Parkinson’s disease is a progressive neurological condition in which tremor is one of the main symptoms. The aim of this project is to research methods of detecting and classifying different types of Parkinsonian tremor from biomedical signals, using advanced signal processing and machine learning techniques. The research has applications in demand-driven brain stimulation approaches to alleviating the symptoms of Parkinson’s disease, with an aim to improving quality of life for patients.
School of Systems Engineering, University of Reading:
The University of Reading is one of the UK’s 20 most research-intensive universities and among the top 200 universities in the world. Achievements include the Queen’s Award for Export Achievement (1989) and the Queen’s Anniversary Prize for Higher Education (1998, 2006 and 2009). This project will take place in the School of Systems Engineering (SSE), which has a strong reputation for its innovative research in computer science, cybernetics, and electronic engineering.
Applicants should have a bachelors (at least 2.1 or equivalent) or masters degree in Signal Processing, Mathematics, Engineering or a strongly-related discipline.
How to apply:
(1) Submit an application for a PhD in Cybernetics using the link below.
(2) After submitting your application you will receive an email to confirm receipt; email should be forwarded along with a covering letter and full CV to Professor Virginie Ruiz ([email protected]
(3) In the online application system, there is a section for “Research proposal” and a box that says “If you have already been in contact with a potential supervisor, please tell us who” – in this box, please enter “Prof. Ruiz”.
Professor Virginie Ruiz, tel: +44 (0) 118 378 8211, email: [email protected]
Carmen Camara, Pedro Isasi, Kevin Warwick, Virginie Ruiz, Tipu Aziz, John Stein, Eduard Bakštein, Resting tremor classification and detection in Parkinson's disease patients, Biomedical Signal Processing and Control, Volume 16, February 2015, Pages 88-97, ISSN 1746-8094, http://dx.doi.org.idpproxy.reading.ac.uk/10.1016/j.bspc.2014.09.006.