AI-based calculating vital cardiovascular metrics

Healthcare and IOT startup focused on cardiac health wanted to develop a prototype system for calculation of vital cardiovascular metrics to test the business hypothesis.

Unstable data coming from the sensors. Missing data points, such as missing peaks and fiducial points, required proper data preprocessing.
High level of noise present in the data from the sensors. Different modalities introduce different noise profiles, requiring the use of a variety of denoising strategies and tuning.
Different natures for the fiducial points of different signal modalities had to be considered during the detection.
A combination of signal modalities had to be examined at the same time to calculate PEP/LVET/IVCT metrics.

Developed an accurate PEP/LVET/IVCT estimation system for patients, validating the business hypothesis with optimal strategies used.
Developed tailored a solution that works seamlessly with the data from the client's hardware (sensors).

An R&D was conducted to quickly estimate the traditional, analytical approaches as well as AI-based approaches for both the data denoising and fiducial points detection.
Traditional approaches offered acceptable results fast to validate business hypothesis in weeks notice. We developed a prototype that dealt with missing data, provided denoising for all the signal modalities, detected fiducial points and peaks, and calculated proposed metrics.
The use of Artificial Intelligence-based solutions such as autoencoders for denoising and CNNs as well as LSTMs for fiducial points detection was proposed for the later stages of development.