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

Supervised machine learning learns a mapping from input data to output labels, based on the patterns and relationships present in a huge labelled training data.Getting labelled data generally requires a substantial allocation of resources in terms of cost and time. In such scenarios, weak supervised...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Machine learning: science and technology Ročník 5; číslo 3; s. 35016 - 35036
Hlavní autoři: Gupta, Priyanka, Gupta, Manik, Kumar, Vijay
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bristol IOP Publishing 01.09.2024
Témata:
Accuracy
active learning
Cardiology
Classification
clustering
Data augmentation
data programming
electrocardiogram
Electrocardiography
Labels
Machine learning
Programming
sampling
Supervised learning
Time series
weak supervised learning
ISSN:2632-2153, 2632-2153
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Supervised machine learning learns a mapping from input data to output labels, based on the patterns and relationships present in a huge labelled training data.Getting labelled data generally requires a substantial allocation of resources in terms of 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 labeled data. This work proposes a novel framework titled AL enhanced data programming (ActDP). It uses a combination of DP and AL for electrocardiogram (ECG) beat classification using single-lead data. ECG beat classification is pivotal in cardiology and healthcare applications 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 into 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%, few-shot learning techniques yield 87.5%–89.2%, and multi-instance learning methods achieve accuracies in the range of 88.9%–94.1%
Bibliografie:MLST-102113.R1
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2632-2153
2632-2153
DOI:10.1088/2632-2153/ad5fda