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US 12,646,622 B2Granted
Detection of cardiac conditions from reduced lead set ECG
AssigneeAlivecor, Inc.
InventorsJoel Q. Xue, Daniel Treiman
Life of this patent
Estimating the term…
Abstract
Embodiments of the present disclosure utilize DNNs for ECG interpretation, where original ECG waveforms are directly ingested by the DNNs using paired interpretation labels for training, without the need for explicatory feature extraction or rule-based criteria.
Claims20
A method comprising:
- training a first deep neural network (DNN) to classify electrocardiogram (ECG) rhythm characteristics, wherein a training weight matrix is used to balance a difference in prevalence among classifications during training;
- training a second DNN to classify ECG morphology characteristics;
- training a third DNN to identify global ECG parameters, wherein both the first and second DNNs comprise a classification layer and the third DNN comprises a regression layer;
- and
- implementing the first, second, and third DNNs as an ensemble DNN for ECG interpretation.
2 more elements
A system comprising:
- an electrocardiogram (ECG) monitor configured to perform an ECG of a person and transmit the ECG;
- and
- a computing device configured to:
- in response to receiving the ECG from the ECG monitor:
- classify rhythm characteristics of the ECG using a first deep neural network (DNN), wherein the first DNN is structured to classify rhythm characteristics of ECG data and wherein a training weight matrix is used to balance a difference in prevalence among classifications during training of the first DNN;
- classify morphology characteristics of the ECG using a second DNN, wherein the second DNN is structured to classify morphology characteristics of ECG data;
- and
- identify global parameters of the ECG using a third DNN, wherein the third DNN is structured to identify global parameters of ECG data;
- and
- apply a set of decision logic to the ECG rhythm characteristics and the ECG morphology characteristics to diagnose one or more conditions indicated by the ECG based at least in part on the ECG global parameters.
7 more elements
A non-transitory computer-readable medium having instructions stored thereon which, when executed by a processing device, cause the processing device to:
- train a first deep neural network (DNN) to classify electrocardiogram (ECG) rhythm characteristics, wherein a training weight matrix is used to balance a difference in prevalence among classifications during the training of the first DNN;
- train a second DNN to classify ECG morphology characteristics;
- train a third DNN to identify global ECG parameters;
- in response to receiving an ECG:
- classify rhythm characteristics of the ECG using the first DNN, wherein the first DNN is structured to classify rhythm characteristics of ECG data;
- classify morphology characteristics of the ECG using the second DNN, wherein the second DNN is structured to classify morphology characteristics of ECG data;
- and
- identify global parameters of the ECG using the third DNN, wherein the third DNN is structured to identify global parameters of ECG data;
- and
- apply a set of decision logic to the ECG rhythm characteristics and the ECG morphology characteristics to diagnose one or more conditions indicated by the ECG based at least in part on the ECG global parameters.
7 more elements
Dependencies are inferred from the claim text.