eHRV
Research Project, 2020
Although the HRV signal has been studied extensively in the clinical settings, the quality of collected data is still a challenge. For example, the signal’s variability needs to be correctly linked/labelled by patient’s status (e.g. sedation, body movement, cerebral ischema), which is difficult to be done á posteriori.
This project addresses problems with labelling of HRV data with events related to the patient’s cerebral diseases. We plan to map methods and techniques to use for highly reliable labelling of continuously streamed HRV signals from individual patients to the events that happen in the patient’s brain by interpreting the EEG signals.
In this project we conduct a series of participatory observations (sv: auskulera) at the SU:s Neurointensive Care Unit and Clinical Neuroscience. These give us the understanding of how toolchains for collecting, processing and visualizing of ECG and HRV are used in practice for setting diagnoses about Delayed Cerebral Ischema and related diseases. They will also provide us with the understanding how to use federated learning to link EEG signals (and their interpretation) with ECG (and HRV) signals to enable high quality learning.
Participants
Miroslaw Staron (contact)
Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers)
Collaborations
Sahlgrenska University Hospital
Gothenburg, Sweden
Funding
Chalmers AI Research Centre (CHAIR)
Funding Chalmers participation during 2020
Related Areas of Advance and Infrastructure
Information and Communication Technology
Areas of Advance
Health Engineering
Areas of Advance