Abstract: A method includes associating beats with respective initial beat classifications using a trained machine learning model and based on electrocardiogram data. Using an encoder machine learning model, latent space representations of the electrocardiogram data are generated for beats associated with the initial beat classifications. Similar shaped beats are associated with each other based on the latent space representations.

Abstract: Techniques for classifying heartbeats using patient electrocardiogram (ECG) data are described. ECG data is received, including waveform data and time interval data relating to a plurality of heartbeats for the patient. A convolutional neural network in a first path of a machine learning architecture generates a first plurality of output values by analyzing the waveform data. A fully-connected neural network in a second path of the machine learning architecture generates a second plurality of output values by analyzing the time interval data. The plurality of heartbeats in the ECG data are classified by concatenating the first plurality of output values and the second plurality of output values using the machine learning architecture.

Abstract: Techniques for classifying heartbeats using patient electrocardiogram (ECG) data are described. ECG data is received, including waveform data and time interval data relating to a plurality of heartbeats for the patient. A convolutional neural network in a first path of a machine learning architecture generates a first plurality of output values by analyzing the waveform data. A fully-connected neural network in a second path of the machine learning architecture generates a second plurality of output values by analyzing the time interval data. The plurality of heartbeats in the ECG data are classified by concatenating the first plurality of output values and the second plurality of output values using the machine learning architecture.

Abstract: A method includes generating first electrocardiogram (ECG) data by adding synthetic noise to naturally occurring ECG data using a first deep neural network (DNN). The method further includes providing one of: (i) the first ECG data, or (ii) second ECG data including naturally occurring noise, to a second DNN. An output is generated by the second DNN indicating whether the second DNN received the first ECG data or the second ECG data.

Abstract: A method includes classifying, using a machine learning model, a portion of an electrocardiogram measurement as an artifact. The method further includes normalizing the electrocardiogram measurement except the portion of the electrocardiogram measurement classified as the artifact. The method further includes applying the machine learning model to the normalized electrocardiogram measurement to detect a cardiac event.

Abstract: A method includes analyzing a first segment of electrocardiogram measurements corresponding to a first heartbeat and a second heartbeat of a patient to identify a set of features of the first segment of electrocardiogram measurements. The method further includes adjusting the set of features based at least on the first segment and a second segment of electrocardiogram measurements corresponding to the first heartbeat. The method further includes predicting, based on the adjusted set of features, a region in the second segment of electrocardiogram measurements comprising a p-wave generated by the second heartbeat.

Abstract: Techniques for classifying heartbeats using patient electrocardiogram (ECG) data are described. ECG data is received, including waveform data and time interval data relating to a plurality of heartbeats for the patient. A convolutional neural network in a first path of a machine learning architecture generates a first plurality of output values by analyzing the waveform data. A fully-connected neural network in a second path of the machine learning architecture generates a second plurality of output values by analyzing the time interval data. The plurality of heartbeats in the ECG data are classified by concatenating the first plurality of output values and the second plurality of output values using the machine learning architecture.