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

Exploring the Synchrony Between Body Temperature and HR, RR, and Aortic Blood Pressure in Viral/Bacterial Disease Onsets with Signal Dynamics