ICP monitoring is used in the diagnosis and treatment of wide-ranging neurological and neurosurgical disorders as well as a part of multimodal monitoring in patients with severe acute brain disease. From some of the first original work on ICP monitoring a few macro-patterns were described (A- and B-waves). Today it is harder to observe and study these patterns as patients are often seen, diagnosed, and treated at a much earlier stage in which these waves are not yet as visible or prominent. This poses a problem concerning both the identification of disease categories for diagnosis and the treatment effect monitoring.
Our goal in this project is to characterize and classify ICP signals in different patient categories, including near-to-normal subjects. The use of both pattern recognition strategies and statistics will play an important role in this process. Additionally, the project will include work on the development of validation procedures for ICP measurements and handling of accompanying biosignals from the subjects and the study of possible relations between disease states and signal characteristics.
Overall, the PhD will involve working with interdisciplinary experimental research rooted in biomedical engineering and signal processing bridging clinical measurements, signal processing, and statistical analysis of long-term ICP monitoring as individual and grouped monitoring data.
The project is made possible by a generous grant from the Novo Nordisk Foundation, and a continued prosperous collaboration with Professor Jens Wilhjelm from the Department of Electrical and Biomedical Engineering, Technical University of Denmark, Lyngby.