Photoplethysmography Data

Photoplethysmography (PPG) data, derived from the measurement of blood volume changes using light, is increasingly used to extract vital physiological information non-invasively. Current research focuses on improving the accuracy and robustness of PPG-based estimations of various parameters, including heart rate, blood pressure, respiratory rate, sleep stages, and even stress levels, often employing deep learning models like convolutional neural networks, recurrent neural networks, transformers, and generative adversarial networks. These advancements are significant because they enable continuous, convenient, and cost-effective health monitoring, with applications ranging from early detection of cardiovascular disease to personalized virtual reality therapy. The field is also actively addressing challenges like motion artifact removal and signal quality assessment to enhance the reliability of PPG-derived measurements.