Anumana, Inc.

Karthik K Bharadwaj

Rohit Jain

Sanjeev Shrinivas Nadapurohit

Animesh Agarwal

Shiva Verma

Yogisha Heggadahalli Jayendra

Yogisha H J

Rakesh Barve

Anand Ramani

Deepak Anand

Leon Ptaszek

Sughosh Indurkar

Shashi Kant

Kappagantula Gopalakrishna Murty

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 system for determining position signals of electrodes using a retrained machine-learning model includes at least a catheter including a plurality of electrodes configured to collect a plurality of potential signals and a magnetic sensor configured to collect magnetic data, and at least a computing device including a memory. The processor receives a first training set, wherein the first training set includes patient-agnostic data; receives a second training set, wherein the second training set includes patient-specific data, trains a mapping machine-learning model using the first training set, retrains the mapping machine-learning model using the second training set, receives at least a first signal, wherein the first signal includes a potential signal of the plurality of potential signal and the magnetic data, and generates, using the retrained machine-learning model, as a function of the at least a first signal, a first position signal for an electrode of the plurality of electrodes.

Methods and systems for determining position signals of electrodes using a retrained machine-learning model

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.

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Bengaluru, India

Karthik K Bharadwaj