Sleep Datasets

Sleep datasets are crucial for developing and validating automated sleep analysis tools, aiming to improve sleep disorder diagnosis and treatment. Current research focuses on creating comprehensive datasets encompassing diverse modalities (EEG, ECG, respiratory signals, even smart garment and Wi-Fi data), employing advanced machine learning models like convolutional neural networks, transformers, and generative adversarial networks to classify sleep stages and detect conditions such as apnea and limb movements. These efforts are significant because they enable more accurate, accessible, and personalized sleep healthcare, potentially reducing the reliance on expensive and time-consuming polysomnography.