Center for Applied Neuroscience

Christina Orphanidou

Dr Christina Orphanidou holds a DPhil in Applied Mathematics and Information Engineering from the University of Oxford, UK. She is currently a Visiting Academic/Special Scientist at the Department of Electrical and Computer Engineering, University of Cyprus.  Christina has over 15 years of research activity in the areas of biomedical engineering and machine learning with a focus on the development of signal processing and predictive analytics tools for Intelligent Patient Monitoring. Prior to joining the University of Cyprus, she was a Senior Research Associate at the Institute of Biomedical Engineering (IBME), University of Oxford, where she lead the “Hospital of the Future” project, investigating the use of data fusion and machine learning for predicting adverse events in ambulatory hospital patients using wearable sensors. In that capacity, she oversaw two large-scale clinical trials on the use of ICT in healthcare at the John Radcliffe hospital in the Oxford NHS Trust and the Guy’s and St Thomas hospital in the London NHS Trust.

Christina has published work in leading biomedical engineering journals and conferences and has authored a book titled “Signal Quality Assessment in Physiological Monitoring”, part of Springer Briefs in Bioengineering, which will be published by Springer International in Spring 2017. Also, related to her DPhil work in Oxford, she was the recipient of the prestigious “Best Young Investigator in Acoustics” award by the Women’s Committee of the Acoustical Society of America. Her present work focuses on the development of machine learning algorithms for predicting secondary insults in Neuro-trauma patients and pulmonary exacerbations in patients with chronic obstructive pulmonary disease (COPD), in collaboration with the Nicosia General Hospital, and in the development of wearable sensors and predictive tools for the intelligent monitoring of athletes during exercise. She will contribute to the statistical analysis and the computational modeling using (Bayesian Networks).

For more information click here.