Heart Rate Variability

Heart rate variability (HRV), the fluctuation in time intervals between heartbeats, reflects the interplay between the sympathetic and parasympathetic nervous systems and serves as a valuable indicator of overall health. Current research focuses on leveraging HRV, often extracted from electrocardiograms (ECGs) or even facial videos via remote photoplethysmography (rPPG), to develop machine learning models for diagnosing conditions like sepsis, mild cognitive impairment, and heart failure, as well as detecting stress and drowsiness. These models frequently employ algorithms such as XGBoost, Random Forests, and neural networks, including convolutional and recurrent architectures, to analyze HRV features and achieve high accuracy in various applications. The ability to accurately and efficiently assess HRV holds significant promise for improving early disease detection, personalized healthcare, and real-time health monitoring.