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Deep Generative Models for Physiological Signals: A Systematic Literature Review
In this paper, we present a systematic literature review on deep generative models for physiological signals, particularly electrocardiogram, electroencephalogram, photoplethysmogram and electromyogram.
DiffECG: A Versatile Probabilistic Diffusion Model for ECG Signals Synthesis
Within cardiovascular disease detection using deep learning applied to ECG signals, the complexities of handling physiological signals have sparked growing interest in leveraging deep generative models for effective data augmentation.
Leveraging Statistical Shape Priors in GAN-based ECG Synthesis
Electrocardiogram (ECG) data collection during emergency situations is challenging, making ECG data generation an efficient solution for dealing with highly imbalanced ECG training datasets.
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