System and method for neural-network-based atrial fibrillation detection with the aid of a digital computer

A method for neural-network-based atrial fibrillation detection with the aid of a digital computer, comprising:

• maintaining in a database a plurality of electrocardiography (ECG) features and annotated patterns of the features, at least some of the patterns associated with atrial fibrillation;
• training by an at least on server interconnected to the database a classifier based on the annotated patterns in the database, the at least one server implementing a convolutional neural network, the convolutional neural network comprising two convolutional hidden layers and two fully-connected hidden layers, wherein the classifier implemented by the convolutional neural network;
• receiving by the at least one server a representation of an ECG signal recorded by one or more cardiac monitors;
• detecting by the at least one server via a first one of the convolutional hidden layers a plurality of the ECG features in at least some of the portions of the representation;
• using by the at least one server the trained classifier via a second one of the convolutional hidden layers to identify patterns of the ECG features within one or more of the portions of the ECG signal;
• generating by the at least one server via a first one of the fully-connected hidden layers at least one matrix with weights for the patterns;
• for each of the portions, calculating by the at least one server via a second one of the fully-connected hidden layers using the at least one matrix a value indicative of whether that portion of the representation is associated the patient experiencing atrial fibrillation;
• determining by the at least one server that one or more of the portions of the ECG signal are associated with the patient experiencing atrial fibrillation using the scores for those portions.