 Recent medical and technological advancements are progressively leading to increased survival rates of term and premature neonates. This project is concerned with detecting early signs of physiological disorders in term and preterm babies based on Eulerian Video Magnification, statistical analysis, and expert knowledge. Video recordings of skin coloration differentials for normal and sick neonates may be used to establish norms for each of the two major populations. Those and possibly other features will be used to develop an automatic contactless, noninvasive monitoring and diagnostic system that may detect early signs of disease. Early detection may lead to early diagnosis and preemptive treatment resulting in better outcome and lower costs of medical intervention. The objective of this project is to adapt the Eulerian Video Magnification for monitoring physiologic changes and then diagnosing potential disturbances in term and preterm neonates of different gestational ages. EVM uses video recordings of magnified skin color signals over time and thus permits analysis of physiological state changes such as heart rate and perfusion. Those changes would then be correlated with particular condition or disease states for the purpose of automatic early diagnosis and alarm issuance. Being contactless and non-invasive, the proposed system would be particularly helpful in vulnerable populations such as sick neonates. It may lead to earlier treatment, improved outcome, and possibly, decreased hospital stay. 