Abstract: A system obtains a maternal electrocardiogram (ECG) signal that represents an ECG of a pregnant mother during a first time interval. The system further obtains a mixed maternal-fetal ECG signal that represents a combined ECG of the mother and her fetus during the first time interval; processes the maternal ECG signal and the mixed maternal-fetal ECG signal to generate a fetal ECG signal that represents the ECG of the fetus during the time interval, the fetal ECG signal substantially excluding the maternal ECG signal; and provides an output based on the fetal ECG signal.

Abstract: Provided herein are methods, systems, and computer program products for the detection and evaluation of cardiac condition in a lead-invariant manner.

Abstract: Systems, methods, devices, and techniques for estimating a heart disease prediction of a mammal. An electrocardiogram (ECG) procedure is performed on a mammal, and a computer system obtains ECG data that describes results of the ECG over a period of time. The system provides a predictive input that is based on the ECG data to a predictive model, such as a neural network or other machine-learning model. In response, the predictive model processes the input to generate an estimated heart disease predictive characteristic of the mammal. The system outputs the estimated heart disease prediction of the mammal for presentation to a user.

Abstract: Systems, methods, devices, and techniques for estimating a heart disease prediction of a mammal. An electrocardiogram (ECG) procedure is performed on a mammal, and a computer system obtains ECG data that describes results of the ECG over a period of time. The system provides a predictive input that is based on the ECG data to a predictive model, such as a neural network or other machine-learning model. In response, the predictive model processes the input to generate an estimated heart disease predictive characteristic of the mammal. The system outputs the estimated heart disease prediction of the mammal for presentation to a user.

Abstract: Provided herein are methods, systems, and computer program products for the detection and evaluation of cardiac condition in a lead-invariant manner.

Abstract: The present disclosure provides methods and systems for displaying a user interface that presents a classifier statistic, mathematical equation output, or ROC curve on a computing device or mobile phone or tablet. The method comprises: receiving a user's input on said screen using a cursor or pointing element whereby user input comprises selection of a point along a plotted curve, ROC curve, or mathematical function so that input results in displaying one or more figures corresponding to the select statistics or threshold term defined by the user input.

Abstract: In some aspects, values of features obtained from training first and second machine-learning models are analyzed to correlate at least a subset of features from the first machine-learning model with at least a subset of features from the second machine-learning model. The correlated features are then applied to update the first or second machine-learning model, or to train a third machine-learning model. In other aspects, a generator machine-learning model processes a seed and a target characteristic indicator to generate a synthetic ECG signal. The synthetic ECG signal is biased according to a target physiological characteristic represented by the target characteristic indicator. The generator machine-learning model can be trained using an expert machine-learning model and in an adversarial process with a discriminator machine-learning model.

Abstract: Apparatuses and methods are provided to predict or diagnose an ischemic event, such as a stroke or a transient ischemic attack (TIA). A machine-learning model such as a neural network is generated that allows for recognition of an ECG consistent with an ischemic event. A system is trained and used to process a recording of ECG data from a patient to generate a prediction indicating a likelihood that the patient will experience a stroke. In other examples, a system is trained and used to process a recording of ECG data from a patient and detect an ischemic event for the patient who did not appear to have such an ischemic event.

Abstract: An apparatus and method for detecting a level of cardiovascular disease. 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 a plurality of voltage-time data, generate at least a feature vector from the voltage-time data by at least a feature model, input the at least feature vector into a cardiovascular classification model, generate at least a disease indication in a subject using the classification model, wherein the disease indication comprises a level of myocarditis, and display the at least a disease indication.

Abstract: Systems, methods, devices, and other techniques for processing an ECG recording to assess a condition of a mammal. Assessing the condition of the mammal can include screening for atrial fibrillation, and screening for atrial fibrillation can include obtaining a first neural network input, the first neural network input representing an electrocardiogram (ECG) recording of the mammal, and processing the first neural network input with a neural network to generate an atrial fibrillation prediction for the mammal.

Abstract: Systems, methods, devices, and techniques for estimating an ejection-fraction characteristic of a mammal. An electrocardiogram (ECG) procedure is performed on a mammal, and a computer system obtains ECG data that describes results of the ECG over a period of time. The system provides a predictive input that is based on the ECG data to an ejection-fraction predictive model, such as a neural network or other machine-learning model. In response, the ejection-fraction predictive model processes the input to generate an estimated ejection-fraction characteristic of the mammal. The system outputs the estimated ejection-fraction characteristic of the mammal for presentation to a user.

Abstract: Systems, methods, devices, and techniques for estimating a heart disease prediction of a mammal. An electrocardiogram (ECG) procedure is performed on a mammal, and a computer system obtains ECG data that describes results of the ECG over a period of time. The system provides a predictive input that is based on the ECG data to a predictive model, such as a neural network or other machine-learning model. In response, the predictive model processes the input to generate an estimated heart disease predictive characteristic of the mammal. The system outputs the estimated heart disease prediction of the mammal for presentation to a user.

Abstract: A method for screening for cardiac amyloidosis by electrocardiography is disclosed. The method includes receiving, using at least a processor, voltage-time data of a subject, wherein the voltage-time data comprises voltage data from a plurality of leads of an electrocardiograph. The method includes generating, using the at least a processor, a feature vector from the voltage-time data. The method includes identifying, using the at least a processor, the presence or absence of cardiac amyloidosis (CA) in the subject as a function of the feature vector using a learning system.

Abstract: Provided herein are methods, systems, and computer program products for the detection and evaluation of coronary artery calcium (CAC) (e.g., CAC scoring) comprising receiving voltage-time data of a plurality of leads of an electrocardiograph subject; generating a feature vector from the voltage-time data; providing the feature vector to a pretrained learning system; and receiving from the pretrained learning system an indication of the level of coronary artery calcium in the subject.

Abstract: Provided herein are methods, systems, and computer program products for the detection and evaluation of cardiac condition in a lead-invariant manner.

Abstract: Apparatuses and methods are provided to predict or diagnose an ischemic event, such as a stroke or a transient ischemic attack (TIA). A machine-learning model such as a neural network is generated that allows for recognition of an ECG consistent with an ischemic event. A system is trained and used to process a recording of ECG data from a patient to generate a prediction indicating a likelihood that the patient will experience a stroke. In other examples, a system is trained and used to process a recording of ECG data from a patient and detect an ischemic event for the patient who did not appear to have such an ischemic event.

Abstract: Systems, methods, devices, and other techniques for processing an ECG recording to assess a condition of a mammal. Assessing the condition of the mammal can include screening for atrial fibrillation, and screening for atrial fibrillation can include obtaining a first neural network input, the first neural network input representing an electrocardiogram (ECG) recording of the mammal, and processing the first neural network input with a neural network to generate an atrial fibrillation prediction for the mammal.

Abstract: Systems, methods, devices, and techniques for estimating a heart disease prediction of a mammal. An electrocardiogram (ECG) procedure is performed on a mammal, and a computer system obtains ECG data that describes results of the ECG over a period of time. The system provides a predictive input that is based on the ECG data to a predictive model, such as a neural network or other machine-learning model. In response, the predictive model processes the input to generate an estimated heart disease predictive characteristic of the mammal. The system outputs the estimated heart disease prediction of the mammal for presentation to a user.

Abstract: Systems and methods are provided for quantifying and monitoring liver disease in a patient from voltage-time data. A method comprises receiving voltage-time data of a subject, the voltage-time data comprising voltage data of a plurality of leads of an electrocardiograph; generating a feature vector from the voltage-time data; providing the feature vector to a pretrained learning system; receiving from the pretrained learning system a status of liver disease in the subject.

Abstract: Systems, methods, devices, and techniques for estimating a heart disease prediction of a mammal. An electrocardiogram (ECG) procedure is performed on a mammal, and a computer system obtains ECG data that describes results of the ECG over a period of time. The system provides a predictive input that is based on the ECG data to a predictive model, such as a neural network or other machine-learning model. In response, the predictive model processes the input to generate an estimated heart disease predictive characteristic of the mammal. The system outputs the estimated heart disease prediction of the mammal for presentation to a user.