Cardioinsight Technologies, Inc.

Ryan M Bokan

Charulatha Ramanathan

Ping Jia

Qingguo Zeng

Brian P George

Maria Strom

Venkatesh Vasudevan

Qing Lou

Connor S Edel

Remi Dubois

Jeffrey B Adair

Meredith E Stone

Ketal C Patel

Vladimir A Turovskiy

Matthew J Sabo

Timothy G Laske

Kevin R Ponziani

Glenn D Raudins

Soniya Bhojwani

Rémi Dubois

Jeff Burrell

Lijun Yu

Raja N Ghanem

John E Anderson

Rémi DuBois

Cheng Yao

Andrew E Hoover

Vivek Jayan

Torsten Konrad

Rahsean Ellis

Koonlawee Nademanee

Quin Lou

Edel S Connor

Andy E Hoover

In a described example, a method includes identifying, by a processor, negative deflections of at least one unipolar signal representing cardiac electrical activity on a surface of interest during a respective interval of the at least one signal. The method also includes determining, by a processor, an activation time for a last identified negative deflection in the respective interval. The method also includes detecting, by the processor, an instance of late activation based on comparing the activation time to a temporal threshold.

Late activation of cardiac signals

A method includes placing a set of electrodes on a body surface of a patient's body. The method also includes digitizing locations for the electrodes across the body surface based on one or more image frames using range imaging and/or monoscopic imaging. The method also includes estimating locations for hidden ones of the electrodes on the body surface not visible during the range imaging and/or monoscopic imaging. The method also includes registering the location for the electrodes on the body surface with predetermined geometry information that includes the body surface and an anatomical envelope within the patient's body. The method also includes storing geometry data in non-transitory memory based on the registration to define spatial relationships between the electrodes and the anatomical envelope.

Imaging to determine electrode geometry

An example method includes applying a localization signal to a source electrode positioned within a conductive volume and a ground electrode at a known location. Electrical activity is sensed at a plurality of sensor electrodes distributed across an outer surface of the conductive volume. The locations of each of the sensor electrodes and the location of the ground electrode being stored in memory as part of geometry data. The electrical activity sensed at each of the sensor electrodes is stored in the memory as electrical measurement data. The method also includes computing a location of the source electrode by minimizing a difference between respective pairs of source voltages determined for the plurality of sensor electrodes. The source voltage for each of the sensor electrodes is determined based on the electrical measurement data and the geometry data.

Localization of objects within a conductive volume

An example method includes analyzing morphology and/or amplitude of each of a plurality of electrophysiological signals across a surface of a patient's body to identify candidate segments of each signal satisfying predetermined conduction pattern criteria. The method also includes determining a conduction timing parameter for each candidate segment in each of the electrophysiological signals.

Detecting conduction timing

This disclosure relates to localization and tracking of an object. As one example, measurement data can be stored in memory to represent measured electrical signals at each of a plurality of known measurement locations in a given coordinate system in response to an applied signal at an unknown location in the given coordinate system. A dipole model cost function has parameters representing a dipole location and moment corresponding to the applied signal. A boundary condition can be imposed on the dipole model cost function. The unknown location in the given coordinate system, corresponding to the dipole location, can then be determined based on the stored measurement data and the dipole model cost function with the boundary condition imposed thereon.

Localization and tracking of an object

One or more non-transitory computer-readable media have instructions executable by a processor and programmed to perform a method. The method includes analyzing the electrical data to locate one or more wave front lines over a given time interval. The electrical data represents electrophysiological signals distributed across a cardiac envelope for one or more time intervals. A respective trajectory is determined for each wave end of each wave front line that is located across the cardiac envelope over the given time interval. A set of connected trajectories are identified based on a duration that the trajectories are connected to each other by a respective wave front line during the given time interval. A connectivity association is characterized for the trajectories in the set of connected trajectories.

Connectivity analysis for arrhythmia drivers

For example, one or more non-transitory computer-readable media includes executable instructions to perform a method. The method includes defining a plurality of spatial regions distributed across a geometric surface. At least one wave front that propagates across the geometric surface is detected based on electrical data representing electrophysiological signals for each of a plurality of nodes distributed on the geometric surface over at least one time interval. An indication of conduction velocity of the wave front is determined for at least one spatial region of the plurality of spatial regions during the time interval based on a duration that the wave front resides within the at least one spatial region. Slow conduction activity is identified for the at least one spatial region based on comparing the indication of conduction velocity relative to a threshold. Conduction data is stored in memory to represent each slow conduction event.

Detection of regions exhibiting irregular electrophysiological activity

An example method includes receiving monitoring data representing one or more substantially real time electrical signals based on measurements from one or more respective electrodes. The method also includes selecting at least one signal of interest (SOI) from the monitoring data, each selected SOI being associated with a respective anatomical location and storing SOI data in memory corresponding to each selected SOI. The method also includes quantifying changes between signal characteristics of real time signals acquired for one or more respective anatomical locations and the at least one SOI that is associated with each of the respective anatomical locations. An output can be generated based on the quantifying to characterize spatially local signal changes with respect to each of the respective anatomical locations.

Signal characterization to facilitate therapy delivery

An example method includes performing amplitude-based detection to determine location of R-peaks for a plurality of electrograms. The method also includes performing wavelet-based detection to determine location of R-peaks for the plurality of electrograms. The method also includes adjusting the location of the R-peaks determined by the wavelet-based detection of R-peaks based on the location of R-peaks determined by the amplitude-based detection of R-peaks. The method also includes storing, in memory, R-peak location data to specify R-peak locations for the plurality of electrograms based on the adjusting.

Detection and analysis of cardiac waveforms

A non-transitory computer-readable medium can have instructions executable by a processor. The instructions can include an electrogram reconstruction method to generate reconstructed electrogram signals for each of a multitude of points residing on or near a predetermined cardiac envelope based on geometry data and non-invasively measured body surface electrical signals. The instructions can include a phase calculator to compute phase signals for the multitude of points based on the reconstructed electrogram signals and a visualization engine to generate an output based on the computed phase signals.

Physiological mapping for arrhythmia

Systems and methods are provided to detect and analyze arrhythmia drivers. In one example, a system can include a wave front analyzer programmed to compute wave front lines extending over a surface for each of the plurality of time samples based on phase information computed from electrical data at nodes distributed across the surface. A trajectory detector can be programmed to compute wave break points for each of the wave front lines and to determine a trajectory of at least one rotor core across the surface. A stability detector can be programmed to identify at least one stable rotor portion corresponding to subtrajectories of the determined trajectory.

Analysis and detection for arrhythmia drivers

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Cleveland, OH, United States of America

Ryan M Bokan