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Cited by 35ECG Monitoring and AnalysisImbalanced Data Classification TechniquesArtificial Intelligence in Healthcare

Generating Healthcare Time Series Data for Improving Diagnostic Accuracy of Deep Neural Networks

Maweu, Barbara Mukami, Shamsuddin, Rittika, Dakshit, Sagnik, Prabhakaran, Balakrishnan·2021·IEEE Transactions on Instrumentation and Measurement
DOI 10.1109/tim.2021.3077049
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Abstract

Data scarcity and class imbalance are common occurrences in healthcare datasets and have an adverse effect on classification performance of machine learning models. Artificial data generation in various applications can be used to handle these challenges. This article proposes a guided evolutionary synthesizer (GES), a tool derived from principles of genetic algorithm, and designed to generate artificial healthcare time series data for improving classification performance of machine learning models. We conducted a series of promising and confirmatory preliminary experiments performance using traditional machine learning and nonresidual convolutional neural network models to evaluate the effectiveness of GES synthetic on data classification. Motivated by the preliminary results, we conducted eight detailed experiments using residual neural network (ResNet), which demonstrated the flexibility of GES and the effectiveness of GES synthetic data in improving the classification performance of deep neural networks. These experiments use GES generated electrocardiogram (ECG) and electroencephalogram (EEG) datasets. Our findings show that models trained with GES synthetic data performed better than models trained with regular perturbed data, had better diagnostic performance for both EEG and ECG datasets, achieved better performance with lower training volume for EEG dataset, eased learning biases seen in the literature for ECG normal sinus and premature ventricular complex rhythms, and had better classification outcomes than comparable related work models trained with similar ECG classes.

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Authors
Maweu, B. M., Shamsuddin, R., Dakshit, S., & Prabhakaran, B.
Affiliations
  • The University of Texas at Dallas (US)
  • Oklahoma State University (US)
ORCID
Venue
IEEE Transactions on Instrumentation and Measurement
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2021
Volume
70
Pages
1-15
ISSN
  • 0018-9456 (ISSN-L)
  • 1557-9662
DOI
10.1109/tim.2021.3077049
OpenAlex
W3158181349
Cited by
35
References
42
Citations by year
2026: 3 · 2025: 9 · 2024: 7 · 2023: 11 · 2022: 5
FWCI
3.89
Type
journal article
Language
en
Open access
closed
Licence
Locations
IEEE Transactions on Instrumentation and Measurement (publishedVersion)
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
  • Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
synthetic data generation, genetic algorithm, data scarcity, class imbalance, ECG classification, EEG classification, ResNet, diagnostic accuracy, data augmentation

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