Anumana, Inc.

Shiva Verma

Shashi Kant

Rohit Jain

Ashim Prasad

Karthik K Bharadwaj

Yash Mishra

Sanjeev Shrinivas Nadapurohit

Rakesh Barve

Uddeshya Upadhyay

Sairam Bade

Samir Awasthi

Anand Ramani

Yogisha H J

Suthirth Vaidya

Animesh Agarwal

Yogisha Heggadahalli Jayendra

Leon Ptaszek

An apparatus and method for training a machine learning model to augment signal data and image data. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a signal data. The memory instructs the processor to generate a digital image, wherein the digital image comprises the signal data. The memory instructs the processor to transmit the digital image to an image processing module, wherein the image processing module produces an augmented image. The memory instructs the processor to transmit the signal data to a signal processing module, wherein the signal processing module produces the augmented image. The memory instructs the processor to train a machine learning model using the augmented image.

Apparatus and method for training a machine learning model to augment signal data and image data

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 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 transforming electrocardiogram images for use in one or more machine learning models, the system including at least a processor configured to receive paired data including a plurality of ECG images correlated to a plurality of standardized images, wherein each ECG image of the plurality of ECG images is correlated with each standardized image of the plurality of standardized images, train an ECG transformation model as a function of the paired data, wherein training the transformation model includes adjusting one or more parameter values of the ECG transformation model as a function of a comparison between at least one predicted standardized image and at least one standardized image of the plurality of standardized images, receive non-conforming data, generate standardized data as a function of the ECG transformation model and the non-conforming data and train an ECG machine learning model as a function of the standardized data.

Systems and methods for transforming electrocardiogram images for use in one or more machine learning models

An apparatus and method for generating a quality diagnostic of analog ECG (electrocardiogram) data. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive ECG data, extract a plurality of ECG parameters from the ECG data, convert the ECG data to one or more digitized ECG signals, validate the digitized ECG signals, wherein validating includes classifying the plurality of digitized ECG signals to a plurality of preliminary ECG parameters and determining an accuracy status of the extracted plurality of ECG parameters by comparing the plurality of preliminary ECG parameters to the extracted plurality of ECG parameters, and generate a quality diagnostic of the ECG data based on validation of the digitized ECG signals.

Apparatus and method for generating a quality diagnostic of ECG (electrocardiogram) data

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Bengaluru, India

Shiva Verma