PPG Signal

Photoplethysmography (PPG) signals, derived from light absorption changes in pulsating blood vessels, are a readily accessible source of physiological information increasingly used for non-invasive health monitoring. Current research focuses on improving the accuracy and robustness of PPG signal analysis using deep learning models, particularly convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, to extract vital signs like heart rate, respiratory rate, and blood pressure, even in the presence of noise and motion artifacts. This work holds significant promise for continuous, remote patient monitoring, enabling early detection of cardiovascular diseases and improved management of chronic conditions through the development of more accurate and efficient wearable health technologies.