Photoplethysmography Signal

Photoplethysmography (PPG) signals, reflecting variations in blood volume, are increasingly used for non-invasive health monitoring, with research focusing on extracting vital information like heart rate, blood pressure, and even detecting arrhythmias and diabetes. Current studies employ various machine learning and deep learning approaches, including convolutional neural networks (CNNs), recurrent neural networks (RNNs like LSTM and GRU), and ensemble methods, often coupled with signal processing techniques like Short-Time Fourier Transform (STFT) to mitigate noise and artifacts. These advancements aim to improve the accuracy and reliability of PPG-based diagnostics, enabling continuous, remote health monitoring and potentially revolutionizing preventative healthcare and personalized medicine.