WO2016033075A1 - Système et procédé de reconstruction d'informations d'activation cardiaques - Google Patents

Système et procédé de reconstruction d'informations d'activation cardiaques Download PDF

Info

Publication number
WO2016033075A1
WO2016033075A1 PCT/US2015/046742 US2015046742W WO2016033075A1 WO 2016033075 A1 WO2016033075 A1 WO 2016033075A1 US 2015046742 W US2015046742 W US 2015046742W WO 2016033075 A1 WO2016033075 A1 WO 2016033075A1
Authority
WO
WIPO (PCT)
Prior art keywords
signal
cardiac
cardiac signal
signals
activation
Prior art date
Application number
PCT/US2015/046742
Other languages
English (en)
Inventor
Sanjiv Narayan
Carey Robert Briggs
Original Assignee
The Regents Of The University Of California
Topera, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US14/473,572 external-priority patent/US9107600B2/en
Application filed by The Regents Of The University Of California, Topera, Inc. filed Critical The Regents Of The University Of California
Priority to CN201580051983.4A priority Critical patent/CN106714677A/zh
Priority to EP15836641.9A priority patent/EP3185768A4/fr
Publication of WO2016033075A1 publication Critical patent/WO2016033075A1/fr
Priority to IL250638A priority patent/IL250638A0/en

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7239—Details of waveform analysis using differentiation including higher order derivatives
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/25—Bioelectric electrodes therefor
    • A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
    • A61B5/28—Bioelectric electrodes therefor specially adapted for particular uses for electrocardiography [ECG]
    • A61B5/283—Invasive
    • A61B5/287—Holders for multiple electrodes, e.g. electrode catheters for electrophysiological study [EPS]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7246—Details of waveform analysis using correlation, e.g. template matching or determination of similarity
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2505/00—Evaluating, monitoring or diagnosing in the context of a particular type of medical care
    • A61B2505/05—Surgical care
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/04—Arrangements of multiple sensors of the same type
    • A61B2562/046—Arrangements of multiple sensors of the same type in a matrix array
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the present application relates generally to heart rhythm disorders. More specifically, the present application is directed to a system and method for reconstructing cardiac activation information (activation onset) associated with heart rhythm disorders.
  • Heart (cardiac) rhythm disorders are common and represent significant causes of morbidity and death throughout the world. Malfunction of the electrical system in the heart represents a proximate cause of heart rhythm disorders.
  • Heart rhythm disorders exist in many forms, of which the most complex and difficult to treat are atrial fibrillation (AF), ventricular tachycardia (VT) and ventricular fibrillation (VF).
  • Other rhythm disorders are more simple to treat, but may also be clinically significant including atrial tachycardia (AT), supraventricular tachycardia (SVT), atrial flutter (AFL), supraventricular ectopic complexes/beats (SVE) and premature ventricular complexes/beats (PVC).
  • AT atrial tachycardia
  • SVT supraventricular tachycardia
  • AFL atrial flutter
  • SVE supraventricular ectopic complexes/beats
  • PVC premature ventricular complexes/beats
  • While under normal conditions the sinus node keeps the heart in sinus rhythm, under
  • Heart rhythm disorders particularly complex rhythm disorders of AF, VF and polymorphic VT— can be very difficult.
  • Pharmacologic therapy for complex rhythm disorder is not optimal, with poor efficacy and significant side effects.
  • Ablation has been used increasingly in connection with heart rhythm disorders by maneuvering a sensor/probe to the heart through the blood vessels, or directly at surgery, and delivering energy to a location of the heart that harbors a cause of the heart rhythm disorder to mitigate and in some cases to eliminate the heart rhythm disorder.
  • ablation is often difficult and ineffectual because tools that identify and locate a cause of the heart rhythm disorder are poor and hinder attempts to deliver energy to the correct region of the heart to eliminate the disorder.
  • Certain systems and methods are known for treating simple heart rhythm disorders.
  • a simple heart rhythm disorder e.g., atrial tachycardia
  • consistent activation onset patterns from beat to beat can generally be traced back to an earliest location, which can be ablated to mitigate and in some cases to eliminate the disorder.
  • ablation of the cause of a heart rhythm disorder is challenging and experienced practitioners often require hours to ablate simple rhythm disorders with consistent beat-to-beat activation patterns, such as atrial tachycardia.
  • Diagnosing and treating heart rhythm disorders often involves the introduction of a catheter having a plurality of sensors/probes into the heart through the blood vessels of a patient.
  • the sensors detect electric activity of the heart at sensor locations in the heart.
  • the electric activity is generally processed into electrogram signals that represent the activation of the heart at the sensor locations.
  • the signal at each sensor location is generally consistent from beat to beat in timing and often in shape and number of its deflections, enabling identification of activation onsets at each sensor location.
  • the signal at each sensor location from beat to beat may transition between one, several, and multiple deflections of various shapes.
  • a signal for a sensor location in AF includes 5, 7, 11 or more deflections
  • the present invention is applicable to reconstructing activation information of various rhythm disorders, including heart rhythm disorders, as well as other biological rhythm disorders, such as neurological seizures, esophageal spasms, bladder instability, irritable bowel syndrome, and other biological disorders for which biological activation information can be reconstructed to permit determination, diagnosis, and/or treatment of the cause or source of the disorders. It is particularly useful, however, in complex rhythm disorders which result in complex activation patterns, and especially useful in complex rhythm disorders of the heart, in order to find the cause(s) or source(s) of the disorders such that they can be treated with expediency.
  • Local activation is activation that originates from, or is associated with, a specific location in the heart (sensed location).
  • a sensor may be proximate to the sensed location (if it is a sensor in direct contact with the sensed location) or associated with the sensed location (if it is a sensor not in direct contact with the sensed location).
  • Far- field activation is activation that originates at a location in the heart that is different than the sensed location associated with the sensor.
  • Conventionally, local activation has been clinically detected by characteristic patterns in individual signals.
  • the present invention provides a relatively few number of steps to reconstruct the activation information in order to determine the activation onset times at various sensor locations for a heartbeat amidst the virtually indiscernible activation patterns.
  • reconstruction is a process of identifying activation onset time in a cardiac or biological signal at a sensor location distinct from nearby or adjacent sensor locations for one or more beats of a biological or cardiac rhythm disorder.
  • activation onset time is a time point at which activation commences in a cell or tissue, as opposed to other time points during activation.
  • activation is a process whereby a cell commences its operation from a quiescent (diastolic) state to an active (electrical) state.
  • an example method of representing cardiac information associated with a heart rhythm disorder includes accessing a plurality of neighboring cardiac signals obtained from a patient. The method also includes eliminating far-field activations from the plurality of neighboring cardiac signals using one or more divergence criteria that define local activations in the plurality of neighboring cardiac signals, the divergence criteria being associated with divergence among the plurality of neighboring cardiac signals.
  • Elimination of the far-field activation from the plurality of neighboring signals includes the following actions.
  • a first cardiac signal and a second cardiac signal of the plurality of neighboring signals are accessed.
  • the first cardiac signal and the second cardiac signal are processed to determine whether there is a point of change in the first cardiac signal at which a derivative of the first cardiac signal diverges with respect to a derivative of the second cardiac signal above a threshold.
  • An activation onset time is assigned in the first cardiac signal at the point of change to define a local activation if the point of change is in the first cardiac signal.
  • the point of change can be determined at about the same time point for the first cardiac signal and the second cardiac signal.
  • the determination of the point of change can include the following actions.
  • a composite cardiac signal can be formed from the first cardiac signal and the second cardiac signal. Ratio values at a plurality of points in the first cardiac signal can be determined. Each ratio value can represent a difference between the derivative of the second cardiac signal and a derivative of the composite cardiac signal to a difference between derivative of the first cardiac signal and the derivative of the composite cardiac signal. A point having a largest ratio value from the determined ratio values can be selected as the point of change in the first cardiac signal.
  • At least one characteristic of the first cardiac signal can be matched to at least one characteristic of a reference cardiac signal in a catalog of cardiac signals. Thereafter, an activation onset time of the reference cardiac signal can then be assigned as an activation onset time in the first cardiac signal to define a local activation in the first cardiac signal.
  • the method of representing cardiac information associated with a heart rhythm disorder can further include iteratively accessing pairs of cardiac signals from the plurality of neighboring cardiac signals. Each pair includes a first cardiac signal and different second cardiac signal. The processing and assigning for each of the pairs can be performed to define multiple local activations in the first cardiac signal in each of the pairs. Thereafter, the clinical represented can be constructed based on multiple local activations of the plurality of neighboring cardiac signals to indicate a source of a cardiac rhythm disorder.
  • the heart rhythm disorder can be treated using the constructed clinical representation.
  • FIG. 1 illustrates an example cardiac activation reconstruction system
  • FIG. 2 illustrates an example simple electrogram signal of a heart rhythm disorder from a sensor positioned at a sensor location in a heart illustrated in FIG. 1;
  • FIG. 3 illustrates an example complex electrogram signal of a heart rhythm disorder from a sensor positioned at a sensor location in a heart illustrated in FIG. 1;
  • FIG. 4 illustrates an example array of sensors of a catheter illustrated in
  • FIG. 1 and an example selection of signals from the sensors to reconstruct cardiac activation information
  • FIG. 5 illustrates example comparison pairs of signals from the sensors of the array illustrated in FIG. 4;
  • FIG. 6 is an illustration of an example signal pair comparison of analysis signal (SIGl) and reference signal (SIG2);
  • FIG. 7 is an illustration of another example signal pair comparison of analysis signal (SIGl) and reference signal (SIG2);
  • FIG. 8 is an illustration of a further example signal pair comparison of analysis signal (SIGl) and reference signal (SIG2) utilizing a composite signal;
  • FIG. 9 is a flowchart that illustrates an example method of reconstructing cardiac activation information associated with heart rhythm disorders
  • FIG. 10 is an illustration of an example signal pair comparison of analysis signal (SIGl) and reference signal (SIG2) that can be processed in accordance with the method of FIG. 9 to reconstruct cardiac activation information;
  • SIGl analysis signal
  • SIG2 reference signal
  • FIG. 11 is an illustration of an example mapping of processed signals in accordance with FIGS. 1-10.
  • FIG. 12 is a block diagram of an illustrative embodiment of a general computer system. DETAILED DESCRIPTION
  • FIG. 1 illustrates an example cardiac activation reconstruction system 100.
  • the example system 100 is configured to detect and reconstruct cardiac activation information collected/detected from a patient's heart in connection with a heart rhythm disorder.
  • the heart includes a right atrium 122, left atrium 124, right ventricle 126 and left ventricle 128.
  • the example system 100 includes a catheter 102, signal processing device
  • computing device 1 16 computing device 1 16 and analysis database 1 18.
  • the catheter 102 is configured to detect cardiac activation information in the heart and to transmit the detected cardiac activation information to the signal processing device 1 14, either via a wireless or wired connection.
  • the catheter includes a plurality of probes/sensors 104-1 12, which can be inserted into the heart through the patient's blood vessels.
  • one or more of the sensors 104-1 12 are not inserted into the patient's heart.
  • some sensors may detect cardiac activation via the patient's surface (e.g., electrocardiogram) or remotely without contact with the patient (e.g., magnetocardiogram).
  • some sensors may also derive cardiac activation information from cardiac motion of a non-electrical sensing device (e.g., echocardiogram).
  • these sensors can be used separately or in different combinations, and further these separate or different combinations can also be used in combination with sensors inserted into the patient's heart.
  • the sensors 104-1 12 which are positioned at sensor locations in the heart under consideration, can detect cardiac activation information at the sensor locations and can further deliver energy to ablate the heart at the sensor locations. It is noted that the sensors 104-1 12 can also detect cardiac activation information from overlapping regions of the heart (e.g., right atrium 122 and left atrium 124).
  • the signal processing device 1 14 is configured to process (e.g., clarify and amplify) the cardiac activation information detected by the sensors 104-1 12 at the sensor locations into electrogram signals and to provide the processed cardiac signals to the computing device 1 16 for analysis or processing in accordance with methods disclosed herein.
  • the signal processing device 1 14 can subtract cardiac activation information from overlapping regions of the heart 120 to provide processed cardiac signals to the computing device 1 16 for analysis. While in some embodiments or aspects, the signal processing device 1 14 is configured to provide unipolar signals, in other embodiments or aspects, the signal processing device 1 14 can provide bipolar signals.
  • the computing device 1 16 is configured to receive (or access) cardiac signals from the signal processing device 1 14 and further configured to analyze or process the cardiac signals in accordance with methods, functions or logic disclosed herein to reconstruct cardiac activation information in the cardiac signals such that it is possible to locate a cause of the heart rhythm disorder and to eliminate the cause.
  • the computing device 1 16 can process a first cardiac signal and a second cardiac signal from the received cardiac signals to determine whether there is a point of change in a derivative of the first cardiac signal with respect to a derivative of the second cardiac signal above a threshold. The computing device 1 16 can then assign an activation onset time in the first signal at the point of change to define cardiac activation indicating a beat in the first signal if it is determined that the point of change is above the threshold.
  • the computing device 1 16 can iteratively select pairs of cardiac signals from the received cardiac signals, each pair having a first cardiac signal and second cardiac signal.
  • the computing device 1 16 can process and assign for each of the pairs in order to define multiple cardiac activations indicating beats for the first cardiac signal in each of the pairs.
  • the computing device 1 16 is configured to perform processing and assigning to define multiple cardiac activations indicating beats in the first cardiac signal.
  • the computing device 1 16 can then reconstruct a cardiac activation pattern based on assigned activation onset times of cardiac activations from the received cardiac signals to indicate a source of a rhythm disorder.
  • the computing device 116 can also display the reconstructed cardiac activation pattern to facilitate treatment of cardiac tissue at the source to suppress, lessen or eliminate the cardiac rhythm disorder.
  • the analysis database 118 is configured to support or aid in the analysis of the signals by the computing device 116.
  • the analysis database 118 can store a catalog of reference signals and associated activations to enable the computing device 116 to determine an activation onset associated with a signal being considered (e.g., when point of change is below threshold during a time window), as will be described in greater detail herein.
  • FIG. 2 illustrates an example simple electrogram signal 200 of a heart rhythm disorder from a sensor positioned at a sensor location in the heart 120.
  • sensor 104 of catheter 102 can be positioned at a sensor location in the right atrium 122, as shown in FIG. 1.
  • the heart rhythm disorder can be a complex rhythm disorder AF, VF and polymorphic VT, or another heart rhythm disorder.
  • the example signal 200 is for a time period between about 300 ms and about 900 ms.
  • the signal 200 is expected to have four (4) local activation onsets 204-208, e.g., those activation onsets that originate at or near (locally to) the sensor location in the heart 120 of sensor 104.
  • cycle length between activation onsets of about 100 ms to about 300 ms can be expected for AF
  • cycle length between activation onsets of about 180 ms to about 240 ms can be expected for complex ventricular arrhythmias.
  • cycle length 210 of about 100 ms to about 300 is expected between activation onset 202 and activation onset 204.
  • the activation onsets 204- 208 are generally identifiable as having a small degree of baseline wander superposed in the local signal with few far-field artifacts that could be mistaken as local activity.
  • Local activity in this example can be characterized by an activation onset with a sharp inflection point and high slope, followed by a period of gentle, low-deviation slope representing repolarization, typically lasting between about 100 ms and 250 ms.
  • an example far-field deflection 212 is illustrated between location activation onset 206 and local activation onset 208, e.g., an activation onset that originates at a location in the heart 120 that is different than the sensor location associated with the sensor 104.
  • the heart 120 at the sensor location associated with sensor 104 cannot physiologically activate again after activation onset 206 in a shorter cycle than about 100 ms to about 300 ms because local tissue must undergo repolarization.
  • the deflection 212 cannot be local to the sensor location associated with the sensor 104 when the deflection 212 is also significantly present in signals collected by neighbor sensors in multiple directions to sensor 104.
  • the far- field deflection 212 detected by sensor 104 can be associated with activation onset at a sensor location associated with sensor 106.
  • FIG. 3 illustrates an example complex electrogram signal 300 of a heart rhythm disorder from a sensor positioned at a sensor location in the heart 120.
  • sensor 106 of catheter 102 can be positioned at a sensor location in the right atrium 122, as shown in FIG. 1.
  • the heart rhythm disorder can be a complex rhythm disorder AF, VF and polymorphic VT, or another heart rhythm disorder.
  • example signal 300 is for a time period between about 300 ms and about 900 ms. During this time period, the signal 300 is expected to have four (4) local activation onsets, e.g., activation onsets that originate locally to the sensor location in the heart 120 of sensor 106. However, in the example signal 300 there are eleven (11) possible activation onsets 302-322. Multiple deflections of short duration (shorter than shortest cycle length of about 100 ms) caused by the heart rhythm disorder makes the discernment of local activation onsets at the sensor location of sensor 104 as opposed to far- field activations or simply noise prohibitively difficult.
  • FIG. 4 illustrates an example array of sensors 400 of catheter 102 and an example selection of signals from the sensors to reconstruct cardiac activation information ⁇ e.g., activation onsets).
  • the array 400 includes fifteen (15) example sensors for simplicity and clarity of the description. It is to be understood that the array 400 can include fewer or more sensors to as may be determined to cover different portions of the heart 120. In some embodiments or aspects, the array 400 can include 160 or more sensors.
  • the sensors of the array 400 are shown in example spatial arrangement with respect to the right atrium 122 of the heart 120.
  • the array 400 can be spatially arranged in other chambers of the heart, e.g., left atrium, right ventricle, left ventricle, or for combinations of chambers including the endocardial or epicardial surfaces.
  • the spatial arrangement of electrodes in the array 400 is shown to be uniform and planar for simplicity and clarity of the description.
  • the heart 120 is not a uniform or planar structure. Accordingly, the spatial arrangement of electrodes in the array 400 can be varied with respect to the shape of the heart 120 to improve detection of electric activity in the heart 120.
  • catheter 102 of FIG. 1 can be a basket catheter with the example sensors of the array 400 disposed in spatial arrangements along splines 406-408 of the basket catheter 102.
  • Different catheters with various spatial arrangements of the sensors in the sensor array 400 can be used, such as spiral, radial spokes or other spatial arrangements.
  • Pairs of sensors (signals of sensors) in the array 400 are iteratively selected for processing as will be described in greater detail herein in order to reconstruct cardiac activation information (activation onsets) of the heart 120 in the right atrium 122, or another chamber in which the array 400 may be disposed.
  • an analysis signal (1) is selected for processing.
  • a reference signal (2)— a neighbor to the analysis signal (1)— is then selected to form a first pair that is processed to determine activation onsets in the analysis signal (1).
  • an analysis signal (1) is selected for processing.
  • a reference signal (2)— another neighbor to the analysis signal (1)— is then selected to form a second pair that is processed to determine activation onsets in the analysis signal (1).
  • the activation onsets from the first pair and the second pair of signals can be stored in memory of computing device 116 or database 118 of FIG. 1.
  • the neighboring sensors (signals) can but do not have to be adjacent, as will be described in greater detail below.
  • FIG. 5 illustrates example comparison pairs of signals from the sensors of the array 400 illustrated in FIG. 4. Neighbor signals can include not only those signals that are immediately adjacent to the analysis signal but also those signals not adjacent to the analysis signal.
  • Spatially separating the paired sensors can have the effect of spatially extending the area over which deflections are considered to be local activity. Local activity is therefore approximately defined by the separation of the paired sensors.
  • selected analysis signal (1) is processed against adjacent signals (2)-(5) and also against a non-adjacent signal (6).
  • selected analysis signal (1) is processed against adjacent signals (2)-(5) and also against a non-adjacent signals (6) and (7). While closest neighbor signals are preferred, neighbor signals in various spatial orientations with respect to the analysis signal can be used.
  • each analysis signal there could be a plurality of reference signals (e.g., four (4) reference signals or greater).
  • a final activation onset in the analysis signal is determined with reference to or based on the combination of the reference signals' possible activation onsets. Specifically, the activation onsets determined from each pair can be referenced against each other to check for correspondence or association of activations in the analysis signal.
  • An activation onset for the analysis signal is finalized based on the possible activation onsets of the referenced pairs of signals.
  • the final activation onset for the analysis signal can be determined in various ways. In one embodiment or aspect, the final activation onset for the analysis signal can be determined based on an average of the possible activation onsets from the various pairs of referenced signals. In another embodiment or aspect, the final activation onset for the analysis signal can be determined based on an average of the possible activation onsets from those pairs of signals in which a majority of the possible activation onsets are within a predetermined time interval of each other (e.g., ⁇ 5 ms). The time interval used can be chosen to be lower or higher.
  • the final activation can also be determined by performing a "center-of-mass" calculation weighted by the significance value of each of the possible activation onsets in the majority, or by analysis of a predominant direction of activation onsets relative to sensor locations.
  • the activation onsets of 190 ms and 193 ms that are outside the time interval can be discounted from the determination of the final activation onset for the analysis signal.
  • the final activation onset determined for each signal can be saved in the database 1 18 of FIG. 1.
  • each signal from a sensor of array 400 can represent multiple successive analysis intervals (e.g., activation cycles) as illustrated in FIG. 2, each of which can have an activation onset as determined based on the same time interval of multiple reference signals (neighboring sensors of array 400).
  • FIG. 6 is an illustration of an example signal pair comparison 600 of example analysis signal (SIG1) and example reference signal (SIG2).
  • the signals can be from comparison pair 402 (or comparison pair 404) illustrated in FIG. 4, or from any comparison pair illustrated in FIG. 5. It is noted that the signals are illustrative and occur during the same analysis interval. As noted herein, the signals can have multiple successive analysis intervals (e.g., activation cycles), as illustrated in FIG. 2.
  • the signals are processed at one or more successive time points (e.g., every millisecond, two milliseconds, or other time points) to determine whether there is a point of change in a derivative of the analysis signal with respect to a derivative of the reference signal above a threshold.
  • the point of change can be determined from one or more of slope, amplitude, timing and shape for the first cardiac signal and the second cardiac signal. It is noted that in some embodiments or aspects, processing of some time points can be omitted (e.g., every other time point or two of three time points).
  • a first derivative (or second derivative can be used) is determined for each of the time points in the signals.
  • a root mean squared is determined for each of the signals.
  • RMS 1 and RMS2 are determined by taking a root mean squared of the derivatives for the entire signal of each of the signals (e.g., all activation cycles). RMS can be used to normalize the amplitude of the signals with respect to one another, such that amplitudes (e.g., voltage) of the deflections in the signals do not affect the processing of the signals as described below.
  • a time point (same time point or about the same time point) is successively selected from each of the signals (SIG1, SIG2) for consideration and processing. For each time point under consideration, a time increment 602, 604 in each signal starting at that time point can be considered. For example, a time increment of 10 ms can be used. Different time increments can be selected.
  • a line which is pinned to the point under consideration in each signal and which provides the best fit to the time points in the time increment of each signal is determined.
  • the determined lines represent the slopes (e.g., volts/per second) of the signals for the selected time point. More specifically, the determined lines represent slopes of the signals at the selected time point for the same time increment (e.g., 10 ms).
  • a significance value ( ⁇ ) is determined with respect to the slopes.
  • the significance threshold indicates that there is a potentially significant point of change (based on slopes) for the time point in the signals under consideration, e.g., that the derivatives diverge sufficiently from each other.
  • the low significance value indicates that the deflection in SIG1 is far- field and not sufficiently local to a sensor location from which the signal originated, e.g., a sensor shown in FIG. 4. Accordingly, there is no potentially significant point of change in the example signal pair comparison 600.
  • Other characteristics of the signals under consideration can also be used to determine divergence criteria in order to separate local activation from far-field activation if one or more of the criteria exceed a significance threshold. These and other divergence criteria can be applied independently, or in combination.
  • a first characteristic is voltage (or amplitude) of the signal, in which voltage exceeding a significance threshold indicates local activation rather than far-field activation.
  • the significance threshold for a voltage (or amplitude) varies in the presence of structural disease or scar which may reduce voltage even at locally-activated sites, poor electrode contact which may also reduce voltage even at locally-activated sites, larger electrical sensors which will alter voltage depending on signal properties within larger sensed areas, signal filtering which may attenuate high or low voltage events if transient, and other factors.
  • a second characteristic is cycle length (CL), in that far-field signals may have a CL that differs by a greater than significance threshold from local signals in a complex rhythm disorder, such as atrial or ventricular fibrillation.
  • This significance threshold will differ based on the rhythm under consideration, the heart chamber under consideration, and properties such as action potential duration (APD), conduction velocity (CV), the presence of structural disease or whether these sites are situated parallel to or perpendicular to fiber orientations.
  • a third characteristic is slope of the signal (upstroke or downstroke; dV/dt)
  • a fourth characteristic is frequency content (or the square of the frequency; energy) - in which local signals have higher frequency (higher energy) than far-field signals.
  • the significance threshold will vary with signal filtering settings, sensor properties (such as size), and contact with the tissue.
  • signals obtained through insulators may have attenuation of certain frequencies, for example, signals detected from the esophagus or body surface have traveled through more tissue that may attenuate high frequency signals compared to signals obtained directly from the heart, and this will alter the signal properties.
  • Situations may arise in which intervening tissue (such as bone) or a device (a signal amplifier) may amplify certain frequencies.
  • repeatability of any of the above criteria over time may be used to determine divergence criteria in order to separate local activation from far-field activation, since in complex rhythm disorders, local activation may remain more consistent than far- field activation which may vary. Repeatability can be measured using correlation values, indexes of disorder such as Shannon entropy, differential entropy, Kolmogorov complexity, and/or other measures of entropy.
  • the signals can have multiple successive analysis intervals
  • FIG. 7 is an illustration of an example signal pair comparison 700 of example analysis signal (SIG1) and example reference signal (SIG2).
  • the signals can be from comparison pair 402 (or comparison pair 404) illustrated in FIG. 4, or from any comparison pair illustrated in FIG. 5.
  • the signals are illustrative and occur during the same analysis interval.
  • the signals can have multiple successive analysis intervals (e.g., activation cycles), as illustrated in FIG. 2.
  • the signals are processed at one or more successive time points to determine whether there is a point of change in a derivative of the analysis signal with respect to a derivative of the reference signal above a threshold.
  • processing of some time points can be omitted (e.g., every other time point or two of three time points).
  • a first derivative (or second derivative) is determined for each of the time points in the signals.
  • a root mean squared is further determined for each of the signals.
  • a time point (same time point or about the same time point) is successively selected from each of the signals (SIG1 , SIG2) for consideration and processing.
  • a time increment 702, 704 (e.g., 10 ms) in each signal starting at that time point can be considered.
  • a line which is pinned to the point under consideration in each signal and which provides the best fit to the time points in the time increment of each signal is determined.
  • the determined lines represent the slopes (e.g., volts/per second) of the signals for the selected time point. More specifically, the determined lines represent the slopes at the selected time point for the same time increment.
  • a significance value ( ⁇ ) is determined with respect to the slopes.
  • a significance threshold e.g. 0.25
  • a noise level can be defined as fraction of the significance threshold or can be defined programmatically in various ways.
  • noise level can be one -tenth (0.025) of the significance threshold (0.25).
  • a different fraction level can be selected.
  • the noise level can be defined as a Gaussian standard deviation of a plurality of significance values.
  • Other ways of defining the noise level are contemplated.
  • the significance threshold e.g., 0.25
  • the signals can have multiple successive analysis intervals
  • FIG. 8 is an illustration of an example signal pair comparison 800 of example analysis signal (SIGl) and example reference signal (SIG2) utilizing a composite signal.
  • the signals can be from comparison pair 402 (or comparison pair 404) illustrated in FIG. 4, or from any comparison pair illustrated in FIG. 5.
  • the signals are illustrative and occur during the same analysis interval.
  • the signals can have multiple successive analysis intervals (e.g., activation cycles), as illustrated in FIG. 2.
  • the signals are processed at one or more successive time points to determine whether there is a point of change in a derivative of the analysis signal with respect to a derivative of the reference signal above a threshold.
  • processing of some time points can be omitted (e.g., every other time point or two of three time points).
  • a first derivative zero order derivative or second derivative
  • a root mean squared is further determined for each of the signals.
  • a time point (same time point or about the same time point) is successively selected from each of the signals (SIGl , SIG2) for consideration and processing.
  • a time increment 802, 804 (e.g., 10 ms) in each signal starting at that time point can be used.
  • a line which is pinned to the point under consideration in each signal and which provides the best fit to the time points in the time increment of each signal is determined.
  • the determined lines represent the slopes (e.g., volts/per second) of the signals for the selected time point. More specifically, the determined lines represent the slopes of the signals at the selected time point for same time increment.
  • a significance value ( ⁇ ) is determined with respect to the slopes.
  • a significance threshold e.g. 0.25
  • the signals can have multiple successive analysis intervals (e.g., activation cycles) and in each analysis interval, it is possible to have zero, one or more potentially significant points of change as described above.
  • the time point under consideration and the potentially significant point(s) of change can be recorded, such as in database 1 18.
  • the significance value can be determined with respect to a composite signal.
  • the composite signal can represent a bipolar signal (COMP) of constituent unipolar signals (SIG1 , SIG2).
  • the composite signal COMP can also be computed by adding signals SIG1 and SIG2.
  • the signals in the signal pair comparison 800 are illustrative and occur during the same analysis interval. As noted herein, the signals can have multiple successive analysis intervals (e.g., activation cycles), as illustrated in FIG. 2.
  • the signals SIG 1 , SIG2 are processed at one or more successive time points with respect to the composite signal COMP to determine whether there is a point of change in a derivative of the analysis signal with respect to a derivative of the reference signal above a threshold.
  • a first derivative (or second derivative) is determined for each of the time points in the signals, SIGl , SIG2, COMP.
  • a time point (same time point or about the same time point) is successively selected from each of the signals (SIGl , SIG2, COMP) for consideration and processing.
  • a time increment 802, 804, 806 e.g., 10 ms in each signal starting at that time point can be considered.
  • a line which is pinned to the point under consideration in each signal and which provides the best fit to the time points in the time increment of each signal is determined.
  • the determined lines represent the slopes (e.g., volts/per second) of the signals for the selected time point. More specifically, the determined lines represent the slopes of the signals at the selected time point for the same time increment.
  • a significance value ( ⁇ ) is determined with respect to the slopes.
  • the significance value ( ⁇ ) can be determined by a ratio taking an absolute value of the second slope and subtracting an absolute value of the composite slope, and dividing by a logarithm of a result of an absolute value the first slope subtracting an absolute value of the composite slope.
  • Significance values can be computed for all points under consideration.
  • a significance threshold can be determined to be an average of the computed significance values ( ⁇ ) plus a standard deviation. Thereafter, only those significance values ( ⁇ ) that are above the significance threshold can be considered to be potentially significant points of change for the comparison pair 800.
  • the determined significance threshold can be 10. It is noted that the significance value(s) that is above the significance threshold generally extends substantially above the significance threshold. For example, a significance value ( ⁇ )— having the largest ratio— can therefore be selected.
  • the signals can have multiple successive analysis intervals (e.g., activation cycles) and in each analysis interval, it is possible to have zero, one or more potentially significant points of change as described above.
  • the time point under consideration and the potentially significant point(s) of change can be recorded, such as in database 1 18.
  • FIG. 9 is a flowchart that illustrates an example method 900 of reconstructing cardiac activation information (activation onset) associated with heart rhythm disorders.
  • the example method 900 can be performed by the computing device 1 16 illustrated in FIG. 1. More specifically, the example method 900 starts at operation 902 at which signals are received by the computing device 1 16 via signal processing device 1 14 from sensors disposed in the heart 120. For example, signals can be received from sensors of the sensor array 400 disposed in the right atrium 122 of the heart 120, as shown in FIGS, 1 and 4. In some embodiments or aspects, at least a portion of the signals from the sensors can be recorded by signal processing device 1 14 and then provided to computing device 1 16.
  • a first signal (analysis signal) is selected.
  • a second signal (reference signal) is selected. Selection of the analysis signal and the reference signal can be performed as described in greater detail with reference to FIGS. 4 and 5. In some embodiments or aspects, a root mean squared (RMS) can be determined for the first signal and for the second signal.
  • RMS root mean squared
  • a time interval over which the first signal and the second signal are to be compared is selected. The time interval can be selected to be an activation cycle (e.g., 100 ms to 300 ms) as described in FIG. 2. In some embodiments or aspects, the time interval can be determined by a dominant frequency analysis or other analysis of the average cycle length of the first (analysis) signal. A default time interval of 200 ms can be used if the time interval cannot be determined computationally.
  • the time interval can be selected manually, computationally by a different analysis method, from a database that catalogs such time intervals for patients of a certain age, gender and type of heart rhythm disorder, or defaulted to a value between about 100 ms and about 300 ms.
  • a composite signal can be determined based on the selected first signal and the second signal, such as by subtracting or adding the signals as described with reference to FIG. 8.
  • a time point is selected for consideration in the selected time interval.
  • the same or about the same time point is selected for consideration in each signal (e.g., first signal and second signal).
  • derivatives are calculated for a time increment (e.g., 10 ms) extending from the point of consideration in each signal.
  • a derivative is also calculated for a time increment (e.g., 10 ms) extending from a time point of consideration in the composite signal.
  • the time point of consideration in the composite signal is the same or about the same as in the other signals (e.g., first signal and second signal).
  • points of change between the derivatives of the first signal with respect to the derivatives of the second signal are determined in the time interval under consideration. For example, a significance value ( ⁇ ) can be determined at each point of change as described with reference to FIGS. 6-8.
  • the threshold can be 0.25 (or another value) as described with reference to FIGS. 6-8, while in those embodiments or aspects that use a composite signal, the threshold can be computed as an average value plus a standard deviation of all points of change as described with reference to FIG. 8.
  • the method 900 continues at operation 920 where the significant point(s) of change is recorded (selected) as a possible activation onset(s) for the time interval under consideration in the first (analysis) signal. If however, it is determined that there is no point of change above the threshold (no significant point of change), the method 900 continues at operation 924 where the first signal is compared over the time interval to a catalog of reference signals. For example, the catalog of reference signals for heart rhythm disorders can be maintained in database 1 18. At operation 926, a determination is made as to whether there is a match to a reference signal in the database.
  • the comparison can be based on at least one characteristic of the first signal to at least one characteristic of the reference signal, such as shape, slope, amplitude, frequency and/or timing. Other characteristics can be used together with or instead of the enumerated characteristics. [00100] If there is no match to a reference signal at operation 926, the method 900 continues at operation 922. Alternatively, the method 900 continues at operation 928 where the point(s) of change in the time interval under consideration is recorded (selected), which would correspond to activation onset(s) in the reference signal that was matched.
  • the subsequent time interval can be determined from the point(s) of change that represents the possible activation onset at 920. Specifically, if only one point of change (above the threshold) is recorded at 920, then the next time interval (e.g., 100 ms to 300 ms) starts at the onset time associated with the point of change plus a half of a cycle length (e.g., 50 ms to 150 ms).
  • the onset time associated with the largest point of change is used to determine the next time interval for operations 908-922. It is noted that the determination of the next time interval can be extended to consider significant points of change from all second (reference) signals for the same time interval under consideration. However, if it is determined that all time intervals have been processed at operation 922, the method 900 continues at operation 930.
  • the method 900 continues to perform operations 906-930 until it is determined that all second (reference) signals have been processed for the first (analysis) signal. However, if it is determined that all second signals have been processed, the method 900 continues to operation 932.
  • an activation onset(s) is assigned in the first signal at the point(s) of change to define cardiac activation(s) indicating a beat(s) in the first signal if it is determined (at operation 918) that the point(s) of change is above the threshold.
  • an activation onset(s) can be assigned in the first signal at the point(s) of change to define cardiac activation(s) indicating a beat(s) in the first signal based on a matched reference signal (at operation 928). More specifically, activation onsets are assigned to the time intervals of the first signal based on the recorded (or significant) point(s) of change of the first signal with reference to the second signal(s).
  • an activation onset is assigned to each time interval in the first (analysis) signal based possible activation onset(s) associated with the significant point(s) of change in the same time interval of the second (reference) signal(s).
  • the activation onset for the time interval of the first (analysis) signal can be determined based on an average of the activation onsets with reference to the second (reference) signals.
  • the activation onset for the time interval of the first signal can be determined based on an average of activation onsets with reference to those second signals in which a majority of activation onsets are within a predetermined time interval of each other (e.g., ⁇ 5 ms).
  • the assigned onset can be recorded for each interval in the first (analysis) signal such as in database 1 18.
  • signals collected from the heart 120 have been reconstructed with cardiac activation information (activation onsets) such that a cause of the heart rhythm disorder can be determined. More specifically, unipolar electrograms or monophasic action potentials (MAPs) can be mapped to the reconstructed activation onsets of the signals to show unipolar or MAP sequences or representations for the signals. An activation map or pattern can be constructed from these unipolar voltage or MAP voltage representations of the signals to locate the cause of the heart rhythm disorder. An example MAP representation and example activation map are illustrated in FIG. 1 1.
  • FIG. 10 is an illustration of an example signal pair comparison 1000 of analysis signal (SIG1) and reference signal (SIG2) that can be processed in accordance with method 900 of FIG. 9 to assign an activation onset 1004.
  • a time interval 1002 e.g., 100 ms - 300 ms
  • the signals in the time interval (SIG1 , SIG2, COMP) are smoothed, such as via median filter.
  • Significance values ( ⁇ ) are determined for the points of changes in the signals' first or second derivative, as described herein with reference to FIGS. 1-9.
  • point of change 1012 in SIGl that is above threshold 1010 is assigned as the activation onset 1004 for the time interval 1002 in SIGl based on the first derivative.
  • point of change 1014 in SIGl that is above threshold 1010 is assigned as the activation onset 1004 for the time interval 1002 in SIGl based on the second derivative.
  • Subsequent time intervals are selected and activation onsets are assigned as described herein with reference to FIGS. 1-9 until the analysis signal (SIGl) is processed.
  • FIG. 11 is an illustration of an example mapping 1000 of processed signals in accordance with FIGS. 1-10.
  • Raw signal 1 101 represents a signal that is processed to assign activation onsets (vertical lines) as described herein.
  • a composite signal 1 102 is shown, which results from the raw (analysis) signal 1 101 and another (reference) signal (not shown).
  • a monophasic action potential (MAP) voltage representation is generated from each processed signal 1 101.
  • Multiple signals are processed as described herein and MAPs generated based on the processed signals.
  • the electrical activity of all MAPs is mapped in a sequence of example activation mappings 1 106 to show activation onsets 1 108, 1 1 10, 1 1 12 and 1 1 14 at each time interval, respectively.
  • These mappings can be displayed by computing device 1 16. Although only four mapping are shown for illustrative purposes, there can be fewer or greater number of mappings 1 106 based on the time intervals represented in the signals.
  • the electrical activity indicates a rotational activation pattern of activation onsets (rotor) in the heart rhythm disorder.
  • a rotational activation pattern of activation onsets rotor
  • At least a portion of the area of the heart 120 indicated by the rotational activation pattern indicated by the arrows in FIG. 1 1 can be treated to eliminate the cause of the heart rhythm disorder, and therefore the heart rhythm disorder itself.
  • Such treatment may be delivered by ablation using various energy sources (including but not limited to radiofrequency, cryoenergy, microwave, and ultrasound), gene therapy, stem cell therapy, pacing stimulation, drug or other therapy.
  • the MAP representation and activation map are examples to illustrate a rotational activation pattern. Other activation patterns can result from different example signals collected by the sensors from the heart 120.
  • FIG. 12 is a block diagram of an illustrative embodiment of a general computer system 1200.
  • the computer system 1200 can be the signal processing device 1 14 and the computing device 1 16 of FIG. 1.
  • the computer system 1200 can include a set of instructions that can be executed to cause the computer system 12800 to perform any one or more of the methods or computer based functions disclosed herein.
  • the computer system 1200, or any portion thereof, may operate as a standalone device or may be connected, e.g., using a network or other connection, to other computer systems or peripheral devices.
  • the computer system 1200 may be operatively connected to signal processing device 114 and analysis database 118.
  • the computer system 1200 may also be implemented as or incorporated into various devices, such as a personal computer (PC), a tablet PC, a personal digital assistant (PDA), a mobile device, a palmtop computer, a laptop computer, a desktop computer, a communications device, a control system, a web appliance, or any other machine capable of executing a set of instructions (sequentially or otherwise) that specify actions to be taken by that machine.
  • PC personal computer
  • PDA personal digital assistant
  • mobile device a palmtop computer
  • laptop computer a laptop computer
  • desktop computer a communications device
  • control system a web appliance
  • the computer system 1200 may include a processor 1202, e.g., a central processing unit (CPU), a graphics-processing unit (GPU), or both. Moreover, the computer system 1200 may include a main memory 1204 and a static memory 1206 that can communicate with each other via a bus 1226. As shown, the computer system 1200 may further include a video display unit 1210, such as a liquid crystal display (LCD), an organic light emitting diode (OLED), a flat panel display, a solid state display, or a cathode ray tube (CRT). Additionally, the computer system 1200 may include an input device 1212, such as a keyboard, and a cursor control device 1214, such as a mouse. The computer system 1200 can also include a disk drive unit 1216, a signal generation device 1222, such as a speaker or remote control, and a network interface device 1208.
  • a processor 1202 e.g., a central processing unit (CPU), a graphics-processing unit (G
  • the disk drive unit 1216 may include a computer-readable medium 1218 in which one or more sets of instructions 1220, e.g., software, can be embedded. Further, the instructions 1220 may embody one or more of the methods or logic as described herein. In a particular embodiment or aspect, the instructions 1220 may reside completely, or at least partially, within the main memory 1204, the static memory 1206, and/or within the processor 1202 during execution by the computer system 1200. The main memory 1204 and the processor 1202 also may include computer-readable media.
  • dedicated hardware implementations such as application specific integrated circuits, programmable logic arrays and other hardware devices, can be constructed to implement one or more of the methods described herein.
  • Applications that may include the apparatus and systems of various embodiments or aspects can broadly include a variety of electronic and computer systems.
  • One or more embodiments or aspects described herein may implement functions using two or more specific interconnected hardware modules or devices with related control and data signals that can be communicated between and through the modules, or as portions of an application-specific integrated circuit. Accordingly, the present system encompasses software, firmware, and hardware implementations.
  • the methods described herein may be implemented by software programs tangibly embodied in a processor- readable medium and may be executed by a processor. Further, in an exemplary, non- limited embodiment or aspect, implementations can include distributed processing, component/object distributed processing, and parallel processing. Alternatively, virtual computer system processing can be constructed to implement one or more of the methods or functionality as described herein.
  • a computer-readable medium includes instructions 1220 or receives and executes instructions 1220 responsive to a propagated signal, so that a device connected to a network 1224 can communicate voice, video or data over the network 1224. Further, the instructions 1220 may be transmitted or received over the network 1224 via the network interface device 1208.
  • the term "computer-readable medium” includes a single medium or multiple media, such as a centralized or distributed database, and/or associated caches and servers that store one or more sets of instructions.
  • the term "computer-readable medium” shall also include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor or that cause a computer system to perform any one or more of the methods or operations disclosed herein.
  • the computer- readable medium can include a solid-state memory, such as a memory card or other package, which houses one or more non-volatile read-only memories.
  • the computer-readable medium can be a random access memory or other volatile re-writable memory.
  • the computer-readable medium can include a magneto-optical or optical medium, such as a disk or tapes or other storage device to capture carrier wave signals, such as a signal communicated over a transmission medium.
  • a digital file attachment to an e-mail or other self-contained information archive or set of archives may be considered a distribution medium that is equivalent to a tangible storage medium. Accordingly, any one or more of a computer-readable medium or a distribution medium and other equivalents and successor media, in which data or instructions may be stored, are included herein.
  • the methods described herein may be implemented as one or more software programs running on a computer processor.
  • Dedicated hardware implementations including, but not limited to, application specific integrated circuits, programmable logic arrays, and other hardware devices can likewise be constructed to implement the methods described herein.
  • alternative software implementations including, but not limited to, distributed processing or component/object distributed processing, parallel processing, or virtual machine processing can also be constructed to implement the methods described herein.
  • software that implements the disclosed methods may optionally be stored on a tangible storage medium, such as: a magnetic medium, such as a disk or tape; a magneto-optical or optical medium, such as a disk; or a solid state medium, such as a memory card or other package that houses one or more read-only (nonvolatile) memories, random access memories, or other re-writable (volatile) memories.
  • the software may also utilize a signal containing computer instructions.
  • a digital file attachment to e-mail or other self-contained information archive or set of archives is considered a distribution medium equivalent to a tangible storage medium.
  • inventive subject matter may be referred to herein, individually and/or collectively, by the term "invention" merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed.
  • inventive subject matter may be referred to herein, individually and/or collectively, by the term "invention" merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed.
  • inventive subject matter merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Biophysics (AREA)
  • General Health & Medical Sciences (AREA)
  • Veterinary Medicine (AREA)
  • Public Health (AREA)
  • Animal Behavior & Ethology (AREA)
  • Surgery (AREA)
  • Molecular Biology (AREA)
  • Medical Informatics (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Pathology (AREA)
  • Biomedical Technology (AREA)
  • Artificial Intelligence (AREA)
  • Physiology (AREA)
  • Cardiology (AREA)
  • Signal Processing (AREA)
  • Psychiatry (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Mathematical Physics (AREA)
  • Fuzzy Systems (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Abstract

L'invention concerne un procédé de représentation d'informations cardiaques associées à un trouble du rythme cardiaque, qui consiste à accéder à une pluralité de signaux cardiaques voisins obtenus à partir d'un patient. Des activations de champ lointain sont éliminées de la pluralité de signaux cardiaques voisins à l'aide d'un ou plusieurs critères de divergence qui définissent des activations locales dans la pluralité de signaux cardiaques voisins, les critères de divergence étant associés à une divergence parmi la pluralité de signaux cardiaques voisins. Le procédé peut également consister à construire une représentation clinique d'activations locales dans la pluralité de signaux cardiaques voisins.
PCT/US2015/046742 2014-08-29 2015-08-25 Système et procédé de reconstruction d'informations d'activation cardiaques WO2016033075A1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
CN201580051983.4A CN106714677A (zh) 2014-08-29 2015-08-25 用于重建心脏激动信息的系统和方法
EP15836641.9A EP3185768A4 (fr) 2014-08-29 2015-08-25 Système et procédé de reconstruction d'informations d'activation cardiaques
IL250638A IL250638A0 (en) 2014-08-29 2017-02-16 System and method for reproducing cardiac activation information

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US14/473,572 US9107600B2 (en) 2011-05-02 2014-08-29 System and method for reconstructing cardiac activation information
US14/473,572 2014-08-29

Publications (1)

Publication Number Publication Date
WO2016033075A1 true WO2016033075A1 (fr) 2016-03-03

Family

ID=55400448

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2015/046742 WO2016033075A1 (fr) 2014-08-29 2015-08-25 Système et procédé de reconstruction d'informations d'activation cardiaques

Country Status (4)

Country Link
EP (1) EP3185768A4 (fr)
CN (1) CN106714677A (fr)
IL (1) IL250638A0 (fr)
WO (1) WO2016033075A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020242940A1 (fr) * 2019-05-24 2020-12-03 St. Jude Medical, Cardiology Division, Inc. Système et procédé de cartographie cardiaque

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090163968A1 (en) * 2007-12-21 2009-06-25 Donofrio William T Optical sensor and method for detecting a patient condition
US20090299203A1 (en) * 2008-06-03 2009-12-03 De Voir Christopher S Heart monitoring apparatus
US20110251505A1 (en) * 2010-04-08 2011-10-13 Topera Medical, Inc. Methods, system and apparatus for the detection, diagnosis and treatment of biological rhythm disorders
US8165666B1 (en) * 2011-05-02 2012-04-24 Topera, Inc. System and method for reconstructing cardiac activation information
US20130150742A1 (en) * 2011-12-09 2013-06-13 The Regents Of The University Of California System and method of identifying sources for biological rhythms

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7117030B2 (en) * 2004-12-02 2006-10-03 The Research Foundation Of State University Of New York Method and algorithm for spatially identifying sources of cardiac fibrillation
US8948837B2 (en) * 2011-01-13 2015-02-03 Rhythmia Medical, Inc. Electroanatomical mapping

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090163968A1 (en) * 2007-12-21 2009-06-25 Donofrio William T Optical sensor and method for detecting a patient condition
US20090299203A1 (en) * 2008-06-03 2009-12-03 De Voir Christopher S Heart monitoring apparatus
US20110251505A1 (en) * 2010-04-08 2011-10-13 Topera Medical, Inc. Methods, system and apparatus for the detection, diagnosis and treatment of biological rhythm disorders
US8165666B1 (en) * 2011-05-02 2012-04-24 Topera, Inc. System and method for reconstructing cardiac activation information
US20130150742A1 (en) * 2011-12-09 2013-06-13 The Regents Of The University Of California System and method of identifying sources for biological rhythms

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
JUNG, TP ET AL.: "Removing Electroencephalographic Artifacts By Blind Source Separation.", PSYCHOPHYSIOLOGY., vol. 37., no. 02, 2000, pages 163 - 178, XP002635432 *
See also references of EP3185768A4 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020242940A1 (fr) * 2019-05-24 2020-12-03 St. Jude Medical, Cardiology Division, Inc. Système et procédé de cartographie cardiaque

Also Published As

Publication number Publication date
IL250638A0 (en) 2017-04-30
EP3185768A4 (fr) 2018-04-11
EP3185768A1 (fr) 2017-07-05
CN106714677A (zh) 2017-05-24

Similar Documents

Publication Publication Date Title
US9913615B2 (en) System and method for reconstructing cardiac activation information
US10149622B2 (en) System and method for reconstructing cardiac activation information
US10271786B2 (en) System and method for reconstructing cardiac activation information
AU2012347478A1 (en) Method and system for determining regularity associated with biological rhythm disorders
JP6386024B2 (ja) 心臓活性化情報を再構築するシステム及び方法
WO2016033075A1 (fr) Système et procédé de reconstruction d'informations d'activation cardiaques

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 15836641

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 250638

Country of ref document: IL

NENP Non-entry into the national phase

Ref country code: DE

REEP Request for entry into the european phase

Ref document number: 2015836641

Country of ref document: EP

WWE Wipo information: entry into national phase

Ref document number: 2015836641

Country of ref document: EP