An active learning enhanced data programming (ActDP) framework for ECG time series

Supervised machine learning requires the estimation of multiple parameters by using large amounts of labelled data. Getting labelled data generally requires a substantial allocation of resources in terms of both cost and time. In such scenarios, weak supervised learning techniques like data programming (DP) and active learning (AL) can be advantageous for time-series classification tasks. These paradigms can be used to assign data labels in an automated manner, and time-series classification can subsequently be carried out on the labelled data. This work proposes a novel framework titled active learning enhanced data programming (ActDP). It uses DP and AL for ECG classification using single-lead data. ECG classification is pivotal in cardiology and healthcare for diagnosing a broad spectrum of heart conditions and arrhythmias. To establish the usefulness of this proposed ActDP framework, the experiments have been conducted using the MIT-BIH dataset with 94,224 ECG beats. DP assigns a probabilistic label to each ECG beat using nine novel polar labelling functions and a generative model in this work. Further, AL improves the result of DP by replacing the labels for sampled ECG beats of a generative model with ground truth. Subsequently, a discriminative model is trained on these labels for each iteration. The experimental results show that by incorporating AL to DP in the ActDP framework, the accuracy of ECG classification strictly increases from 85.7 % to 97.34 % in 58 iterations. Comparatively, the proposed framework (ActDP) has demonstrated a higher classification accuracy of 97.34 % In contrast, DP with data augmentation (DA) achieves an accuracy of 92.2 %, while DP without DA results in an accuracy of 85.7 %, majority vote yields an accuracy of 50.2 %, and the generative model achieves an accuracy of only 66.5 %.