Multi-channel and with rhythm transfer learning

A non-transitory computer-readable medium containing computer program code that, when executed by operation of a computer processor, performs an operation comprising:

• generating a feature set by analyzing electrocardiogram (ECG) data for a patient using a first phase in a machine learning architecture, the first phase comprising a deep learning neural network;
• classifying, based on the feature set and using the first phase, a first cardiac event in the ECG data, wherein the first phase is configured to classify cardiac events of a plurality of types;
• classifying, based on the feature set and the classified first event and using a second phase in the machine learning architecture, a second cardiac event in the ECG data, wherein the second phase is configured to classify cardiac events of a single type;
• and
• classifying, based on the feature set and the classified first event and using a third phase in the machine learning architecture, a third cardiac event in the ECG data,
• wherein the third phase is configured to classify cardiac events of a single type different from the second phase, and
• wherein the second and third cardiac events occur in the patient at least partially at the same time as the first cardiac event.

A system, comprising:

• a computer processor;
• and
• a memory having instructions stored thereon which, when executed on the computer processor, performs an operation comprising:
• generating a feature set by analyzing electrocardiogram (ECG) data for a patient using a first phase in a machine learning architecture, the first phase comprising a deep learning neural network;
• classifying, based on the feature set and using the first phase, a first cardiac event in the ECG data, wherein the first phase is configured to classify cardiac events of a plurality of types;
• classifying, based on the feature set and the classified first event and using a second phase in the machine learning architecture, a second cardiac event in the ECG data, wherein the second phase is configured to classify cardiac events of a single type;
• and
• classifying, based on the feature set and the classified first event and using a third phase in the machine learning architecture, a third cardiac event in the ECG data,
• wherein the third phase is configured to classify cardiac events of a single type different from the second phase, and
• wherein the second and third cardiac events occur in the patient at least partially at the same time as the first cardiac event.