Abstract: Cardiac activity can be mapped by receiving an electrogram, transforming the electrogram into the wavelet domain (e.g., using a continuous wavelet transformation) to create a scalogram of the electrogram, computing at least one energy function of the scalogram, and computing at least one metric of the electrogram using the at least one energy function. The metrics of the electrogram can include, without limitation: a QRS activity duration for the electrogram; a near-field component duration for the electrogram; a far-field component duration for the electrogram; a number of multiple components for the electrogram; a slope of a sharpest component of the electrogram; a scalogram width; an energy ratio in the electrogram; and a cycle-length based metric of the electrogram.

Abstract: A map of cardiac activation wavefronts can be created from a plurality of mesh nodes, each of which is assigned a conduction velocity vector. Directed edges are defined to interconnect the mesh nodes, and weights are assigned to the directed edges, thereby creating a weighted directed conduction velocity graph. A user can select one or more points within the weighted directed conduction velocity graph (which do not necessarily correspond to nodes), and one or more cardiac activation wavefronts passing through these points can be identified using the weighted directed conduction velocity graph. The cardiac activation wavefronts can then be displayed on a graphical representation of the cardiac geometry.

Abstract: A plurality of electrophysiology (EP) data points, each including an electrogram signal, can be used to visualize cardiac activity. Each EP data point can be characterized as substrate or healthy, and a cloud map of the substrate EP data points can be generated. A graphical representation of the cloud map can be output in combination with a graphical representation of an electrophysiology map of the healthy EP data points. In alternative embodiments, the electrogram signals can be transformed into the wavelet domain, thereby computing a plurality of scalograms, and computing a wave function of each scalogram, thereby computing a plurality of wave functions. A propagation map, such as a propagation wave map and/or propagation wave trail map, can then be generated from the wave functions and output graphically.

Abstract: An electroanatomical mapping system graphically represents local activation time (LAT) information contained in data set including a plurality of electrophysiology (EP) data points. The system computes a spatial gradient over a spatial kernel centered at an EP data point and a plurality of temporal gradients for the EP data points set. Using the gradients, the electroanatomical mapping system can detect spatial outlier EP data points and temporal outlier EP data points. These outlier EP data points can then be corrected prior to outputting a graphical representation of the LAT map on a model of a cardiac surface.

Abstract: The present disclosure provides systems and methods for generating a local activation time (LAT) map. A method includes receiving at least one reference electrogram, receiving at least one roving electrogram, detecting cardiac activations in the at least one reference electrogram, detecting roving cardiac activations in the at least one roving electrogram, identifying, at a trigger time, a most recent reference cardiac activation of the detected reference cardiac activations, identifying, a corresponding roving cardiac activation of the detected roving cardiac activations that is closest in time to the most recent reference cardiac activation, the corresponding roving cardiac activation identified independent of any roving activation interval (RAI), computing, a LAT as a time difference between the most recent reference cardiac activation and the corresponding roving cardiac activation, and generating and displaying, a LAT map based on the computed LAT.

Abstract: The present disclosure provides systems and methods for detecting cardiac activations of a patient. A system includes a data acquisition system (DAQ) communicatively coupled to an activation detection module. The DAQ detects an electrogram generated at an electrode disposed on or in the patient. The activation detection module is configured to receive the electrogram from the DAQ and compute an activation response. The activation detection module is further configured to determine a set of candidate detection time points (CDTPs) in the activation response. The activation detection module is configured to compute respective deflection characteristics for each CDTP. The activation detection module is configured to identify a group of final detection time points (FDTPs) among the set of CDTPs for a metric corresponding to the respective deflection characteristics. The group of FDTPs has similar deflection characteristics.

Abstract: An electrophysiology map, for example a map of arrhythmic substrate, can be generated by acquiring both geometry information and electrophysiology information pertaining to an anatomical region, and associating the acquired geometry and electrophysiology information as a plurality of electrophysiology data points. A user can select two (or more) electrophysiological characteristics for display, and can further elect to apply various filters to the selected electrophysiological characteristics. The user can also define various relationships (e.g., Boolean ANDS, ORs, and the like) between the selected and/or filtered characteristics. The user-selected filtering criteria can be applied to the electrophysiology data points to output various subsets thereof. These subsets can then be graphically rendered using various combinations of colorscale, monochrome scale, and iconography, for example as a three-dimensional cardiac electrophysiology model.

Abstract: The local conduction velocity of a cardiac activation wavefront can be computed by collecting a plurality of electrophysiology (“EP”) data points using a multi-electrode catheter, with each EP data point including both position data and local activation time (“LAT”) data. For any EP data point, a neighborhood of EP data points, including the selected EP data point and at least two additional EP data points, can be defined. Planes of position and LATs can then be defined using the positions and LATs, respectively, of the EP data points within the neighborhood. A conduction velocity can be computed from an intersection of the planes of positions and LATs. The resultant plurality of conduction velocities can be output as a graphical representation (e.g., an electrophysiology map), for example by displaying vector icons arranged in a uniform grid over a three-dimensional cardiac model.

Abstract: The present disclosure provides systems and methods for detecting cardiac activation times of a patient. A system includes a data acquisition system and a processor communicatively coupled thereto. The data acquisition system is configured to detect a plurality of electrograms generated at a plurality of respective electrodes coupled to the patient. The processor is configured to receive the plurality of electrograms from the data acquisition system. The processor is further configured to compute respective energies of the plurality of electrograms. The processor is further configured to detect a cardiac activation time for a first electrogram among the plurality of electrograms based on the respective energy of the first electrogram and the respective energy of a second electrogram that neighbors the first electrogram.

Abstract: Cardiac repolarization activity can be mapped using action potential duration (“APD”) and/or activation recovery interval (“ARI”). APD can be measured using a bipolar electrogram signal measured, for example, using a monophasic action potential (“MAP”) catheter. ARI can be measured using unipolar electrogram signals. The electrogram signal is used to identify a depolarization tick time. A repolarization tick time can be identified using either a point in time when the electrogram signal passes below a threshold or via local maxima and minima of a first derivative of the electrogram signal. Diastolic intervals can also be computed using depolarization and repolarization tick times.

Abstract: Systems and methods for evaluating electrograms are described. An example method of evaluating an electrogram such as an atrial and/or ventricular electrogram containing a plurality of data samples each having a voltage includes selecting an activity interval for the electrogram, calculating an energy level for each window of a plurality of windows of the electrogram based on the voltages of the data samples in each window, assigning the calculated energy levels to a plurality of bins, and calculating an index based at least in part on a number of energy levels assigned to a particular bin of the plurality of bins.

Abstract: Systems and methods for evaluating electrograms are described. An example method of evaluating an electrogram such as an atrial and/or ventricular electrogram containing a plurality of data samples each having a voltage includes selecting an activity interval for the electrogram, calculating an energy level for each window of a plurality of windows of the electrogram based on the voltages of the data samples in each window, assigning the calculated energy levels to a plurality of bins, and calculating an index based at least in part on a number of energy levels assigned to a particular bin of the plurality of bins.

Abstract: Two or more electrophysiology characteristics can be graphically represented in a single representation output, for example, by an electroanatomical mapping system. The system can generate or receive multiple electrophysiology maps, one for each of a corresponding number of electrophysiological characteristics. The system can also generate or receive a three-dimensional anatomical model, such as a cardiac surface model, that includes a focal point. The system can identify a display region about the focal point and transform the display region from a three-dimensional surface into a plane. One or more of the electrophysiology maps can be represented by varying the elevation of the plane, e.g., according to value(s) of the represented electrophysiological characteristic(s). One or more additional electrophysiology maps can be represented on the elevation-varied plane, e.g., in color scale, grey scale, or the like.

Abstract: Cardiac activity can be mapped by receiving an electrogram, transforming the electrogram into the wavelet domain (e.g., using a continuous wavelet transformation) to create a scalogram of the electrogram, computing at least one energy function of the scalogram, and computing at least one metric of the electrogram using the at least one energy function. The metrics of the electrogram can include, without limitation: a QRS activity duration for the electrogram; a near-field component duration for the electrogram; a far-field component duration for the electrogram; a number of multiple components for the electrogram; a slope of a sharpest component of the electrogram; a scalogram width; an energy ratio in the electrogram; and a cycle-length based metric of the electrogram.

Abstract: A map of cardiac activation wavefronts can be created from a plurality of mesh nodes, each of which is assigned a conduction velocity vector. Directed edges are defined to interconnect the mesh nodes, and weights are assigned to the directed edges, thereby creating a weighted directed conduction velocity graph. A user can select one or more points within the weighted directed conduction velocity graph (which do not necessarily correspond to nodes), and one or more cardiac activation wavefronts passing through these points can be identified using the weighted directed conduction velocity graph. The cardiac activation wavefronts can then be displayed on a graphical representation of the cardiac geometry.

Abstract: Cardiac activity can be mapped by receiving an electrogram, transforming the electrogram into the wavelet domain (e.g., using a continuous wavelet transformation) to create a scalogram of the electrogram, computing at least one energy function of the scalogram, and computing at least one metric of the electrogram using the at least one energy function. The metrics of the electrogram can include, without limitation: a QRS activity duration for the electrogram; a near-field component duration for the electrogram; a far-field component duration for the electrogram; a number of multiple components for the electrogram; a slope of a sharpest component of the electrogram; a scalogram width; an energy ratio in the electrogram; and a cycle-length based metric of the electrogram.

Abstract: The present disclosure provides systems and methods for detecting cardiac activation times of a patient. A system includes a data acquisition system and a processor communicatively coupled thereto. The data acquisition system is configured to detect a plurality of electrograms generated at a plurality of respective electrodes coupled to the patient. The processor is configured to receive the plurality of electrograms from the data acquisition system. The processor is further configured to compute respective energies of the plurality of electrograms. The processor is further configured to detect a cardiac activation time for a first electrogram among the plurality of electrograms based on the respective energy of the first electrogram and the respective energy of a second electrogram that neighbors the first electrogram.

Abstract: An electrophysiology map, for example a map of arrhythmic substrate, can be generated by acquiring both geometry information and electrophysiology information pertaining to an anatomical region, and associating the acquired geometry and electrophysiology information as a plurality of electrophysiology data points. A user can select two (or more) electrophysiological characteristics for display, and can further elect to apply various filters to the selected electrophysiological characteristics. The user can also define various relationships (e.g., Boolean ANDS, ORs, and the like) between the selected and/or filtered characteristics. The user-selected filtering criteria can be applied to the electrophysiology data points to output various subsets thereof. These subsets can then be graphically rendered using various combinations of colorscale, monochrome scale, and iconography, for example as a three-dimensional cardiac electrophysiology model.

Abstract: A map of cardiac activation wavefronts can be created from a plurality of mesh nodes, each of which is assigned a conduction velocity vector. Directed edges are defined to interconnect the mesh nodes, and weights are assigned to the directed edges, thereby creating a weighted directed conduction velocity graph. A user can select one or more points within the weighted directed conduction velocity graph (which do not necessarily correspond to nodes), and one or more cardiac activation wavefronts passing through these points can be identified using the weighted directed conduction velocity graph. The cardiac activation wavefronts can then be displayed on a graphical representation of the cardiac geometry.

Abstract: A method and system for presenting information representative of lesion formation is provided. The system comprises an electronic control unit (ECU). The ECU is configured to acquire a value for an ablation description parameter and/or a position signal metric, wherein the value corresponds to a location in the tissue. The ECU is further configured to evaluate the value, assign it a visual indicator of a visualization scheme associated with the parameter/metric corresponding to the value, and generate a marker comprising the visual indicator such that the marker is indicative of the acquired value. The method comprises acquiring a value for the parameter/metric, and evaluating the value. The method further includes assigning a visual indicator of a visualization scheme associated with the parameter/metric corresponding to the value, and generating a marker comprising the visual indicator.