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US 2026/0215720 A1Published application
Apparatus and method for medical signal interpretation
AssigneeAnumana, Inc.
InventorsUddeshya Upadhyay, Sairam Bade, Ashim Prasad, Rakesh Barve
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Abstract
Disclosed herein is an apparatus and method for medical signal interpretation. An apparatus may train an encoder on a first training dataset comprising a plurality of elements of medical time series training data, generating a predictive machine learning model by combining the encoder with a medical prediction downstream head, and using the predictive machine learning model, generating a medical prediction as a function of medical time series input data.
Claims24
An apparatus for medical signal interpretation, the apparatus comprising:
- an electrocardiogram device comprising a 12 lead electrocardiogram device, wherein the electrocardiogram device is configured to collect medical time series input data from a subject comprising at least a right arm electrode voltage, a left arm electrode voltage and a V1 electrode voltage;
- 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:
- train an encoder on a first training dataset comprising a plurality of elements of medical time series training data by:
- masking a subset of an element of medical time series training data of the plurality of elements of medical time series training data to generate at least a masked element of medical time series training data, wherein the medical time series training data of the first training dataset comprises 12 lead ECG data and components selected from the at least a right arm electrode voltage, left arm electrode voltage and a V1 electrode voltage, and a plurality of unmasked elements of medical time series training data;
- generating, using an encoder, a plurality of medical time series training data tokens as a function of the plurality of unmasked elements of medical time series training data wherein the encoder is applied to the unmasked elements of medical time series training data to reduce computation needs of training the encoder;
- generating, using a decoder, a reconstruction of the element of medical time series training data as a function of the plurality of medical time series training data tokens;
- and
- modifying the encoder as a function of the element of medical time series training data and the reconstruction of the element of medical time series training data, wherein the encoder is modified by the at least a processor operating on 12-lead electrocardiogram signals acquired from the subject;
- generating a predictive machine learning model by combining the encoder with a medical prediction downstream head;
- and
- using the predictive machine learning model, generating a medical prediction as a function of the medical time series input data.
10 more elements
A method of medical signal interpretation, the method comprising:
- an electrocardiogram device comprising a 12 lead electrocardiogram device, wherein the electrocardiogram device is configured to collect medical time series input data from a subject comprising at least a right arm electrode voltage, a left arm electrode voltage and a V1 electrode voltage;
- using at least a processor, training an encoder on a first training dataset comprising a plurality of elements of medical time series training data by:
- masking a subset of an element of medical time series training data of the plurality of elements of medical time series training data to generate at least a masked element of medical time series training data wherein the medical time series training data of the first training dataset comprises 12 lead ECG data and components selected from the at least a right arm electrode voltage, left arm electric voltage and a V1 electrode voltage and a plurality of unmasked elements of medical time series training data;
- generating, using an encoder, a plurality of medical time series training data tokens as a function of the plurality of unmasked elements of medical time series training data wherein the encoder is applied to the unmasked elements of medical time series training data to reduce computation needs of training the encoder;
- generating, using a decoder, a reconstruction of the element of medical time series training data as a function of the plurality of medical time series training data tokens;
- and
- modifying the encoder as a function of the element of medical time series training data and the reconstruction of the element of medical time series training data, wherein the encoder is modified by the at least a processor operating on 12-lead electrocardiogram signals acquired from the subject;
- using the at least a processor to generate a predictive machine learning model by combining the encoder with a medical prediction downstream head;
- and
- using the at least a processor and the predictive machine learning model to generate a medical prediction as a function of medical time series input data.
7 more elements
Dependencies are inferred from the claim text.