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.

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: Provided herein are methods, systems, and computer program products for the detection of pulmonary hypertension comprising receiving voltage-time data of a plurality of leads of an electrocardiograph of a 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 presence or absence of pulmonary hypertension in the subject.

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: 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: Systems, methods, devices, and other techniques for processing an ECG recording to assess a condition of a mammal.

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: Systems and methods for assessing the condition of a heart of an individual. A system obtains electrocardiogram (ECG) data that describes a result of an ECG of the individual. The ECG data can be provided to a machine-learning model, which processes the data and generates an output indicative of the condition of the heart. The output relates to a level of troponin in a bloodstream of the individual. The system can then provide the output of the machine-learning model to a post-processing resource.

Abstract: Systems, methods, and other techniques for estimating the level of an analyte present in a patient during a time interval using electrocardiogram (ECG) signals. The estimate can be improved using information about the patient's posture.

Abstract: Systems, methods, devices, and other techniques for estimating the age and sex of a person through analysis of an electrocardiogram (ECG) recording for the person. Some aspects include recording an ECG of a person, processing data representing the ECG with an age-estimation neural network to generate an estimated age of the person, and outputting an indication of the estimated age of the person. Other aspects include processing the ECG with a sex prediction neural network to generate a predicted sex of the person and outputting an indication of the predicted sex of the person.

Abstract: Data is generated that describes features of an ECG of a subject. This generation can include receiving ECG data that was generated to reflect cardiac activity of a particular mammal; submitting the ECG data to a plurality of cardiac classifiers, each cardiac classifier configured to identify, in the ECG, at least some of a plurality of cardiac features that are within a particular feature-class; receiving, from each of the plurality of cardiac classifiers, a classification message containing data of the cardiac classifiers identifying of cardiac features in the ECG; and assembling, from the received classification messages, ECG features for the ECG, the ECG features identifying at least some features of different feature-classes.

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: 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: Systems, methods, and other techniques for estimating the level of an analyte present in a patient during a time interval using electrocardiogram (ECG) signals. The estimate can be improved using information about the patient's posture.

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.