Automatic diagnosis of arrhythmia with electrocardiogram using multiple instance learning: From rhythm annotation to heartbeat prediction

The electrocardiogram (ECG) is a commonly used technique for detecting arrhythmias and many other cardiac diseases. Automatic ECG diagnosis has seen tremendous success in recent years, owing to the rapid development of the deep learning (DL) approach. Existing works on automatic ECG diagnosis can be divided roughly into two categories: prediction at the rhythm level from an ECG record, and prediction at the heartbeat level, although their relationship was seldom studied previously. In this paper, we address the following question: can we train an abnormal heartbeat detection model using solely data annotated at the rhythm level? We first used multiple instance learning (MIL) to model the relationship between an ECG record (whose label is given at the rhythm level and is provided as an input) and the heartbeats in the ECG (whose labels are to be predicted). Then, we sequentially trained two models, a rhythm model for detecting abnormal heartbeats in an ECG record labeled as arrhythmia, and a heartbeat model for classifying heartbeats as normal or various types of arrhythmias. We trained and tested our models using 61,853 ECG records with rhythm annotations. The experimental results demonstrate that the heartbeat model achieves a macro-average F1 score of 0.807 in classifying four types of arrhythmias as well as normal heartbeats. Our model significantly outperforms the model directly trained with 15,385 ECG heartbeats with heartbeat annotations, demonstrating the viability of our strategy for training a high-performing heartbeat-level automatic diagnostic model using only rhythm annotation.