  Signal-based early detection of illnesses has been a key topic in research
and hospital settings; it reduces technological costs and paves the way for
quick and effective patient-care operations. Elementary machine learning and
signal processing algorithms have proven to be sufficient in classifying the
onset of viral and bacterial conditions before clinical symptoms are shown.
Inspired by these recent developments, this project employs signal dynamics
analysis to infer changes in vital signs (temperature, respiration, and heart
rate). The results demonstrate that the trends of one vital function can be
predicted from that of another. In particular, it is shown that heart rate and
respiration typically change shortly after body temperature, and aortic blood
pressure follows. This is not an etiologically specific approach, but if
advanced further, it can enable patients and wearable system users to tame
these changes and prevent immediate symptoms.
