Robust Active Learning for Electrocardiographic Signal Classification

TL;DR

This paper introduces a robust active learning approach for ECG signal classification that effectively handles class imbalance and noisy labels by combining data clustering, local instance selection, and a noise reduction scheme, improving classification performance.

Contribution

It presents a novel active learning method that integrates clustering and noise reduction to enhance ECG classification robustness against data imbalance and label noise.

Findings

Improved classification accuracy on MIT-BIH dataset.

Effective handling of class imbalance and noisy labels.

Enhanced robustness of ECG signal classification.

Abstract

The classification of electrocardiographic (ECG) signals is a challenging problem for healthcare industry. Traditional supervised learning methods require a large number of labeled data which is usually expensive and difficult to obtain for ECG signals. Active learning is well-suited for ECG signal classification as it aims at selecting the best set of labeled data in order to maximize the classification performance. Motivated by the fact that ECG data are usually heavily unbalanced among different classes and the class labels are noisy as they are manually labeled, this paper proposes a novel solution based on robust active learning for addressing these challenges. The key idea is to first apply the clustering of the data in a low dimensional embedded space and then select the most information instances within local clusters. By selecting the most informative instances relying on local average minimal distances, the algorithm tends to select the data for labelling in a more diversified way. Finally, the robustness of the model is further enhanced by adding a novel noisy label reduction scheme after the selection of the labeled data. Experiments on the ECG signal classification from the MIT-BIH arrhythmia database demonstrate the effectiveness of the proposed algorithm.