Key result
Wasserstein GAN generates synthetic ECG signals for oversampling to evaluate quality and classification performance.
Why the study?
Oversampling is vital for improving minority-class classification accuracy in imbalanced biomedical datasets, prompting the development of a synthetic biosignal data generation method.
WGAN-based oversampling is proposed as a method to generate synthetic ECG signals to improve classification accuracy in imbalanced biomedical datasets.
WGAN oversampling may boost ECG classification in imbalanced datasets; extends generative methods for cardiovascular signal analysis.
Oversampling plays a vital role in improving the minority-class classification accuracy for imbalanced biomedical datasets. In this work, we propose a single-channel biosignal data generation method by exploiting the advancements in well-established image-based Generative Adversarial Networks (GANs). We have implemented a Wasserstein GAN (WGAN) to generate synthetic electrocardiogram (ECG) signal, due to their stability in training as well as correlation of the loss function with the generated image quality. We first trained the WGAN with fixed-dimensional images of the signal and generated synthetic data with similar characteristics. Two evaluation methods were then used for evaluating the efficiency of the proposed technique in generating synthetic ECG data. We used Frechet Inception Distance score for measuring synthetic image quality. We then performed a binary classification of normal and abnormal (Anterior Myocardial Infarction) ECG using Support Vector Machine to verify the performance of the proposed method as an oversampling technique.
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Munia et al. (2020) studied Anterior Myocardial Infarction. Wasserstein Generative Adversarial Network (WGAN) was evaluated on Synthetic image quality (Frechet Inception Distance) and binary classification performance. A Wasserstein Generative Adversarial Network was implemented to generate synthetic electrocardiogram signals for oversampling, evaluated using Frechet Inception Distance and SVM classification.
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