Continuous Cardiovascular Function Monitoring for Risk Model Development
Spinal Cord Injury (SCI) is a lifelong lasting condition that can affect anyone. Although much advanced care have provided improved prognosis post-SCI, we still see a remarkable difference in the mortality rate of persons with SCI compared to the general population from 2 times in paraplegics to over 8 times in tetraplegic persons. Where the main cause of reduced life expectancy is cardiovascular disease (CVD).
Continuous monitoring of the physical activity levels, social behaviors, and environmental factors together with cardiovascular function in a continuous and unobtrusive manner will enable us to develop methods and models to quantify CVD risk models and compare them with the general population models.
Although some CVD risk factors could be measured during annual check-ups, such as lipid spectrum, blood pressure, heart rate or electrocardiograms (ECG). Relevant information during daily life portrays a different picture of the health state and cardiovascular function of the individual, e.g. blood pressure curves, long-term heart rate variability, and continuous ECG during ADLs. Something yet unseen in clinical practice.
We aim to develop a measurement systems and ambulatory continuous metrics that could provide risk models of CVD by quantifying physical activities while relating them to known cardiovascular function metrics. Herewith, developing measurement protocols that could be exported from inpatient evaluations towards outpatient monitoring and prevention in telemedicine.
We make use of continuous measurements of cardiovascular activity through hear rate, blood pressure, and ECG data combine with environmental measurement and activity tracking. Multimodal data fusion is then used for building anomaly detections and a feedback loop for the medical doctors to assess the onset of multiple related conditions which would enable the development of detection algorithms and risk models for outpatient care.
Founded by the ETH-SPS Digital Transformation in Personalized Health Care for SCI Project