Our bodies are constantly communicating information about our health. This information can be captured through physiological instruments that measure heart rate, blood pressure, oxygen saturation levels, blood glucose, nerve conduction, brain activity and so forth. Traditionally, such measurements are taken at specific points in time and noted on a patient’s chart. Physicians actually see less than one percent of these values as they make their rounds, and treatment decisions are made based upon these isolated readings.
Biomedical signal processing involves the analysis of these measurements to provide useful information upon which clinicians can make decisions. In our research group, we are focused on developing new ways to process these signals using a variety of mathematical formulae and algorithms. Working with traditional bio-measurement tools, the signals can be computed by software to provide physicians with real-time data and greater insights to aid in clinical assessments. By using more sophisticated means to analyse what our bodies are saying, we can potentially determine the state of a patient’s health through more non-invasive measures.
Our work has been directed toward the definition of novel computational models able to explain the dynamics of the signals time series, such as to extract features and quantifiers summarizing physiological system’s activity and healthiness.
Catrambone, V., Talebi, A., Barbieri, R., & Valenza, G. (2021). "Time-resolved Brain-to-Heart Probabilistic Information Transfer Estimation Using Inhomogeneous Point-Process Models". IEEE Transactions on Biomedical Engineering, 8.
Valenza, Gaetano, et al. "Revealing real-time emotional responses: a personalized assessment based on heartbeat dynamics". Scientific reports 4.1 (2014): 1-13.