Active learning and margin strategies for arrhythmia classification in implantable devices

The massive storage of cardiac arrhythmic episodes from Implantable Cardioverter Defibrillators (ICD) and the advent of new artificial intelligence algorithms are opening up new opportunities for electrophysiological knowledge extraction. However, in this context, accurate and reliable episode labeling by expert cardiologists still remains a manual, costly, and time-consuming process.

In this work, we propose using Active Learning (AL) to design classification models that streamline the manual labeling of cardiac arrhythmic episodes. When AL is used, relevant episodes for classification are selected and then presented to the human expert for labeling, thereby dramatically reducing the manual labeling burden.

We adapted four large-margin-based AL strategies to a previously proposed classification methodology. We benchmarked them on problems involving 3 and 8 arrhythmia types using 9908 episodes from a massive national ICD data repository. Specifically, the relevance of episode-patient diversity for classification was evaluated. Results showed that the gold standard performance, achieved using all episodes, was reached by using approximately 20% (50%) of episodes from 60% (85%) of patients in the 3-class (8-class) model design.

We can conclude that AL techniques are advantageous for designing classification models and can streamline the human labeling process of large ICD datasets.