Active broad learning system for ECG arrhythmia classification

This paper presents an active and incremental learning system called active broad learning system (ABLS) for ECG arrhythmia classification to reduce the time-consumption of training and labor cost of experts labeling beats. An effective strategy is designed to convert the actual outputs in broad learning system (BLS) into approximated posterior probabilities for active learning to select the most valuable beats from unlabeled beats. The proposed ABLS is first pre-trained with a small number of labeled beats and then incremental trained with the selected beats labeled by the expert to fine-tune the connection weight. Due to the structural characteristics, ABLS does not need to retrain all the beats, which can greatly reduce the time-consumption. The experimental results on the MIT-BIH arrhythmia database show ABLS can greatly reduce the number of beats that need to be labeled and consume very little training time while maintaining excellent performance compared to state-of-theart methods.

Automated summary

The ABLS system presented in the paper reduces training time and expert labor costs for ECG arrhythmia classification by using an active learning strategy, while maintaining excellent performance compared to state-of-the-art methods.