Anumana, Inc.

Shashi Kant

Rakesh Barve

Ashim Prasad

Uddeshya Upadhyay

Sairam Bade

Shiva Verma

Yash Mishra

Samir Awasthi

Yash Gupta

Rohit Jain

Suthirth Vaidya

Animesh Agarwal

Anand Ramani

Karthik K Bharadwaj

Sanjeev Shrinivas Nadapurohit

Mohan Krishna Ranganathan

Yogisha Heggadahalli Jayendra

Shayan Ghosh

Shiv Pratap Singh

Mayank Sharma

Durgaprasad Dodle

Shahir Asfahan

Wui Ip

An apparatus and method for generating output data as a function of digital image data and an analysis module. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to present a graphical user interface comprising medical time-series data, receive a capture event as a function of a first user command, generate, as a function of the capture event, digital image data, wherein the digital image data comprises a window of the graphical user interface, verify, using user confirmation, the window of the graphical user interface, process, using one or more analysis models, the digital image data, wherein processing the digital image data comprises extracting at least a cardiac metric from the digital image data, and generate, using the analysis module, output data based on the at least a cardiac metric extracted from the digital image data.

Apparatus and method for generating output data as a function of digital image data and an analysis module

A system for tracking cardiac values including at least a processor configured to receive electrocardiogram (ECG) input data associated with a patient wherein the ECG input data includes ECG signals, input the ECG input data into one or more cardiac panels, wherein each cardiac panel of the one or more cardiac panels is configured to calculate a cardiac value associated with a heart condition and each cardiac panel comprises at least one ECG machine-learning model configured to receive ECG input data and output cardiac values, generate the cardiac values from the cardiac panels as a function of the ECG input data wherein at least one cardiac value of the one or more cardiac values includes a probability of the patient satisfying at least one ejection fraction level threshold and the one or more ECG machine-learning models and display the cardiac values through a graphical user interface.

Systems and methods for tracking cardiac values

An apparatus and method for static image of time series measured data to time series translation is disclosed. The apparatus comprises at least a processor configured to receive a static image of time series measured data, convert that static image from its initial domain to a usable time series within another user-selected domain, then to validate the conversion against a confidence threshold.

Apparatus and method for time series data format conversion and analysis

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 unpaired time series to time series translation is disclosed. The apparatus comprises at least a processor configured to receive an automated analysis of a time series, convert that time series from its initial domain to a usable time series within another user-selected domain, then to validate the conversion against a confidence threshold.

Apparatus and method for unpaired time series data translation

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

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/or method) for training machine-learning model to make determinations from mismatched channel signals, wherein the apparatus includes a processor and memory communicatively connected to the processor. The memory containing instructions configuring the processor to receive a first set of signals using a first number of channels, process the first set of signals to simulate a second set of signals from a second number of channels, train a signal conversion model using the first set of signals and the second set of signals, and output a set of converted signals having the first number of channels using the third set of signals and the trained signal conversion model.

Apparatus (and/or method) of training a machine-learning model to generate determinations using mismatched-channel signals

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

An apparatus for standardization of electrocardiogram signal images, the apparatus having an imaging device, at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive an overlay image from the imaging device, identify a captured fixed background image and a captured primary image within the overlay image, wherein the captured primary image includes a plurality of electrocardiogram signals, compare the captured fixed background image to one or more image quality thresholds, determine an image quality score of the primary image as a function of the captured fixed background image, and output one or more image modification datum as a function of the image quality score.

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Bengaluru, India

Shashi Kant