Key result
Wasserstein Generative Adversarial Networks (WGAN) successfully synthesized realistic ECG signals with a high structural similarity index of 0.9695 and cross-correlation coefficient of 0.9601.
Why the study?
Machine learning algorithms require large amounts of training data, creating privacy risks when using authentic patient records to develop automated diagnostic systems.
Can Generative Adversarial Networks (GAN and WGAN) synthesize realistic artificial ECG signals to augment training data?
Can Generative Adversarial Networks (GAN and WGAN) synthesize realistic artificial ECG signals to augment training data?
Generative Adversarial Networks, particularly WGAN, can successfully generate highly realistic synthetic ECG signals, offering a potential solution to data scarcity and privacy concerns in training deep learning models for arrhythmia detection.
AI/ML ECG tools may aid early cardiac diagnosis; leaves open need for large training datasets and validation.
Analyzing the data from an electrocardiogram (ECG) can reveal important details about a patient's heart health. A key component of modern medicine is the use of AI and ML-based computer-aided diagnosis tools to aid in making life-or-death decisions. It is common practice to use them in cardiology for the automatic early diagnosis of a variety of potentially fatal illnesses. The machine learning algorithm's need for a large amount of training data to build the learning model is an empirical challenge in the medical domain. To address this challenge, study into methods for creating synthetic patient data has blossomed. There is a higher risk of privacy invasion due to the need for massive amounts of training data for deep learning automated medical diagnostic systems that may help assess the state of the heart from this signal. To combat this issue, researchers have tried to create artificial ECG readings by analyzing only the statistical distributions of the accessible authentic training data.The primary goal of this study is to learn how generative adversarial networks can be used to create artificial ECG signals for use as training data in a classification task. In this study, we used both GAN and WGAN for generation of artificial ECG signals.
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Rayavarapu et al. (2023) studied Arrhythmia (n=290). Generative Adversarial Networks (GAN and WGAN) vs. Real ECG signals was evaluated on Structural Similarity Index (SSIM) and Cross Correlation Coefficient. Wasserstein Generative Adversarial Networks (WGAN) successfully synthesized realistic ECG signals with a high structural similarity index of 0.9695 and cross-correlation coefficient of 0.9601.
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