Anumana, Inc.

Neutrace, Inc.

Neucures Inc.

Unisense Tech, Inc.

Rohit Jain

Karthik K Bharadwaj

Robert L Lux

Animesh Agarwal

Anand Ramani

Sanjeev Shrinivas Nadapurohit

Shiva Verma

Yogisha H J

Kappagantula Gopalakrishna Murty

Rakesh Barve

Padmaja Narsipur

Leon Ptaszek

Yogisha Heggadahalli Jayendra

Vijay Raghavan Dharmapuri Murali

Chandan Chikkanayakanahally Kodandaswamy

Nandeesha Gowdagere Madalingaiah

Suthirth Vaidya

Abhijith Chunduru

Shashi Kant

Deepak Anand

Bharat Vijay

Sughosh Indurkar

Amar K Ray

K N Amarnath

Karpagam Gobalakrishna

An apparatus and method for determining a label dynamically using a potential signal. The apparatus includes a memory and at least a processor communicatively connected to the memory, wherein the memory contains instructions configuring the at least a processor to receive a user input comprising a labeled datum associated with the at least a potential signal, receive a plurality of stored data associated with the at least a potential signal, generate, using the at least a processor, a plurality of canonicalized data by processing the plurality of stored data, transmit, using a real time data simulator, the plurality of canonicalized data to a simulation module, generate, using the simulation module, a labeled prediction corresponding to a segmented datum.

Apparatus and method for dynamically determining a label for a cardiac potential signal via simulation of neural network trained data

An apparatus and method for validating, using user input, labeled data generated by a prediction generator. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The processor receives at least a potential signal, generates, processed data using the at least a potential signal, trains a prediction generator on a plurality of labeled training data, wherein the plurality of labeled training data comprises the processed data associated with at least an annotation, generates, using the prediction generator, a plurality of labeled data as a function of dynamic data, displays the plurality of labeled data to a user interface of a graphical user interface, receives a user input associated with the plurality of labeled data, and retrains the prediction generator using the user input.

Apparatus and method for validating labeled data generated by a prediction generator

An apparatus and method for generating clinical decision support is disclosed. The apparatus includes at least a processor and a computer-readable storage medium communicatively connected to the at least a processor, wherein the computer-readable storage medium contains instructions configuring the at least processor to receive user data, generate a fused feature vector correlating the user data to a plurality of clinical outcomes by training a plurality of deep neural networks (DNNs) to output a first set of feature vectors, a second set of feature vectors and a third set of feature vectors, fusing the first, second, and third set of features vectors to form the fused feature vector, generate a procedural output using the fused feature vector, and display the procedural output through a user interface.

Apparatus and method for generating clinical decision support

An apparatus for labeling time-series data using machine learning models, comprising a processor and a memory containing instructions configuring the processor to receive time-series data, identify a plurality of time-series segments from received time-series data, pre-train at least a classifier using labeled training data, annotate, at a labeling module, each time-series segment of the plurality of time-series segments with at least one segment identification, retrain the at least a classifier using the annotated plurality of time-series segments, generate, using the at least a classifier, one or more segment identifications at each time-series segment subsequently identified based on continuous time-series data, and display a visual representation of the continuous time-series data with the segment identifications on a user interface.

Apparatus and methods for labeling time-series data using machine learning models

A method for calculating conduction velocity of a cardiac activation wavefront from electrophysiological ('EP') data points generated by a mapping system during a mapping procedure for a heart is provided. The method comprises for each EP data point comprising a local activation time, and position data defining a location within the heart corresponding to the local activation time: defining a neighborhood of EP points comprising the EP data point and a selection of neighboring EP data points; representing local activation time as a function f(x,y), where x and y our coordinates within a hyperplane defined to contain the neighborhood of EP data points based on the position data for each EP data point in the neighborhood; and calculating conduction velocity at the EP data point as a norm function of a gradient for the function f(x,y).

Method and system for calculating conduction velocity of a cardiac activation wavefront

Techniques are disclosed to identify if a current chamber being mapped is a target chamber that is desired to be mapped based on the relative position of the local activation time relative to QRS onset. Techniques are also disclosed whereby cardiac data can be labeled to indicate whether a beat was acquired while in the ventricle, or in the atrium.

Methods and systems for classifying a heartbeat associated with patients in sinus rhythm

Apparatus for generating electro-anatomical mapping and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to receive input data, generate, using at least a machine learning model, an electro-anatomical mapping as a function of the input data, and display the electro-anatomical mapping using a user interface, wherein receiving the input data includes receiving, from an imaging device, at least a medical image and receiving, from a signal capturing device, at least an electrogram, wherein the at least a machine learning model is trained using electro-anatomical mapping training data including exemplary medical images and exemplary electrograms as input correlated to exemplary electro-anatomical mappings as output.

Apparatus and methods for generating electro-anatomical mapping

According to one embodiment, a method for generating a cardiac map is disclosed. The method comprises during cardiac mapping in which a mapping catheter is inserted into a heart, receiving a plurality of recordings from the mapping catheter, each recording includes a voltage, and the spatial coordinates of a recording site within the heart indicative of an electrode position of the mapping catheter at the time of acquisition of said voltage, constructing a three-dimensional surface of the heart based on the spatial coordinates of each recording site, performing surface analysis to determine off-surface points in the plurality of recordings where each off-surface point corresponds to a recording from the plurality of recordings which does not lie on the three-dimensional surface, and selectively moving at least some of the off-surface points to the three-dimensional surface based on a predefined threshold.

Method and system to assess whether electrical recordings during cardiac mapping lie on the myocardial surface

An apparatus for the generation of an impedance model of a biological chamber is disclosed. The apparatus includes a catheter assembly comprising a plurality of electrodes arranged into one or more constraint pairs. The apparatus includes a processor and a memory communicatively connected to the processor. The memory instructs the processor to receive tank data. The memory instructs the processor to generate a plurality of relative configurations of the plurality of electrodes as a function of the tank data using a compliant configuration generator. The memory instructs the processor to record voltage data from the plurality of electrodes within a biological chamber as a function of the plurality of relative configurations. The memory instructs the processor to map a plurality of impedance metrics for each relative configuration as a function of the voltage data. The memory instructs the processor to generate an impedance model as a function of the map.

Apparatus and a method for the generation of an impedance model of a biological chamber

Described herein is an apparatus and method for predicting Pulsed Field Ablation (PFA) durability. An apparatus may include at least a processor; and a memory communicatively connected to the at least processor, wherein the memory contains instructions configuring the at least processor to receive a training dataset comprising a plurality of example PFA device parameters correlated to a plurality of example PFA outcomes; train a PFA durability machine learning model using the training dataset; receive a PFA device parameter; and generate a PFA durability datum as a function of the PFA device parameter using a trained PFA durability machine learning model.

Method and apparatus for predicting pulsed field ablation durability

Described herein is a method by which more than one person is enabled to actively participate in the process of finalizing a real estate property either for purchase or rent. Each deciding party is enabled to create a custom style profile capturing their individual preferences at an attribute level by providing both visual and verbal feedback. A Collective Preferences Profile (CPP) is created by integrating the multiple style profiles of the chosen deciding parties. The generated CPP is then utilized to curate the different houses available and to surface those houses that are most likely to fit the aesthetic and requirements of the combined audience. The CPP evolves on an ongoing basis by active solicitation of feedback on properties viewed or waitlisted to accommodate changing preferences and provide the most suited recommendation at any time.

Multi user collective preferences profile

A method for a graphical user interface (GUI) of a computer system is provided. The method comprises displaying within a first portion the GUI, an electro-anatomical map of the heart, said electro-anatomical map comprising a plurality of measurement points within the heart for which data was collected; displaying a plurality of markers, each representing a respective one of said measurement points spatially in relation to the geometry of the heart within said first portion; determining, by a processor, user-selection of a particular measurement point within the heart through selection of its associated marker; responsive to said user-selection of the particular measurement point, retrieving at least one metric associated with said measurement point; and displaying in a second portion of the GUI, each retrieved metric associated with a particular measurement point within the heart.

User-interface for visualizing electro-anatomical data with selective control for exposing metrics

Disclosed is a method for generating high resolution point cloud data for electro-anatomical mapping comprising receiving sparsely measured point cloud data having a plurality of data points. Surface mesh data comprising mesh points defining triangles on a myocardial surface is generated. The point cloud data is mapped to the surface mesh data. For each point of the surface mesh data that cannot be mapped to the point cloud data because there is a missing data point in point cloud data, an interpolation operation is performed based on the point cloud data within the neighbourhood of the point to generate a value for the missing data point. The interpolation operation is repeated N times. For every repetition, a difference between the value for the missing data point generated from the current iteration and the value for the missing data point generated from the immediately preceding iteration is compared, until the difference is below a threshold.

Growing community of inventors

Danville, CA, United States of America

Rohit Jain