A system for machine-learning-based atrial fibrillation detection with the aid of a digital computer, comprising: a database operable to maintain a plurality of electrocardiography features and annotated patterns of the features, at least some of the patterns associated with atrial fibrillation; at least one server interconnected to the database, the at least one server configured to: train a classifier based on the annotated patterns in the database; receive a representation of an ECG signal recorded by an ambulatory monitor recorder during a plurality of temporal windows; detect a plurality of the ECG features in at least some of the portions of the representation falling within each of the temporal windows; use the trained classifier to identify repeating patterns of the ECG features within one or more of the portions of the ECG signal; for each of the portions, calculate using the repeating patterns a score indicative of whether the portion of the representation within that ECG signal is associated with the patient experiencing atrial fibrillation; calculate using the repeating patterns a further score indicative of whether the portion of the representation within that ECG signal is associated with the patient not experiencing atrial fibrillation; compare the further score to the score; determine that the portion of the ECG signal is associated with the patient experiencing atrial fibrillation based on the comparison; and take an action based on the determination that the portion of the ECG signal is associated with the patient experiencing atrial fibrillation. a database operable to maintain a plurality of electrocardiography features and annotated patterns of the features, at least some of the patterns associated with atrial fibrillation; at least one server interconnected to the database, the at least one server configured to: train a classifier based on the annotated patterns in the database; receive a representation of an ECG signal recorded by an ambulatory monitor recorder during a plurality of temporal windows; detect a plurality of the ECG features in at least some of the portions of the representation falling within each of the temporal windows; use the trained classifier to identify repeating patterns of the ECG features within one or more of the portions of the ECG signal; for each of the portions, calculate using the repeating patterns a score indicative of whether the portion of the representation within that ECG signal is associated with the patient experiencing atrial fibrillation; calculate using the repeating patterns a further score indicative of whether the portion of the representation within that ECG signal is associated with the patient not experiencing atrial fibrillation; compare the further score to the score; determine that the portion of the ECG signal is associated with the patient experiencing atrial fibrillation based on the comparison; and take an action based on the determination that the portion of the ECG signal is associated with the patient experiencing atrial fibrillation. train a classifier based on the annotated patterns in the database; receive a representation of an ECG signal recorded by an ambulatory monitor recorder during a plurality of temporal windows; detect a plurality of the ECG features in at least some of the portions of the representation falling within each of the temporal windows; use the trained classifier to identify repeating patterns of the ECG features within one or more of the portions of the ECG signal; for each of the portions, calculate using the repeating patterns a score indicative of whether the portion of the representation within that ECG signal is associated with the patient experiencing atrial fibrillation; calculate using the repeating patterns a further score indicative of whether the portion of the representation within that ECG signal is associated with the patient not experiencing atrial fibrillation; compare the further score to the score; determine that the portion of the ECG signal is associated with the patient experiencing atrial fibrillation based on the comparison; and take an action based on the determination that the portion of the ECG signal is associated with the patient experiencing atrial fibrillation.