Abstract: A system for interpreting an electrocardiogram identifies a segment of the electrocardiogram having an abnormality using a machine learning algorithm. The system assigns an interpretation output to the segment of the electrocardiogram having the abnormality. The interpretation output describes a morphology of the abnormality.

Abstract: Systems and methods for detecting brief episodes of atrial fibrillation are described. The methods may comprise receiving from one or more sensors, data including ECG information, generating preprocessed data based on the ECG information, generating, based at least in part on the preprocessed data, a visual illustration associated with the ECG information, the visual illustration including a first section associated with a first time resolution and a second section associated with a second time resolution, receiving, as an output of a neural network, an indication of whether the visual illustration corresponds to a classification of atrial fibrillation, and assigning the visual illustrations to a classification based at least in part on the indication.