S4H

Respiration studies have assumed increased relevance in the biomedical analytics research.  Concerns related to the patient’s levels of comfort and the accuracy of detecting respiratory patterns, have resulted in the development of several respiration monitoring systems. There are different approaches for respiration monitoring such as electrocardiogram derived respiration (EDR). Using the EDR method it is possible to monitor concomitantly the electrical activity of the heart and to detect respiratory disorders such as central and mixed apnea, hypopnea, and tachypnea in polymedicated elderly individuals following cardiorespiratory therapeutical changes. 

We propose to do the following:

- To implement ant optimize an advanced algorithm based on the EDR method. 

- To test and validate the algorithm using CardioID technology.

- To develop a supervised learning method of classification for respiration monitoring.

It is the aim of this multidisciplinary group, ranging from the pharmaceutical industry to biomedical engineering, leveraging the power of machine learning associated with biosignals, to further contribute to an ever evolving field of science.