Identification of CVD Risk through Physiological Parameters

Riccardo Barbieri1, Marianna Meo2, Anna Maria Bianchi

  • 1Politecnico di Milano
  • 2Brigham and Women's Hospital, Harvard Medical School



Special Session Lectures


08:00 - 09:30 | Wed 17 Aug | Grand Republic D | WeAT19

Research and Innovation Forum. Needs and Trends in Algorithms for P-Health Solutions Addressing CVD Management


Parameters obtained from physiological signals have been found to have good prognostic value in cardiovascular disease (CVD). Such measures might provide key elements in the estimation of CVD risk stratification, especially when associated to other clinical and demographic data. In regard to ambulatory and home monitoring of CVD patients, the possibility of using signals recorded non-invasively and with small wearable systems may greatly reduce the impact on daily life for the individual. Our contribution is aimed at presenting recent and prospective advances in using physiological parameters for estimating CVD risk. In particular, we will discuss specific identifications characterizing autonomic dynamics in different physio-pathological states.

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