WO2023223007A1 - Appareil de surveillance de l'activation dans le coeur - Google Patents

Appareil de surveillance de l'activation dans le coeur Download PDF

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Publication number
WO2023223007A1
WO2023223007A1 PCT/GB2023/051270 GB2023051270W WO2023223007A1 WO 2023223007 A1 WO2023223007 A1 WO 2023223007A1 GB 2023051270 W GB2023051270 W GB 2023051270W WO 2023223007 A1 WO2023223007 A1 WO 2023223007A1
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WIPO (PCT)
Prior art keywords
unipolar
processor
electrodes
electrograms
heart
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PCT/GB2023/051270
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English (en)
Inventor
Prapa KANAGARATNAM
Nick Linton
Darrel Francis
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Imperial College Of Science, Technology And Medicine
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Priority claimed from US17/745,342 external-priority patent/US20230019503A1/en
Application filed by Imperial College Of Science, Technology And Medicine filed Critical Imperial College Of Science, Technology And Medicine
Publication of WO2023223007A1 publication Critical patent/WO2023223007A1/fr

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Classifications

    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/24Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316Modalities, i.e. specific diagnostic methods
    • A61B5/318Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/367Electrophysiological study [EPS], e.g. electrical activation mapping or electro-anatomical mapping
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/24Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316Modalities, i.e. specific diagnostic methods
    • A61B5/318Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346Analysis of electrocardiograms
    • A61B5/349Detecting specific parameters of the electrocardiograph cycle
    • A61B5/361Detecting fibrillation
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271Specific aspects of physiological measurement analysis
    • A61B5/7282Event detection, e.g. detecting unique waveforms indicative of a medical condition

Definitions

  • the present invention relates to the detection and pictorial representation of cardiac activation, and in particular of the progression of activation through the heart. It has application in locating sites suitable for ablation therapy, for example to cure atrial fibrillation.
  • Irregular heart beat (arrhythmia) is commonly treated with ablation therapy in a cardiac catheter laboratory.
  • a method is needed to identify where the ablation therapy should be delivered, in order to cure the arrhythmia without having to ablate an unnecessarily large amount of the heart.
  • the abnormal sequences may be of two categories: those that show a surface ECG pattern that is obviously regular and repetitive, or those that appear to be irregular and do not show a simple repeating pattern.
  • Fibrillatory electrical activation of the myocardium is one of the most common causes of cardiac morbidity and mortality. It can affect either the atrium (atrial fibrillation) or the ventricle (ventricular fibrillation).
  • Mathematical modelling and in-vitro studies have suggested that fibrillatory electrical activation can often be driven by electrical rotors (Skanes AC, Mandapati R, Berenfeld O, Davidenko JM, Jalife J. Spatiotemporal periodicity during atrial fibrillation in the isolated sheep heart. Circulation 1998;98: 1236-48) and that a small number, for example one to three, of such rotors may be sufficient to maintain fibrillation in the human heart.
  • fibrillatory activity is that there exist areas of the heart that act as focal sources of atrial fibrillation giving rise to focal activation. The repetitive firing from these areas may then perpetuate atrial fibrillation (Lee MS, Sahadevan J, Khrestian CM, Durand DM, Waldo AL. High Density Mapping of Atrial Fibrillation During Vagal Nerve Stimulation in the Canine Heart: Restudying the Moe Hypothesis. Journal of Cardiovascular Electrophysiology, 24: 328–335. doi:10.1111/jce.12032).
  • Atrial activation during human atrial fibrillation is not identical between different patients. It can be anywhere on a spectrum between completely organised to highl y disorganised activation (Kanagaratnam P, Cherian A, Stanbridge RD, Glenville B, Severs NJ, Peters NS Relationship between connexins and atrial activation during human atrial fibrillation. J Cardiovasc Electrophysiol. 2004 Feb;15(2):206 -16).
  • the current state of the art is that, using multi-electrode catheters, it is possible to map the activation sequences using isochronal mapping. i.e. mapping of the timing of activation over the cardiac surface with points which activate at the same time being identified, eg by the same colour.
  • WO2014/174274 discloses a system for locating and representing activation wave fronts and rotors.
  • the present invention aims to further improve on the systems described therein. Summary of the Invention
  • the present invention provides apparatus for monitoring activation in a heart, the apparatus comprising a probe, a plurality of electrodes supported on the probe and each arranged to detect electrical potential, for example at a respective position in the heart during a series of activations, and processing means arranged to analyse the detected electrical potentials, for example to identify a propagation direction of the activation, and optionally to generate an output indicative of that direction.
  • the processing means may be arranged to record values of the electrical potential at each of the electrodes as a respective unipolar electrogram.
  • the processing means may be arranged to perform filtering on the unipolar electrograms to filter out noise from the unipolar electrograms.
  • the processing means may be arranged, in filtering the unipolar electrograms, to filter out from the unipolar electrograms noise features which are common to a plurality of the unipolar electrograms.
  • the processing means may be arranged to retain in the unipolar electrograms, features which are unique to each of the unipolar electrograms.
  • the processing means may be arranged, in filtering the unipolar electrograms, to define a multi-electrode model which defines the unipolar signal at each of the electrodes, and to identify a best fit of the model to the unipolar electrograms.
  • the model may define the difference between each of a plurality of pairs of the unipolar signals and the processing means may be arranged to determine from the unipolar electrograms measured values of said differences.
  • the best fit may be arranged to include said differences.
  • the processor may be configured to identify at least one of the wavefronts as belonging to one of a plurality of classifications depending on at least one of its shape, its position, and its direction of travel.
  • the present invention further provides apparatus for monitoring activation in a heart, the apparatus comprising a probe, a plurality of electrodes supported on the probe and extending over a detection area of the probe, the detection area being arranged to contact a detection region of the heart, wherein each of the electrodes is arranged to detect an electrical potential at a respective position in the heart during movement of a series of activation wavefronts across the detection region, a display screen, and a processor configured to: record the detected electrical potential at each of the electrodes as a unipolar electrogram; forming an electrogram combination which is a linear combination of at least two of the unipolar electrograms; and filter out noise in the unipolar electrograms based on the degree to which the noise is more present in the individual unipolar electrograms than it is in the electrogram combination.
  • the linear combination may be affected more by a change in just one of the two electrical potentials than it is by the same change in both of the two electrical potentials.
  • the linear combination may be a subtraction of values in one unipolar electrogram from values in another unipolar electrogram.
  • the values in each of the unipolar electrograms may be weighted.
  • the processor may be configured to filter out the noise by: defining a model of the electrical potentials at all of the electrodes; defining a voltage combination which is a linear combination of at least two of the electrical potentials in the model; finding a best fit of the model and the voltage combination to the unipolar electrograms and the electrogram combination.
  • the processor may be configured to define a measurement matrix recording the unipolar electrograms and the electrogram combination, and an observer matrix which relates the measurement matrix to the model.
  • the observer matrix may define a plurality of voltage combinations each of which is a linear combination of a different pair of the electrical potentials.
  • the voltage combination may be a difference between two of the electrical potentials.
  • the processor may be configured to define a weighting for each of the unipolar electrograms and the electrogram combination or combinations.
  • the weightings may be applied in the finding of the best fit, for example in a least squares fit method.
  • the weightings may be chosen to determine the frequency response. This enables the system to retain the most useful information from all of the measurements.
  • the processor may be configured to analyse each of the unipolar electrograms, for example using a convolution with an inverse function of time, to identify any tissue activation features that they contain which are indicative of one of the activation wavefronts
  • the electrodes may extend over a detection area of the probe.
  • the de tection area may be arranged to contact a detection region of the heart.
  • the processing means may be arranged to analyse the detected potentials to i dentify a plurality of propagation directions of a single wavefront as the wavefront moves across the detection region.
  • the processing means may be arranged to allocate the electrodes to a plurality of groups.
  • the processing means may be arranged, within each group, to allocate the electrodes into a plurality of pairs each comprising a first electrode and a second electrode.
  • the processing means may be arranged, for each of the pairs, to determine the time delay between a wave front passing the first elect rode and the wavefront passing the second electrode.
  • the processing means may be arranged, from the time delays, to determine a direction of propagation of the wavefront past the group.
  • the processing means may be arranged to identify a plurality of wavef ronts and a plurality of propagation directions of each of the plurality of wavefronts.
  • the apparatus may further comprise display means, such as a display screen.
  • the processing means may be arranged to control the display means to generate a display indicative of the direction or directions.
  • the processing means may be arranged to analyse detected potentials for a plurality of positions of the probe in the heart.
  • the processing means may be arranged to control the display means to indicate simultaneously the direction of propagation at each of the positions of the probe.
  • the processing means may be arranged, for each wavefront passing the detection region, to define a series of update intervals over the transition period during which the wavefront crosses the detection region.
  • the processing means may be arranged, for each of the update intervals, to determine a propagation direction.
  • the processing means may be arranged to control the display to generate an image comprising a series of features. One of the features may be added after each update interval in a position determined by the propagation direction for that update interval.
  • the processing means may be arranged to identify an update interval in which a wavefront first enters the detection region and to control the display in response thereto to add one of the features at an origin position of the image, whereby each wavefront crossing the detection region is indicated by a line of said features extending away from the origin position.
  • the processing means may be arranged to determine the direction of propagation at each of a plurality of points in the detection region.
  • the processing means may be arranged to determine from the directions of propagation a value for the curl , or divergence, of the propagation direction at at least one point in the detection region.
  • the processing means may be arranged, from the at least one value of the curl or divergence, to locate a source of fibrillation within the detection region.
  • the present invention further provides a method of locating a source of fibrillation of a heart, the method comprising: providing a probe having a detection area, and a plurality of electrodes supported on the probe and extending over the detection area of the probe, placing the detection area of the probe in contact with a detection region of the heart, detecting the electrical potent ial of each of the electrodes, for example, during movement of a series of activation wavefronts across the detection region, analysing the detected electrical potentials to identify at least one propagation direction of one or more of the wavefronts, and identifying from the propagation directions the location of a source of fibrillation.
  • the step of analysing the detected potentials may identify a plurality of propagation directions of a single wavefront as the wavefront moves across the detection region .
  • the step of analysing the detected electrical potentials may comprise allocating the electrodes to a plurality of groups, and within each group allocating the electrodes into a plurality of pairs each comprising a first electrode and a second electrode . It may further comprise for each of the pairs determining the time delay between a wave front passing the first electrode and the wavefront passing the second electrode. It may further comprise, from the time delays, determining a direction of propagation of the wavefront past the group.
  • the method may comprise identifying a plurality of wavefronts and a plurality of propagation directions of each of the plurality of wavefronts.
  • the method may further comprise generating an image indicative of the direction or directions.
  • the method may further comprise analysing detected potentials for a plurality of positions of the probe in the heart, and generating the image ssoo aass to indicate simultaneously the direction of propagation at each of the positions of the probe.
  • the method may further comprise, for each wavefront passing the detection region, defining a series of update intervals over the transition period during which the wavefront crosses the detection region.
  • the method may further comprise, for each of the update intervals determining a propagation direction.
  • the image may comprise a series of features, One of the features may be added after each update interval in a position determined by the propagation direction for that update interval.
  • the method may further comprise identifying an update interval in which a wavefront first enters the detection region.
  • Generating the image may comprise adding one of the features at an origin position of the image, whereby each wavefront crossing the detection region may be indicated by a line of said features extending away from the origin position.
  • the present invention further provides a method of treating cardiac fibrillation comprising locating a source of fibrillation according to the method described above and ablating a region of the heart at the location of the source of fibrillation.
  • the system may further comprise any one or more features, in any combination, of the embodiments of the invention which will now be described by way of example only with reference to the accompanying drawings.
  • Figure 1 is a diagram of a system according to an embodiment of the invention
  • Figure 2 is a set of plots showing variation in wave front propagation direction during atrial fibrillation
  • Figure 3 is a plot of a windowing function used in the system of Figure 1
  • Figure 4 is a plot illustrating an algorithm for determining relative activation times used in the system of Figure 1
  • Figure 5 shows an image displayed in the system of Figure 1 showing multiple propagation directions
  • Figure 6 shows a set of three plots generated by the system of Figure 1 showing most recent wave front directions at three different times during passage of a further wave front
  • Figure 7 shows a polar histogram plot of wave front directions generated by the system of Figure 1
  • Figure 8 shows the main steps in a method according to an embodiment of the invention
  • Figure 9 shows examples of wave front patterns detected an analysed by the system of Figure 1
  • Figure 10 shows the identification of a series of wavefront positions during transition of a wavefront across a region of the heart
  • Figure 11 shows how different types of wavefront can be identified
  • a cardiac monitoring system comprises a catheter 100 having a probe 101 at one end with a set of electrodes 102 located on it.
  • Each of the electrodes 102 is connected independently through the probe catheter 100 to a computer 104 which is arranged to ac quire, store and analyse the voltages detected by the electrodes 102.
  • the computer 104 comprises a memory 106 and a processor 108.
  • the processor is arranged to sample the voltages detected by the electrodes 102 at a regular sample rate and sto re the values of the sampled voltages, which form a time series of sample values, in the memory 106, and then to analyse the stored voltage values so as to analyse the activation of the heart in the area contacted by the probe 101. Specifically the data can be analysed to identify focal targets within areas of irregular activation.
  • the processor 108 is arranged to generate from the sampled voltage data, an image data set which it then provides to the display screen 110 which displays an image showing the ac tivation pattern in the heart so that a user can interpret it.
  • the probe 101 can be moved from region to region within the heart to focus attention in the regions where the targets are suspected.
  • the catheter 100 may further comprise an ablation tip 114 which is connected to a radio frequency (RF) power source.
  • the ablation tip 114 can therefore be used for ablation of regions of the heart which are found to be sources of atrial fibrillation, whether focal sources or rotors.
  • the catheter may for example be a Smart-Touch catheter (Biosense-Webster) or a Tacticath catheter (Abbott).
  • separate catheters may be used, one such as the AFocus catheter for diagnosis or location of the source of fibrillation, and the other for example a Navistar cathete r for ablation.
  • the variety of directions of activation wave fronts at a particular location is illustrated for example in Figure 2.
  • the system is therefore arranged to analyse the signals from the probe electrodes 102 so as to detect each of the different directions of propagation a cross each point, and then to analysis those as will be described in more detail below so as to locate, and enable treatment of, the rotors or other problematic regions.
  • the data acquisition, data processing, and image display will now be described in more detail.
  • the processor 108 is arranged to perform each of these steps. For any particular position of the catheter 100, a stream of raw signal data is acquired from each of the numerous electrodes 102 of the catheter. The position of each electrode 102 is known through one of a variety of methods well known to those skilled in the art, such as those marketed as CARTOTM or NavXTM. The following steps are then carried out by the system under the control of the processor 108.
  • the catheter 100 and computer 104 are arranged to acquire unipolar or bipolar electrogram data.
  • a standard definition of unipolar electrogram data for a particular site is the potential difference between an intracardiac electrode at that site and a reference potential, for example at Wilson’s central terminal, or any other combination of skin surface electrodes.
  • a unipolar electrogram can be defined as the potential difference recorded between an intracardiac electrode and an electrode placed within the body at a site outside the heart, for example in the inferior vena cava, a large vein adjacent to the heart in which an electrode can very conveniently be located. Therefore for unipolar electrogram data a further electrode, not shown, is also provided and connected to the computer to provide the reference signal in known manner.
  • bipolar electrogram data can be used, being defined as the potential difference between two of the intracardiac electrodes 102. In this case no further reference electrode is needed. Whether unipolar or bipolar electrogram signals are used, the electrical signal (voltage) from each electrode (or electrode pair) is sampled at a regular sample frequency and the sampled values stored in memory for analysis.
  • the electrogram data obtained is filtered to remove noise and baseline artefact.
  • filtering algorithms are well known to those skilled in the art. It is possible to apply one or more in sequence, using software programs coded operating on the microcomputer system 104 as in this embodiment. In other embodiments the processing is performed by hardware circuitry specifically designed or customised for filtering, known as digital signal processing hardware. For example a simple band pass filter may be used, which may be at 10-250Hz.
  • An example of filtered electrogram signals is shown in Figure 3, in which the electrogram data for electrodes numbered 12, 13, 14, 15 and 16 are shown, together with the results of analysis of pairs of the electrograms which are shown on lines 12.5, 13.5, 14.5 and 15.5, as will be described in more detail below.
  • the electrode locations at which the electrograms are obtained will be referred to as 'nodes'.
  • the nodes are triangulated, i.e. grouped into sets of three nodes 102a, 102b, 102c which form a triangle.
  • Each edge 104 of the triangle extends between a pair of the three nodes, e.g. 102a and 102b, with a corresponding pair of electrograms.
  • Each of these three pairs of electrograms is then processed to determine a time delay between the activation times of the two electrodes in the pair. From the three time delays, the direction and speed of the wave front passing the group of three nodes can be determined.
  • each direction may be calculated and defined as components in two orthogonal directions x, and y, of a vector of unit length in the direction of propagation.
  • an autocorrelation algorithm is used, performed by the processor 108 to compare the activation times of the two electrodes in each electrogram pair.
  • Autocorrelation is used because closely spaced electrograms usually have a morphology that is similar.
  • Autocorrelation provides a way of determining relative times of activation between the pair.
  • the total width of the windowing function is 51 (2k+1) samples, corresponding to approximately 100ms at a sample rate of 500Hz.
  • the windowing function is centred on one sample, indicated as sample 0 in Figure 5, and has its highest value for sample 0. It falls away to zero at the samples at either end of the window, i.e. –k and +k.
  • the windowed electrogram at time corresponding to one of the sample times, is obtained by multiplying the electrogram values, centred on the sample at time t, with the values of the windowing function: Then windowed electrograms from two neighbouring nodes are selected - e 1w and e 2w .
  • cross correlation R(t, ⁇ ) between the two windowed electrograms is determined for all sample times t and all possible time delays (i.e. differences in time of activation between the two electrodes in the pair) ⁇ within a range: For example, for a catheter where electrograms are recorded 10mm apart , allowing for slow conduction of 0.5 m/s, this corresponds to a maximum transit time of 20ms. Therefore, values of may be limited to ⁇ 2 0 ms .
  • to find values for t and local maxima in R(t, ⁇ ) are determined. Only maxima above a sensitivity threshold are considered.
  • T e1 and T e2 of activation at each of the two electrodes in the pair can be determined as follows: Information from multiple electrograms can, optionally, be compared by performing the above analysis on, for example, all possible pairs from groups of three electrograms, with their positions, after they have been triangulated. The node positions 102a, 102b, 102c on the catheter are then transformed onto a 2 dimensional surface and a grid defined, with a spacing that is substantially smaller than the inter-electrode distance.
  • the nearest edge 104 is determined and the relative distances from the two nodes at the ends of that edge are calculated.
  • the activation timing T g at each grid point g is then calculated as: where has a value of 0 if the grid location is next to node 1 and has a value of 1 if the location is next to node 2.
  • Each grid value is assigned to 1 at times where there exists t-T g ⁇ 1 0 0 m s . This produces a set of activation times, one of each grid point g. However because of the method of calculation, the times will not be representative of a smoothly propagating wave front. Standard image smoothing algorithms are then used to create a visual display of smooth wavefront propagation.
  • a Sobel edge detector or other suitable edge detector, is used to identify the wavefront direction at each grid point. From the smoothed timing information describing wavefront propagation times at each grid point, direction data at each grid point is calculated.
  • the direction of a wavefront passing grid point, g , at time,t be whree are the magnitudes in two orthogonal directions ( i and j ) and That direction is determined using triangulation from any group of three grid points. For example it may be calculated for each grid point using a triangular group of that grid point and two adjacent grid points g.
  • the wavefront propagation across an area of interest can be described by taking the most recent activation at each point within a specified time range.
  • the direction of the latest wave front to pass a grid point which is a vector of unit length
  • any defined surface usually the surface covered by the electrodes that have been analysed
  • Wavefront direction, at a given sample time, for a particular wavefront travelling over the analysed region G(T) is calculated as the integral of latest directions for all grid points over a fixed time period, for example 200ms, which will typically be approximately long enough so that each transition period is covered completely by one integration period, though this will of course not always be the case: where, the wavefront direction is given by and the wavefront 'coherence' is given by It will be appreciated that, for a given wave front, G(T) is a vector sum of directional vectors d latest over the transition period during which the wave front is passing through the analysed region. Therefore, assuming the wave front moves in one direction the length of the vector G(T) will increase over the transition period.
  • the direction of the vector G(T) will also vary over that period.
  • This can be displayed as a series of dots on an x-y plot having an origin, with the dots each being located at a point which is displaced from the origin by a distance and direction corresponding to the length and direction of the integral G(T) at the time the dot is added to the display.
  • a new dot may be displayed at regular update intervals during the transition period, and each dot displayed may be displayed for a display period, which is much longer than those regular update intervals, so that the dots are superimposed on the image during the transition period.
  • each dot may be removed from the display, or faded out.
  • the dots may be each be displayed continuously so that the number of dots displayed increases until the end of the measurement.
  • This addition of a series of dots generates a line of dots for each wavefront that starts at the origin and is extended after each update interval in the direction of travel of the wavefront during that update interval. Therefore if the direction of travel is constant, the line will be straight, whereas if the direction of travel varies over the transition period, the line will be curved.
  • This display therefore gives information about the direction and coherence of wavefronts.
  • no further dots will be added to the line representing that wavefront.
  • a new wavefront is detected entering the analysed region, e.g.
  • FIG. 6 shows the appearance of the plot as a wavefront passes the recording electrodes in a direction from bottom-right towards top-left. Panels are shown at 15ms intervals, which is equal to the sample interval.
  • the dots in the example shown are roughly circular, but it will be appreciated that they can be of a variety of shapes such as square or triangular.
  • the integral G(T) is calculated over much shorter time periods, for example once for each sample period.
  • the information on wavefront direction can also be amalgamated into a polar histogram plot, weighted according to the number and coherence of wavefronts in each direction.
  • the wavefront direction is calculated at regular intervals, for example using the integral G(T) as described above either once for each sample interval, or once every two or three sample intervals. This generates a set of sample wavefront directions which can be displayed on a polar histogram as shown in Figure 7.
  • the vector quantities d latest at each of the grid points any one time define a vector field over the detected region and can therefore be analysed to determine the vector operators curl and divergence at points within that r egion.
  • the curl can be defined as: and the divergence can be defined as These are calculated for each point g on the grid from the values of d latest .
  • the divergence and curl are calculated at each grid point g using the vector that represents the last wavefront direction at neighbouring grid points. (Thus the speed of the wavefront is not used.)
  • the divergence and curl are calculated using standard algorithms. In a neighbourhood, let x and y be the distance along two orthogonal vectors (i and j) on the heart surface.
  • the wavefront direction, d can de represented as the sum of two vectors: Where d(x,y) has been normalised to have a magnitude of 1. Divergence and Curl are then calculated for each grid point g as: The wavefront divergence and curvature may be used to highlight locations where there is rotor (or rotational) activity and also where there are focal sources or wavefront collision. Specifically maxima, or high values, of curl are associated with rotors, and maxima or values of high divergence are associated with focal sources. Therefore these maxima or high values can be located as described below and used to indicate the location of tissue that can be ablated. Referring to Figure 8, the operation of the system can be described as follows.
  • step A the electrogram data for each of the electrodes 102 on the probe 101 is acquired for a first position of the probe 101 and a corresponding region of the heart.
  • step B the electrogram data for that position over a sample time period is analysed as described above to determine the direction of propagation, and optionally also the coherence, of each wave front passing through that region in the total sampling time is determined.
  • the probe 101 has a spiral array of electrodes 102, with the electrode positions or nodes shown in Figure 8.
  • step C data relating to the directions of wavefront propagation is displayed as an image on the display screen 110, for example as vectors on a polar plot, or a moving dot pattern as described above with reference to Figure 6, for the region in which the probe 101 is located.
  • the probe 101 is then moved to a second position and then subsequent positions, and in each position further data acquired, analysed and displayed.
  • the data for each position is stored, together with probe locat ion data indicative of the location of the probe and hence the position on the heart of the region currently being examined.
  • step D from this location data, the individual wave front direction images are mapped onto an image of the heart .
  • the individual wavefront direction images may be shown as vector plots or dot images, or indeed as histogram plots or simple directional indicators as shown in Figure 8.
  • the location of sources of fibrillation may then be determined by the user from the directional information displayed.
  • the processor 108 may be arranged to determine the location of sources and to control the display 110 to indicate the location of the sources on the image.
  • the processor may be arranged to calculate the curl of the wavefront direction vector at positions on the heart as described above, locate a maximum of the curl, and identify the position of that maximum as the position of a rotor.
  • the processor may then be arranged to control the display 110 to highlight, for example using arrows or colour or an outline, the position on the heat of the rotor.
  • the processor may be arranged to calculate the divergence of the wavefront direction vector at positions on the heart as described above, locate a maximum of the divergence, and identify the position of that maximum as the position of a focal source.
  • the processor may then be arranged to control the display 110 to highlight, for example using arrows or colour or an outline, the position on the heat of the focal source.
  • the ablation tip 114 of the catheter, or a separate ablation catheter is used to ablate heart tissue at the location of the rotor.
  • the system of Figure 1 can be arranged to differentiate between further different wave front patterns using the divergence and curvature (curl) of the wave front directions.
  • the first set of four images in Figure 9 shows the detection of a relatively uniform wave front passing across the region of the heart that is being contacted by the probe 101.
  • the second set of four images shows the detection of a wave front propagating out from a focal point in the imaged region of the heart.
  • the third set of four images shows a wave front that is rotating about a point generally at the centre of the imaged region of the heart.
  • the fourth set of four images shows the detection of a wave front that is turning about a point outside (in this case below as seen in Figure 9) the imaged region of the heart.
  • the last set of four images in Figure 9 shows the detection of a pair of wave fronts colliding at the centre of the imaged region of the heart.
  • the processor is arranged to identify at each of a series of update intervals those electrodes at which an activation has been detected.
  • a map of the monitored region of the heart can then be defined which identifies an area 300 in which electrodes have experienced an activation and another area 302 in which the electrodes have not experienced an activation.
  • the top row of Figure 10 shows a sequence of maps for a corresponding sequence of update intervals in which the activated area 300, which is shown in black, moves or expands across the monitored region from bottom left to top right.
  • the boundary 304 between the activated area 300 and the non-activated area 302 defines the wavefront.
  • the bottom row of Figure 10 shows the current wavefront after each update interval, together with the locations of the wavefront after each previous update interval.
  • the wavefronts may be displayed as an image which updates after each update interval and adds the latest wavefront position to those already displayed. This would result in an image that changes from the left hand image to the right hand image of the bottom row of Figure 10 as the wavefront crosses the monitored region, moving from position 304a to 304e through the intermediate positions of 304b, 304c, 304d in between.
  • the latest wavefront position after each update interval may be indicted more brightly than the others as shown in Figure 10.
  • Each wavefront may be further faded in each subsequent image update in which it is displayed.
  • the system may be arranged to characterize the wavefronts.
  • a wavefront is a child wavefront of a wavefront that precedes it, for example 304e is a child of 304d
  • a wavefront is a parent wavefront of a wavefront that follows it, so 304a is a parent of 304b, and 304b is a parent of 304c.
  • the point at which a wavefront has an end, either within the image or where the wavefront meets the edge of the image (i.e. the edge of the monitored region) is defined as an endpoint 306.
  • Plane wavefront (a) satisfies all of: • A wavefront with both endpoints at the edge of the field of view (i.e. on the outer circle). • Either 1 parent or no parents. • Either 1 child or no children.
  • Focal wavefront (b) satisfies all of: • Either 1 parent wavefront which is also focal, or no parents • The endpoint of the wavefront meets the wavefront (i.e. the wavefront is a loop).
  • Rotating wavefront (c) satisfies all of: • One endpoint is within the field of view (i.e. one endpoint is ‘floating’ within the outer circle).
  • Collision wavefront (d) satisfies all of: • 2 or more parent wavefronts .
  • These categorizations may be used displayed to a user in various ways, for example using different colours on a display for different categorizations of wavefront, or using labelling, depending on the purpose of the investigation being carried out.
  • the raw (un-filtered) electrogram data from the individual electrodes, plotted as voltage V as a function of time t includes both the underlying unipolar voltages, which include sharp drops caused by the activations, and noise which is superimposed on the underlying voltages, and shown as a separate signal varying about each of the underlying voltage signals.
  • the no ise is typically of a relatively high frequency, but needs to be filtered out without removing the sharp drops in the underlying voltages.
  • the noise in the signals will be, in general, common to all, or several, of the individual signals. This means that features having (approximately) the same shape occur at (approximately) the same time in some or all of the individual signals.
  • the sharp variations in voltage which are used to identify the activations occur at different times in the different signals and are therefore not common, at least in timing, to all of the signals.
  • a filtering method is therefore used which takes into account not just the individual unipolar electrograms, but measurements of the difference between voltages at a pair of electrodes.
  • the aim is to estimate unipolar signals utilising the information from all the measurements that have been made: in clinical practice, the bipolar measurements (i.e. differences between unipolar electrograms) have much less noise because of common mode noise rejection.
  • the aim is to estimate the underlying state, i.e. the actual voltages at each of the electrodes:
  • the noisy measurements Y are used, which are made up of the noisy measurements of u 1 , u 2 and b 1 : [2]
  • H is an observer matrix: [3]
  • the observer matrix defines the linear combination, here a simple difference between the two electrograms, which is used in addition to the individual unipolar electrograms.
  • the observer matrix defines for which pairs of electrodes the voltage difference is measured and used.
  • an observer matrix can be used in a system with multiple (more than two) electrodes and time varying signals to estimate the unipolar voltages.
  • a method based on a Savitsky Golay filter may be used. This estimates each of the unipolar signals to be modelled by a polynomial having a number of terms with respective weights, and estimates the weights using a least squares fit to the measured data, which includes the unipolar electrograms and measurements of the difference between pairs of the electrodes.
  • a timeframe z that consists of M samples at interval dt around t 0 .
  • the difference between the voltage at each electrode and the adjacent electrode is included, i.e. the unipoles and ‘sequential bipoles’.
  • the weighting of the errors used in the minimisation can be arranged to relate to the expected noise level.
  • the weights W k that are chosen for each signal measurement can be used to select the relative weight of the different signal measurements.
  • Figure 13 shows the results of a filter as described above, in which the gradient (time differential) of one of the unipolar electrograms is plotted, as a function of time, for the raw unfiltered and the filtered electrograms.
  • the filtered data shows much less noise and the steep negative gradients which are indicative of activation events are much more clearly identified.
  • the shape of the electrogram plot around the time of an activation event is similar to a plot of 1/t as a function of t. Therefore in order to locate the activation events, a convolution with 1/t is applied to the filtered electrograms of Figure 12 (for example a Hilbert filter) which results, after filtering out low frequency noise using a digital linear phase filter, in the plots shown in Figure 15 with peaks cleraly identifying the location in time of the activation events.
  • Figure 12 for example a Hilbert filter

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Abstract

Un appareil pour surveiller l'activation dans un cœur comprend une sonde (101), une pluralité d'électrodes (102) soutenues sur la sonde et s'étendant sur une zone de détection de la sonde, la zone de détection étant agencée pour entrer en contact avec une région de détection du cœur. Chacune des électrodes (102) est agencée pour détecter un potentiel électrique à une position respective dans le cœur pendant le déplacement d'une série de fronts d'onde d'activation à travers la région de détection. Un processeur est agencé pour analyser les potentiels électriques détectés afin d'identifier une direction de propagation d'au moins un des fronts d'onde, et pour générer une sortie indicative de cette direction.
PCT/GB2023/051270 2022-05-16 2023-05-15 Appareil de surveillance de l'activation dans le coeur WO2023223007A1 (fr)

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WO2014174274A1 (fr) 2013-04-22 2014-10-30 Imperial Innovations Limited Interfaces d'affichage d'image
WO2014182842A1 (fr) * 2013-05-07 2014-11-13 Boston Scientific Scimed Inc. Système d'identification de vecteurs de propagation rotationnelle
WO2018197865A1 (fr) * 2017-04-25 2018-11-01 Imperial Innovations Limited Systèmes et procédés de traitement d'arythmies cardiaques
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