Active learning applied to patient-adaptive heartbeat classification

While clinicians can accurately identify different types of heartbeats in electro-cardiograms (ECGs) from different patients, researchers have had limited success in applying supervised machine learning to the same task. The problem is made challenging by the variety of tasks, inter- and intra-patient differences, an often severe class imbalance, and the high cost of getting cardiologists to label data for individual patients. We address these difficulties using active learning to per-form patient-adaptive and task-adaptive heartbeat classification. When tested on a benchmark database of cardiologist annotated ECG recordings, our method had considerably better performance than other recently proposed methods on the two primary classification tasks recommended by the Association for the Advance-ment of Medical Instrumentation. Additionally, our method required over 90% less patient-specific training data than the methods to which we compared it.