Abstract: Systems, methods, and devices involve approaches for limiting the number of detected cardiac events for which a physician is alerted. Approaches involve detecting cardiac events based on electrocardiogram (ECG) data and associating the cardiac events with a criticality. ECG data associated with a subset of the cardiac events is transmitted to a remote computing system based, at least in part, on the criticality. An electronic notification is transmitted to a patient care group regarding at least one of the cardiac events in the subset. Approaches further include determining whether an acknowledgement of the electronic notification has been received by the server from the patient care group within a set period of time.

Abstract: Systems and methods involve generating, by one or more machine learning models, a first set of classifications for a first strip of electrocardiogram (ECG) data; generating, by the one or more machine learning models, a second set of classifications for a second strip of ECG data; generating, by the one or more machine learning models, one or more new classifications in response to inputting only a portion of the first strip of ECG data along with only a portion of the second strip of ECG data into the machine learning model; and updating the first set of classification and/or the second set of classifications with the one or more new classifications.

Abstract: Systems of methods involve receiving a package of data comprising strips of electrocardiogram (ECG) data and metadata; displaying, on a user interface, at least some of the strips of ECG data, including a strip of ECG data associated with an initial maximum heart rate; and overriding the initial maximum heart rate in response to selecting a strip of ECG data associated a lower maximum heart rate.

Abstract: A method includes associating beats with respective initial beat classifications using a trained machine learning model and based on electrocardiogram data. Using an encoder machine learning model, latent space representations of the electrocardiogram data are generated for beats associated with the initial beat classifications. Similar shaped beats are associated with each other based on the latent space representations.

Abstract: A method includes completing, at a server, a mobile cardiac telemetry (MCT) study based on packets of electrocardiogram (ECG) data periodically received by the server. The method further includes completing, at the server, a Holter study based on the packets of ECG data. Completing the Holter study includes generating a report that includes results of the Holter study and the MCT study.

Abstract: A method includes receiving, by a first computing system, electrocardiogram (ECG) data and metadata associated with the ECG data. The metadata includes an initial cardiac event classification and an initial beat classification for beats occurring during a first event associated with the initial cardiac event classification. The method further includes displaying the ECG data in a user interface, receiving a command to change the initial cardiac event classification to a subsequent cardiac event classification, and automatically modifying the initial beat classifications to subsequent beat classifications based on the subsequent cardiac event classification.

Abstract: A method includes obtaining test criteria for a monitor configured to be attached to a user; obtaining reference data; determining, based on the test criteria and the reference data, a detached state of the monitor. The determining the detached state includes performing a set of tests indicated in the test criteria. The set of tests includes a first test corresponding to a first power consumption by the monitor; and a second test different from the first test. The second test corresponds to a second power consumption by the monitor, and the second power consumption is larger than the first power consumption. The method further includes automatically modifying, in response to the determining the detached state, an operating mode of the monitor.

Abstract: A method includes receiving—by a first computing system—electrocardiogram (ECG) data and metadata associated with the ECG data, where the metadata includes an initial cardiac event classification and an initial beat classification for beats occurring during a first event associated with the initial cardiac event classification. The method further includes causing the ECG data to be displayed in a user interface (UI) and receiving a command, via the UI, to change the initial beat classifications to subsequent beat classifications. The method also includes automatically modifying, by the first computing system, the initial cardiac event classification to a subsequent cardiac event classification based on the subsequent beat classifications.

Abstract: Techniques for classifying cardiac events in electrocardiogram (ECG) data. A feature set is generated by analyzing ECG data for a patient using a first phase in a machine learning architecture. A first cardiac event in the ECG data is classified based on the feature set, using the first phase in the machine learning architecture. A second cardiac event in the ECG data is classified based on the classified first cardiac event and the feature set, using a second phase in the machine learning architecture. The second cardiac event overlaps at least partially in time with the first cardiac event. Further, a plurality of feature sets, corresponding to a plurality channels of ECG data, are generated using paths in a machine learning architecture. A cardiac event in the ECG data is classified using the machine learning architecture based on the plurality of feature sets.

Abstract: Techniques for classifying cardiac events in electrocardiogram (ECG) data. A feature set is generated by analyzing ECG data for a patient using a first phase in a machine learning architecture. A first cardiac event in the ECG data is classified based on the feature set, using the first phase in the machine learning architecture. A second cardiac event in the ECG data is classified based on the classified first cardiac event and the feature set, using a second phase in the machine learning architecture. The second cardiac event overlaps at least partially in time with the first cardiac event. Further, a plurality of feature sets, corresponding to a plurality channels of ECG data, are generated using paths in a machine learning architecture. A cardiac event in the ECG data is classified using the machine learning architecture based on the plurality of feature sets.