Approved Research

Estimation of Continuous Blood Pressure Using Theoretical/Data-Driven Models

Lay summary

Emphasis of this research project is placed on investigating and developing multimodal physiological signals-based model for early prediction and prevention of acute cardiovascular diseases (CVDs) and the monitoring of accurate wearable and non-invasive tonoarteriogram (TAG) which is the recording of noninvasive continuous arterial blood pressure.

We set out the following specific objectives to: 1) develop a new model by using multi-modality signals including electrocardiogram (ECG), photoplethysmogram (PPG), and other possible modal signals for accurate TAG estimation; and 2) further integrate with AI technologies for stable and dynamic long-term TAG estimation.

Roughly it will take 1 year in total with the detail below:

*            Design of multi-modal TAG estimation model

*            Training and validation of multi-modal TAG estimation model on database.

*            Improvement and optimization of multi-modal TAG estimation model

The model/algorithm can be easily implemented into various wearable electronics such as watch, glasses, finger ring etc. These works can be applied to new generations of home-use medical devices, empowering the user with its health status and allowing the users proactively take steps to improve one's health conditions before facing acute CVD events. These works have potential for commercialization with huge market, which could be applied in different scenarios for effective management of CVD diseases if validated.