The present invention relates to a method to provide a mean temporal spatial isochrone (TSI) path relating to an ECG feature (wave form) of interest, such as the activation of the heart from a single point (QRS), relative to the heart in a torso while using an ECG measurement from an ECG recording device. The method includes: receiving ECG measuring data from the ECG recording device; determining vector cardiogram (VCG) data; receiving a model of the heart, preferably with torso, as an input, preferably based on a request including request parameters; determining mean TSI data values representing the TSI path relating to an electrophysiological phase representing the ECG feature, the mean TSI providing a location within the heart representing the mean location of the ECG feature at the corresponding time; positioning the mean TSI path and preferably the vector cardiogram data points in the model of the heart and/or torso at an initial position; and rendering the model of the heart, preferably with torso, with the mean TSI path, preferably with VCG data related to the TSI, for displaying on a display screen for interpretation of the displayed rendering.

The present invention relates to a method to provide a mean temporal spatial isochrone (TSI) path relating to an ECG feature (wave form) of interest, such as the activation of the heart from a single point (QRS), relative to the heart in a torso while using an ECG measurement from an ECG recording device. The method includes: receiving ECG measuring data from the ECG recording device; determining vector cardiogram (VCG) data; receiving a model of the heart, preferably with torso, as an input, preferably based on a request including request parameters; determining mean TSI data values representing the TSI path relating to an electrophysiological phase representing the ECG feature, the mean TSI providing a location within the heart representing the mean location of the ECG feature at the corresponding time; positioning the mean TSI path and preferably the vector cardiogram data points in the model of the heart and/or torso at an initial position; and rendering the model of the heart, preferably with torso, with the mean TSI path, preferably with VCG data related to the TSI, for displaying on a display screen for interpretation of the displayed rendering.

Disclosed are various examples and embodiments of systems, devices, components and methods configured to detect a location of a source of at least one cardiac rhythm disorder in a patient's heart. In some embodiments, electrogram signals are acquired from a patient's body surface, and subsequently normalized, adjusted and/or filtered, followed by generating a two-dimensional spatial map, grid or representation of the electrode positions, processing the amplitude-adjusted and filtered electrogram signals to generate a plurality of three-dimensional electrogram surfaces corresponding at least partially to the 2D map, one surface being generated for each or selected discrete times, and processing the plurality of three-dimensional electrogram surfaces through time to generate a velocity vector or other type of map using one or more of optical flow, video tracking analysis, motion capture analysis, motion estimation analysis, data association and segmentation tracking analysis, particle tracking analysis, and single-particle tracking analysis methods corresponding at least partially to the 2D map. Trained atrial discriminative machine learning models that facilitate the foregoing systems and methods, and that provide predictions or results concerning a patient's condition, are also disclosed.

Disclosed are various examples and embodiments of systems, devices, components and methods configured to detect a location of a source of at least one cardiac rhythm disorder in a patient's heart. In some embodiments, electrogram signals are acquired from a patient's body surface, and subsequently normalized, adjusted and/or filtered, followed by generating a two-dimensional spatial map, grid or representation of the electrode positions, processing the amplitude-adjusted and filtered electrogram signals to generate a plurality of three-dimensional electrogram surfaces corresponding at least partially to the 2D map, one surface being generated for each or selected discrete times, and processing the plurality of three-dimensional electrogram surfaces through time to generate a velocity vector or other type of map using one or more of optical flow, video tracking analysis, motion capture analysis, motion estimation analysis, data association and segmentation tracking analysis, particle tracking analysis, and single-particle tracking analysis methods corresponding at least partially to the 2D map. Trained atrial discriminative machine learning models that facilitate the foregoing systems and methods, and that provide predictions or results concerning a patient's condition, are also disclosed.

Disclosed includes a body surface device for diagnosing locations associated with electrical rhythm disorders to guide therapy. The device can sense electrical signals and determine multiple sites that may be operative in that patient. The patch may encompass the heart regions from where the heart rhythm disorder originates. The patch comprises an array of electrodes configured to detect electrical signals generated by a heart. A controller may determine the locations of interest based on detected electrical signals. The controller is configured to locate these regions relative to the surface patch. The system may be coupled to a sensor or therapy device inside the heart, to guide this device to a region of interest. The controller is further configured to instruct the operator to use the trigger or source information to treat the heart rhythm disorder in an individual using additional clinical data and methods for personalization such as machine learning.

Disclosed includes a body surface device for diagnosing locations associated with electrical rhythm disorders to guide therapy. The device can sense electrical signals and determine multiple sites that may be operative in that patient. The patch may encompass the heart regions from where the heart rhythm disorder originates. The patch comprises an array of electrodes configured to detect electrical signals generated by a heart. A controller may determine the locations of interest based on detected electrical signals. The controller is configured to locate these regions relative to the surface patch. The system may be coupled to a sensor or therapy device inside the heart, to guide this device to a region of interest. The controller is further configured to instruct the operator to use the trigger or source information to treat the heart rhythm disorder in an individual using additional clinical data and methods for personalization such as machine learning.

A set of training electrocardiograms (ECGs) for each of a plurality of subjects is processed using a machine learning model to generate an output for each training ECG of each of the plurality of subjects. Training ECGs for each subject are labeled with an identity of the subject. A machine learning model is trained by comparing the output generated for each training ECG to a corresponding label of the training ECG to generate an identity model to identify ECGs of a first subject of the plurality of subjects. A first ECG is received from an ECG sensor and input to the identity model, which generates an output indicating whether the first ECG corresponds to the first subject. In response to the output indicating that the first ECG does not correspond to the first subject, a condition that the first subject has or may develop is determined based on the output.

A set of training electrocardiograms (ECGs) for each of a plurality of subjects is processed using a machine learning model to generate an output for each training ECG of each of the plurality of subjects. Training ECGs for each subject are labeled with an identity of the subject. A machine learning model is trained by comparing the output generated for each training ECG to a corresponding label of the training ECG to generate an identity model to identify ECGs of a first subject of the plurality of subjects. A first ECG is received from an ECG sensor and input to the identity model, which generates an output indicating whether the first ECG corresponds to the first subject. In response to the output indicating that the first ECG does not correspond to the first subject, a condition that the first subject has or may develop is determined based on the output.

A method includes receiving, for at least a region of an anatomical map of at least a portion of a heart, positions and respective electrophysiological (EP) wave propagation velocity vectors, the vectors having respective magnitudes. The magnitudes are nonlinearly scaled. Scaled vectors having the scaled magnitudes, are presented by being overlaid on the anatomical map.

A method includes receiving, for at least a region of an anatomical map of at least a portion of a heart, positions and respective electrophysiological (EP) wave propagation velocity vectors, the vectors having respective magnitudes. The magnitudes are nonlinearly scaled. Scaled vectors having the scaled magnitudes, are presented by being overlaid on the anatomical map.