Source: https://patents.google.com/patent/JP2019141619A/en
Timestamp: 2020-04-04 22:35:28
Document Index: 358581820

Matched Legal Cases: ['Application No. 61', 'art. 6', 'art 152', 'art 152', 'art 152', 'art 152', 'art 152', 'art 152', 'art 152', 'art 152']

JP2019141619A - Physiological mapping for arrhythmia - Google Patents
Physiological mapping for arrhythmia Download PDF
JP2019141619A
JP2019141619A JP2019070850A JP2019070850A JP2019141619A JP 2019141619 A JP2019141619 A JP 2019141619A JP 2019070850 A JP2019070850 A JP 2019070850A JP 2019070850 A JP2019070850 A JP 2019070850A JP 2019141619 A JP2019141619 A JP 2019141619A
JP2019070850A
デュボワ，レミ
ピー． ジョージ，ブライアン
P George Brian
ラマナサン，シャルラタ
Ramanathan Charulatha
ツェン，チングオ
Qingguo Zeng
ストロム，マリア
Strom Maria
ヴァスデヴァン，ヴェンカテッシュ
Vasudevan Venkatesh
ボカン，ライアン
Bokan Ryan
ジア，ピン
カーディオインサイト テクノロジーズ インコーポレイテッド
2012-09-21 Priority to US201261704227P priority Critical
2012-09-21 Priority to US61/704,227 priority
2019-04-02 Application filed by カーディオインサイト テクノロジーズ インコーポレイテッド, Cardioinsight Technologies Inc, カーディオインサイト テクノロジーズ インコーポレイテッド filed Critical カーディオインサイト テクノロジーズ インコーポレイテッド
2019-08-29 Publication of JP2019141619A publication Critical patent/JP2019141619A/en
206010003119 Arrhythmia Diseases 0 title claims description 22
206010007521 Cardiac arrhythmias Diseases 0 title claims description 22
230000002018 arrhythmia Effects 0 title claims description 22
230000000747 cardiac Effects 0 claims abstract description 25
238000002679 ablation Methods 0 claims abstract description 10
206010003658 Atrial fibrillation Diseases 0 claims description 18
208000003663 Ventricular Fibrillation Diseases 0 claims description 14
206010003668 Atrial tachycardia Diseases 0 claims description 4
206010003130 Arrhythmia supraventricular Diseases 0 claims description 3
206010047281 Ventricular arrhythmia Diseases 0 claims description 3
206010047302 Ventricular tachycardia Diseases 0 claims 1
An accurate phase map for a human atrium is generated. An electrocardiogram mapping system includes a signal processing method storable on a non-transitory machine-readable medium as instructions executable by a processor. The signal processing method 12 is programmed to calculate the phase data 14 based on the non-invasive electrical data. The non-invasive electrical data 16 may be obtained from previously non-invasive data or non-invasive electrodes (eg, body surface electrodes) during a procedure, such as during a treatment procedure (which may include cardiac ablation). ) May correspond to the data that can be obtained. The generated phase data can be stored in memory and corresponds to the phase of the electrical activity at each of the plurality of identified locations on the three-dimensional cardiac envelope. Such a cardiac envelope may correspond to the epicardial and endocardial surfaces or any 3D membrane having a known relative position in three dimensions with respect to the patient's heart. [Selection diagram] Fig. 1
The present disclosure relates to physiological mapping for arrhythmias such as fibrillation or tachycardia.
Electrocardiogram mapping (ECM) is a technique used to determine and display cardiac electrical information from sensed electrical signals. The mapping of cardiac electrical activity is further complicated by the presence of certain types of arrhythmias such as fibrillation, including atrial fibrillation and ventricular fibrillation. The heart signal includes several successive depolarizations of the tissue. Most methods in practice today that describe the analysis of cardiac depolarization or excitation rely on speculative knowledge about signal periodicity or cycle length (eg, for 3D excitation mapping). For heart excitement without a specific periodicity or cycle length, analytical techniques such as phase mapping have been developed. However, existing phase mapping techniques are invasive, including one or more basket catheters inserted into the heart or optical imaging by injecting dye into the myocardium. Optical imaging dyes are toxic in humans, and the resolution obtained with basket catheters is not sufficient to produce an accurate phase map for the human atrium.
As an example, a system may include a memory that stores machine readable instructions and data, and a processor that executes instructions to access the memory and perform the method. The method reconstructs processed electrical data corresponding to non-invasive electrical data obtained from a patient during at least one time interval, on a predetermined cardiac envelope. Converting to a generated electrical signal. The reconstructed electrical signal may be space-time consistent. The method may also include calculating phase data based on the reconstructed electrical signal.
As another example, non-transitory computer readable media may have instructions executable by a processor. These instructions are reconstructed electrograms for each of a number of points on or near a predetermined cardiac envelope based on geometric data and non-invasively measured body surface electrical signals. An electrogram reconstruction method for generating a signal may be included. These instructions include a phase calculator for calculating a phase signal for a number of points based on the reconstructed electrogram signal, and a visualization engine for generating an output based on the calculated phase signal. But you can.
== Cross-reference to related applications ==
This application is filed with the title of the invention “PHYSIOLOGICAL MAPPING FOR ARRHYTHMIA”, US Provisional Patent Application No. 61 / 704,227 filed Sep. 21, 2012 (the entire contents of which are hereby incorporated by reference). Insist on the interests of priority).
FIG. 1 shows an example of a system for performing phase mapping for arrhythmias. FIG. 2 shows an example of a pre-inverse solution process that can be implemented for atrial fibrillation. FIG. 3 shows an example of a reverse processing that can be implemented for ventricular fibrillation. FIG. 4 shows an example of a post-inverse solution process. FIG. 5 shows an example of a methodology that can be used as part of the baseline removal process. FIG. 6 shows an example of an atrial signal and a corresponding baseline signal. FIG. 7 shows a plot of the ventricular signal before and after filtering. FIG. 8 shows an example of a phase calculator tool that can be implemented to generate phase data used to generate a visible image. FIG. 9 shows an example of an atrial signal and its corresponding conversion result. FIG. 10 shows an example of the phase of the atrial signal plotted on the complex plane. FIG. 11 shows an example of a ventricular signal plotted in a complex plane. FIG. 12 shows an example of a diagnostic treatment system. FIG. 13 shows an example of a phase map. FIG. 14 shows an example of a phase map that clearly identifies the turning center of the spiral wave. FIG. 15 shows an example of the cycle length map. FIG. 16 shows an example of an electrocardiogram map that clearly shows a singular point. FIG. 17 illustrates an example of a cardiac motion phase map that can be generated based on the systems and methods disclosed herein. FIG. 18 illustrates different portions of an algorithm that can be used to determine the position and trajectory of the spiral center of rotation based on the phase map data calculated by the systems and methods disclosed herein. FIG. 19 illustrates different portions of the algorithm that can be used to determine the position and trajectory of the spiral center of rotation based on the phase map data calculated by the systems and methods disclosed herein. FIG. 20 illustrates different portions of an algorithm that can be used to determine the position and trajectory of the spiral center of rotation based on the phase map data calculated by the systems and methods disclosed herein. FIG. 21 shows an example of a graph demonstrating helical wave motion as a function of time that may include fragmentation and interaction between each helical wave. FIG. 22 shows an example of a graphic element that can be applied to a phase map to demonstrate different helical wave characteristics. FIG. 23 shows a moving image representation of a phase map of atrial flutter. FIG. 24 shows an example of a neighborhood view of a part of the heart envelope surface. FIG. 25 illustrates an exemplary computer environment in which systems and methods can be performed.
The present disclosure relates to non-invasive electrophysiological mapping for arrhythmias such as fibrillation. This mapping technique can be used as part of a diagnostic and / or therapeutic workflow to facilitate the identification and localization of fibrillation mechanisms based on body surface electrical activity measured non-invasively on the body surface Is possible. Using non-invasive body surface measurements, high resolution electrical signals can be reconstructed on a heart envelope, such as the heart surface. The techniques disclosed herein can employ signal processing techniques including processing before, during, and / or after calculating the inverse solution for electrical data. Such signal processing allows for high resolution output and allows the treatment target to be identified from the above output without injecting dyes or using invasive measurement catheters as in optical mapping. .
The techniques disclosed herein can also be used in real time (eg, to provide guidance during electrophysiology research or during treatment), or can be pre-obtained and stored in a particular patient. It can also be implemented in the context of electrical data. In this signal processing, physiologically relevant information can be extracted from non-invasive electrical data and the phase over one or more time intervals can be calculated. Utilizing this calculated phase, mechanisms of fibrillation in the patient's heart (including but not limited to local spiral waves, micro and macro reentry circuits, evoked activity, focus points, etc.) Can be identified and characterized. Further, the obtained phase data may be used to generate a graphic visible image and present space-time consistent information from one or more maps. 3D anatomical maps such as dynamic animated images that show these mapping outputs in addition to spiral waves and their associated movements and other features of persistent arrhythmias in spacetime as clinical targets Can also be displayed as a graphic.
FIG. 1 shows an example of an electrocardiogram mapping system 10. The system 10 includes a signal processing method 12 that can be stored on a non-transitory machine-readable medium as instructions executable by a processor. The signal processing method 12 is programmed to calculate phase data 14 based on non-invasive electrical data 16. The non-invasive electrical data 16 may correspond to stored electrical data that has been acquired non-invasively in advance and stored in memory for subsequent processing. Alternatively, non-invasive electrical data 16 may be non-invasive electrodes (eg, body surface electrodes) during a procedure, such as during electrophysiological studies or during a treatment procedure (which may include cardiac ablation). May correspond to a real-time data flow obtainable by The generated phase data 14 can be stored in memory and corresponds to the phase of electrical activity at each of a plurality of specified locations in the three-dimensional heart envelope. Such cardiac envelopes may correspond to epicardial and endocardial surfaces or any 3D membrane that has a known relative position in three dimensions relative to the patient's heart.
The system 10 may include a visualization engine 19 that is programmed to produce a corresponding visible image that is manifested as a graphic output 18. The output 18 may include, for example, a phase map generated based on the phase data 14 calculated for points distributed on the heart envelope. Alternatively or in addition, the phase data 14 corresponds to a phase singularity (eg, a spiral center of rotation) or other phase characteristic that is calculated based on the signal represented by the non-invasive electrical data 16. May be. For example, the graphic output 18 may include a map showing an integral of a phase gradient (eg, an integrated phase gradient map) that demonstrates the spatio-temporal consistency of the spiral center of rotation. Unlike existing invasive techniques, the integral phase map can be spatio-temporal consistent for multiple chambers of the heart (eg, with respect to current bi-atrial or bi-ventricular simultaneous phase characteristics).
As another example, the visualization engine 19 can generate a phase map in a movie or cinematographic format and dynamically demonstrate spiral wave motion and interaction over one or more time intervals. In still other examples, the visualization engine 19 may generate graphic output and maps based on other data that may include potential maps, excitement maps, dominant frequency maps, and the like. Such additional maps related to electrical potential activity may be combined with phase data into a single display. Alternatively or additionally, visualization engine 19 may display a plurality of displays that display a phase map or associated phase characteristics in a window or display adjacent to a potential-based electrocardiogram mapping (eg, excitement map, dominant frequency map, etc.). Can be generated simultaneously.
In the example of FIG. 1, signal processing 12 may include a plurality of steps (eg, program modules) that can be performed to process non-invasive electrical data 16 to generate phase data 14. For example, each signal included in the non-invasive electrical data 16 is obtained by applying a pre-reverse solution process (also referred to as pre-processing in this specification) 20 to the electrical data 16 acquired non-invasively for subsequent processing. May be extracted (or removed unrelated parts). In this way, the preprocessing 20 can provide preprocessed electrical data that is used to drive the inverse solution 22.
The inverse solution 22 can calculate a reconstructed electrogram on the heart envelope based on preprocessed electrical data and geometry data (not shown) supplied to the inverse solution. The geometric shape data may correspond to actual geometric shape data acquired for a particular patient, a general anatomical model, or a combination thereof. The pre-reverse processing 20 gives pre-processed electrical data to reduce the overall sensitivity of input data supplied to the reverse solution 22 while reducing the overall sensitivity of the phase data 14 (for example, atrial arrhythmia). The specificity of each of fibrillation (AF), atrial tachycardia (AT), ventricular fibrillation (VF), ventricular (VT)) may be increased. In addition to removing signal features by pre-reverse processing 20, such pre-processing may include noise (eg, unwanted vibrations) from each signal channel by filtering such as notch filtering, low pass filtering, and bad channel exclusion. Can also be removed.
As an example, pre-reverse processing 20 removes signal contributions due to ventricular function (eg, one or both ventricular depolarization, ventricular repolarization, or both) when calculating AF phase data. Is programmable. For example, prior to calculating the inverse solution 22, QRS wave cancellation and / or T wave cancellation (eg, QT interval or selected portion thereof) in the signal removes signal features due to ventricular electrical activity. It is possible to perform feature extraction and reconstruct an electrocardiogram on the heart envelope. Such cancellation can remove any portion sufficient to enhance the specificity of the entire wave or other selected signal component of interest. It should be understood that other methods besides the signal pre-processing 20 may be utilized to mitigate the effects of ventricular signals while calculating values for AF phase diagnosis. Such methods may include the use of chemicals (eg, drugs), the use of electrical stimulation, and combinations thereof. This type of pre-processing 20 may be further programmed to remove unwanted signal components from non-invasively acquired electrical signals depending on which type of arrhythmia is being analyzed.
As disclosed herein, the inverse solution 22 is programmed to provide a reconstructed electrogram based on the preprocessed data. Examples of inverse algorithms that can be used in system 10 are disclosed in US Pat. Nos. 7,983,743 and 6,772,004, each incorporated herein by reference. The inverse solution 22 can reconstruct electrical activity at multiple locations on the heart envelope (eg, more than 1000 locations, such as about 2000 locations or more). As a result, the phase data 14 provided by the process can be of a correspondingly high resolution that is significantly greater than can be achieved in an invasive manner (eg, using a basket catheter contact electrode). Also, the phase data 14 can be significantly safer than that generated using optical mapping techniques that use toxic chemical dyes. By performing a pre-processing 20 that removes unnecessary information from the input signal represented by the non-invasive electrical data 16, the inverse solution 22 is applied to the remaining signal information and the specificity of the selected type of phase analysis. It is possible to provide a corresponding electrical signal reconstructed at the heart envelope, specially adapted to the height of the heart.
The additional post-inverse processing 24 can be applied to the reconstructed electrical data. The post-inverse processing 24 may include one or more filtering methods, such as baseline removal, bandpass filtering, and low-pass filtering (see, eg, FIG. 4).
The phase calculation 26 is programmable to calculate the phase data 14 from the reconstructed electrogram data after filtering. For example, the phase calculator 26 can perform signal decomposition based on the filtered electrogram data and calculate a phase signal for each of a plurality of points on the surface of the heart envelope. The phase calculation can be implemented according to various solutions as disclosed herein (see, eg, the phase calculation described with respect to FIG. 8).
As disclosed herein, the visualization engine 19 can further calculate a phase map and other visible images based on the phase signal calculated for each position of the heart envelope. The non-invasive input electrical data 16 from which the phase signal is calculated can present consistent phase information in space and time across the surface of the patient's heart or other region of interest selected by the user, so that the phase map Can be viewed as a general true phase map of the heart envelope (eg, the entire epicardial surface). Unlike existing invasive phase mapping techniques, an overview visual image provides consistent spatio-temporal information about the entire heart, so that the spatial movement of the spiral wave and the interaction in the heart (across multiple cavities) Including interactions). For example, the visualization engine 19 may generate a graphical output 18 from the phase data 14 and include biventricular and / or biatrial maps to characterize fibrillation mechanisms across multiple heart chambers.
The graphic output 18 may correspond to phase information at a specific time or may be animated to show the phase change at the heart envelope as a function of time. For example, depending on how and when non-invasive electrical data was acquired, the time may further include successive times, or multiple time intervals (eg, minutes, Other intervals separated by hours or days). Despite this delay between measurements, the generated phase data 14 can still be spatially consistent for each measurement set. Such an approach would not be possible with existing invasive techniques without dramatically increasing patient risk. The change can correspond to cardiac electrical activity obtained from one or more time intervals. A particular time interval may include one or more phase cycles for each position for which phase data is calculated.
FIG. 2 illustrates an example of a pre-reverse process (eg, corresponding to pre-process 20 in FIG. 1) 50 that can be configured to facilitate analysis of atrial fibrillation (AF) 50. The method 50 receives non-invasive electrical data from a vest of sensors or other electrical array that can be distributed on the surface of the patient's torso. The method 50 includes a notch filter 52 that can be configured to remove line filter frequencies and harmonics and other frequency ranges that can be specified by the user in response to user input. For this reason, the notch filter 52 may be programmable. In some examples, the notch filter 52 can be implemented using a fast Fourier transform (FFT) method. The filtered data can be provided to a channel selection block 54 that can be used to select which input channel to use in subsequent processing.
Channel selection 54 may include, for example, detection and removal of bad channels. Bad channel detection can identify channels that are determined to be detrimental to the calculation of the reverse solution (eg, reverse solution 22 in FIG. 1). For example, the bad channel may be a channel having distorted data as well as a disconnected channel and a missing channel. Channel selection 54 can be implemented, for example, by visual inspection of the signal and / or by an automatic bad channel detection algorithm. Alternatively or in addition, channel selection 54 may pre-select the signal to be detected to contribute positively to a particular type of analysis (eg, AF or AT) and evaluation made on the heart or selected area of interest. It can be implemented to select a set of input channels corresponding to one or more regions of a defined patient torso.
Filtered data for the selected channel may be provided to ventricular exciter removal block 56 to remove signal features corresponding to ventricular excitability. As an example, ventricular excitation removal 56 may include a QRS wave subtraction method 58. For example, QRS subtraction can be implemented by principal component analysis of virtual leads. In this case, the position of the template region of the QRS wave is specified, the template region is averaged, the corresponding region of interest template is created, and the template is moved. The adjusted template is subtracted from the input signal after filtering in the detected template region.
Alternatively or additionally, ventricular excitation cancellation 56 may include T-wave subtraction 60 performed on the filtered signal provided by the selected channel. The T subtraction may be similar to the QRS wave subtraction 58, but another template configured to correspond to the T wave subtraction may be used. Alternatively, a single template corresponding to both QRS waves and T waves may be used to remove both QRS waves and T waves from the filtered input signal provided for the selected channel.
In this way, ventricular exciter removal 56 can provide a processed signal. This processed signal includes a portion of the signal identified in connection with the corresponding atrial analysis. By subtracting such features from the signal, some level of sensitivity may be sacrificed to increase the specificity of the phase analysis to be implemented. Ventricular exciter removal 56 can provide a signal corresponding to low pass filter 62. The low pass filter 62 can be configured to pass bands below a predetermined cutoff frequency, such as about 40 Hertz. As an example, the low-pass filter 62 can be implemented as a Savitzy Golay filter. Other types of filters may be used.
FIG. 3 illustrates an example of a anteversion process 70 that can be implemented to facilitate analysis of ventricular arrhythmias (eg, VF or VT). For example, this may correspond to the pre-inverse solution processing 20 of FIG. As long as there are two or more types of different pre-inverse processing, the method disclosed herein allows any type of pre-processing depending on the diagnosis and evaluation of the subject to be performed (selectable in response to user input). Can be selectively controlled.
In the example of FIG. 3, the pre-inverse processing 70 includes a notch filter 72 that can be implemented using the FFT method. The notch filter 72 provides a corresponding filtered signal to a channel selector 74 that can be configured to select which channels are used or excluded in subsequent processing. For example, the channel selection block 74 can be configured to hold channels in the input data that are determined to contribute more to ventricular activity than to atrial activity. Additional signal subtraction or filtering may be utilized to remove atrial features from non-invasive input data (eg, input data 16 of FIG. 1). The channel selection 74 provides the low-pass filter 76 with a filtered signal of the selected channel and provides a corresponding filtered output that includes a signal component of interest for performing phase diagnosis for ventricular arrhythmia evaluation. May be.
FIG. 4 shows an example of a post-inverse method process 80 that can correspond to the post-inverse process 24 in FIG. Post-inverse processing 80 can be applied to the reconstructed electrogram data corresponding to electrical activity at the heart envelope (eg, the epicardial surface of the patient's heart). Post-inverse processing 80 may include a bandpass filter 82 that is programmed to pass one or more bands at a specified frequency. As an example, the bandpass filter 82 can be implemented as an FIR filter.
The bandpass filtered signal can be provided to a baseline removal function 84 that is configured to remove baseline wander from the filtered signal. For example, baseline wander can be offset by, for example, subtraction of signal average values for atrial signals. In another example, baseline removal 84 can be implemented by a method programmed to interpolate the baseline with a polynomial function. As an example, baseline interpolation can be calculated from the anchor points selected for each signal to remove baseline wander.
An example of a methodology that can be used to remove the baseline is shown in FIG. After spline interpolation according to the methodology of FIG. 5 and linking anchor points from a particular signal, the resulting baseline can be subtracted from the particular signal. Thereafter, each of the resulting baseline correction signals may be passed through a low pass filter 86 to provide the corresponding processed signal for subsequent calculations, including phase calculation (eg, by phase calculation 26 of FIG. 1). .
As yet another example, baseline removal 84 can be implemented by wavelet decomposition, such as using multiple levels of decomposition. For example, the filtered signal can be decomposed into orthogonal wavelet bases, such as using higher order coiflets or other wavelets (eg, Mexican hat wavelets or Morlet wavelets).
As yet another example, when the time signal is set to x, the wavelet decomposition reaches a scale function s i that satisfies the following.
Here, r is a remainder function.
Filtering 84 can be done by removing the s i function from the sum total of information not relevant to diagnosis. One way towards this goal is the Fourier transform of each s i function calculates, dominant frequency (e.g., highest peak of the frequency) physiological frequency (e.g., typically from about 4 10 Hz) out of the range of It may be to remove a certain function. In this way, it is possible to remove both low frequency components from the baseline and high frequency components from noise. In addition, such wavelet filtering can be used to undulate so that the signal can be reconstructed using only the component corresponding to the AF cycle length with high reliability, i.e., the data includes a signal component corresponding to true atrial depolarization. The large waveform is decomposed to remove components contributing to unnecessary vibration.
FIG. 6 illustrates an example of an atrial signal 90 with a corresponding baseline signal 92 calculated, such as according to one of the techniques disclosed herein. For each signal, the calculated baseline signal 92 can be subtracted from the respective signal 90 to provide a corresponding signal that is substantially free of baseline drift.
FIG. 7 shows an example of signal plots 94, 96, 98 that can be provided by the post-inverse processing 80 of FIG. In FIG. 7, a raw ventricular fibrillation signal 94 is illustrated. FIG. 7 also shows a signal 96 after frequency filtering to remove vibration due to noise. FIG. 6 also shows the signal 98 after wavelet filtering to mitigate baseline wander as disclosed herein.
FIG. 8 is a program to perform a method for calculating phase and phase characteristics from processed reconstructed electrogram data (eg, corresponding to the signal provided by method 24 of FIG. 1 or method 80 of FIG. 4). An example of a possible phase calculator tool 100 is shown. The phase calculator tool 100 can be activated in response to user input, and for this purpose may use a graphical user interface (GUI) or the like that provides access to the respective methods shown with the tool 100. Each method implemented by the tool can be performed during the inverse mapping calculation, the output of which can be used to generate a corresponding graphical visual image (eg, graphic output 18 of FIG. 1). is there.
For example, the tool 100 may include a phase calculation method 102 that is programmed to calculate corresponding phase data 104 (eg, corresponding to the phase data 14 of FIG. 1). The phase calculation 102 can be calculated with a reconstructed electrogram after processing (eg, as provided by the post-inverse processing 24 of FIG. 1 or 80 of FIG. 4), where the reconstructed electrogram is From the gram signal, signal components outside the range of the physiological frequency of interest (eg, about 4-10 hertz) have been removed.
As an example, the phase calculation 102 can be implemented using a Hilbert transform to obtain corresponding phase data 104 for each signal across the heart envelope. Other types of signal decomposition methods may be used. As such, the phase data 104 can represent one or more time intervals over which the reconstructed electrogram is constructed.
As yet another example, if −π is an arbitrary starting point of a cycle, π is the starting point of the next cycle. Each time point in between is assigned an incremental phase value between [−π, π]. For example, assume that the obtained phase is a complex phase of magnitude 1. As such, each cycle can be transformed into one cycle centered at 0 in complex space.
For example, let x be a reconstructed electrocardiogram at a specific position on the heart envelope. In order to find a phase signal that satisfies the above requirements for a particular x, the following phase space “theory” can be used.
Any real-time signal can be seen as an orthogonal projection of the real axis of the complex signal.
Therefore, given x,
It is possible to find a complex signal X that satisfies. In order to represent the calculated phase for a particular electrical signal reconstructed and filtered at the heart envelope,
And Given X,
Is unique, but given x, there are an infinite number of signals X. The following
Here are some possible solutions for X that match
Is the diagnostic part of interest (eg, relevant) of x extracted from the signal using the processing techniques disclosed herein (herein
Here, τ needs to be estimated.
In such a case, X is
Therefore, it can be expressed as follows.
Or it corresponds to the complex wavelet of x,
Any X that satisfies the above condition may be used.
To calculate phase data 104 and give a unique solution (convert one cycle to a value between [−π, π]) and perform phase mapping, the requirement for X is (0,0) for each cycle. ) Get one circle around. This is accomplished by the preprocessing disclosed herein (eg, FIGS. 3-5) to remove noise, ie, unrelated vibrations of the signal to extract the salient features of X, thereby The accuracy and reproducibility of the above-described technique of phase mapping is enhanced. At various points in time, it is possible to calculate phase information for several segments of data and to make the analysis robust in the sense of space-time consistency. Phase information from multiple data segments in the same patient may be combined using various computational and visualization techniques as disclosed herein.
FIG. 9 shows the Hilbert transform of the signal specified at 22 (which corresponds to the calculated phase data 104), along with an example of the atrial signal 120 after processing. Thus, the phase data can provide a corresponding phase signal for each location (eg, about 2000 points or more) on the heart envelope at one or more intervals at which electrical data is obtained.
FIG. 10 shows an example of an atrial signal plotted on a complex plane. In the figure, the real axis corresponds to the signal itself, and the imaginary axis corresponds to the Hilbert transform of the signal. FIG. 11 is an example of a ventricular fibrillation signal plotted in the same manner as FIG.
For each phase signal calculated at each position on the heart envelope from the reconstructed electrogram to make an assessment to facilitate diagnosis and / or treatment of an arrhythmia such as AF or VF, Corresponding phase maps or other information can be generated and displayed. A corresponding phase signal can be displayed on the map for each electrogram location of the 3D geometry corresponding to the heart envelope (eg, the epicardial surface) of the patient's heart. The nature of the phase is such that -π is equal to π, so the range of color codes or other measures used to visualize the phase must be implemented to reflect this cyclicity of the phase signal. is there. The phase map can be displayed as an integral phase at a specific point in time for each position on the surface simultaneously. Moreover, this map may be displayed as a moving image phase map, and a temporal pattern of a phase spatially present on the entire surface may be clearly indicated. The 3D surface can be rotated in response to user input to display other portions of the surface according to the phase signal calculated as disclosed herein. Examples of different phase maps that can be created are disclosed herein in FIGS.
Returning again to FIG. 8, the phase calculator tool 100 may include a phase singularity calculation 106 that is programmed to calculate the position of the phase singularity. A phase singularity can be useful for diagnosis as the location where all phases in the atrium or ventricle meet. In a phase map where the calculated phase is spatially represented by the color scale, the phase singularity corresponds to the region where all the colors of the corresponding color scale of the phase map occur at the same position. For example, in the phase map 200 of FIG. 13, the phase singularity is shown as a region with “PS”. This is the location on the right atrial front of a particular patient's heart.
As described above, the phase singularity occurs at a spatial position where all phases merge. This can be determined as an integral of the phase gradient. For example, from the mathematical definition at a specific time t, the phase singularity occurs at the position x in the case of Equation 6 below.
Here, Φ (t) is a phase value calculated for the closed contour line L around x.
Therefore, each position and time satisfying Equation 6 can be regarded as a singularity point (that is, the spiral center of the spiral wave). Here, the surface of the heart (or other membranes of the heart) can be represented by a mesh structure, and the integral value of Equation 6 is given by the sum of phase changes along the closed contour around position X: For example, it can be estimated as follows.
Here, as shown in the neighborhood view of FIG. 24, N is a position near x on the mesh surface, which is arranged in order clockwise with respect to x, and x i and x j are adjacent positions. It is.
In the example of FIG. 24, the position x is surrounded by a set of neighboring elements having phase values Φ1, Φ2, Φ3, Φ4, and Φ5, respectively. Information on the spiral center of the spiral wave can be expressed on a dynamic map for tracking their position and trajectory in space over time. Alternatively, the information on the spiral center of the spiral wave may be averaged over time to obtain a single map for the entire episode.
The phase calculator tool 100 may include a focus source calculator 107 that is programmed to determine the position of one or more focus points based on the phase data 104. Therefore, the focus source calculation unit 107 can calculate local excitation based on the phase signal represented by the data 104, and can specify one or more wavefront focus sources. A particular position x can be defined as the focus source when the wavefront extends from X to its neighboring elements (using the neighborhood view of FIG. 24). At a specific time t, the excitement wavefront is located where the phase is equal to π / 2. Therefore, when the phase value of X when the wavefront passes through the X position is earlier than the phase of the neighboring element at the same moment, X is the focus position. In the neighborhood view of FIG. 24, the position X can be determined as the focus source (by the focus source calculation unit 107) when the following is satisfied.
In the example of FIG. 24, there are five neighboring elements, but in other examples, there may be any number of neighboring elements. Using the above-described focus source identification, it is possible to create one or more corresponding visible images, such as, for example, graphically displaying one or more focus firing index. Each such map can be calculated over one or more time intervals to provide space-time consistent data for the entire heart. In some examples, the focus activity (given by the calculator 107) and the spiral activity indicator (given by the calculations 106 and / or 108) can be visualized on separate maps. . In another example, focus activity and spiral wave activity may be combined and visualized simultaneously on a single map to facilitate understanding of fibrillation mechanisms.
In addition to locating phase singularities in a three-dimensional map, the tool 100 shown in FIG. 8 may also include helical wave identification 108 and helical wave characterization 112. Spiral wave identification 108 is programmable to calculate statistics related to the position and trajectory of a particular helical wave in time and space. Such a statistic can be used to determine the position of the spiral center of rotation over a time interval or multiple time intervals, for example to create a single map for a particular helical wave that can cover one or more episodes. It may include averaging. For example, the helical characterization function 112 can be programmed to calculate various statistics, for example, the excitatory wavefront statistics (for each of the reconstructed electrogram positions in the three-dimensional heart envelope) ( For example, average value, standard deviation, median value) can be included.
Alternatively or additionally, the helical characterization function 112 can calculate statistics (eg, mean, standard deviation, median, etc.) between two successive excitements for each location in the heart envelope. is there. As yet another example, the helical characterization function 112 can be programmed to calculate a helical characterization statistic (eg, mean, standard deviation, median, etc.) for each location on the heart envelope. It is.
The helical characterization function 112 may include an interval selector that can select one or more time intervals in response to user input. In addition to characterizing a particular interval, other acts may be performed on the selected intervals (eg, in response to user input). For example, the helical characterization can be programmed to combine multiple intervals and display a phase map for the selected multiple intervals. The helical characterization function 112 can further compare the calculated statistics and data during different time intervals selected by the user in response to user input. For example, the length may be different if the interval is different. However, it is also possible to calculate the number of excitatory wavefronts per unit time for each and compare them to ascertain additional information regarding the wavefront and number of swirl centers. Such other information may include not only the number of spiral centers of rotation and the number of times the focus source is excited, but also the position of the focus source over time on the heart envelope.
The tool 100 may also include a cycle length calculation function that is programmed to calculate the cycle length from the calculated phase signal. Further, the calculation of cycle length may include calculating a cycle length statistic that may change during a user selected time interval when fibrillation or other arrhythmia is occurring. Based on the filtering performed, including pre-reverse and post-reverse processing, these signals reflect those corresponding to true atrial or ventricular depolarization. For example, the cycle length calculation can calculate the cycle length of atrial fibrillation over a selected time interval. Corresponding maps can be created to specify calculated cycle length statistics, for example corresponding to the average cycle length displayed on the three-dimensional representation of the heart. FIG. 15 shows an example of a cycle length map that can be created from the calculated cycle length and that shows the average cycle length on a color scale.
Other information that can be calculated by the tool 100, including the helical characterization 112, includes integral phase gradients (e.g., formulas) for purposes such as demonstrating the spatial consistency of the spiral center of rotation on the 3D representation of the heart. 6 or Equation 7). For example, the integration phase can be calculated from the phase map by setting an upper and lower threshold, on which the corresponding integration phase for each position on the surface (eg, including multiple cavities simultaneously) Can be determined. A corresponding integral phase gradient map can be created (see, eg, FIG. 14) to specify phase singularities.
Yet another example of the helical wave characterization 112 may include calculating the time frequency of the pivot center position of the helical wave. Such a time frequency can be calculated for a single time interval during which electrical activity data is acquired. In addition, the helical wave characterization function 112 can also calculate the time frequency for the entire set combining multiple intervals by combining multiple intervals. In addition to combining the intervals, the helical characterization feature is programmable to compare these intervals to allow the user to visually compare multiple intervals on separate maps.
FIG. 12 illustrates an example of a system 150 that can be utilized to perform patient diagnosis and / or treatment. In some examples, the system 150 can evaluate the heart 152 in real time as part of a diagnostic or treatment procedure. This diagnosis or treatment procedure is intended, for example, to facilitate determination of parameters (eg, location of treatment, amount and type of treatment, etc.) for the physician to treat the patient. For example, a catheter, such as a pacing catheter or ablation catheter, with one or more therapeutic action delivery devices 156 secured thereto can be inserted into the body 154 such that it contacts the patient's heart 152 endocardially or epicardially. One of ordinary skill in the art will understand and appreciate the various types and configurations of therapeutic action implementation devices 156 that may be used depending on the procedure and type of treatment.
In some examples, the therapeutic action performing device 156 is configured to generate heat to cauterize the tissue in response to an electrical signal (eg, radio frequency energy) provided by the treatment system 158. One or more electrodes disposed at the tip of the substrate may be included. In other examples, the therapeutic action performing device 156 may deliver a coolant for performing ablation (eg, cryoablation), chemical (eg, drug) delivery, ultrasonic ablation, radiofrequency ablation, or ablation thereof. It may be configured to perform a combination of mechanisms or other ablation mechanisms. In yet another example, the therapeutic action performing device 156 is a pacing catheter tip for performing electrical stimulation in response to an electrical signal (eg, a pacing pulse) provided by the treatment system 158, such as for cardiac pacing. May include one or more electrodes disposed on the substrate. Other types of treatment can also be performed by the treatment system 158 and the invasive therapeutic action performing device 156 placed in the body.
The treatment system 158 may be located outside the patient's body 154 and can be configured to control the treatment performed by the device 156. For example, the treatment system 158 includes a control circuit 160. The control circuit 160 can communicate (eg, supply) electrical signals via a conductive link that is electrically connected between the device (eg, electrode) 156 and the treatment system 158. The control system 160 may provide signal parameters (eg, current, voltage) supplied to the device 156 for applying treatment (eg, ablation or stimulation) with one or more electrodes 154 at one or more locations in the heart 152. , Repeat count, trigger delay, detection trigger amplitude). The control circuit 160 can set treatment parameters and apply stimuli automatically, manually (eg, user input), or a combination of automatic and manual (eg, semi-automatic control). One or more sensors (not shown) can also communicate with the treatment system 158 to return sensor information. The position of the device 156 relative to the heart 152 can be determined and tracked intraoperatively by diagnostic imaging techniques (eg, fluoroscopy, x-ray), mapping system 162, direct viewing, and the like. In this way, the position of device 156 and treatment parameters can be combined to provide corresponding treatment parameter data.
Another system or subsystem can be utilized to obtain the patient's electrophysiological information before, during and / or after treatment with the therapy system 158. In the example of FIG. 12, sensor array 164 includes one or more electrodes that can be used to record patient activity. As an example, sensor array 164 may be a densely arranged body surface sensor (eg, as part of an electrocardiogram mapping procedure) that is distributed over a portion of a patient's torso to measure electrical activity associated with the patient's heart (eg, , The number of electrodes exceeds 200, etc.). An example of a non-invasive sensor array that can be used is shown and described in International Patent Application PCT / US2009 / 063803 filed Nov. 10, 2009 (incorporated herein). Other sensing electrodes in other arrangements can be used as the sensor array 164. This array is specifically designed for the analysis of a reduced number of electrodes (eg, AF and / or VF) designed to measure electrical activity for a specific purpose without covering the entire patient's torso. Electrode array).
One or more sensors may be placed on a device 156 that is inserted into the patient's body. Such electrodes can be utilized with a sensor array 164 for mapping the electrical activity of an endocardial surface such as the heart chamber wall. Such electrodes may also be used to help locate the device 156 within the heart and may be displayed on an image or map created by the system 150.
Each of these exemplary methods for obtaining electrical information of a patient, including an invasive sensor, a non-invasive sensor, or a combination of invasive and non-invasive sensors, each with one or more sensor arrays 164, The detected electrical information is supplied to the corresponding measurement system 166. Measurement system 166 may include appropriate control and signal processing circuitry 168 to provide corresponding measurement data 170 indicative of electrical activity detected by the sensors of sensor array 164. The measurement data 170 may include analog information or digital information.
The control 168 can also be configured to control a data acquisition process for measuring electrical activity and providing measurement data 170. The measurement data 170 can be acquired simultaneously with the treatment by the treatment system. The measurement data 170 is for detecting electrical activity of the heart 152 that occurs, for example, in response to applying a particular treatment (eg, according to treatment parameters). For example, an appropriate time stamp can be used to index the temporal relationship between each data 170 and treatment parameter to facilitate its evaluation and analysis.
The mapping system 162 applies measurement processing 170 and geometry data 172 corresponding to the electrical activity of the heart 152 by applying appropriate processing and calculations (eg, as disclosed with respect to FIGS. 1-4). Programmed to provide a combination and corresponding output data 174. The output data 174 may represent or characterize the phase of the heart envelope (eg, on the surface of the heart 152).
As an example, the output data 174 is obtained non-invasively based on phase data calculated for the epicardial surface of the patient's heart 152 (eg, using sensors 164 distributed on the surface of the patient's body 154). It may correspond to a phase map or another characterization (based on electrical data). Alternatively or in addition, the output data 174 is reconstructed for the surface of the patient's heart based on, for example, the same electrical activity that is recorded using the body surface sensor 164 to generate phase data. A potential map may be included.
In some examples, the measurement system 166 can simultaneously measure electrical activity throughout the heart, so that the resulting output data (eg, phase characterization and / or other electrocardiogram maps) can also be in a time-space consistent manner. It is possible to represent simultaneous data of the heart. The time interval for calculating the output data / map can be selected based on user input. Alternatively or in addition, the selected interval can be synchronized to the application of treatment by the treatment system 158.
Mapping system 162 includes a signal processing method indicated at 178. The signal processing method 178 may include pre-inverse processing and post-inverse processing (for example, pre-processing 50 in FIG. 2, pre-processing 70 in FIG. 3, post-processing 80 in FIG. 4) disclosed herein. Thus, signal processing can pre-process electrical measurement data 170 by extracting the relevant signal components in response to user input. For example, the user input may specify the type of fibrillation (such as AF or VF) to be mapped. Depending on the type of phase evaluation selected, signal processing is appropriately configured to provide appropriate filtering and baseline removal to remove extraneous signal components and unwanted vibrations as disclosed herein. It is possible.
Electrogram reconstruction 180 can then calculate an inverse solution of the processed signal based on the processed signal and geometry data 172 to provide a corresponding reconstructed electrogram. Thus, the reconstructed electrogram can accommodate electrocardiographic activity on the heart envelope, and can be static (three-dimensional at a specific time) or dynamic (eg, change over time) A four-dimensional map).
The cardiac envelope disclosed herein may correspond to the actual three-dimensional surface of the patient's heart. This surface may be the epicardium or the endocardium. Alternatively or in addition, the cardiac envelope may correspond to a surface that exists between the epicardial surface of the patient's heart and the patient's body surface on which the sensor array 164 is located. Also, the geometry data 172 utilized by the electrogram reconstruction 180 may correspond to the patient's actual geometry, a general model, or a combination thereof.
As an example, the geometric shape data 172 may be in the form of a graphic representation of the patient's torso, such as image data obtained about the patient. Such image processing may include extracting and segmenting anatomical features (including one or more organs and other structures) from the set of digital images. Also, for example, the position of each electrode in the sensor array 164 can be geometrically acquired by acquiring an image while the electrode is attached to the patient and identifying the position of the electrode in a coordinate system by appropriate extraction and segmentation. It may be included in the shape data 172. The segmented image data may be converted into a two-dimensional or three-dimensional graphic representation that includes the region of interest of the patient.
Alternatively, the geometric shape data 172 may correspond to a mathematical model, which may be a general model or constructed based on patient image data. Good. Appropriate anatomical landmarks or other landmarks, including the position of the electrodes in the sensor array 164, are identified in the geometry data 172 to facilitate matching of the electrical measurement data 170 and the inverse method there It may be easy to perform. Such landmark identification may be performed manually (eg, by a human using image editing software) or automatically (eg, by image processing techniques).
As yet another example, geometric shape data 172 may be used in any imaging technique (eg, x-ray, ultrasound, computed tomography, magnetic resonance) that can constitute the corresponding representation described herein. It can also be obtained using imaging. Such imaging may be performed simultaneously with the recording of electrical activity used to generate the patient's electrical measurement data 170, or imaging may be performed separately (eg, prior to acquiring measurement data).
For the reconstructed electrogram data calculated by the EGM reconstruction method (eg, reverse solution) 180, for example, according to the post-processing method disclosed herein (eg, post-reverse processing 80 of FIG. 4), Further, signal processing 178 may be performed. Following post-inverse processing performed by the signal processing function 178, the phase calculator 182 may calculate phase data for each signal as disclosed herein (eg, for more than 1000 points). it can. The obtained phase is stored in a memory as phase data and can be used by the map generator 184 to generate corresponding high-resolution output data 174.
Visualization engine 186 can provide output data 174 corresponding to a graphical representation of the phase information. Parameters associated with the visualization, such as including selection of time intervals and types of information to be visualized, can be selected in response to user input via the corresponding visualization GUI 188. In this manner, the mapping system 174 can generate corresponding output data 174 that is provided as a corresponding graphic output by the visualization engine on the display 192, including, for example, an electrocardiogram phase map 194.
In addition to the mapping system 162 that creates a phase map and phase characterization map for each fibrillation interval, other types of electrocardiogram mapping may be utilized including, for example, excitement maps, dominant frequency maps, and the like. For example, the display 192 may include one or more regions for displaying phase map data simultaneously with a corresponding excitement or dominant frequency map to facilitate AF or VF diagnosis and evaluation.
FIGS. 13-23 illustrate examples of phase maps and associated graphical representations that can be created by the systems and method systems disclosed herein.
FIG. 13 shows an example of a phase map 200 rendered on a graphical representation of the heart. The map 200 includes a color scale 202 corresponding to the phase (eg, between ± π) calculated for each position on the three-dimensional surface displayed on the map. In the example of FIG. 13, the spiral center of the spiral wave is indicated by PS. This corresponds to a single location where all phases meet (eg, a region such as a two-dimensional spatial region). In other examples, there may be more than one spiral wave on the surface of the heart at a particular point in time, and the techniques disclosed herein provide electricity that is obtained simultaneously from the entire heart surface for a particular time interval. As phase data is calculated based on the data, it will be apparent that each of the phase singularities as described above can be manifested in a single 3D graphic display.
FIG. 14 illustrates an example of a cardiac graphic map (eg, an integral phase gradient map) showing the position of the spiral center of rotation for an AF case, calculated over a 500 millisecond time interval. In the example of FIG. 14, the spiral center of the spiral wave is indicated by 210 and 212 in each 3D map. The dark area of this map corresponds to the position where the spiral center of rotation was not determined to be anchored (eg, according to Equation 6 or Equation 7). The data used to create the map of FIG. 14 can be determined by a phase singularity or helical wave identification function (eg, executable code corresponding to 106 or 108 of FIG. 8).
FIG. 15 shows an example of a cycle length map that can be created by the mapping system. In the example of FIG. 15, the cycle length is calculated as the average cycle length for phases occurring over a 500 millisecond time interval, which is selectable by the user. The average cycle length is plotted in the three-dimensional geometry of the heart. The color code scale 220 can be used to characterize the calculated statistics (eg, median, standard deviation) for the cycle length and the color spectrum (eg, from 120 milliseconds to about 230 milliseconds). It is also possible to characterize the dominant frequency in the range. In the example of FIG. 15, maps 216 and 218 are each shown in different orientations for the same heart in three-dimensional space. This is, for example, to show a time consistent cycle length for different surface areas of the heart.
FIG. 16 is an electrocardiogram map showing a phase map corresponding to an integral phase calculated for a cardiac envelope (eg, multiple chambers, which may be the entire epicardial surface, etc.) over a time interval. An example of 230 is shown. In the example of FIG. 16, the information presented in the map can represent the spatial consistency of the spiral center of rotation (shown as region 232) in the heart envelope. The phase characteristic in the map 230 represents the sum of integral phases over one or more intervals, such as plotting data if the calculated phase is greater than a pre-established predetermined phase threshold. it can. This threshold may be selected by the user to change the sensitivity of the presented information (eg, using the GUI 188 of FIG. 12).
FIG. 17 shows an example of a phase map that clearly shows the format of a movie or movie in different cases over a time interval. In FIG. 17, the upper part indicated by 17A clearly shows a phase map from 1050 milliseconds to 1080 milliseconds. In the lower part indicated by 17B, the time range is from 2300 milliseconds to 2660 milliseconds. Video rendering of phase maps over time can help the user identify spiral wave motion and interactions between spiral waves.
In addition to plotting the calculated phase statically or animatedly as shown in FIG. 17, identify the spiral trajectory and other helical wave characteristics as disclosed herein. For this purpose, other visualization mapping methods may be used.
As an example, each point on the heart envelope (eg, corresponding to the epicardial surface of the heart) can be used to project the corresponding surface onto a cylinder as shown in the example of FIG. . The corresponding heart envelope or heart surface can be segmented into a plurality of regions of interest based on, for example, anatomical locations associated with a particular analysis. FIG. 19 shows points that are segmented into multiple spatial regions with different color codes. Each segmented spatial region (eg, from FIG. 19) can be mapped onto a two-dimensional plane from the projected cylinder points of FIG.
In other words, FIGS. 18-19 show an intermediate process of how to reach the plot shown in FIG. That is, FIGS. 18 and 19 show how each region of the geometric shape can be divided and projected onto a 2D surface for analysis of the trajectory and characterization of the spiral wave. FIG. 18 shows the geometric projection process, and FIG. 19 shows how each region of the heart can be identified relative to the 2D surface. After 2D projection, these regions can be displayed as a single region or a combination of multiple regions.
FIG. 20 shows a plot in which one or more segmented regions are projected onto a 2D plane 251, which can be determined, for example, based on the analysis of the spatial region of the surface specified in FIGS. 18 and 19 It is. Other forms of analysis, such as spiral wave trajectory tracking, can also be derived from such analysis. Also shown in FIG. 20 is a vertical axis corresponding to time. From the plot 250, several helical wave characteristics (eg, chirality, wheel speed, etc.) can be calculated for each of the specified spiral centers 252, 254, and 256. In this characterization method, the swirl center of the spiral wave can be specified, and the trajectory of the swivel center can be measured over a certain time interval. For example, since each pivot center is specified by a two-dimensional surface projection, a specific surface is spatially represented with respect to a nearby surface. For this reason, it is possible to track the movement of the corresponding spiral wave and the trajectory of the spiral wave with the passage of time with each surface projection. It is also possible to specify the interaction between the spiral waves and the fragmentation of the spiral waves between the respective spiral waves in the surface region. By overlapping the calculations, the surface projection 251 shown in FIG. 20 is spatially related and can be used to identify the swirl center of the spiral wave that moves the corresponding surface projection.
In the example of FIG. 20, the turning centers of three spiral waves over time are clearly indicated by 252, 254, and 256. Spiral wave 256 remains spatially fairly stationary at the beginning (eg, from 0 to about 400 milliseconds), but rotates around spatial projection 251 later in the cycle time. To do. Around 300 to 350 milliseconds, spiral waves 252 and 254 interact across their respective regions, as indicated at 258.
Once the spiral center of the spiral wave has been spatially identified and the interaction has been determined, the identified spiral center orbit of the spiral wave is projected back to the corresponding area of the 3D surface and is supported with a map that changes over time It is possible to visualize the motion of a helical wave (see, eg, FIG. 17). Once the spiral center of rotation is spatially identified in the cardiac anatomy graphic map, the catheter design can be created for use in cauterizing the tissue where the spiral center of rotation occurs. This catheter design may include identification of the number and spatial arrangement of electrodes. In some examples, the catheter design can identify one of a plurality of preconfigured catheters, such as by model number. In another example, the catheter design corresponds to a custom configuration made with configurations and dimensions that have dedicated electrodes for ablating a tissue region in which one or more helical wave pivots are identified. It may be.
Alternatively, a spiral wave interaction map may be created to visualize the interaction of the spiral waves by a graphical method. For example, different color codes or other markings may be placed on the 3D visible image of the heart to identify such interactions. You can also see the chirality on the spiral interaction map by showing a symbol in each circle or square that shows the spin direction of the spiral center of rotation. The wheel speed can also be displayed on the map as the intensity of the color of the line indicating the interaction of the spiral waves on the map. Alternatively, the wheel speed may be displayed as rotations per second and fit to a corresponding color scale that can also be presented on a 3D graphic map.
For example, additional maps as shown in FIGS. 21 and 22 may be created. FIG. 21 shows a spiral wave swirl center interaction map that can be created based on, for example, the calculation shown in FIG. 20 and the interaction between the spiral wave swirl centers. FIG. 22 is a map that is presented on a graphical map to show information that can indicate chirality (eg, the direction of rotation of a particular spiral wave), as well as the wheel speed of each identified spiral wave. Another example of the color change applicable to is shown below.
In addition to AF and VF, the phase calculation disclosed herein can also be used to provide a graphical representation of atrial flutter, for example as shown in the example of FIG. In FIG. 23, only the phases within the small integration phase are color coded. This can be used, for example, to facilitate tracking of the propagation path of atrial flutter to accommodate counter-clockwise rotation of the phase relative to the heart valve. As an example, one or more maps or animation maps can be shown to demonstrate the situation where the calculated phase Φ (x) = constant as shown in FIG. 23 for the case of atrial flutter. is there. In the color version of this figure, for example, the condition Φ (x) = π / 2 can be graphically represented in purple, and all other conditions can be represented in gray (or other different colors). Similar diagnostic information can be visualized for other types of arrhythmias, for example to visualize when the phase of atrial fibrillation or ventricular fibrillation is constant.
In view of the above structural or functional description, it will be apparent to those skilled in the art that portions of the present invention may be implemented as a method, data processing system, or computer program product. Accordingly, such portions of the present invention may include software and hardware as illustrated or described in an embodiment that is entirely hardware, an embodiment that is entirely software, or the computer system of FIG. Any of the embodiments may be adopted. Further, part of the present invention may be a computer program product on a computer usable storage medium having computer readable program code on the medium. Any suitable computer-readable medium may be utilized, examples of which include static and dynamic storage devices, hard disks, optical storage devices and magnetic storage devices. It is not limited to.
Certain embodiments of the present invention have also been described herein with reference to block diagrams of methods, systems, and computer program products. It will be understood that the illustrated blocks, as well as combinations of the illustrated blocks, may be implemented by computer-executable instructions. These computer-executable instructions are transmitted to one or more processors of a general purpose computer, a dedicated computer, or other programmable data processing device (or combination of devices and circuits) for manufacturing a machine, Instructions executed by the processor may be provided to implement the functionality indicated in one or more blocks.
These computer-executable instructions are instructions that cause a computer or other programmable data processing device to implement the functions stored in the computer-readable memory, where the instructions are shown in one or more blocks of the flowchart. May be stored in a computer readable memory that can be operated in a specific manner to lead to a product containing. A computer program or other programmable device is also loaded with instructions of the computer program loaded onto the computer or other programmable data processing device to perform a series of operational steps on the computer or other programmable device. Computer-implemented processes may be generated such that the instructions executing above provide steps for implementing the functionality shown in one or more blocks of the flowchart.
In view of this, FIG. 25 illustrates an example of a computer system 300 that can be used to implement one or more embodiments of the present invention. These embodiments include, for example, analysis of converted sensor data and image data associated with analysis of cardiac electrical activity as well as acquisition and processing of sensor data, processing of image data. The computer system 300 can be implemented in one or more networked general purpose computer systems, embedded computer systems, routers, switches, server devices, client devices, various intermediate devices / nodes, or stand-alone computer systems. is there. The computer system 300 can also be implemented on a variety of mobile clients such as personal digital assistants (PDAs), laptop computers, pagers, etc., assuming that it has sufficient processing power.
The computer system 300 includes a processing device 301, a system memory 302, and a system bus 303 that connects various system components (including system memory) to the processing device 301. A multiprocessor architecture such as a dual microprocessor may be used as the processing unit 301. The system bus 303 is any of various types of bus structures, including memory buses or memory controllers, peripheral buses, and local buses that use any of a wide variety of bus architectures. There may be. The system memory 302 includes a read only memory (ROM) 304 and a random access memory (RAM) 305. A basic input / output system (BIOS) 306 can be resident in a ROM 304 that includes basic routines that facilitate the transfer of information between elements within the computer system 300.
The computer system 300 reads / writes data from / to a hard disk drive 307, a magnetic disk drive 308 for reading / writing, for example, a removable disk 309, and / or a CD-ROM disk 311, for example. And an optical disk drive 310 for the purpose. The hard disk drive 307, magnetic disk drive 308, and optical disk drive 310 are connected to the system bus 303 via a hard disk drive interface 312, a magnetic disk drive interface 313, and an optical drive interface 314, respectively. The drive and associated computer-readable medium provide a non-volatile storage device that stores data, data structures, and computer-executable instructions for the computer system 300. In the above description of the computer readable medium, the hard disk, the removable magnetic disk, and the CD are mentioned. However, in this operating environment, the computer readable medium in various forms such as a magnetic cassette, a flash memory card, and a digital video disk is used. The following types of media may be used. In addition, any such media may include computer readable instructions for implementing one or more portions of this specification.
A number of program modules may be stored in the drive and RAM 305, including the operating system 315, one or more application programs 316, other program modules 317, and program data 318. Application programs and program data may include functions and methods that are programmed to obtain, process, and display electrical data from one or more sensors as shown and described herein. Application programs and program data may include functions and methods programmed to process signals and calculate phase data as disclosed herein. Application programs and program data may also include functions and methods that are programmed to create a phase map or other electrocardiogram map as disclosed herein.
A user may enter commands and information into the computer system 300 via one or more input devices 320. The input device 320 is, for example, a pointing device (for example, a mouse or a touch screen), a keyboard, a microphone, a joystick, a game pad, a scanner, or the like. For example, the user can use the input device 320 to edit or modify the domain model. These input devices and other input devices 320 are often connected to the processing unit 301 via a corresponding port interface 322 connected to the system bus, but may be a parallel port, serial port, or universal serial bus (USB). ) And other interfaces. One or more output devices 324 (eg, a display, monitor, printer, projector or other type of display device) are also connected to the system bus 303 via an interface 326 such as a video adapter.
Computer system 300 may operate in a network environment using logical connections to one or more remote computers, such as remote computer 328. The remote computer 328 may be a workstation, computer system, router, peer device, or other common network node and generally includes many or all of the elements described with respect to the computer system 300. The logical connections shown schematically at 330 may include a local area network (LAN) and a wide area network (WAN).
When used in a LAN network environment, the computer system 300 may be connected to a local network through a network interface or adapter 332. When used in a WAN network environment, the computer system 300 may include a modem or may be connected to a communication server on the LAN. A modem (which may be internal or external) can be connected to the system bus 303 via a suitable port interface. In a network environment, the application program 316 or program data 318 shown in connection with the computer system 300, or a portion thereof, may be stored in the remote memory storage device 340.
What have been described above are examples and embodiments of the present invention. Of course, for the purpose of illustrating the invention, it is not possible to describe all possible combinations of components or methodologies, but it will be appreciated by those skilled in the art that many other combinations and permutations of the invention are possible. You will recognize it. Accordingly, the present invention is intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. In the claims, the article “a” indicates “one or more” unless stated otherwise.
A memory for storing machine-readable instructions and data;
A processor for accessing the memory and executing the instructions, the machine-readable instructions comprising:
Processed electrical data corresponding to non-invasive electrical data obtained from the patient for at least one time interval is converted into a corresponding reconstructed electrical signal on a predetermined cardiac envelope. Converted,
For performing a method comprising calculating phase data based on the reconstructed electrical signal;
The system, wherein the reconstructed electrical signal is space-time consistent.
The system of claim 1, wherein the method further comprises creating a phase map based on the calculated phase data.
Based on the phase data, calculate the helical wave characteristics,
The system of claim 2, further comprising creating a spiral map over the at least one time interval based on the calculated phase data.
A set of electrodes configured to cover a predetermined portion of the patient's torso and non-invasively detect electrical activity from an outer surface of the patient's torso;
The system of claim 2, wherein the detected electrical activity corresponds to the non-invasive electrical data.
The system of claim 4, further comprising a treatment device configured to treat cardiac tissue at a spatial location identified based on the calculated phase data.
The system of claim 4, further comprising an ablation catheter configured to cauterize heart tissue at a spatial location corresponding to a spiral center of rotation identified based on the calculated phase data.
The system of claim 2, wherein the method further comprises calculating a temporal frequency of a spatial position about a spiral center of rotation over at least one time interval.
Estimating an indicator of the integral of the phase gradient of the heart envelope,
Creating an integrated phase gradient map based on the estimated index to present spatio-temporal consistent phase information for a plurality of cavities of the patient's heart corresponding to the cardiac envelope surface; The system according to claim 2.
The method of claim 2, further comprising: determining a position of at least one phase singularity on the heart envelope and displaying the position of the at least one phase singularity on the phase map for the heart envelope. The described system.
The method further comprises determining a position of at least one focus source on the heart envelope and displaying the position of the at least one focus source on the phase map for the heart envelope. 2. The system according to 2.
The method pre-processes non-invasive electrical data obtained from the patient for at least one time interval prior to the conversion to extract features of the signal determined to contribute to arrhythmia; Providing the processed electrical data corresponding to the characteristics of the processed signal,
The system of claim 1, wherein the reconstructed electrical signal is derived from the processed electrical data.
The method further includes processing the reconstructed electrical signal after the conversion to provide a processed reconstructed electrical signal;
The system of claim 1, wherein the calculated phase data is calculated based on the processed reconstructed electrical signal.
The method pre-processes non-invasive electrical data obtained from a patient for at least one time interval prior to the conversion to remove signal features determined not to contribute to a predefined arrhythmia. And further comprising providing the processed electrical data,
14. The system of claim 13, wherein the preprocessing further comprises removing signal features from ventricular electrical activity from the non-invasive electrical data to increase specificity for an atrial type arrhythmia.
The removing of the signal features further comprises at least one of canceling a QRS wave or canceling a T wave from the signal represented by the non-invasive electrical data. 14. The system according to 14.
The method further includes selectively controlling the pre-processing performed in response to user input depending on whether an atrial arrhythmia or a ventricular arrhythmia is selected as a pre-defined arrhythmia;
14. The system of claim 13, wherein the preprocessing is programmed to remove signal features representative of ventricular electrical activity when the user input selects the atrial arrhythmia.
The system of claim 1, wherein the reconstructed electrical signal provides electrical information about over 1000 locations distributed on the heart envelope.
2. The cardiac envelope is the epicardial surface and the predefined arrhythmia is at least one of atrial fibrillation, atrial tachycardia, ventricular tachycardia, and ventricular fibrillation. The system described.
Said transforming by computing an inverse solution based on a geometric shape that is at least one of actual geometric data and a general anatomical model acquired for a particular patient The system of claim 1, wherein the system is performed.
The system of claim 1, wherein the method is further programmed to calculate a cycle length indicator based on the calculated phase data.
A non-transitory computer readable medium having instructions executable by a processor, the instructions comprising:
For generating a reconstructed electrogram signal for each of a number of points on or near a predetermined cardiac envelope based on geometry data and non-invasively measured body surface electrical signals An electrogram reconstruction method;
A phase calculator for calculating a phase signal for the multiple points based on the reconstructed electrogram signal;
A visualization engine for generating an output based on the calculated phase signal.
The instructions process the non-invasively measured body surface electrical signal, remove signal characteristics determined not to contribute to a predefined arrhythmia, and provide a corresponding processed electrical signal Further comprising a pre-processing method programmed into
The medium of claim 21, wherein the electrogram reconstruction method generates the reconstructed electrical signal from the corresponding processed electrical signal.
The pre-processing method removes signal features from the non-invasively measured body surface electrical signals due to electrical activity of the ventricle and increases the specificity of the corresponding processed electrical signals for atrial type arrhythmias. 24. The medium of claim 22, further programmed.
The pre-processing method performs at least one of QRS wave cancellation or T wave cancellation from the non-invasively measured body surface electrical signal, and the non-invasively measured body surface electrical 24. The medium of claim 23, further programmed to remove the signal features from the signal.
The instructions further include a graphical user interface programmed to specify a predetermined type of arrhythmia for evaluation in response to user input;
23. The medium of claim 22, wherein the preprocessing method is programmed to selectively control the preprocessing in response to the user input.
The preprocessing method is programmed to remove signal features related to ventricular electrical activity from the non-invasively measured body surface electrical signal when the user input selects an atrial type arrhythmia assessment. 26. The medium of claim 25, wherein:
The instructions further include a phase singularity calculation programmed to determine the position of at least one phase singularity on the cardiac envelope based on the calculated phase signal;
The medium of claim 21, wherein the location of the at least one phase singularity is identified in the output.
The instructions further include a focus source calculator programmed to determine a position of at least one focus source on the cardiac envelope based on the calculated phase signal;
The medium of claim 21, wherein the at least one focus source is identified in the output.
The visualization engine is further programmed to create a consistent map of at least one space-time and to characterize one or more mechanisms of arrhythmia for multiple chambers of the patient's heart over multiple time intervals The medium according to claim 21.
The output includes a phase map, an integral phase gradient map, a spiral road map, a cycle length map for presenting space-time consistent information for a plurality of cavities of the patient's heart based on the calculated phase signal 30. The medium of claim 29, comprising at least one of:
The medium of claim 21, wherein the instructions further include a helical wave identification function programmed to identify a position of a spiral center of a helical wave as a target for ablation based on the calculated phase signal.
The geometry data utilized by the electrogram reconstruction method is at least one of actual geometry data acquired for a particular patient and a general anatomical model. The medium according to 21.
The medium of claim 21, wherein the instructions further comprise a cycle length calculation function programmed to calculate a cycle length indicator based on the calculated phase signal.
JP2019070850A 2012-09-21 2019-04-02 Physiological mapping for arrhythmia Pending JP2019141619A (en)
US201261704227P true 2012-09-21 2012-09-21
US61/704,227 2012-09-21
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JP2015533210A Active JP6556624B2 (en) 2012-09-21 2013-09-20 Physiological mapping for arrhythmia
JP2019070850A Pending JP2019141619A (en) 2012-09-21 2019-04-02 Physiological mapping for arrhythmia
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