be downloaded to the processor in electronic form, over a network, for example. Alternatively or additionally, the software may be provided on non-transitory tangible media, such as optical, magnetic, or electronic storage media.

In order to operate apparatus <NUM>, module bank <NUM> typically comprises modules other than the ECG module described above, such as one or more tracking modules allowing the processor to track the distal end of probe <NUM>. For simplicity, such other modules are not illustrated in <FIG>. All modules may comprise hardware as well as software elements.

In addition to display screen <NUM> presenting ECG signals acquired by electrode <NUM>, results <NUM> of the algorithms described herein may also be presented to the algorithm user on the display screen.

Claim 1:
A computer-implemented method for identifying times of activation of the myocardium, comprising:
receiving a bipolar signal from a pair of electrodes in proximity to a myocardium of a human subject;
receiving a unipolar signal from a selected one of the pair of electrodes;
pre-processing the bipolar signal and the unipolar signal, wherein pre-processing comprises removing baseline wander, applying a low pass filter and differentiation;
extracting features for each of a plurality of candidate annotations, wherein the extracted features comprise a time duration of a downslope segment of the unipolar signal;
discarding candidate annotations based on a subset of the extracted features;
discarding one candidate annotation from any pairs of candidate annotations which arise from a single activation;
computing a score for each candidate annotation based on the extracted features, wherein computing the score comprises computing the score based on the time duration of the downslope segment of the unipolar signal; and
identifying candidate annotations for which the score is greater than a score threshold as times of activation of the myocardium.