Intelligent fault diagnosis, detection, and prognostics (DDP) for complex equipment
prognostics and health management (PHM) have achieved remarkable breakthroughs …

The Electrocardiogram (ECG) remains a fundamental tool in cardiac diagnostics, yet its
interpretation has traditionally relied on cardiologists' expertise. Deep learning has …

Generating synthetic data is a promising solution for addressing privacy concerns that arise
when distributing sensitive health data. In recent years, diffusion models have become the …

Diffusion models have demonstrated impressive results in both data generation and
downstream tasks such as inverse problems, text-based editing, classification, and more …

Cardiovascular diseases (CVDs) are a major global health concern, causing significant
morbidity and mortality. AI's integration with healthcare offers promising solutions, with data …

Large high-quality datasets are essential for building powerful artificial intelligence (AI)
algorithms capable of supporting advancement in cardiac clinical research. However …

Generative models realized with Machine Learning (ML) techniques are powerful tools to
infer complex and unknown data distributions from a finite number of training samples in …

Electromyography (EMG) signals find wide applications in the fields of medicine, sports, and
rehabilitation. However, the collection of EMG signals is a laborious process constrained by …

Within cardiovascular disease detection using deep learning applied to ECG signals, the
complexities of handling physiological signals have sparked growing interest in leveraging …

In this paper, we present a systematic literature review on deep generative models for
physiological signals, particularly electrocardiogram, electroencephalogram …