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: In one embodiment, a processing device receives a first digital fingerprint of a media item along with a first content management rule. The processing device separately receives a second digital fingerprint of the media item along with a second content management rule. The processing device determines that the received digital fingerprints are for the same media item based upon a match between the first digital fingerprint and the second digital fingerprint. The processing device determines that all rights to the media item have been accounted for, and then determines a set of actions to be performed for hosted media items comprising the media item based at least in part upon the first content management rule and the second content management rule. Processing logic may also perform a conflict resolution process for conflicting rights claims to the media item.

Abstract: In one embodiment, a processing device receives a first digital fingerprint of a media item along with a first content management rule. The processing device separately receives a second digital fingerprint of the media item along with a second content management rule. The processing device determines that the received digital fingerprints are for the same media item based upon a match between the first digital fingerprint and the second digital fingerprint. The processing device determines that all rights to the media item have been accounted for, and then determines a set of actions to be performed for hosted media items comprising the media item based at least in part upon the first content management rule and the second content management rule. Processing logic may also perform a conflict resolution process for conflicting rights claims to the media item.

Abstract: A media item may be received by a computing device. One or more works may be identified in the media item. One or more ratios may be determined based on the length of the media item and/or lengths of one or more identified works in the media item. An output may be determined based on the one or more ratios.

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: In one embodiment, a processing device receives a request to claim ownership of a first hosted media item on a first media sharing platform, wherein the request is received from a rights holder that holds one or more rights to a claimed media item that is incorporated into the first hosted media item. The processing device adds an indicator of the ownership of the rights holder to a first entry for the first hosted media item in a data store. The processing device determines that a second hosted media item having a threshold similarity to the first hosted media item is hosted by a second media sharing platform. The processing device automatically adds an indicator of the ownership of the rights holder to a second entry for the second hosted media item without receiving a separate request to claim ownership of the second hosted media item.

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: In one embodiment, a processing device receives a first digital fingerprint of a media item along with a first content management rule. The processing device separately receives a second digital fingerprint of the media item along with a second content management rule. The processing device determines that the received digital fingerprints are for the same media item based upon a match between the first digital fingerprint and the second digital fingerprint. The processing device determines that all rights to the media item have been accounted for, and then determines a set of actions to be performed for hosted media items comprising the media item based at least in part upon the first content management rule and the second content management rule. Processing logic may also perform a conflict resolution process for conflicting rights claims to the media item.

Abstract: In one embodiment, a processing device receives a request to claim ownership of a first hosted media item on a first media sharing platform, wherein the request is received from a rights holder that holds one or more rights to a claimed media item that is incorporated into the first hosted media item. The processing device adds an indicator of the ownership of the rights holder to a first entry for the first hosted media item in a data store. The processing device determines that a second hosted media item having a threshold similarity to the first hosted media item is hosted by a second media sharing platform. The processing device automatically adds an indicator of the ownership of the rights holder to a second entry for the second hosted media item without receiving a separate request to claim ownership of the second hosted media item.

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: A media item may be received by a computing device. One or more works may be identified in the media item. One or more ratios may be determined based on the length of the media item and/or lengths of one or more identified works in the media item. An output may be determined based on the one or more ratios.

Abstract: A media item may be received by a computing device. One or more works may be identified in the media item. One or more ratios may be determined based on the length of the media item and/or lengths of one or more identified works in the media item. An output may be determined based on the one or more ratios.

Abstract: A media item may be received by a computing device. One or more works may be identified in the media item. One or more ratios may be determined based on the length of the media item and/or lengths of one or more identified works in the media item. An output may be determined based on the one or more ratios.