Data Analytics for Cardiovascular Resilience Experiment bij Department M&CS

Background:

To investigate the resilience of the autonomous nerve system (ANS) a resilience experiment, with five different stages (mental, hand grip, supine, standing, and walking), was executed in the third-generation participants of the Framingham Heart Study (around 1300 participants). Several vital signs of the heart (systolic blood pressure, diastolic blood pressure, mean arterial pressure, heart rate, and respiratory rate) were continuously observed throughout the experiment (25–30 minutes of monitoring). The experiment is visualized in the following figure.

Data Analytic Questions:

This type of data is not easy to analyze due to the large number of repeats and its high correlation among sequential data points. One example of the data that is generated is provided in the figure below.

Each person will have their own vital sign profile and all participants react differently to the different stages. Additionally, we have already information on sex and age on each participant, but we are currently trying to add other information (e.g., BMI, smoking, hypertension, past heart problems, treatments) from the participants. 

For the master thesis project, we are trying to answer several data analytical questions:

·        Can we describe the longitudinal vital sign signals properly (functional data analysis, time-series analysis, longitudinal smoothing) and find clusters of participants?

·        Can we define important features calculated per stage and rest period (e.g., average, variation, delta response, autocorrelation, time at maximum response) that summarize the most relevant information in the trajectories?

·        Can we determine the association between certain participant characteristic (age, sex, smoking, etc.) and the vital sign profiles (using the profiles or the features).