Abstract: An example method includes identifying data indicative of measurements of a physiological parameter of a subject over a time period. The data is divided into multiple segments including a first segment and a second segment. The method further includes determining a first label indicating whether the first segment is indicative of a condition; generating a first image that is indicative of the first segment; and determining, by inputting the first image into a trained machine learning (ML) model, that the first label is inaccurate. The method additionally includes determining a second label indicating whether the second segment is indicative of the condition; generating a second image that is indicative of the second segment; and determining, by inputting the second image into the trained ML model, that the second label is accurate. The method additionally includes outputting an indication of whether the subject has the condition based on the second label.

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.