Most of Facial Expression Recognition (FER) systems rely on machine learning approaches that require large databases (DBs) for an effective training. As these are not easily available, a good solution is to augment the DBs with appropriate techniques, which are typically based on either geometric transformation or deep learning based technologies (e.g., Generative Adversarial Networks (GANs)). Whereas the first category of techniques have been fairly adopted in the past, studies that use GAN-based techniques are limited for FER systems.

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This is the dataset associated with the IEEE-JBHI submission "Synthesizing Electrocardiograms With Atrial Fibrillation Characteristics Using Generative Adversarial Networks". This dataset contains 4,768 synthesized atrial fibrillation (AF)-like ECG signals stored in PhysioNet MAT/HEA format.

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