Mobilmed’s BEAT a personalized health monitoring system that can detect early occurrences of arrhythmias from an individual’s electrocardiogram (ECG) signal. We have modelled the common causes of arrhythmias in the signal domain as a degradation of normal ECG beats to abnormal beats. Using the degradation models, we perform abnormal beat synthesis which created potential abnormal beats from the average normal beat of the individual. Finally, a machine learning model based on adaptive 1-D Convolutional convolutional neural network (CNN) was trained using real
normal and synthesized abnormal beats. As a personalized classifier, the trained network can monitor ECG beats in real time for arrhythmia detection.